Datasets:

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ftopal

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huggingface-models-processed

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[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "war", "es", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

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[ "### opus-mt-war-es\n\n\n* source languages: war\n* target languages: es\n* OPUS readme: war-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 28.7, chr-F: 0.470" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #war #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-war-es\n\n\n* source languages: war\n* target languages: es\n* OPUS readme: war-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 28.7, chr-F: 0.470" ]

[ 51, 105 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #war #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-war-es\n\n\n* source languages: war\n* target languages: es\n* OPUS readme: war-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 28.7, chr-F: 0.470" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "war", "fi", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

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[]

[ "### opus-mt-war-fi\n\n\n* source languages: war\n* target languages: fi\n* OPUS readme: war-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 26.9, chr-F: 0.507" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #war #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-war-fi\n\n\n* source languages: war\n* target languages: fi\n* OPUS readme: war-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 26.9, chr-F: 0.507" ]

[ 51, 106 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #war #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-war-fi\n\n\n* source languages: war\n* target languages: fi\n* OPUS readme: war-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 26.9, chr-F: 0.507" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "war", "fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-war-fr\n\n\n* source languages: war\n* target languages: fr\n* OPUS readme: war-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.2, chr-F: 0.482" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #war #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-war-fr\n\n\n* source languages: war\n* target languages: fr\n* OPUS readme: war-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.2, chr-F: 0.482" ]

[ 51, 106 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #war #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-war-fr\n\n\n* source languages: war\n* target languages: fr\n* OPUS readme: war-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.2, chr-F: 0.482" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "war", "sv", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

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[ "### opus-mt-war-sv\n\n\n* source languages: war\n* target languages: sv\n* OPUS readme: war-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.4, chr-F: 0.505" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #war #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-war-sv\n\n\n* source languages: war\n* target languages: sv\n* OPUS readme: war-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.4, chr-F: 0.505" ]

[ 51, 105 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #war #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-war-sv\n\n\n* source languages: war\n* target languages: sv\n* OPUS readme: war-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.4, chr-F: 0.505" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "wls", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-wls-en\n\n\n* source languages: wls\n* target languages: en\n* OPUS readme: wls-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.8, chr-F: 0.471" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #wls #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-wls-en\n\n\n* source languages: wls\n* target languages: en\n* OPUS readme: wls-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.8, chr-F: 0.471" ]

[ 52, 109 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #wls #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-wls-en\n\n\n* source languages: wls\n* target languages: en\n* OPUS readme: wls-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.8, chr-F: 0.471" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "wls", "fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-wls-fr\n\n\n* source languages: wls\n* target languages: fr\n* OPUS readme: wls-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.6, chr-F: 0.389" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #wls #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-wls-fr\n\n\n* source languages: wls\n* target languages: fr\n* OPUS readme: wls-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.6, chr-F: 0.389" ]

[ 52, 109 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #wls #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-wls-fr\n\n\n* source languages: wls\n* target languages: fr\n* OPUS readme: wls-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.6, chr-F: 0.389" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "wls", "sv", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-wls-sv\n\n\n* source languages: wls\n* target languages: sv\n* OPUS readme: wls-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 23.8, chr-F: 0.408" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #wls #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-wls-sv\n\n\n* source languages: wls\n* target languages: sv\n* OPUS readme: wls-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 23.8, chr-F: 0.408" ]

[ 52, 109 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #wls #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-wls-sv\n\n\n* source languages: wls\n* target languages: sv\n* OPUS readme: wls-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 23.8, chr-F: 0.408" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "xh", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-xh-en\n\n\n* source languages: xh\n* target languages: en\n* OPUS readme: xh-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 45.8, chr-F: 0.610" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #xh #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-xh-en\n\n\n* source languages: xh\n* target languages: en\n* OPUS readme: xh-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 45.8, chr-F: 0.610" ]

[ 52, 108 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #xh #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-xh-en\n\n\n* source languages: xh\n* target languages: en\n* OPUS readme: xh-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 45.8, chr-F: 0.610" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "xh", "es", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-xh-es\n\n\n* source languages: xh\n* target languages: es\n* OPUS readme: xh-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 32.3, chr-F: 0.505" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #xh #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-xh-es\n\n\n* source languages: xh\n* target languages: es\n* OPUS readme: xh-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 32.3, chr-F: 0.505" ]

[ 52, 108 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #xh #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-xh-es\n\n\n* source languages: xh\n* target languages: es\n* OPUS readme: xh-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 32.3, chr-F: 0.505" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "xh", "fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-xh-fr\n\n\n* source languages: xh\n* target languages: fr\n* OPUS readme: xh-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.6, chr-F: 0.487" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #xh #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-xh-fr\n\n\n* source languages: xh\n* target languages: fr\n* OPUS readme: xh-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.6, chr-F: 0.487" ]

[ 52, 109 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #xh #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-xh-fr\n\n\n* source languages: xh\n* target languages: fr\n* OPUS readme: xh-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.6, chr-F: 0.487" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "xh", "sv", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-xh-sv\n\n\n* source languages: xh\n* target languages: sv\n* OPUS readme: xh-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.1, chr-F: 0.522" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #xh #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-xh-sv\n\n\n* source languages: xh\n* target languages: sv\n* OPUS readme: xh-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.1, chr-F: 0.522" ]

[ 52, 109 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #xh #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-xh-sv\n\n\n* source languages: xh\n* target languages: sv\n* OPUS readme: xh-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.1, chr-F: 0.522" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "yap", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-yap-en\n\n\n* source languages: yap\n* target languages: en\n* OPUS readme: yap-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.2, chr-F: 0.452" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #yap #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-yap-en\n\n\n* source languages: yap\n* target languages: en\n* OPUS readme: yap-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.2, chr-F: 0.452" ]

[ 52, 109 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #yap #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-yap-en\n\n\n* source languages: yap\n* target languages: en\n* OPUS readme: yap-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.2, chr-F: 0.452" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "yap", "fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-yap-fr\n\n\n* source languages: yap\n* target languages: fr\n* OPUS readme: yap-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.2, chr-F: 0.381" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #yap #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-yap-fr\n\n\n* source languages: yap\n* target languages: fr\n* OPUS readme: yap-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.2, chr-F: 0.381" ]

[ 52, 108 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #yap #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-yap-fr\n\n\n* source languages: yap\n* target languages: fr\n* OPUS readme: yap-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.2, chr-F: 0.381" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "yap", "sv", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-yap-sv\n\n\n* source languages: yap\n* target languages: sv\n* OPUS readme: yap-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.6, chr-F: 0.399" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #yap #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-yap-sv\n\n\n* source languages: yap\n* target languages: sv\n* OPUS readme: yap-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.6, chr-F: 0.399" ]

[ 52, 109 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #yap #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-yap-sv\n\n\n* source languages: yap\n* target languages: sv\n* OPUS readme: yap-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.6, chr-F: 0.399" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "yo", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-yo-en\n\n\n* source languages: yo\n* target languages: en\n* OPUS readme: yo-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.8, chr-F: 0.496" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #yo #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-yo-en\n\n\n* source languages: yo\n* target languages: en\n* OPUS readme: yo-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.8, chr-F: 0.496" ]

[ 51, 106 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #yo #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-yo-en\n\n\n* source languages: yo\n* target languages: en\n* OPUS readme: yo-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.8, chr-F: 0.496" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "yo", "es", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-yo-es\n\n\n* source languages: yo\n* target languages: es\n* OPUS readme: yo-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.0, chr-F: 0.393" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #yo #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-yo-es\n\n\n* source languages: yo\n* target languages: es\n* OPUS readme: yo-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.0, chr-F: 0.393" ]

[ 51, 106 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #yo #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-yo-es\n\n\n* source languages: yo\n* target languages: es\n* OPUS readme: yo-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.0, chr-F: 0.393" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "yo", "fi", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-yo-fi\n\n\n* source languages: yo\n* target languages: fi\n* OPUS readme: yo-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 21.5, chr-F: 0.434" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #yo #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-yo-fi\n\n\n* source languages: yo\n* target languages: fi\n* OPUS readme: yo-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 21.5, chr-F: 0.434" ]

[ 51, 106 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #yo #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-yo-fi\n\n\n* source languages: yo\n* target languages: fi\n* OPUS readme: yo-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 21.5, chr-F: 0.434" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "yo", "fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-yo-fr\n\n\n* source languages: yo\n* target languages: fr\n* OPUS readme: yo-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 24.1, chr-F: 0.408" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #yo #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-yo-fr\n\n\n* source languages: yo\n* target languages: fr\n* OPUS readme: yo-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 24.1, chr-F: 0.408" ]

[ 51, 106 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #yo #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-yo-fr\n\n\n* source languages: yo\n* target languages: fr\n* OPUS readme: yo-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 24.1, chr-F: 0.408" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "yo", "sv", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-yo-sv\n\n\n* source languages: yo\n* target languages: sv\n* OPUS readme: yo-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.2, chr-F: 0.434" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #yo #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-yo-sv\n\n\n* source languages: yo\n* target languages: sv\n* OPUS readme: yo-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.2, chr-F: 0.434" ]

[ 51, 106 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #yo #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-yo-sv\n\n\n* source languages: yo\n* target languages: sv\n* OPUS readme: yo-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.2, chr-F: 0.434" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "zai", "es", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-zai-es\n\n\n* source languages: zai\n* target languages: es\n* OPUS readme: zai-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 20.8, chr-F: 0.372" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zai #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-zai-es\n\n\n* source languages: zai\n* target languages: es\n* OPUS readme: zai-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 20.8, chr-F: 0.372" ]

[ 52, 109 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zai #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-zai-es\n\n\n* source languages: zai\n* target languages: es\n* OPUS readme: zai-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 20.8, chr-F: 0.372" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "zh", "bg", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "zh", "bg" ]

[ "### zho-bul\n\n\n* source group: Chinese\n* target group: Bulgarian\n* OPUS readme: zho-bul\n* model: transformer\n* source language(s): cmn cmn\\_Hans cmn\\_Hant zho zho\\_Hans zho\\_Hant\n* target language(s): bul\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: Tatoeba-test.cmn\\_Hani.bul, BLEU: 29.6, chr-F: 0.497\ntestset: URL, BLEU: 29.6, chr-F: 0.497", "### System Info:\n\n\n* hf\\_name: zho-bul\n* source\\_languages: zho\n* target\\_languages: bul\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'bg']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'bul', 'bul\\_Latn'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: bul\n* short\\_pair: zh-bg\n* chrF2\\_score: 0.49700000000000005\n* bleu: 29.6\n* brevity\\_penalty: 0.883\n* ref\\_len: 3113.0\n* src\\_name: Chinese\n* tgt\\_name: Bulgarian\n* train\\_date: 2020-07-03\n* src\\_alpha2: zh\n* tgt\\_alpha2: bg\n* prefer\\_old: False\n* long\\_pair: zho-bul\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #bg #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zho-bul\n\n\n* source group: Chinese\n* target group: Bulgarian\n* OPUS readme: zho-bul\n* model: transformer\n* source language(s): cmn cmn\\_Hans cmn\\_Hant zho zho\\_Hans zho\\_Hant\n* target language(s): bul\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: Tatoeba-test.cmn\\_Hani.bul, BLEU: 29.6, chr-F: 0.497\ntestset: URL, BLEU: 29.6, chr-F: 0.497", "### System Info:\n\n\n* hf\\_name: zho-bul\n* source\\_languages: zho\n* target\\_languages: bul\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'bg']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'bul', 'bul\\_Latn'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: bul\n* short\\_pair: zh-bg\n* chrF2\\_score: 0.49700000000000005\n* bleu: 29.6\n* brevity\\_penalty: 0.883\n* ref\\_len: 3113.0\n* src\\_name: Chinese\n* tgt\\_name: Bulgarian\n* train\\_date: 2020-07-03\n* src\\_alpha2: zh\n* tgt\\_alpha2: bg\n* prefer\\_old: False\n* long\\_pair: zho-bul\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ 53, 194, 727 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #bg #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zho-bul\n\n\n* source group: Chinese\n* target group: Bulgarian\n* OPUS readme: zho-bul\n* model: transformer\n* source language(s): cmn cmn\\_Hans cmn\\_Hant zho zho\\_Hans zho\\_Hant\n* target language(s): bul\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: Tatoeba-test.cmn\\_Hani.bul, BLEU: 29.6, chr-F: 0.497\ntestset: URL, BLEU: 29.6, chr-F: 0.497### System Info:\n\n\n* hf\\_name: zho-bul\n* source\\_languages: zho\n* target\\_languages: bul\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'bg']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'bul', 'bul\\_Latn'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: bul\n* short\\_pair: zh-bg\n* chrF2\\_score: 0.49700000000000005\n* bleu: 29.6\n* brevity\\_penalty: 0.883\n* ref\\_len: 3113.0\n* src\\_name: Chinese\n* tgt\\_name: Bulgarian\n* train\\_date: 2020-07-03\n* src\\_alpha2: zh\n* tgt\\_alpha2: bg\n* prefer\\_old: False\n* long\\_pair: zho-bul\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "zh", "de", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "zh", "de" ]

[ "### zho-deu\n\n\n* source group: Chinese\n* target group: German\n* OPUS readme: zho-deu\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hang cmn\\_Hani cmn\\_Hira cmn\\_Kana cmn\\_Latn lzh\\_Hani wuu\\_Hani yue\\_Hani\n* target language(s): deu\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 32.1, chr-F: 0.522", "### System Info:\n\n\n* hf\\_name: zho-deu\n* source\\_languages: zho\n* target\\_languages: deu\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'de']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'deu'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: deu\n* short\\_pair: zh-de\n* chrF2\\_score: 0.522\n* bleu: 32.1\n* brevity\\_penalty: 0.9540000000000001\n* ref\\_len: 19102.0\n* src\\_name: Chinese\n* tgt\\_name: German\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: de\n* prefer\\_old: False\n* long\\_pair: zho-deu\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zho-deu\n\n\n* source group: Chinese\n* target group: German\n* OPUS readme: zho-deu\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hang cmn\\_Hani cmn\\_Hira cmn\\_Kana cmn\\_Latn lzh\\_Hani wuu\\_Hani yue\\_Hani\n* target language(s): deu\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 32.1, chr-F: 0.522", "### System Info:\n\n\n* hf\\_name: zho-deu\n* source\\_languages: zho\n* target\\_languages: deu\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'de']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'deu'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: deu\n* short\\_pair: zh-de\n* chrF2\\_score: 0.522\n* bleu: 32.1\n* brevity\\_penalty: 0.9540000000000001\n* ref\\_len: 19102.0\n* src\\_name: Chinese\n* tgt\\_name: German\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: de\n* prefer\\_old: False\n* long\\_pair: zho-deu\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ 52, 190, 713 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zho-deu\n\n\n* source group: Chinese\n* target group: German\n* OPUS readme: zho-deu\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hang cmn\\_Hani cmn\\_Hira cmn\\_Kana cmn\\_Latn lzh\\_Hani wuu\\_Hani yue\\_Hani\n* target language(s): deu\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 32.1, chr-F: 0.522### System Info:\n\n\n* hf\\_name: zho-deu\n* source\\_languages: zho\n* target\\_languages: deu\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'de']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'deu'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: deu\n* short\\_pair: zh-de\n* chrF2\\_score: 0.522\n* bleu: 32.1\n* brevity\\_penalty: 0.9540000000000001\n* ref\\_len: 19102.0\n* src\\_name: Chinese\n* tgt\\_name: German\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: de\n* prefer\\_old: False\n* long\\_pair: zho-deu\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "transformers", "pytorch", "tf", "rust", "marian", "text2text-generation", "translation", "zh", "en", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "zh", "en" ]

[ "### zho-eng\n\n\nTable of Contents\n-----------------\n\n\n* Model Details\n* Uses\n* Risks, Limitations and Biases\n* Training\n* Evaluation\n* Citation Information\n* How to Get Started With the Model\n\n\nModel Details\n-------------\n\n\n* Model Description:\n* Developed by: Language Technology Research Group at the University of Helsinki\n* Model Type: Translation\n* Language(s):\n\t+ Source Language: Chinese\n\t+ Target Language: English\n* License: CC-BY-4.0\n* Resources for more information:\n\t+ GitHub Repo\n\n\nUses\n----", "#### Direct Use\n\n\nThis model can be used for translation and text-to-text generation.\n\n\nRisks, Limitations and Biases\n-----------------------------\n\n\nCONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.\n\n\nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).\n\n\nFurther details about the dataset for this model can be found in the OPUS readme: zho-eng\n\n\nTraining\n--------", "#### System Information\n\n\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41\n* src\\_multilingual: False\n* tgt\\_multilingual: False", "#### Training Data", "##### Preprocessing\n\n\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* ref\\_len: 82826.0\n* dataset: opus\n* download original weights: URL\n* test set translations: URL\n\n\nEvaluation\n----------", "#### Results\n\n\n* test set scores: URL\n* brevity\\_penalty: 0.948\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 36.1, chr-F: 0.548\n\n\nHow to Get Started With the Model\n---------------------------------" ]

[ "TAGS\n#transformers #pytorch #tf #rust #marian #text2text-generation #translation #zh #en #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zho-eng\n\n\nTable of Contents\n-----------------\n\n\n* Model Details\n* Uses\n* Risks, Limitations and Biases\n* Training\n* Evaluation\n* Citation Information\n* How to Get Started With the Model\n\n\nModel Details\n-------------\n\n\n* Model Description:\n* Developed by: Language Technology Research Group at the University of Helsinki\n* Model Type: Translation\n* Language(s):\n\t+ Source Language: Chinese\n\t+ Target Language: English\n* License: CC-BY-4.0\n* Resources for more information:\n\t+ GitHub Repo\n\n\nUses\n----", "#### Direct Use\n\n\nThis model can be used for translation and text-to-text generation.\n\n\nRisks, Limitations and Biases\n-----------------------------\n\n\nCONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.\n\n\nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).\n\n\nFurther details about the dataset for this model can be found in the OPUS readme: zho-eng\n\n\nTraining\n--------", "#### System Information\n\n\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41\n* src\\_multilingual: False\n* tgt\\_multilingual: False", "#### Training Data", "##### Preprocessing\n\n\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* ref\\_len: 82826.0\n* dataset: opus\n* download original weights: URL\n* test set translations: URL\n\n\nEvaluation\n----------", "#### Results\n\n\n* test set scores: URL\n* brevity\\_penalty: 0.948\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 36.1, chr-F: 0.548\n\n\nHow to Get Started With the Model\n---------------------------------" ]

[ 56, 134, 150, 126, 6, 71, 99 ]

[ "TAGS\n#transformers #pytorch #tf #rust #marian #text2text-generation #translation #zh #en #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zho-eng\n\n\nTable of Contents\n-----------------\n\n\n* Model Details\n* Uses\n* Risks, Limitations and Biases\n* Training\n* Evaluation\n* Citation Information\n* How to Get Started With the Model\n\n\nModel Details\n-------------\n\n\n* Model Description:\n* Developed by: Language Technology Research Group at the University of Helsinki\n* Model Type: Translation\n* Language(s):\n\t+ Source Language: Chinese\n\t+ Target Language: English\n* License: CC-BY-4.0\n* Resources for more information:\n\t+ GitHub Repo\n\n\nUses\n----#### Direct Use\n\n\nThis model can be used for translation and text-to-text generation.\n\n\nRisks, Limitations and Biases\n-----------------------------\n\n\nCONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.\n\n\nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).\n\n\nFurther details about the dataset for this model can be found in the OPUS readme: zho-eng\n\n\nTraining\n--------#### System Information\n\n\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41\n* src\\_multilingual: False\n* tgt\\_multilingual: False#### Training Data##### Preprocessing\n\n\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* ref\\_len: 82826.0\n* dataset: opus\n* download original weights: URL\n* test set translations: URL\n\n\nEvaluation\n----------#### Results\n\n\n* test set scores: URL\n* brevity\\_penalty: 0.948\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 36.1, chr-F: 0.548\n\n\nHow to Get Started With the Model\n---------------------------------" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "zh", "fi", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "zh", "fi" ]

[ "### zho-fin\n\n\n* source group: Chinese\n* target group: Finnish\n* OPUS readme: zho-fin\n* model: transformer-align\n* source language(s): cmn\\_Bopo cmn\\_Hani cmn\\_Latn nan\\_Hani yue yue\\_Hani\n* target language(s): fin\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 35.1, chr-F: 0.579", "### System Info:\n\n\n* hf\\_name: zho-fin\n* source\\_languages: zho\n* target\\_languages: fin\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'fi']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'fin'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: fin\n* short\\_pair: zh-fi\n* chrF2\\_score: 0.579\n* bleu: 35.1\n* brevity\\_penalty: 0.935\n* ref\\_len: 1847.0\n* src\\_name: Chinese\n* tgt\\_name: Finnish\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: fi\n* prefer\\_old: False\n* long\\_pair: zho-fin\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zho-fin\n\n\n* source group: Chinese\n* target group: Finnish\n* OPUS readme: zho-fin\n* model: transformer-align\n* source language(s): cmn\\_Bopo cmn\\_Hani cmn\\_Latn nan\\_Hani yue yue\\_Hani\n* target language(s): fin\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 35.1, chr-F: 0.579", "### System Info:\n\n\n* hf\\_name: zho-fin\n* source\\_languages: zho\n* target\\_languages: fin\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'fi']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'fin'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: fin\n* short\\_pair: zh-fi\n* chrF2\\_score: 0.579\n* bleu: 35.1\n* brevity\\_penalty: 0.935\n* ref\\_len: 1847.0\n* src\\_name: Chinese\n* tgt\\_name: Finnish\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: fi\n* prefer\\_old: False\n* long\\_pair: zho-fin\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ 52, 162, 701 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zho-fin\n\n\n* source group: Chinese\n* target group: Finnish\n* OPUS readme: zho-fin\n* model: transformer-align\n* source language(s): cmn\\_Bopo cmn\\_Hani cmn\\_Latn nan\\_Hani yue yue\\_Hani\n* target language(s): fin\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 35.1, chr-F: 0.579### System Info:\n\n\n* hf\\_name: zho-fin\n* source\\_languages: zho\n* target\\_languages: fin\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'fi']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'fin'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: fin\n* short\\_pair: zh-fi\n* chrF2\\_score: 0.579\n* bleu: 35.1\n* brevity\\_penalty: 0.935\n* ref\\_len: 1847.0\n* src\\_name: Chinese\n* tgt\\_name: Finnish\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: fi\n* prefer\\_old: False\n* long\\_pair: zho-fin\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "zh", "he", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "zh", "he" ]

[ "### zho-heb\n\n\n* source group: Chinese\n* target group: Hebrew\n* OPUS readme: zho-heb\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hang cmn\\_Hani cmn\\_Hira cmn\\_Kana cmn\\_Latn cmn\\_Yiii lzh lzh\\_Bopo lzh\\_Hang lzh\\_Hani lzh\\_Hira lzh\\_Kana lzh\\_Yiii\n* target language(s): heb\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 28.5, chr-F: 0.469", "### System Info:\n\n\n* hf\\_name: zho-heb\n* source\\_languages: zho\n* target\\_languages: heb\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'he']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'heb'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: heb\n* short\\_pair: zh-he\n* chrF2\\_score: 0.469\n* bleu: 28.5\n* brevity\\_penalty: 0.986\n* ref\\_len: 3654.0\n* src\\_name: Chinese\n* tgt\\_name: Hebrew\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: he\n* prefer\\_old: False\n* long\\_pair: zho-heb\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #he #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zho-heb\n\n\n* source group: Chinese\n* target group: Hebrew\n* OPUS readme: zho-heb\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hang cmn\\_Hani cmn\\_Hira cmn\\_Kana cmn\\_Latn cmn\\_Yiii lzh lzh\\_Bopo lzh\\_Hang lzh\\_Hani lzh\\_Hira lzh\\_Kana lzh\\_Yiii\n* target language(s): heb\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 28.5, chr-F: 0.469", "### System Info:\n\n\n* hf\\_name: zho-heb\n* source\\_languages: zho\n* target\\_languages: heb\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'he']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'heb'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: heb\n* short\\_pair: zh-he\n* chrF2\\_score: 0.469\n* bleu: 28.5\n* brevity\\_penalty: 0.986\n* ref\\_len: 3654.0\n* src\\_name: Chinese\n* tgt\\_name: Hebrew\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: he\n* prefer\\_old: False\n* long\\_pair: zho-heb\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ 52, 216, 707 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #he #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zho-heb\n\n\n* source group: Chinese\n* target group: Hebrew\n* OPUS readme: zho-heb\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hang cmn\\_Hani cmn\\_Hira cmn\\_Kana cmn\\_Latn cmn\\_Yiii lzh lzh\\_Bopo lzh\\_Hang lzh\\_Hani lzh\\_Hira lzh\\_Kana lzh\\_Yiii\n* target language(s): heb\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 28.5, chr-F: 0.469### System Info:\n\n\n* hf\\_name: zho-heb\n* source\\_languages: zho\n* target\\_languages: heb\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'he']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'heb'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: heb\n* short\\_pair: zh-he\n* chrF2\\_score: 0.469\n* bleu: 28.5\n* brevity\\_penalty: 0.986\n* ref\\_len: 3654.0\n* src\\_name: Chinese\n* tgt\\_name: Hebrew\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: he\n* prefer\\_old: False\n* long\\_pair: zho-heb\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "zh", "it", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "zh", "it" ]

[ "### zho-ita\n\n\n* source group: Chinese\n* target group: Italian\n* OPUS readme: zho-ita\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hang cmn\\_Hani cmn\\_Hira cmn\\_Kana cmn\\_Latn lzh lzh\\_Hang lzh\\_Hani lzh\\_Hira lzh\\_Yiii wuu\\_Bopo wuu\\_Hani wuu\\_Latn yue\\_Hani\n* target language(s): ita\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 27.9, chr-F: 0.508", "### System Info:\n\n\n* hf\\_name: zho-ita\n* source\\_languages: zho\n* target\\_languages: ita\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'it']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'ita'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: ita\n* short\\_pair: zh-it\n* chrF2\\_score: 0.508\n* bleu: 27.9\n* brevity\\_penalty: 0.935\n* ref\\_len: 19684.0\n* src\\_name: Chinese\n* tgt\\_name: Italian\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: it\n* prefer\\_old: False\n* long\\_pair: zho-ita\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #it #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zho-ita\n\n\n* source group: Chinese\n* target group: Italian\n* OPUS readme: zho-ita\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hang cmn\\_Hani cmn\\_Hira cmn\\_Kana cmn\\_Latn lzh lzh\\_Hang lzh\\_Hani lzh\\_Hira lzh\\_Yiii wuu\\_Bopo wuu\\_Hani wuu\\_Latn yue\\_Hani\n* target language(s): ita\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 27.9, chr-F: 0.508", "### System Info:\n\n\n* hf\\_name: zho-ita\n* source\\_languages: zho\n* target\\_languages: ita\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'it']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'ita'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: ita\n* short\\_pair: zh-it\n* chrF2\\_score: 0.508\n* bleu: 27.9\n* brevity\\_penalty: 0.935\n* ref\\_len: 19684.0\n* src\\_name: Chinese\n* tgt\\_name: Italian\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: it\n* prefer\\_old: False\n* long\\_pair: zho-ita\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ 52, 222, 707 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #it #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zho-ita\n\n\n* source group: Chinese\n* target group: Italian\n* OPUS readme: zho-ita\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hang cmn\\_Hani cmn\\_Hira cmn\\_Kana cmn\\_Latn lzh lzh\\_Hang lzh\\_Hani lzh\\_Hira lzh\\_Yiii wuu\\_Bopo wuu\\_Hani wuu\\_Latn yue\\_Hani\n* target language(s): ita\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 27.9, chr-F: 0.508### System Info:\n\n\n* hf\\_name: zho-ita\n* source\\_languages: zho\n* target\\_languages: ita\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'it']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'ita'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: ita\n* short\\_pair: zh-it\n* chrF2\\_score: 0.508\n* bleu: 27.9\n* brevity\\_penalty: 0.935\n* ref\\_len: 19684.0\n* src\\_name: Chinese\n* tgt\\_name: Italian\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: it\n* prefer\\_old: False\n* long\\_pair: zho-ita\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "zh", "ms", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "zh", "ms" ]

[ "### zho-msa\n\n\n* source group: Chinese\n* target group: Malay (macrolanguage)\n* OPUS readme: zho-msa\n* model: transformer-align\n* source language(s): cmn\\_Bopo cmn\\_Hani cmn\\_Latn hak\\_Hani yue\\_Bopo yue\\_Hani\n* target language(s): ind zsm\\_Latn\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 13.9, chr-F: 0.390", "### System Info:\n\n\n* hf\\_name: zho-msa\n* source\\_languages: zho\n* target\\_languages: msa\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'ms']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'zsm\\_Latn', 'ind', 'max\\_Latn', 'zlm\\_Latn', 'min'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: msa\n* short\\_pair: zh-ms\n* chrF2\\_score: 0.39\n* bleu: 13.9\n* brevity\\_penalty: 0.9229999999999999\n* ref\\_len: 2762.0\n* src\\_name: Chinese\n* tgt\\_name: Malay (macrolanguage)\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: ms\n* prefer\\_old: False\n* long\\_pair: zho-msa\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #ms #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zho-msa\n\n\n* source group: Chinese\n* target group: Malay (macrolanguage)\n* OPUS readme: zho-msa\n* model: transformer-align\n* source language(s): cmn\\_Bopo cmn\\_Hani cmn\\_Latn hak\\_Hani yue\\_Bopo yue\\_Hani\n* target language(s): ind zsm\\_Latn\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 13.9, chr-F: 0.390", "### System Info:\n\n\n* hf\\_name: zho-msa\n* source\\_languages: zho\n* target\\_languages: msa\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'ms']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'zsm\\_Latn', 'ind', 'max\\_Latn', 'zlm\\_Latn', 'min'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: msa\n* short\\_pair: zh-ms\n* chrF2\\_score: 0.39\n* bleu: 13.9\n* brevity\\_penalty: 0.9229999999999999\n* ref\\_len: 2762.0\n* src\\_name: Chinese\n* tgt\\_name: Malay (macrolanguage)\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: ms\n* prefer\\_old: False\n* long\\_pair: zho-msa\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ 52, 208, 756 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #ms #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zho-msa\n\n\n* source group: Chinese\n* target group: Malay (macrolanguage)\n* OPUS readme: zho-msa\n* model: transformer-align\n* source language(s): cmn\\_Bopo cmn\\_Hani cmn\\_Latn hak\\_Hani yue\\_Bopo yue\\_Hani\n* target language(s): ind zsm\\_Latn\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 13.9, chr-F: 0.390### System Info:\n\n\n* hf\\_name: zho-msa\n* source\\_languages: zho\n* target\\_languages: msa\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'ms']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'zsm\\_Latn', 'ind', 'max\\_Latn', 'zlm\\_Latn', 'min'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: msa\n* short\\_pair: zh-ms\n* chrF2\\_score: 0.39\n* bleu: 13.9\n* brevity\\_penalty: 0.9229999999999999\n* ref\\_len: 2762.0\n* src\\_name: Chinese\n* tgt\\_name: Malay (macrolanguage)\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: ms\n* prefer\\_old: False\n* long\\_pair: zho-msa\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "zh", "nl", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "zh", "nl" ]

[ "### zho-nld\n\n\n* source group: Chinese\n* target group: Dutch\n* OPUS readme: zho-nld\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hani cmn\\_Hira cmn\\_Kana cmn\\_Latn\n* target language(s): nld\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.5, chr-F: 0.525", "### System Info:\n\n\n* hf\\_name: zho-nld\n* source\\_languages: zho\n* target\\_languages: nld\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'nl']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'nld'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: nld\n* short\\_pair: zh-nl\n* chrF2\\_score: 0.525\n* bleu: 31.5\n* brevity\\_penalty: 0.9309999999999999\n* ref\\_len: 13575.0\n* src\\_name: Chinese\n* tgt\\_name: Dutch\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: nl\n* prefer\\_old: False\n* long\\_pair: zho-nld\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #nl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zho-nld\n\n\n* source group: Chinese\n* target group: Dutch\n* OPUS readme: zho-nld\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hani cmn\\_Hira cmn\\_Kana cmn\\_Latn\n* target language(s): nld\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.5, chr-F: 0.525", "### System Info:\n\n\n* hf\\_name: zho-nld\n* source\\_languages: zho\n* target\\_languages: nld\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'nl']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'nld'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: nld\n* short\\_pair: zh-nl\n* chrF2\\_score: 0.525\n* bleu: 31.5\n* brevity\\_penalty: 0.9309999999999999\n* ref\\_len: 13575.0\n* src\\_name: Chinese\n* tgt\\_name: Dutch\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: nl\n* prefer\\_old: False\n* long\\_pair: zho-nld\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ 52, 167, 718 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #nl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zho-nld\n\n\n* source group: Chinese\n* target group: Dutch\n* OPUS readme: zho-nld\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hani cmn\\_Hira cmn\\_Kana cmn\\_Latn\n* target language(s): nld\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.5, chr-F: 0.525### System Info:\n\n\n* hf\\_name: zho-nld\n* source\\_languages: zho\n* target\\_languages: nld\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'nl']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'nld'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: nld\n* short\\_pair: zh-nl\n* chrF2\\_score: 0.525\n* bleu: 31.5\n* brevity\\_penalty: 0.9309999999999999\n* ref\\_len: 13575.0\n* src\\_name: Chinese\n* tgt\\_name: Dutch\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: nl\n* prefer\\_old: False\n* long\\_pair: zho-nld\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "zh", "sv", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "zh", "sv" ]

[ "### zho-swe\n\n\n* source group: Chinese\n* target group: Swedish\n* OPUS readme: zho-swe\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hani cmn\\_Latn\n* target language(s): swe\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 46.1, chr-F: 0.621", "### System Info:\n\n\n* hf\\_name: zho-swe\n* source\\_languages: zho\n* target\\_languages: swe\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'sv']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'swe'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: swe\n* short\\_pair: zh-sv\n* chrF2\\_score: 0.621\n* bleu: 46.1\n* brevity\\_penalty: 0.956\n* ref\\_len: 6223.0\n* src\\_name: Chinese\n* tgt\\_name: Swedish\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: sv\n* prefer\\_old: False\n* long\\_pair: zho-swe\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zho-swe\n\n\n* source group: Chinese\n* target group: Swedish\n* OPUS readme: zho-swe\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hani cmn\\_Latn\n* target language(s): swe\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 46.1, chr-F: 0.621", "### System Info:\n\n\n* hf\\_name: zho-swe\n* source\\_languages: zho\n* target\\_languages: swe\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'sv']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'swe'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: swe\n* short\\_pair: zh-sv\n* chrF2\\_score: 0.621\n* bleu: 46.1\n* brevity\\_penalty: 0.956\n* ref\\_len: 6223.0\n* src\\_name: Chinese\n* tgt\\_name: Swedish\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: sv\n* prefer\\_old: False\n* long\\_pair: zho-swe\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ 52, 156, 707 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zho-swe\n\n\n* source group: Chinese\n* target group: Swedish\n* OPUS readme: zho-swe\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hani cmn\\_Latn\n* target language(s): swe\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 46.1, chr-F: 0.621### System Info:\n\n\n* hf\\_name: zho-swe\n* source\\_languages: zho\n* target\\_languages: swe\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'sv']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'swe'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: swe\n* short\\_pair: zh-sv\n* chrF2\\_score: 0.621\n* bleu: 46.1\n* brevity\\_penalty: 0.956\n* ref\\_len: 6223.0\n* src\\_name: Chinese\n* tgt\\_name: Swedish\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: sv\n* prefer\\_old: False\n* long\\_pair: zho-swe\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "zh", "uk", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "zh", "uk" ]

[ "### zho-ukr\n\n\n* source group: Chinese\n* target group: Ukrainian\n* OPUS readme: zho-ukr\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hang cmn\\_Hani cmn\\_Kana cmn\\_Latn cmn\\_Yiii yue\\_Bopo yue\\_Hang yue\\_Hani yue\\_Hira yue\\_Kana\n* target language(s): ukr\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 10.4, chr-F: 0.259", "### System Info:\n\n\n* hf\\_name: zho-ukr\n* source\\_languages: zho\n* target\\_languages: ukr\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'uk']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'ukr'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm4k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: ukr\n* short\\_pair: zh-uk\n* chrF2\\_score: 0.259\n* bleu: 10.4\n* brevity\\_penalty: 0.9059999999999999\n* ref\\_len: 9193.0\n* src\\_name: Chinese\n* tgt\\_name: Ukrainian\n* train\\_date: 2020-06-16\n* src\\_alpha2: zh\n* tgt\\_alpha2: uk\n* prefer\\_old: False\n* long\\_pair: zho-ukr\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #uk #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zho-ukr\n\n\n* source group: Chinese\n* target group: Ukrainian\n* OPUS readme: zho-ukr\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hang cmn\\_Hani cmn\\_Kana cmn\\_Latn cmn\\_Yiii yue\\_Bopo yue\\_Hang yue\\_Hani yue\\_Hira yue\\_Kana\n* target language(s): ukr\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 10.4, chr-F: 0.259", "### System Info:\n\n\n* hf\\_name: zho-ukr\n* source\\_languages: zho\n* target\\_languages: ukr\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'uk']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'ukr'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm4k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: ukr\n* short\\_pair: zh-uk\n* chrF2\\_score: 0.259\n* bleu: 10.4\n* brevity\\_penalty: 0.9059999999999999\n* ref\\_len: 9193.0\n* src\\_name: Chinese\n* tgt\\_name: Ukrainian\n* train\\_date: 2020-06-16\n* src\\_alpha2: zh\n* tgt\\_alpha2: uk\n* prefer\\_old: False\n* long\\_pair: zho-ukr\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ 52, 196, 719 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #uk #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zho-ukr\n\n\n* source group: Chinese\n* target group: Ukrainian\n* OPUS readme: zho-ukr\n* model: transformer-align\n* source language(s): cmn cmn\\_Bopo cmn\\_Hang cmn\\_Hani cmn\\_Kana cmn\\_Latn cmn\\_Yiii yue\\_Bopo yue\\_Hang yue\\_Hani yue\\_Hira yue\\_Kana\n* target language(s): ukr\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 10.4, chr-F: 0.259### System Info:\n\n\n* hf\\_name: zho-ukr\n* source\\_languages: zho\n* target\\_languages: ukr\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'uk']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'ukr'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm4k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: ukr\n* short\\_pair: zh-uk\n* chrF2\\_score: 0.259\n* bleu: 10.4\n* brevity\\_penalty: 0.9059999999999999\n* ref\\_len: 9193.0\n* src\\_name: Chinese\n* tgt\\_name: Ukrainian\n* train\\_date: 2020-06-16\n* src\\_alpha2: zh\n* tgt\\_alpha2: uk\n* prefer\\_old: False\n* long\\_pair: zho-ukr\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "zh", "vi", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "zh", "vi" ]

[ "### zho-vie\n\n\n* source group: Chinese\n* target group: Vietnamese\n* OPUS readme: zho-vie\n* model: transformer-align\n* source language(s): cmn\\_Hani cmn\\_Latn\n* target language(s): vie\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 20.0, chr-F: 0.385", "### System Info:\n\n\n* hf\\_name: zho-vie\n* source\\_languages: zho\n* target\\_languages: vie\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'vi']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'vie', 'vie\\_Hani'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: vie\n* short\\_pair: zh-vi\n* chrF2\\_score: 0.385\n* bleu: 20.0\n* brevity\\_penalty: 0.917\n* ref\\_len: 4667.0\n* src\\_name: Chinese\n* tgt\\_name: Vietnamese\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: vi\n* prefer\\_old: False\n* long\\_pair: zho-vie\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #vi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zho-vie\n\n\n* source group: Chinese\n* target group: Vietnamese\n* OPUS readme: zho-vie\n* model: transformer-align\n* source language(s): cmn\\_Hani cmn\\_Latn\n* target language(s): vie\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 20.0, chr-F: 0.385", "### System Info:\n\n\n* hf\\_name: zho-vie\n* source\\_languages: zho\n* target\\_languages: vie\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'vi']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'vie', 'vie\\_Hani'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: vie\n* short\\_pair: zh-vi\n* chrF2\\_score: 0.385\n* bleu: 20.0\n* brevity\\_penalty: 0.917\n* ref\\_len: 4667.0\n* src\\_name: Chinese\n* tgt\\_name: Vietnamese\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: vi\n* prefer\\_old: False\n* long\\_pair: zho-vie\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ 52, 144, 710 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zh #vi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zho-vie\n\n\n* source group: Chinese\n* target group: Vietnamese\n* OPUS readme: zho-vie\n* model: transformer-align\n* source language(s): cmn\\_Hani cmn\\_Latn\n* target language(s): vie\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 20.0, chr-F: 0.385### System Info:\n\n\n* hf\\_name: zho-vie\n* source\\_languages: zho\n* target\\_languages: vie\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['zh', 'vi']\n* src\\_constituents: {'cmn\\_Hans', 'nan', 'nan\\_Hani', 'gan', 'yue', 'cmn\\_Kana', 'yue\\_Hani', 'wuu\\_Bopo', 'cmn\\_Latn', 'yue\\_Hira', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn', 'lzh\\_Hang', 'lzh\\_Hira', 'cmn\\_Hant', 'lzh\\_Bopo', 'zho', 'zho\\_Hans', 'zho\\_Hant', 'lzh\\_Hani', 'yue\\_Hang', 'wuu', 'yue\\_Kana', 'wuu\\_Latn', 'yue\\_Bopo', 'cjy\\_Hant', 'yue\\_Hans', 'lzh', 'cmn\\_Hira', 'lzh\\_Yiii', 'lzh\\_Hans', 'cmn\\_Bopo', 'cmn\\_Hang', 'hak\\_Hani', 'cmn\\_Yiii', 'yue\\_Hant', 'lzh\\_Kana', 'wuu\\_Hani'}\n* tgt\\_constituents: {'vie', 'vie\\_Hani'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zho\n* tgt\\_alpha3: vie\n* short\\_pair: zh-vi\n* chrF2\\_score: 0.385\n* bleu: 20.0\n* brevity\\_penalty: 0.917\n* ref\\_len: 4667.0\n* src\\_name: Chinese\n* tgt\\_name: Vietnamese\n* train\\_date: 2020-06-17\n* src\\_alpha2: zh\n* tgt\\_alpha2: vi\n* prefer\\_old: False\n* long\\_pair: zho-vie\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "be", "ru", "uk", "zle", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "be", "ru", "uk", "zle", "en" ]

[ "### zle-eng\n\n\n* source group: East Slavic languages\n* target group: English\n* OPUS readme: zle-eng\n* model: transformer\n* source language(s): bel bel\\_Latn orv\\_Cyrl rue rus ukr\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.1, chr-F: 0.579\ntestset: URL, BLEU: 24.9, chr-F: 0.522\ntestset: URL, BLEU: 27.9, chr-F: 0.563\ntestset: URL, BLEU: 26.8, chr-F: 0.541\ntestset: URL, BLEU: 25.8, chr-F: 0.535\ntestset: URL, BLEU: 29.1, chr-F: 0.561\ntestset: URL, BLEU: 25.4, chr-F: 0.537\ntestset: URL, BLEU: 26.8, chr-F: 0.545\ntestset: URL, BLEU: 38.3, chr-F: 0.569\ntestset: URL, BLEU: 50.1, chr-F: 0.656\ntestset: URL, BLEU: 6.9, chr-F: 0.217\ntestset: URL, BLEU: 15.4, chr-F: 0.345\ntestset: URL, BLEU: 52.5, chr-F: 0.674\ntestset: URL, BLEU: 52.1, chr-F: 0.673", "### System Info:\n\n\n* hf\\_name: zle-eng\n* source\\_languages: zle\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['be', 'ru', 'uk', 'zle', 'en']\n* src\\_constituents: {'bel', 'orv\\_Cyrl', 'bel\\_Latn', 'rus', 'ukr', 'rue'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zle\n* tgt\\_alpha3: eng\n* short\\_pair: zle-en\n* chrF2\\_score: 0.6559999999999999\n* bleu: 50.1\n* brevity\\_penalty: 0.97\n* ref\\_len: 69599.0\n* src\\_name: East Slavic languages\n* tgt\\_name: English\n* train\\_date: 2020-08-01\n* src\\_alpha2: zle\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: zle-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #be #ru #uk #zle #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zle-eng\n\n\n* source group: East Slavic languages\n* target group: English\n* OPUS readme: zle-eng\n* model: transformer\n* source language(s): bel bel\\_Latn orv\\_Cyrl rue rus ukr\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.1, chr-F: 0.579\ntestset: URL, BLEU: 24.9, chr-F: 0.522\ntestset: URL, BLEU: 27.9, chr-F: 0.563\ntestset: URL, BLEU: 26.8, chr-F: 0.541\ntestset: URL, BLEU: 25.8, chr-F: 0.535\ntestset: URL, BLEU: 29.1, chr-F: 0.561\ntestset: URL, BLEU: 25.4, chr-F: 0.537\ntestset: URL, BLEU: 26.8, chr-F: 0.545\ntestset: URL, BLEU: 38.3, chr-F: 0.569\ntestset: URL, BLEU: 50.1, chr-F: 0.656\ntestset: URL, BLEU: 6.9, chr-F: 0.217\ntestset: URL, BLEU: 15.4, chr-F: 0.345\ntestset: URL, BLEU: 52.5, chr-F: 0.674\ntestset: URL, BLEU: 52.1, chr-F: 0.673", "### System Info:\n\n\n* hf\\_name: zle-eng\n* source\\_languages: zle\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['be', 'ru', 'uk', 'zle', 'en']\n* src\\_constituents: {'bel', 'orv\\_Cyrl', 'bel\\_Latn', 'rus', 'ukr', 'rue'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zle\n* tgt\\_alpha3: eng\n* short\\_pair: zle-en\n* chrF2\\_score: 0.6559999999999999\n* bleu: 50.1\n* brevity\\_penalty: 0.97\n* ref\\_len: 69599.0\n* src\\_name: East Slavic languages\n* tgt\\_name: English\n* train\\_date: 2020-08-01\n* src\\_alpha2: zle\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: zle-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ 58, 444, 455 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #be #ru #uk #zle #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zle-eng\n\n\n* source group: East Slavic languages\n* target group: English\n* OPUS readme: zle-eng\n* model: transformer\n* source language(s): bel bel\\_Latn orv\\_Cyrl rue rus ukr\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.1, chr-F: 0.579\ntestset: URL, BLEU: 24.9, chr-F: 0.522\ntestset: URL, BLEU: 27.9, chr-F: 0.563\ntestset: URL, BLEU: 26.8, chr-F: 0.541\ntestset: URL, BLEU: 25.8, chr-F: 0.535\ntestset: URL, BLEU: 29.1, chr-F: 0.561\ntestset: URL, BLEU: 25.4, chr-F: 0.537\ntestset: URL, BLEU: 26.8, chr-F: 0.545\ntestset: URL, BLEU: 38.3, chr-F: 0.569\ntestset: URL, BLEU: 50.1, chr-F: 0.656\ntestset: URL, BLEU: 6.9, chr-F: 0.217\ntestset: URL, BLEU: 15.4, chr-F: 0.345\ntestset: URL, BLEU: 52.5, chr-F: 0.674\ntestset: URL, BLEU: 52.1, chr-F: 0.673### System Info:\n\n\n* hf\\_name: zle-eng\n* source\\_languages: zle\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['be', 'ru', 'uk', 'zle', 'en']\n* src\\_constituents: {'bel', 'orv\\_Cyrl', 'bel\\_Latn', 'rus', 'ukr', 'rue'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zle\n* tgt\\_alpha3: eng\n* short\\_pair: zle-en\n* chrF2\\_score: 0.6559999999999999\n* bleu: 50.1\n* brevity\\_penalty: 0.97\n* ref\\_len: 69599.0\n* src\\_name: East Slavic languages\n* tgt\\_name: English\n* train\\_date: 2020-08-01\n* src\\_alpha2: zle\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: zle-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "be", "ru", "uk", "zle", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "be", "ru", "uk", "zle" ]

[ "### zle-zle\n\n\n* source group: East Slavic languages\n* target group: East Slavic languages\n* OPUS readme: zle-zle\n* model: transformer\n* source language(s): bel bel\\_Latn orv\\_Cyrl rus ukr\n* target language(s): bel bel\\_Latn orv\\_Cyrl rus ukr\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 57.1, chr-F: 0.758\ntestset: URL, BLEU: 55.5, chr-F: 0.751\ntestset: URL, BLEU: 58.0, chr-F: 0.742\ntestset: URL, BLEU: 5.8, chr-F: 0.226\ntestset: URL, BLEU: 2.5, chr-F: 0.161\ntestset: URL, BLEU: 50.5, chr-F: 0.714\ntestset: URL, BLEU: 0.3, chr-F: 0.129\ntestset: URL, BLEU: 63.9, chr-F: 0.794\ntestset: URL, BLEU: 51.3, chr-F: 0.719\ntestset: URL, BLEU: 0.3, chr-F: 0.106\ntestset: URL, BLEU: 68.7, chr-F: 0.825", "### System Info:\n\n\n* hf\\_name: zle-zle\n* source\\_languages: zle\n* target\\_languages: zle\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['be', 'ru', 'uk', 'zle']\n* src\\_constituents: {'bel', 'orv\\_Cyrl', 'bel\\_Latn', 'rus', 'ukr', 'rue'}\n* tgt\\_constituents: {'bel', 'orv\\_Cyrl', 'bel\\_Latn', 'rus', 'ukr', 'rue'}\n* src\\_multilingual: True\n* tgt\\_multilingual: True\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zle\n* tgt\\_alpha3: zle\n* short\\_pair: zle-zle\n* chrF2\\_score: 0.742\n* bleu: 58.0\n* brevity\\_penalty: 1.0\n* ref\\_len: 62731.0\n* src\\_name: East Slavic languages\n* tgt\\_name: East Slavic languages\n* train\\_date: 2020-07-27\n* src\\_alpha2: zle\n* tgt\\_alpha2: zle\n* prefer\\_old: False\n* long\\_pair: zle-zle\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #be #ru #uk #zle #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zle-zle\n\n\n* source group: East Slavic languages\n* target group: East Slavic languages\n* OPUS readme: zle-zle\n* model: transformer\n* source language(s): bel bel\\_Latn orv\\_Cyrl rus ukr\n* target language(s): bel bel\\_Latn orv\\_Cyrl rus ukr\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 57.1, chr-F: 0.758\ntestset: URL, BLEU: 55.5, chr-F: 0.751\ntestset: URL, BLEU: 58.0, chr-F: 0.742\ntestset: URL, BLEU: 5.8, chr-F: 0.226\ntestset: URL, BLEU: 2.5, chr-F: 0.161\ntestset: URL, BLEU: 50.5, chr-F: 0.714\ntestset: URL, BLEU: 0.3, chr-F: 0.129\ntestset: URL, BLEU: 63.9, chr-F: 0.794\ntestset: URL, BLEU: 51.3, chr-F: 0.719\ntestset: URL, BLEU: 0.3, chr-F: 0.106\ntestset: URL, BLEU: 68.7, chr-F: 0.825", "### System Info:\n\n\n* hf\\_name: zle-zle\n* source\\_languages: zle\n* target\\_languages: zle\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['be', 'ru', 'uk', 'zle']\n* src\\_constituents: {'bel', 'orv\\_Cyrl', 'bel\\_Latn', 'rus', 'ukr', 'rue'}\n* tgt\\_constituents: {'bel', 'orv\\_Cyrl', 'bel\\_Latn', 'rus', 'ukr', 'rue'}\n* src\\_multilingual: True\n* tgt\\_multilingual: True\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zle\n* tgt\\_alpha3: zle\n* short\\_pair: zle-zle\n* chrF2\\_score: 0.742\n* bleu: 58.0\n* brevity\\_penalty: 1.0\n* ref\\_len: 62731.0\n* src\\_name: East Slavic languages\n* tgt\\_name: East Slavic languages\n* train\\_date: 2020-07-27\n* src\\_alpha2: zle\n* tgt\\_alpha2: zle\n* prefer\\_old: False\n* long\\_pair: zle-zle\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ 56, 418, 478 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #be #ru #uk #zle #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zle-zle\n\n\n* source group: East Slavic languages\n* target group: East Slavic languages\n* OPUS readme: zle-zle\n* model: transformer\n* source language(s): bel bel\\_Latn orv\\_Cyrl rus ukr\n* target language(s): bel bel\\_Latn orv\\_Cyrl rus ukr\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 57.1, chr-F: 0.758\ntestset: URL, BLEU: 55.5, chr-F: 0.751\ntestset: URL, BLEU: 58.0, chr-F: 0.742\ntestset: URL, BLEU: 5.8, chr-F: 0.226\ntestset: URL, BLEU: 2.5, chr-F: 0.161\ntestset: URL, BLEU: 50.5, chr-F: 0.714\ntestset: URL, BLEU: 0.3, chr-F: 0.129\ntestset: URL, BLEU: 63.9, chr-F: 0.794\ntestset: URL, BLEU: 51.3, chr-F: 0.719\ntestset: URL, BLEU: 0.3, chr-F: 0.106\ntestset: URL, BLEU: 68.7, chr-F: 0.825### System Info:\n\n\n* hf\\_name: zle-zle\n* source\\_languages: zle\n* target\\_languages: zle\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['be', 'ru', 'uk', 'zle']\n* src\\_constituents: {'bel', 'orv\\_Cyrl', 'bel\\_Latn', 'rus', 'ukr', 'rue'}\n* tgt\\_constituents: {'bel', 'orv\\_Cyrl', 'bel\\_Latn', 'rus', 'ukr', 'rue'}\n* src\\_multilingual: True\n* tgt\\_multilingual: True\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zle\n* tgt\\_alpha3: zle\n* short\\_pair: zle-zle\n* chrF2\\_score: 0.742\n* bleu: 58.0\n* brevity\\_penalty: 1.0\n* ref\\_len: 62731.0\n* src\\_name: East Slavic languages\n* tgt\\_name: East Slavic languages\n* train\\_date: 2020-07-27\n* src\\_alpha2: zle\n* tgt\\_alpha2: zle\n* prefer\\_old: False\n* long\\_pair: zle-zle\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "hr", "mk", "bg", "sl", "zls", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "hr", "mk", "bg", "sl", "zls", "en" ]

[ "### zls-eng\n\n\n* source group: South Slavic languages\n* target group: English\n* OPUS readme: zls-eng\n* model: transformer\n* source language(s): bos\\_Latn bul bul\\_Latn hrv mkd slv srp\\_Cyrl srp\\_Latn\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 54.9, chr-F: 0.693\ntestset: URL, BLEU: 55.7, chr-F: 0.700\ntestset: URL, BLEU: 54.6, chr-F: 0.681\ntestset: URL, BLEU: 53.6, chr-F: 0.676\ntestset: URL, BLEU: 25.6, chr-F: 0.407", "### System Info:\n\n\n* hf\\_name: zls-eng\n* source\\_languages: zls\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['hr', 'mk', 'bg', 'sl', 'zls', 'en']\n* src\\_constituents: {'hrv', 'mkd', 'srp\\_Latn', 'srp\\_Cyrl', 'bul\\_Latn', 'bul', 'bos\\_Latn', 'slv'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zls\n* tgt\\_alpha3: eng\n* short\\_pair: zls-en\n* chrF2\\_score: 0.6759999999999999\n* bleu: 53.6\n* brevity\\_penalty: 0.98\n* ref\\_len: 68623.0\n* src\\_name: South Slavic languages\n* tgt\\_name: English\n* train\\_date: 2020-08-01\n* src\\_alpha2: zls\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: zls-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #hr #mk #bg #sl #zls #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zls-eng\n\n\n* source group: South Slavic languages\n* target group: English\n* OPUS readme: zls-eng\n* model: transformer\n* source language(s): bos\\_Latn bul bul\\_Latn hrv mkd slv srp\\_Cyrl srp\\_Latn\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 54.9, chr-F: 0.693\ntestset: URL, BLEU: 55.7, chr-F: 0.700\ntestset: URL, BLEU: 54.6, chr-F: 0.681\ntestset: URL, BLEU: 53.6, chr-F: 0.676\ntestset: URL, BLEU: 25.6, chr-F: 0.407", "### System Info:\n\n\n* hf\\_name: zls-eng\n* source\\_languages: zls\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['hr', 'mk', 'bg', 'sl', 'zls', 'en']\n* src\\_constituents: {'hrv', 'mkd', 'srp\\_Latn', 'srp\\_Cyrl', 'bul\\_Latn', 'bul', 'bos\\_Latn', 'slv'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zls\n* tgt\\_alpha3: eng\n* short\\_pair: zls-en\n* chrF2\\_score: 0.6759999999999999\n* bleu: 53.6\n* brevity\\_penalty: 0.98\n* ref\\_len: 68623.0\n* src\\_name: South Slavic languages\n* tgt\\_name: English\n* train\\_date: 2020-08-01\n* src\\_alpha2: zls\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: zls-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ 61, 255, 484 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #hr #mk #bg #sl #zls #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zls-eng\n\n\n* source group: South Slavic languages\n* target group: English\n* OPUS readme: zls-eng\n* model: transformer\n* source language(s): bos\\_Latn bul bul\\_Latn hrv mkd slv srp\\_Cyrl srp\\_Latn\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 54.9, chr-F: 0.693\ntestset: URL, BLEU: 55.7, chr-F: 0.700\ntestset: URL, BLEU: 54.6, chr-F: 0.681\ntestset: URL, BLEU: 53.6, chr-F: 0.676\ntestset: URL, BLEU: 25.6, chr-F: 0.407### System Info:\n\n\n* hf\\_name: zls-eng\n* source\\_languages: zls\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['hr', 'mk', 'bg', 'sl', 'zls', 'en']\n* src\\_constituents: {'hrv', 'mkd', 'srp\\_Latn', 'srp\\_Cyrl', 'bul\\_Latn', 'bul', 'bos\\_Latn', 'slv'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zls\n* tgt\\_alpha3: eng\n* short\\_pair: zls-en\n* chrF2\\_score: 0.6759999999999999\n* bleu: 53.6\n* brevity\\_penalty: 0.98\n* ref\\_len: 68623.0\n* src\\_name: South Slavic languages\n* tgt\\_name: English\n* train\\_date: 2020-08-01\n* src\\_alpha2: zls\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: zls-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "hr", "mk", "bg", "sl", "zls", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "hr", "mk", "bg", "sl", "zls" ]

[ "### zls-zls\n\n\n* source group: South Slavic languages\n* target group: South Slavic languages\n* OPUS readme: zls-zls\n* model: transformer\n* source language(s): bul mkd srp\\_Cyrl\n* target language(s): bul mkd srp\\_Cyrl\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 19.3, chr-F: 0.514\ntestset: URL, BLEU: 31.9, chr-F: 0.669\ntestset: URL, BLEU: 18.0, chr-F: 0.636\ntestset: URL, BLEU: 19.4, chr-F: 0.322\ntestset: URL, BLEU: 44.6, chr-F: 0.679\ntestset: URL, BLEU: 5.5, chr-F: 0.152\ntestset: URL, BLEU: 26.5, chr-F: 0.563", "### System Info:\n\n\n* hf\\_name: zls-zls\n* source\\_languages: zls\n* target\\_languages: zls\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['hr', 'mk', 'bg', 'sl', 'zls']\n* src\\_constituents: {'hrv', 'mkd', 'srp\\_Latn', 'srp\\_Cyrl', 'bul\\_Latn', 'bul', 'bos\\_Latn', 'slv'}\n* tgt\\_constituents: {'hrv', 'mkd', 'srp\\_Latn', 'srp\\_Cyrl', 'bul\\_Latn', 'bul', 'bos\\_Latn', 'slv'}\n* src\\_multilingual: True\n* tgt\\_multilingual: True\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zls\n* tgt\\_alpha3: zls\n* short\\_pair: zls-zls\n* chrF2\\_score: 0.563\n* bleu: 26.5\n* brevity\\_penalty: 1.0\n* ref\\_len: 58.0\n* src\\_name: South Slavic languages\n* tgt\\_name: South Slavic languages\n* train\\_date: 2020-07-27\n* src\\_alpha2: zls\n* tgt\\_alpha2: zls\n* prefer\\_old: False\n* long\\_pair: zls-zls\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #hr #mk #bg #sl #zls #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zls-zls\n\n\n* source group: South Slavic languages\n* target group: South Slavic languages\n* OPUS readme: zls-zls\n* model: transformer\n* source language(s): bul mkd srp\\_Cyrl\n* target language(s): bul mkd srp\\_Cyrl\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 19.3, chr-F: 0.514\ntestset: URL, BLEU: 31.9, chr-F: 0.669\ntestset: URL, BLEU: 18.0, chr-F: 0.636\ntestset: URL, BLEU: 19.4, chr-F: 0.322\ntestset: URL, BLEU: 44.6, chr-F: 0.679\ntestset: URL, BLEU: 5.5, chr-F: 0.152\ntestset: URL, BLEU: 26.5, chr-F: 0.563", "### System Info:\n\n\n* hf\\_name: zls-zls\n* source\\_languages: zls\n* target\\_languages: zls\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['hr', 'mk', 'bg', 'sl', 'zls']\n* src\\_constituents: {'hrv', 'mkd', 'srp\\_Latn', 'srp\\_Cyrl', 'bul\\_Latn', 'bul', 'bos\\_Latn', 'slv'}\n* tgt\\_constituents: {'hrv', 'mkd', 'srp\\_Latn', 'srp\\_Cyrl', 'bul\\_Latn', 'bul', 'bos\\_Latn', 'slv'}\n* src\\_multilingual: True\n* tgt\\_multilingual: True\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zls\n* tgt\\_alpha3: zls\n* short\\_pair: zls-zls\n* chrF2\\_score: 0.563\n* bleu: 26.5\n* brevity\\_penalty: 1.0\n* ref\\_len: 58.0\n* src\\_name: South Slavic languages\n* tgt\\_name: South Slavic languages\n* train\\_date: 2020-07-27\n* src\\_alpha2: zls\n* tgt\\_alpha2: zls\n* prefer\\_old: False\n* long\\_pair: zls-zls\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ 59, 316, 529 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #hr #mk #bg #sl #zls #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zls-zls\n\n\n* source group: South Slavic languages\n* target group: South Slavic languages\n* OPUS readme: zls-zls\n* model: transformer\n* source language(s): bul mkd srp\\_Cyrl\n* target language(s): bul mkd srp\\_Cyrl\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 19.3, chr-F: 0.514\ntestset: URL, BLEU: 31.9, chr-F: 0.669\ntestset: URL, BLEU: 18.0, chr-F: 0.636\ntestset: URL, BLEU: 19.4, chr-F: 0.322\ntestset: URL, BLEU: 44.6, chr-F: 0.679\ntestset: URL, BLEU: 5.5, chr-F: 0.152\ntestset: URL, BLEU: 26.5, chr-F: 0.563### System Info:\n\n\n* hf\\_name: zls-zls\n* source\\_languages: zls\n* target\\_languages: zls\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['hr', 'mk', 'bg', 'sl', 'zls']\n* src\\_constituents: {'hrv', 'mkd', 'srp\\_Latn', 'srp\\_Cyrl', 'bul\\_Latn', 'bul', 'bos\\_Latn', 'slv'}\n* tgt\\_constituents: {'hrv', 'mkd', 'srp\\_Latn', 'srp\\_Cyrl', 'bul\\_Latn', 'bul', 'bos\\_Latn', 'slv'}\n* src\\_multilingual: True\n* tgt\\_multilingual: True\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zls\n* tgt\\_alpha3: zls\n* short\\_pair: zls-zls\n* chrF2\\_score: 0.563\n* bleu: 26.5\n* brevity\\_penalty: 1.0\n* ref\\_len: 58.0\n* src\\_name: South Slavic languages\n* tgt\\_name: South Slavic languages\n* train\\_date: 2020-07-27\n* src\\_alpha2: zls\n* tgt\\_alpha2: zls\n* prefer\\_old: False\n* long\\_pair: zls-zls\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "pl", "cs", "zlw", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "pl", "cs", "zlw", "en" ]

[ "### zlw-eng\n\n\n* source group: West Slavic languages\n* target group: English\n* OPUS readme: zlw-eng\n* model: transformer\n* source language(s): ces csb\\_Latn dsb hsb pol\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.7, chr-F: 0.536\ntestset: URL, BLEU: 24.6, chr-F: 0.530\ntestset: URL, BLEU: 25.0, chr-F: 0.540\ntestset: URL, BLEU: 25.9, chr-F: 0.539\ntestset: URL, BLEU: 24.8, chr-F: 0.533\ntestset: URL, BLEU: 27.8, chr-F: 0.551\ntestset: URL, BLEU: 30.3, chr-F: 0.585\ntestset: URL, BLEU: 27.5, chr-F: 0.542\ntestset: URL, BLEU: 29.1, chr-F: 0.564\ntestset: URL, BLEU: 26.0, chr-F: 0.537\ntestset: URL, BLEU: 27.3, chr-F: 0.544\ntestset: URL, BLEU: 53.3, chr-F: 0.691\ntestset: URL, BLEU: 10.2, chr-F: 0.313\ntestset: URL, BLEU: 11.7, chr-F: 0.296\ntestset: URL, BLEU: 24.6, chr-F: 0.426\ntestset: URL, BLEU: 51.8, chr-F: 0.680\ntestset: URL, BLEU: 50.4, chr-F: 0.667", "### System Info:\n\n\n* hf\\_name: zlw-eng\n* source\\_languages: zlw\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['pl', 'cs', 'zlw', 'en']\n* src\\_constituents: {'csb\\_Latn', 'dsb', 'hsb', 'pol', 'ces'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zlw\n* tgt\\_alpha3: eng\n* short\\_pair: zlw-en\n* chrF2\\_score: 0.68\n* bleu: 51.8\n* brevity\\_penalty: 0.9620000000000001\n* ref\\_len: 75742.0\n* src\\_name: West Slavic languages\n* tgt\\_name: English\n* train\\_date: 2020-08-01\n* src\\_alpha2: zlw\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: zlw-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #pl #cs #zlw #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zlw-eng\n\n\n* source group: West Slavic languages\n* target group: English\n* OPUS readme: zlw-eng\n* model: transformer\n* source language(s): ces csb\\_Latn dsb hsb pol\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.7, chr-F: 0.536\ntestset: URL, BLEU: 24.6, chr-F: 0.530\ntestset: URL, BLEU: 25.0, chr-F: 0.540\ntestset: URL, BLEU: 25.9, chr-F: 0.539\ntestset: URL, BLEU: 24.8, chr-F: 0.533\ntestset: URL, BLEU: 27.8, chr-F: 0.551\ntestset: URL, BLEU: 30.3, chr-F: 0.585\ntestset: URL, BLEU: 27.5, chr-F: 0.542\ntestset: URL, BLEU: 29.1, chr-F: 0.564\ntestset: URL, BLEU: 26.0, chr-F: 0.537\ntestset: URL, BLEU: 27.3, chr-F: 0.544\ntestset: URL, BLEU: 53.3, chr-F: 0.691\ntestset: URL, BLEU: 10.2, chr-F: 0.313\ntestset: URL, BLEU: 11.7, chr-F: 0.296\ntestset: URL, BLEU: 24.6, chr-F: 0.426\ntestset: URL, BLEU: 51.8, chr-F: 0.680\ntestset: URL, BLEU: 50.4, chr-F: 0.667", "### System Info:\n\n\n* hf\\_name: zlw-eng\n* source\\_languages: zlw\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['pl', 'cs', 'zlw', 'en']\n* src\\_constituents: {'csb\\_Latn', 'dsb', 'hsb', 'pol', 'ces'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zlw\n* tgt\\_alpha3: eng\n* short\\_pair: zlw-en\n* chrF2\\_score: 0.68\n* bleu: 51.8\n* brevity\\_penalty: 0.9620000000000001\n* ref\\_len: 75742.0\n* src\\_name: West Slavic languages\n* tgt\\_name: English\n* train\\_date: 2020-08-01\n* src\\_alpha2: zlw\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: zlw-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ 57, 509, 446 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #pl #cs #zlw #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zlw-eng\n\n\n* source group: West Slavic languages\n* target group: English\n* OPUS readme: zlw-eng\n* model: transformer\n* source language(s): ces csb\\_Latn dsb hsb pol\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.7, chr-F: 0.536\ntestset: URL, BLEU: 24.6, chr-F: 0.530\ntestset: URL, BLEU: 25.0, chr-F: 0.540\ntestset: URL, BLEU: 25.9, chr-F: 0.539\ntestset: URL, BLEU: 24.8, chr-F: 0.533\ntestset: URL, BLEU: 27.8, chr-F: 0.551\ntestset: URL, BLEU: 30.3, chr-F: 0.585\ntestset: URL, BLEU: 27.5, chr-F: 0.542\ntestset: URL, BLEU: 29.1, chr-F: 0.564\ntestset: URL, BLEU: 26.0, chr-F: 0.537\ntestset: URL, BLEU: 27.3, chr-F: 0.544\ntestset: URL, BLEU: 53.3, chr-F: 0.691\ntestset: URL, BLEU: 10.2, chr-F: 0.313\ntestset: URL, BLEU: 11.7, chr-F: 0.296\ntestset: URL, BLEU: 24.6, chr-F: 0.426\ntestset: URL, BLEU: 51.8, chr-F: 0.680\ntestset: URL, BLEU: 50.4, chr-F: 0.667### System Info:\n\n\n* hf\\_name: zlw-eng\n* source\\_languages: zlw\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['pl', 'cs', 'zlw', 'en']\n* src\\_constituents: {'csb\\_Latn', 'dsb', 'hsb', 'pol', 'ces'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zlw\n* tgt\\_alpha3: eng\n* short\\_pair: zlw-en\n* chrF2\\_score: 0.68\n* bleu: 51.8\n* brevity\\_penalty: 0.9620000000000001\n* ref\\_len: 75742.0\n* src\\_name: West Slavic languages\n* tgt\\_name: English\n* train\\_date: 2020-08-01\n* src\\_alpha2: zlw\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: zlw-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "transformers", "pytorch", "tf", "safetensors", "marian", "text2text-generation", "translation", "zlw", "fiu", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "dsb", "cs", "csb_Latn", "hsb", "pl", "zlw", "hu", "vro", "fi", "liv_Latn", "mdf", "krl", "fkv_Latn", "mhr", "et", "sma", "udm", "vep", "myv", "kpv", "se", "izh", "fiu" ]

[ "### zlw-fiu\n* source language name: West Slavic languages\n* target language name: Finno-Ugrian languages\n* OPUS readme: URL\n* model: transformer\n* source language codes: dsb, cs, csb_Latn, hsb, pl, zlw\n* target language codes: hu, vro, fi, liv_Latn, mdf, krl, fkv_Latn, mhr, et, sma, udm, vep, myv, kpv, se, izh, fiu\n* dataset: opus \n* release date: 2021-02-18\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* a sentence-initial language token is required in the form of >>id<<(id = valid, usually three-letter target language ID)\n* Training data: \n * ces-fin: Tatoeba-train (1000000)\n * ces-hun: Tatoeba-train (1000000)\n * pol-est: Tatoeba-train (1000000)\n * pol-fin: Tatoeba-train (1000000)\n * pol-hun: Tatoeba-train (1000000)\n* Validation data: \n * ces-fin: Tatoeba-dev, 1000\n * ces-hun: Tatoeba-dev, 1000\n * est-pol: Tatoeba-dev, 1000\n * fin-pol: Tatoeba-dev, 1000\n * hun-pol: Tatoeba-dev, 1000\n * mhr-pol: Tatoeba-dev, 461\n * total-size-shuffled: 5426\n * devset-selected: top 5000 lines of URL.shuffled!\n* Test data: \n * URL-hun: 502/9733\n * URL-hun: 2525/54965\n * URL-fin: 88/408\n * URL-hun: 1911/10336\n * URL-multi: 4562/25497\n * URL-chm: 5/36\n * URL-est: 15/98\n * URL-fin: 609/3293\n * URL-hun: 1934/11285\n* test set translations file: URL\n* test set scores file: URL\n* BLEU-scores\n|Test set|score|\n|---|---|\n|URL-fin|57.2|\n|URL-hun|42.6|\n|URL-multi|39.4|\n|URL-hun|36.6|\n|URL-fin|36.1|\n|URL-est|20.9|\n|URL-hun|13.9|\n|URL-hun|13.9|\n|URL-chm|2.0|\n* chr-F-scores\n|Test set|score|\n|---|---|\n|URL-fin|0.71|\n|URL-hun|0.637|\n|URL-multi|0.616|\n|URL-hun|0.605|\n|URL-fin|0.592|\n|URL-hun|0.449|\n|URL-hun|0.443|\n|URL-est|0.372|\n|URL-chm|0.007|", "### System Info: \n* hf_name: zlw-fiu\n* source_languages: dsb,cs,csb_Latn,hsb,pl,zlw\n* target_languages: hu,vro,fi,liv_Latn,mdf,krl,fkv_Latn,mhr,et,sma,udm,vep,myv,kpv,se,izh,fiu\n* opus_readme_url: URL\n* original_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['dsb', 'cs', 'csb_Latn', 'hsb', 'pl', 'zlw', 'hu', 'vro', 'fi', 'liv_Latn', 'mdf', 'krl', 'fkv_Latn', 'mhr', 'et', 'sma', 'udm', 'vep', 'myv', 'kpv', 'se', 'izh', 'fiu']\n* src_constituents: ['dsb', 'ces', 'csb_Latn', 'hsb', 'pol']\n* tgt_constituents: ['hun', 'vro', 'fin', 'liv_Latn', 'mdf', 'krl', 'fkv_Latn', 'mhr', 'est', 'sma', 'udm', 'vep', 'myv', 'kpv', 'sme', 'izh']\n* src_multilingual: True\n* tgt_multilingual: True\n* helsinki_git_sha: a0966db6db0ae616a28471ff0faf461b36fec07d\n* transformers_git_sha: 3857f2b4e34912c942694489c2b667d9476e55f5\n* port_machine: bungle\n* port_time: 2021-06-29-15:24" ]

[ "TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #zlw #fiu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zlw-fiu\n* source language name: West Slavic languages\n* target language name: Finno-Ugrian languages\n* OPUS readme: URL\n* model: transformer\n* source language codes: dsb, cs, csb_Latn, hsb, pl, zlw\n* target language codes: hu, vro, fi, liv_Latn, mdf, krl, fkv_Latn, mhr, et, sma, udm, vep, myv, kpv, se, izh, fiu\n* dataset: opus \n* release date: 2021-02-18\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* a sentence-initial language token is required in the form of >>id<<(id = valid, usually three-letter target language ID)\n* Training data: \n * ces-fin: Tatoeba-train (1000000)\n * ces-hun: Tatoeba-train (1000000)\n * pol-est: Tatoeba-train (1000000)\n * pol-fin: Tatoeba-train (1000000)\n * pol-hun: Tatoeba-train (1000000)\n* Validation data: \n * ces-fin: Tatoeba-dev, 1000\n * ces-hun: Tatoeba-dev, 1000\n * est-pol: Tatoeba-dev, 1000\n * fin-pol: Tatoeba-dev, 1000\n * hun-pol: Tatoeba-dev, 1000\n * mhr-pol: Tatoeba-dev, 461\n * total-size-shuffled: 5426\n * devset-selected: top 5000 lines of URL.shuffled!\n* Test data: \n * URL-hun: 502/9733\n * URL-hun: 2525/54965\n * URL-fin: 88/408\n * URL-hun: 1911/10336\n * URL-multi: 4562/25497\n * URL-chm: 5/36\n * URL-est: 15/98\n * URL-fin: 609/3293\n * URL-hun: 1934/11285\n* test set translations file: URL\n* test set scores file: URL\n* BLEU-scores\n|Test set|score|\n|---|---|\n|URL-fin|57.2|\n|URL-hun|42.6|\n|URL-multi|39.4|\n|URL-hun|36.6|\n|URL-fin|36.1|\n|URL-est|20.9|\n|URL-hun|13.9|\n|URL-hun|13.9|\n|URL-chm|2.0|\n* chr-F-scores\n|Test set|score|\n|---|---|\n|URL-fin|0.71|\n|URL-hun|0.637|\n|URL-multi|0.616|\n|URL-hun|0.605|\n|URL-fin|0.592|\n|URL-hun|0.449|\n|URL-hun|0.443|\n|URL-est|0.372|\n|URL-chm|0.007|", "### System Info: \n* hf_name: zlw-fiu\n* source_languages: dsb,cs,csb_Latn,hsb,pl,zlw\n* target_languages: hu,vro,fi,liv_Latn,mdf,krl,fkv_Latn,mhr,et,sma,udm,vep,myv,kpv,se,izh,fiu\n* opus_readme_url: URL\n* original_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['dsb', 'cs', 'csb_Latn', 'hsb', 'pl', 'zlw', 'hu', 'vro', 'fi', 'liv_Latn', 'mdf', 'krl', 'fkv_Latn', 'mhr', 'et', 'sma', 'udm', 'vep', 'myv', 'kpv', 'se', 'izh', 'fiu']\n* src_constituents: ['dsb', 'ces', 'csb_Latn', 'hsb', 'pol']\n* tgt_constituents: ['hun', 'vro', 'fin', 'liv_Latn', 'mdf', 'krl', 'fkv_Latn', 'mhr', 'est', 'sma', 'udm', 'vep', 'myv', 'kpv', 'sme', 'izh']\n* src_multilingual: True\n* tgt_multilingual: True\n* helsinki_git_sha: a0966db6db0ae616a28471ff0faf461b36fec07d\n* transformers_git_sha: 3857f2b4e34912c942694489c2b667d9476e55f5\n* port_machine: bungle\n* port_time: 2021-06-29-15:24" ]

[ 58, 759, 501 ]

[ "TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #zlw #fiu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zlw-fiu\n* source language name: West Slavic languages\n* target language name: Finno-Ugrian languages\n* OPUS readme: URL\n* model: transformer\n* source language codes: dsb, cs, csb_Latn, hsb, pl, zlw\n* target language codes: hu, vro, fi, liv_Latn, mdf, krl, fkv_Latn, mhr, et, sma, udm, vep, myv, kpv, se, izh, fiu\n* dataset: opus \n* release date: 2021-02-18\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* a sentence-initial language token is required in the form of >>id<<(id = valid, usually three-letter target language ID)\n* Training data: \n * ces-fin: Tatoeba-train (1000000)\n * ces-hun: Tatoeba-train (1000000)\n * pol-est: Tatoeba-train (1000000)\n * pol-fin: Tatoeba-train (1000000)\n * pol-hun: Tatoeba-train (1000000)\n* Validation data: \n * ces-fin: Tatoeba-dev, 1000\n * ces-hun: Tatoeba-dev, 1000\n * est-pol: Tatoeba-dev, 1000\n * fin-pol: Tatoeba-dev, 1000\n * hun-pol: Tatoeba-dev, 1000\n * mhr-pol: Tatoeba-dev, 461\n * total-size-shuffled: 5426\n * devset-selected: top 5000 lines of URL.shuffled!\n* Test data: \n * URL-hun: 502/9733\n * URL-hun: 2525/54965\n * URL-fin: 88/408\n * URL-hun: 1911/10336\n * URL-multi: 4562/25497\n * URL-chm: 5/36\n * URL-est: 15/98\n * URL-fin: 609/3293\n * URL-hun: 1934/11285\n* test set translations file: URL\n* test set scores file: URL\n* BLEU-scores\n|Test set|score|\n|---|---|\n|URL-fin|57.2|\n|URL-hun|42.6|\n|URL-multi|39.4|\n|URL-hun|36.6|\n|URL-fin|36.1|\n|URL-est|20.9|\n|URL-hun|13.9|\n|URL-hun|13.9|\n|URL-chm|2.0|\n* chr-F-scores\n|Test set|score|\n|---|---|\n|URL-fin|0.71|\n|URL-hun|0.637|\n|URL-multi|0.616|\n|URL-hun|0.605|\n|URL-fin|0.592|\n|URL-hun|0.449|\n|URL-hun|0.443|\n|URL-est|0.372|\n|URL-chm|0.007|### System Info: \n* hf_name: zlw-fiu\n* source_languages: dsb,cs,csb_Latn,hsb,pl,zlw\n* target_languages: hu,vro,fi,liv_Latn,mdf,krl,fkv_Latn,mhr,et,sma,udm,vep,myv,kpv,se,izh,fiu\n* opus_readme_url: URL\n* original_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['dsb', 'cs', 'csb_Latn', 'hsb', 'pl', 'zlw', 'hu', 'vro', 'fi', 'liv_Latn', 'mdf', 'krl', 'fkv_Latn', 'mhr', 'et', 'sma', 'udm', 'vep', 'myv', 'kpv', 'se', 'izh', 'fiu']\n* src_constituents: ['dsb', 'ces', 'csb_Latn', 'hsb', 'pol']\n* tgt_constituents: ['hun', 'vro', 'fin', 'liv_Latn', 'mdf', 'krl', 'fkv_Latn', 'mhr', 'est', 'sma', 'udm', 'vep', 'myv', 'kpv', 'sme', 'izh']\n* src_multilingual: True\n* tgt_multilingual: True\n* helsinki_git_sha: a0966db6db0ae616a28471ff0faf461b36fec07d\n* transformers_git_sha: 3857f2b4e34912c942694489c2b667d9476e55f5\n* port_machine: bungle\n* port_time: 2021-06-29-15:24" ]

[ "transformers", "pytorch", "marian", "text2text-generation", "translation", "pl", "cs", "zlw", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "pl", "cs", "zlw" ]

[ "### zlw-zlw\n\n\n* source group: West Slavic languages\n* target group: West Slavic languages\n* OPUS readme: zlw-zlw\n* model: transformer\n* source language(s): ces dsb hsb pol\n* target language(s): ces dsb hsb pol\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 2.6, chr-F: 0.167\ntestset: URL, BLEU: 44.0, chr-F: 0.649\ntestset: URL, BLEU: 8.5, chr-F: 0.250\ntestset: URL, BLEU: 9.6, chr-F: 0.276\ntestset: URL, BLEU: 38.8, chr-F: 0.580\ntestset: URL, BLEU: 43.4, chr-F: 0.620\ntestset: URL, BLEU: 2.1, chr-F: 0.159", "### System Info:\n\n\n* hf\\_name: zlw-zlw\n* source\\_languages: zlw\n* target\\_languages: zlw\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['pl', 'cs', 'zlw']\n* src\\_constituents: {'csb\\_Latn', 'dsb', 'hsb', 'pol', 'ces'}\n* tgt\\_constituents: {'csb\\_Latn', 'dsb', 'hsb', 'pol', 'ces'}\n* src\\_multilingual: True\n* tgt\\_multilingual: True\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zlw\n* tgt\\_alpha3: zlw\n* short\\_pair: zlw-zlw\n* chrF2\\_score: 0.58\n* bleu: 38.8\n* brevity\\_penalty: 0.99\n* ref\\_len: 7792.0\n* src\\_name: West Slavic languages\n* tgt\\_name: West Slavic languages\n* train\\_date: 2020-07-27\n* src\\_alpha2: zlw\n* tgt\\_alpha2: zlw\n* prefer\\_old: False\n* long\\_pair: zlw-zlw\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "TAGS\n#transformers #pytorch #marian #text2text-generation #translation #pl #cs #zlw #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### zlw-zlw\n\n\n* source group: West Slavic languages\n* target group: West Slavic languages\n* OPUS readme: zlw-zlw\n* model: transformer\n* source language(s): ces dsb hsb pol\n* target language(s): ces dsb hsb pol\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 2.6, chr-F: 0.167\ntestset: URL, BLEU: 44.0, chr-F: 0.649\ntestset: URL, BLEU: 8.5, chr-F: 0.250\ntestset: URL, BLEU: 9.6, chr-F: 0.276\ntestset: URL, BLEU: 38.8, chr-F: 0.580\ntestset: URL, BLEU: 43.4, chr-F: 0.620\ntestset: URL, BLEU: 2.1, chr-F: 0.159", "### System Info:\n\n\n* hf\\_name: zlw-zlw\n* source\\_languages: zlw\n* target\\_languages: zlw\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['pl', 'cs', 'zlw']\n* src\\_constituents: {'csb\\_Latn', 'dsb', 'hsb', 'pol', 'ces'}\n* tgt\\_constituents: {'csb\\_Latn', 'dsb', 'hsb', 'pol', 'ces'}\n* src\\_multilingual: True\n* tgt\\_multilingual: True\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zlw\n* tgt\\_alpha3: zlw\n* short\\_pair: zlw-zlw\n* chrF2\\_score: 0.58\n* bleu: 38.8\n* brevity\\_penalty: 0.99\n* ref\\_len: 7792.0\n* src\\_name: West Slavic languages\n* tgt\\_name: West Slavic languages\n* train\\_date: 2020-07-27\n* src\\_alpha2: zlw\n* tgt\\_alpha2: zlw\n* prefer\\_old: False\n* long\\_pair: zlw-zlw\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ 52, 310, 474 ]

[ "TAGS\n#transformers #pytorch #marian #text2text-generation #translation #pl #cs #zlw #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### zlw-zlw\n\n\n* source group: West Slavic languages\n* target group: West Slavic languages\n* OPUS readme: zlw-zlw\n* model: transformer\n* source language(s): ces dsb hsb pol\n* target language(s): ces dsb hsb pol\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 2.6, chr-F: 0.167\ntestset: URL, BLEU: 44.0, chr-F: 0.649\ntestset: URL, BLEU: 8.5, chr-F: 0.250\ntestset: URL, BLEU: 9.6, chr-F: 0.276\ntestset: URL, BLEU: 38.8, chr-F: 0.580\ntestset: URL, BLEU: 43.4, chr-F: 0.620\ntestset: URL, BLEU: 2.1, chr-F: 0.159### System Info:\n\n\n* hf\\_name: zlw-zlw\n* source\\_languages: zlw\n* target\\_languages: zlw\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['pl', 'cs', 'zlw']\n* src\\_constituents: {'csb\\_Latn', 'dsb', 'hsb', 'pol', 'ces'}\n* tgt\\_constituents: {'csb\\_Latn', 'dsb', 'hsb', 'pol', 'ces'}\n* src\\_multilingual: True\n* tgt\\_multilingual: True\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: zlw\n* tgt\\_alpha3: zlw\n* short\\_pair: zlw-zlw\n* chrF2\\_score: 0.58\n* bleu: 38.8\n* brevity\\_penalty: 0.99\n* ref\\_len: 7792.0\n* src\\_name: West Slavic languages\n* tgt\\_name: West Slavic languages\n* train\\_date: 2020-07-27\n* src\\_alpha2: zlw\n* tgt\\_alpha2: zlw\n* prefer\\_old: False\n* long\\_pair: zlw-zlw\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "zne", "es", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-zne-es\n\n\n* source languages: zne\n* target languages: es\n* OPUS readme: zne-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 21.1, chr-F: 0.382" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zne #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-zne-es\n\n\n* source languages: zne\n* target languages: es\n* OPUS readme: zne-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 21.1, chr-F: 0.382" ]

[ 52, 109 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zne #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-zne-es\n\n\n* source languages: zne\n* target languages: es\n* OPUS readme: zne-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 21.1, chr-F: 0.382" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "zne", "fi", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-zne-fi\n\n\n* source languages: zne\n* target languages: fi\n* OPUS readme: zne-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.8, chr-F: 0.432" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zne #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-zne-fi\n\n\n* source languages: zne\n* target languages: fi\n* OPUS readme: zne-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.8, chr-F: 0.432" ]

[ 52, 109 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zne #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-zne-fi\n\n\n* source languages: zne\n* target languages: fi\n* OPUS readme: zne-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.8, chr-F: 0.432" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "zne", "fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-zne-fr\n\n\n* source languages: zne\n* target languages: fr\n* OPUS readme: zne-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.3, chr-F: 0.416" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zne #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-zne-fr\n\n\n* source languages: zne\n* target languages: fr\n* OPUS readme: zne-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.3, chr-F: 0.416" ]

[ 52, 109 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zne #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-zne-fr\n\n\n* source languages: zne\n* target languages: fr\n* OPUS readme: zne-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.3, chr-F: 0.416" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "zne", "sv", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "### opus-mt-zne-sv\n\n\n* source languages: zne\n* target languages: sv\n* OPUS readme: zne-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.2, chr-F: 0.425" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zne #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### opus-mt-zne-sv\n\n\n* source languages: zne\n* target languages: sv\n* OPUS readme: zne-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.2, chr-F: 0.425" ]

[ 52, 108 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #zne #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-zne-sv\n\n\n* source languages: zne\n* target languages: sv\n* OPUS readme: zne-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.2, chr-F: 0.425" ]

[ "transformers", "pytorch", "safetensors", "marian", "text2text-generation", "translation", "af", "ru", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "af", "ru" ]

[ "### af-ru\n\n\n* source group: Afrikaans\n* target group: Russian\n* OPUS readme: afr-rus\n* model: transformer-align\n* source language(s): afr\n* target language(s): rus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.2, chr-F: 0.580", "### System Info:\n\n\n* hf\\_name: af-ru\n* source\\_languages: afr\n* target\\_languages: rus\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['af', 'ru']\n* src\\_constituents: ('Afrikaans', {'afr'})\n* tgt\\_constituents: ('Russian', {'rus'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: afr-rus\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: afr\n* tgt\\_alpha3: rus\n* chrF2\\_score: 0.58\n* bleu: 38.2\n* brevity\\_penalty: 0.992\n* ref\\_len: 1213\n* src\\_name: Afrikaans\n* tgt\\_name: Russian\n* train\\_date: 2020-01-01 00:00:00\n* src\\_alpha2: af\n* tgt\\_alpha2: ru\n* prefer\\_old: False\n* short\\_pair: af-ru\n* helsinki\\_git\\_sha: e8c308a96c1bd0b4ca6a8ce174783f93c3e30f25\n* transformers\\_git\\_sha: 31245775e5772fbded1ac07ed89fbba3b5af0cb9\n* port\\_machine: URL\n* port\\_time: 2021-02-12-14:52" ]

[ "TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #af #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### af-ru\n\n\n* source group: Afrikaans\n* target group: Russian\n* OPUS readme: afr-rus\n* model: transformer-align\n* source language(s): afr\n* target language(s): rus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.2, chr-F: 0.580", "### System Info:\n\n\n* hf\\_name: af-ru\n* source\\_languages: afr\n* target\\_languages: rus\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['af', 'ru']\n* src\\_constituents: ('Afrikaans', {'afr'})\n* tgt\\_constituents: ('Russian', {'rus'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: afr-rus\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: afr\n* tgt\\_alpha3: rus\n* chrF2\\_score: 0.58\n* bleu: 38.2\n* brevity\\_penalty: 0.992\n* ref\\_len: 1213\n* src\\_name: Afrikaans\n* tgt\\_name: Russian\n* train\\_date: 2020-01-01 00:00:00\n* src\\_alpha2: af\n* tgt\\_alpha2: ru\n* prefer\\_old: False\n* short\\_pair: af-ru\n* helsinki\\_git\\_sha: e8c308a96c1bd0b4ca6a8ce174783f93c3e30f25\n* transformers\\_git\\_sha: 31245775e5772fbded1ac07ed89fbba3b5af0cb9\n* port\\_machine: URL\n* port\\_time: 2021-02-12-14:52" ]

[ 52, 132, 411 ]

[ "TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #af #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### af-ru\n\n\n* source group: Afrikaans\n* target group: Russian\n* OPUS readme: afr-rus\n* model: transformer-align\n* source language(s): afr\n* target language(s): rus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.2, chr-F: 0.580### System Info:\n\n\n* hf\\_name: af-ru\n* source\\_languages: afr\n* target\\_languages: rus\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['af', 'ru']\n* src\\_constituents: ('Afrikaans', {'afr'})\n* tgt\\_constituents: ('Russian', {'rus'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: afr-rus\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: afr\n* tgt\\_alpha3: rus\n* chrF2\\_score: 0.58\n* bleu: 38.2\n* brevity\\_penalty: 0.992\n* ref\\_len: 1213\n* src\\_name: Afrikaans\n* tgt\\_name: Russian\n* train\\_date: 2020-01-01 00:00:00\n* src\\_alpha2: af\n* tgt\\_alpha2: ru\n* prefer\\_old: False\n* short\\_pair: af-ru\n* helsinki\\_git\\_sha: e8c308a96c1bd0b4ca6a8ce174783f93c3e30f25\n* transformers\\_git\\_sha: 31245775e5772fbded1ac07ed89fbba3b5af0cb9\n* port\\_machine: URL\n* port\\_time: 2021-02-12-14:52" ]

[ "transformers", "pytorch", "tf", "safetensors", "marian", "text2text-generation", "translation", "de", "ro", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "de", "ro" ]

[ "### de-ro\n\n\n* source group: German\n* target group: Romanian\n* OPUS readme: deu-ron\n* model: transformer-align\n* source language(s): deu\n* target language(s): mol ron\n* raw source language(s): deu\n* raw target language(s): mol ron\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* valid language labels: >>mol<< >>ron<<\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------", "### System Info:\n\n\n* hf\\_name: de-ro\n* source\\_languages: deu\n* target\\_languages: ron\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['de', 'ro']\n* src\\_constituents: ('German', {'deu'})\n* tgt\\_constituents: ('Romanian', {'ron'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: deu-ron\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: deu\n* tgt\\_alpha3: ron\n* chrF2\\_score: 0.636\n* bleu: 42.0\n* src\\_name: German\n* tgt\\_name: Romanian\n* train\\_date: 2021-10-22 00:00:00\n* src\\_alpha2: de\n* tgt\\_alpha2: ro\n* prefer\\_old: False\n* short\\_pair: de-ro\n* helsinki\\_git\\_sha: 2ef219d5b67f0afb0c6b732cd07001d84181f002\n* transformers\\_git\\_sha: df1f94eb4a18b1a27d27e32040b60a17410d516e\n* port\\_machine: URL\n* port\\_time: 2021-11-08-16:45" ]

[ "TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #de #ro #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### de-ro\n\n\n* source group: German\n* target group: Romanian\n* OPUS readme: deu-ron\n* model: transformer-align\n* source language(s): deu\n* target language(s): mol ron\n* raw source language(s): deu\n* raw target language(s): mol ron\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* valid language labels: >>mol<< >>ron<<\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------", "### System Info:\n\n\n* hf\\_name: de-ro\n* source\\_languages: deu\n* target\\_languages: ron\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['de', 'ro']\n* src\\_constituents: ('German', {'deu'})\n* tgt\\_constituents: ('Romanian', {'ron'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: deu-ron\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: deu\n* tgt\\_alpha3: ron\n* chrF2\\_score: 0.636\n* bleu: 42.0\n* src\\_name: German\n* tgt\\_name: Romanian\n* train\\_date: 2021-10-22 00:00:00\n* src\\_alpha2: de\n* tgt\\_alpha2: ro\n* prefer\\_old: False\n* short\\_pair: de-ro\n* helsinki\\_git\\_sha: 2ef219d5b67f0afb0c6b732cd07001d84181f002\n* transformers\\_git\\_sha: df1f94eb4a18b1a27d27e32040b60a17410d516e\n* port\\_machine: URL\n* port\\_time: 2021-11-08-16:45" ]

[ 55, 176, 394 ]

[ "TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #de #ro #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### de-ro\n\n\n* source group: German\n* target group: Romanian\n* OPUS readme: deu-ron\n* model: transformer-align\n* source language(s): deu\n* target language(s): mol ron\n* raw source language(s): deu\n* raw target language(s): mol ron\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* valid language labels: >>mol<< >>ron<<\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------### System Info:\n\n\n* hf\\_name: de-ro\n* source\\_languages: deu\n* target\\_languages: ron\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['de', 'ro']\n* src\\_constituents: ('German', {'deu'})\n* tgt\\_constituents: ('Romanian', {'ron'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: deu-ron\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: deu\n* tgt\\_alpha3: ron\n* chrF2\\_score: 0.636\n* bleu: 42.0\n* src\\_name: German\n* tgt\\_name: Romanian\n* train\\_date: 2021-10-22 00:00:00\n* src\\_alpha2: de\n* tgt\\_alpha2: ro\n* prefer\\_old: False\n* short\\_pair: de-ro\n* helsinki\\_git\\_sha: 2ef219d5b67f0afb0c6b732cd07001d84181f002\n* transformers\\_git\\_sha: df1f94eb4a18b1a27d27e32040b60a17410d516e\n* port\\_machine: URL\n* port\\_time: 2021-11-08-16:45" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "en", "ja", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "en", "ja" ]

[ "### en-ja\n\n\n* source group: English\n* target group: Japanese\n* OPUS readme: eng-jpn\n* model: transformer-align\n* source language(s): eng\n* target language(s): jpn\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: opus+URL\n* test set translations: opus+URL\n* test set scores: opus+URL\n\n\nBenchmarks\n----------", "### System Info:\n\n\n* hf\\_name: en-ja\n* source\\_languages: eng\n* target\\_languages: jpn\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['en', 'ja']\n* src\\_constituents: ('English', {'eng'})\n* tgt\\_constituents: ('Japanese', {'jpn', 'jpn\\_Latn', 'jpn\\_Yiii', 'jpn\\_Kana', 'jpn\\_Hira', 'jpn\\_Hang', 'jpn\\_Bopo', 'jpn\\_Hani'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: eng-jpn\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: eng\n* tgt\\_alpha3: jpn\n* chrF2\\_score: 0.258\n* bleu: 15.2\n* src\\_name: English\n* tgt\\_name: Japanese\n* train\\_date: 2021-04-10 00:00:00\n* src\\_alpha2: en\n* tgt\\_alpha2: ja\n* prefer\\_old: False\n* short\\_pair: en-ja\n* helsinki\\_git\\_sha: 70b0a9621f054ef1d8ea81f7d55595d7f64d19ff\n* transformers\\_git\\_sha: 12b4d66a80419db30a15e7b9d4208ceb9887c03b\n* port\\_machine: URL\n* port\\_time: 2021-10-12-11:13" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #en #ja #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### en-ja\n\n\n* source group: English\n* target group: Japanese\n* OPUS readme: eng-jpn\n* model: transformer-align\n* source language(s): eng\n* target language(s): jpn\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: opus+URL\n* test set translations: opus+URL\n* test set scores: opus+URL\n\n\nBenchmarks\n----------", "### System Info:\n\n\n* hf\\_name: en-ja\n* source\\_languages: eng\n* target\\_languages: jpn\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['en', 'ja']\n* src\\_constituents: ('English', {'eng'})\n* tgt\\_constituents: ('Japanese', {'jpn', 'jpn\\_Latn', 'jpn\\_Yiii', 'jpn\\_Kana', 'jpn\\_Hira', 'jpn\\_Hang', 'jpn\\_Bopo', 'jpn\\_Hani'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: eng-jpn\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: eng\n* tgt\\_alpha3: jpn\n* chrF2\\_score: 0.258\n* bleu: 15.2\n* src\\_name: English\n* tgt\\_name: Japanese\n* train\\_date: 2021-04-10 00:00:00\n* src\\_alpha2: en\n* tgt\\_alpha2: ja\n* prefer\\_old: False\n* short\\_pair: en-ja\n* helsinki\\_git\\_sha: 70b0a9621f054ef1d8ea81f7d55595d7f64d19ff\n* transformers\\_git\\_sha: 12b4d66a80419db30a15e7b9d4208ceb9887c03b\n* port\\_machine: URL\n* port\\_time: 2021-10-12-11:13" ]

[ 51, 116, 456 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #en #ja #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### en-ja\n\n\n* source group: English\n* target group: Japanese\n* OPUS readme: eng-jpn\n* model: transformer-align\n* source language(s): eng\n* target language(s): jpn\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: opus+URL\n* test set translations: opus+URL\n* test set scores: opus+URL\n\n\nBenchmarks\n----------### System Info:\n\n\n* hf\\_name: en-ja\n* source\\_languages: eng\n* target\\_languages: jpn\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['en', 'ja']\n* src\\_constituents: ('English', {'eng'})\n* tgt\\_constituents: ('Japanese', {'jpn', 'jpn\\_Latn', 'jpn\\_Yiii', 'jpn\\_Kana', 'jpn\\_Hira', 'jpn\\_Hang', 'jpn\\_Bopo', 'jpn\\_Hani'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: eng-jpn\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: eng\n* tgt\\_alpha3: jpn\n* chrF2\\_score: 0.258\n* bleu: 15.2\n* src\\_name: English\n* tgt\\_name: Japanese\n* train\\_date: 2021-04-10 00:00:00\n* src\\_alpha2: en\n* tgt\\_alpha2: ja\n* prefer\\_old: False\n* short\\_pair: en-ja\n* helsinki\\_git\\_sha: 70b0a9621f054ef1d8ea81f7d55595d7f64d19ff\n* transformers\\_git\\_sha: 12b4d66a80419db30a15e7b9d4208ceb9887c03b\n* port\\_machine: URL\n* port\\_time: 2021-10-12-11:13" ]

[ "transformers", "pytorch", "safetensors", "marian", "text2text-generation", "translation", "en", "ro", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "en", "ro" ]

[ "### en-ro\n\n\n* source group: English\n* target group: Romanian\n* OPUS readme: eng-ron\n* model: transformer-align\n* source language(s): eng\n* target language(s): mol ron\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* valid language labels:\n* download original weights: opus+URL\n* test set translations: opus+URL\n* test set scores: opus+URL\n\n\nBenchmarks\n----------", "### System Info:\n\n\n* hf\\_name: en-ro\n* source\\_languages: eng\n* target\\_languages: ron\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['en', 'ro']\n* src\\_constituents: ('English', {'eng'})\n* tgt\\_constituents: ('Romanian', {'ron'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: eng-ron\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: eng\n* tgt\\_alpha3: ron\n* chrF2\\_score: 0.678\n* bleu: 46.9\n* src\\_name: English\n* tgt\\_name: Romanian\n* train\\_date: 2021-03-07 00:00:00\n* src\\_alpha2: en\n* tgt\\_alpha2: ro\n* prefer\\_old: False\n* short\\_pair: en-ro\n* helsinki\\_git\\_sha: 2ef219d5b67f0afb0c6b732cd07001d84181f002\n* transformers\\_git\\_sha: 12b4d66a80419db30a15e7b9d4208ceb9887c03b\n* port\\_machine: URL\n* port\\_time: 2021-11-08-09:31" ]

[ "TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #en #ro #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### en-ro\n\n\n* source group: English\n* target group: Romanian\n* OPUS readme: eng-ron\n* model: transformer-align\n* source language(s): eng\n* target language(s): mol ron\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* valid language labels:\n* download original weights: opus+URL\n* test set translations: opus+URL\n* test set scores: opus+URL\n\n\nBenchmarks\n----------", "### System Info:\n\n\n* hf\\_name: en-ro\n* source\\_languages: eng\n* target\\_languages: ron\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['en', 'ro']\n* src\\_constituents: ('English', {'eng'})\n* tgt\\_constituents: ('Romanian', {'ron'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: eng-ron\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: eng\n* tgt\\_alpha3: ron\n* chrF2\\_score: 0.678\n* bleu: 46.9\n* src\\_name: English\n* tgt\\_name: Romanian\n* train\\_date: 2021-03-07 00:00:00\n* src\\_alpha2: en\n* tgt\\_alpha2: ro\n* prefer\\_old: False\n* short\\_pair: en-ro\n* helsinki\\_git\\_sha: 2ef219d5b67f0afb0c6b732cd07001d84181f002\n* transformers\\_git\\_sha: 12b4d66a80419db30a15e7b9d4208ceb9887c03b\n* port\\_machine: URL\n* port\\_time: 2021-11-08-09:31" ]

[ 52, 148, 391 ]

[ "TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #en #ro #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### en-ro\n\n\n* source group: English\n* target group: Romanian\n* OPUS readme: eng-ron\n* model: transformer-align\n* source language(s): eng\n* target language(s): mol ron\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* valid language labels:\n* download original weights: opus+URL\n* test set translations: opus+URL\n* test set scores: opus+URL\n\n\nBenchmarks\n----------### System Info:\n\n\n* hf\\_name: en-ro\n* source\\_languages: eng\n* target\\_languages: ron\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['en', 'ro']\n* src\\_constituents: ('English', {'eng'})\n* tgt\\_constituents: ('Romanian', {'ron'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: eng-ron\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: eng\n* tgt\\_alpha3: ron\n* chrF2\\_score: 0.678\n* bleu: 46.9\n* src\\_name: English\n* tgt\\_name: Romanian\n* train\\_date: 2021-03-07 00:00:00\n* src\\_alpha2: en\n* tgt\\_alpha2: ro\n* prefer\\_old: False\n* short\\_pair: en-ro\n* helsinki\\_git\\_sha: 2ef219d5b67f0afb0c6b732cd07001d84181f002\n* transformers\\_git\\_sha: 12b4d66a80419db30a15e7b9d4208ceb9887c03b\n* port\\_machine: URL\n* port\\_time: 2021-11-08-09:31" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "en", "tr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "en", "tr" ]

[ "### en-tr\n\n\n* source group: English\n* target group: Turkish\n* OPUS readme: eng-tur\n* model: transformer-align\n* source language(s): eng\n* target language(s): tur\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: opus+URL\n* test set translations: opus+URL\n* test set scores: opus+URL\n\n\nBenchmarks\n----------", "### System Info:\n\n\n* hf\\_name: en-tr\n* source\\_languages: eng\n* target\\_languages: tur\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['en', 'tr']\n* src\\_constituents: ('English', {'eng'})\n* tgt\\_constituents: ('Turkish', {'tur'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: eng-tur\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: eng\n* tgt\\_alpha3: tur\n* chrF2\\_score: 0.684\n* bleu: 41.5\n* src\\_name: English\n* tgt\\_name: Turkish\n* train\\_date: 2021-04-10 00:00:00\n* src\\_alpha2: en\n* tgt\\_alpha2: tr\n* prefer\\_old: False\n* short\\_pair: en-tr\n* helsinki\\_git\\_sha: a6bd0607aec9603811b2b635aec3f566f3add79d\n* transformers\\_git\\_sha: 12b4d66a80419db30a15e7b9d4208ceb9887c03b\n* port\\_machine: URL\n* port\\_time: 2021-10-05-12:13" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #en #tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### en-tr\n\n\n* source group: English\n* target group: Turkish\n* OPUS readme: eng-tur\n* model: transformer-align\n* source language(s): eng\n* target language(s): tur\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: opus+URL\n* test set translations: opus+URL\n* test set scores: opus+URL\n\n\nBenchmarks\n----------", "### System Info:\n\n\n* hf\\_name: en-tr\n* source\\_languages: eng\n* target\\_languages: tur\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['en', 'tr']\n* src\\_constituents: ('English', {'eng'})\n* tgt\\_constituents: ('Turkish', {'tur'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: eng-tur\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: eng\n* tgt\\_alpha3: tur\n* chrF2\\_score: 0.684\n* bleu: 41.5\n* src\\_name: English\n* tgt\\_name: Turkish\n* train\\_date: 2021-04-10 00:00:00\n* src\\_alpha2: en\n* tgt\\_alpha2: tr\n* prefer\\_old: False\n* short\\_pair: en-tr\n* helsinki\\_git\\_sha: a6bd0607aec9603811b2b635aec3f566f3add79d\n* transformers\\_git\\_sha: 12b4d66a80419db30a15e7b9d4208ceb9887c03b\n* port\\_machine: URL\n* port\\_time: 2021-10-05-12:13" ]

[ 51, 116, 395 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #en #tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### en-tr\n\n\n* source group: English\n* target group: Turkish\n* OPUS readme: eng-tur\n* model: transformer-align\n* source language(s): eng\n* target language(s): tur\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: opus+URL\n* test set translations: opus+URL\n* test set scores: opus+URL\n\n\nBenchmarks\n----------### System Info:\n\n\n* hf\\_name: en-tr\n* source\\_languages: eng\n* target\\_languages: tur\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['en', 'tr']\n* src\\_constituents: ('English', {'eng'})\n* tgt\\_constituents: ('Turkish', {'tur'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: eng-tur\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: eng\n* tgt\\_alpha3: tur\n* chrF2\\_score: 0.684\n* bleu: 41.5\n* src\\_name: English\n* tgt\\_name: Turkish\n* train\\_date: 2021-04-10 00:00:00\n* src\\_alpha2: en\n* tgt\\_alpha2: tr\n* prefer\\_old: False\n* short\\_pair: en-tr\n* helsinki\\_git\\_sha: a6bd0607aec9603811b2b635aec3f566f3add79d\n* transformers\\_git\\_sha: 12b4d66a80419db30a15e7b9d4208ceb9887c03b\n* port\\_machine: URL\n* port\\_time: 2021-10-05-12:13" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "es", "zh", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "es", "zh" ]

[ "### es-zh\n\n\n* source group: Spanish\n* target group: Chinese\n* OPUS readme: spa-zho\n* model: transformer\n* source language(s): spa\n* target language(s): cjy\\_Hans cjy\\_Hant cmn cmn\\_Hans cmn\\_Hant hsn hsn\\_Hani lzh nan wuu yue\\_Hans yue\\_Hant\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.8, chr-F: 0.324", "### System Info:\n\n\n* hf\\_name: es-zh\n* source\\_languages: spa\n* target\\_languages: zho\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['es', 'zh']\n* src\\_constituents: ('Spanish', {'spa'})\n* tgt\\_constituents: ('Chinese', {'wuu\\_Bopo', 'wuu', 'cmn\\_Hang', 'lzh\\_Kana', 'lzh', 'wuu\\_Hani', 'lzh\\_Yiii', 'yue\\_Hans', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn\\_Hans', 'cmn\\_Kana', 'zho\\_Hans', 'zho\\_Hant', 'yue', 'cmn\\_Bopo', 'yue\\_Hang', 'lzh\\_Hans', 'wuu\\_Latn', 'yue\\_Hant', 'hak\\_Hani', 'lzh\\_Bopo', 'cmn\\_Hant', 'lzh\\_Hani', 'lzh\\_Hang', 'cmn', 'lzh\\_Hira', 'yue\\_Bopo', 'yue\\_Hani', 'gan', 'zho', 'cmn\\_Yiii', 'yue\\_Hira', 'cmn\\_Latn', 'yue\\_Kana', 'cjy\\_Hant', 'cmn\\_Hira', 'nan\\_Hani', 'nan'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: spa-zho\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: spa\n* tgt\\_alpha3: zho\n* chrF2\\_score: 0.324\n* bleu: 38.8\n* brevity\\_penalty: 0.878\n* ref\\_len: 22762.0\n* src\\_name: Spanish\n* tgt\\_name: Chinese\n* train\\_date: 2021-01-04 00:00:00\n* src\\_alpha2: es\n* tgt\\_alpha2: zh\n* prefer\\_old: False\n* short\\_pair: es-zh\n* helsinki\\_git\\_sha: dfdcef114ffb8a8dbb7a3fcf84bde5af50309500\n* transformers\\_git\\_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de\n* port\\_machine: URL\n* port\\_time: 2021-01-04-18:53" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #es #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### es-zh\n\n\n* source group: Spanish\n* target group: Chinese\n* OPUS readme: spa-zho\n* model: transformer\n* source language(s): spa\n* target language(s): cjy\\_Hans cjy\\_Hant cmn cmn\\_Hans cmn\\_Hant hsn hsn\\_Hani lzh nan wuu yue\\_Hans yue\\_Hant\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.8, chr-F: 0.324", "### System Info:\n\n\n* hf\\_name: es-zh\n* source\\_languages: spa\n* target\\_languages: zho\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['es', 'zh']\n* src\\_constituents: ('Spanish', {'spa'})\n* tgt\\_constituents: ('Chinese', {'wuu\\_Bopo', 'wuu', 'cmn\\_Hang', 'lzh\\_Kana', 'lzh', 'wuu\\_Hani', 'lzh\\_Yiii', 'yue\\_Hans', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn\\_Hans', 'cmn\\_Kana', 'zho\\_Hans', 'zho\\_Hant', 'yue', 'cmn\\_Bopo', 'yue\\_Hang', 'lzh\\_Hans', 'wuu\\_Latn', 'yue\\_Hant', 'hak\\_Hani', 'lzh\\_Bopo', 'cmn\\_Hant', 'lzh\\_Hani', 'lzh\\_Hang', 'cmn', 'lzh\\_Hira', 'yue\\_Bopo', 'yue\\_Hani', 'gan', 'zho', 'cmn\\_Yiii', 'yue\\_Hira', 'cmn\\_Latn', 'yue\\_Kana', 'cjy\\_Hant', 'cmn\\_Hira', 'nan\\_Hani', 'nan'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: spa-zho\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: spa\n* tgt\\_alpha3: zho\n* chrF2\\_score: 0.324\n* bleu: 38.8\n* brevity\\_penalty: 0.878\n* ref\\_len: 22762.0\n* src\\_name: Spanish\n* tgt\\_name: Chinese\n* train\\_date: 2021-01-04 00:00:00\n* src\\_alpha2: es\n* tgt\\_alpha2: zh\n* prefer\\_old: False\n* short\\_pair: es-zh\n* helsinki\\_git\\_sha: dfdcef114ffb8a8dbb7a3fcf84bde5af50309500\n* transformers\\_git\\_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de\n* port\\_machine: URL\n* port\\_time: 2021-01-04-18:53" ]

[ 52, 202, 719 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #es #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### es-zh\n\n\n* source group: Spanish\n* target group: Chinese\n* OPUS readme: spa-zho\n* model: transformer\n* source language(s): spa\n* target language(s): cjy\\_Hans cjy\\_Hant cmn cmn\\_Hans cmn\\_Hant hsn hsn\\_Hani lzh nan wuu yue\\_Hans yue\\_Hant\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.8, chr-F: 0.324### System Info:\n\n\n* hf\\_name: es-zh\n* source\\_languages: spa\n* target\\_languages: zho\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['es', 'zh']\n* src\\_constituents: ('Spanish', {'spa'})\n* tgt\\_constituents: ('Chinese', {'wuu\\_Bopo', 'wuu', 'cmn\\_Hang', 'lzh\\_Kana', 'lzh', 'wuu\\_Hani', 'lzh\\_Yiii', 'yue\\_Hans', 'cmn\\_Hani', 'cjy\\_Hans', 'cmn\\_Hans', 'cmn\\_Kana', 'zho\\_Hans', 'zho\\_Hant', 'yue', 'cmn\\_Bopo', 'yue\\_Hang', 'lzh\\_Hans', 'wuu\\_Latn', 'yue\\_Hant', 'hak\\_Hani', 'lzh\\_Bopo', 'cmn\\_Hant', 'lzh\\_Hani', 'lzh\\_Hang', 'cmn', 'lzh\\_Hira', 'yue\\_Bopo', 'yue\\_Hani', 'gan', 'zho', 'cmn\\_Yiii', 'yue\\_Hira', 'cmn\\_Latn', 'yue\\_Kana', 'cjy\\_Hant', 'cmn\\_Hira', 'nan\\_Hani', 'nan'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: spa-zho\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: spa\n* tgt\\_alpha3: zho\n* chrF2\\_score: 0.324\n* bleu: 38.8\n* brevity\\_penalty: 0.878\n* ref\\_len: 22762.0\n* src\\_name: Spanish\n* tgt\\_name: Chinese\n* train\\_date: 2021-01-04 00:00:00\n* src\\_alpha2: es\n* tgt\\_alpha2: zh\n* prefer\\_old: False\n* short\\_pair: es-zh\n* helsinki\\_git\\_sha: dfdcef114ffb8a8dbb7a3fcf84bde5af50309500\n* transformers\\_git\\_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de\n* port\\_machine: URL\n* port\\_time: 2021-01-04-18:53" ]

[ "transformers", "pytorch", "tf", "safetensors", "marian", "text2text-generation", "translation", "fi", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "fi", "en" ]

[ "### fi-en\n\n\n* source group: Finnish\n* target group: English\n* OPUS readme: fin-eng\n* model: transformer-align\n* source language(s): fin\n* target language(s): eng\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: opusTCv20210807+URL\n* test set translations: opusTCv20210807+URL\n* test set scores: opusTCv20210807+URL\n\n\nBenchmarks\n----------", "### System Info:\n\n\n* hf\\_name: fi-en\n* source\\_languages: fin\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['fi', 'en']\n* src\\_constituents: ('Finnish', {'fin'})\n* tgt\\_constituents: ('English', {'eng'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: fin-eng\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: fin\n* tgt\\_alpha3: eng\n* chrF2\\_score: 0.7\n* bleu: 54.1\n* src\\_name: Finnish\n* tgt\\_name: English\n* train\\_date: 2021-08-25 00:00:00\n* src\\_alpha2: fi\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* short\\_pair: fi-en\n* helsinki\\_git\\_sha: 2ef219d5b67f0afb0c6b732cd07001d84181f002\n* transformers\\_git\\_sha: 12b4d66a80419db30a15e7b9d4208ceb9887c03b\n* port\\_machine: URL\n* port\\_time: 2021-11-04-21:36" ]

[ "TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #fi #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### fi-en\n\n\n* source group: Finnish\n* target group: English\n* OPUS readme: fin-eng\n* model: transformer-align\n* source language(s): fin\n* target language(s): eng\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: opusTCv20210807+URL\n* test set translations: opusTCv20210807+URL\n* test set scores: opusTCv20210807+URL\n\n\nBenchmarks\n----------", "### System Info:\n\n\n* hf\\_name: fi-en\n* source\\_languages: fin\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['fi', 'en']\n* src\\_constituents: ('Finnish', {'fin'})\n* tgt\\_constituents: ('English', {'eng'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: fin-eng\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: fin\n* tgt\\_alpha3: eng\n* chrF2\\_score: 0.7\n* bleu: 54.1\n* src\\_name: Finnish\n* tgt\\_name: English\n* train\\_date: 2021-08-25 00:00:00\n* src\\_alpha2: fi\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* short\\_pair: fi-en\n* helsinki\\_git\\_sha: 2ef219d5b67f0afb0c6b732cd07001d84181f002\n* transformers\\_git\\_sha: 12b4d66a80419db30a15e7b9d4208ceb9887c03b\n* port\\_machine: URL\n* port\\_time: 2021-11-04-21:36" ]

[ 55, 135, 390 ]

[ "TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #fi #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### fi-en\n\n\n* source group: Finnish\n* target group: English\n* OPUS readme: fin-eng\n* model: transformer-align\n* source language(s): fin\n* target language(s): eng\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: opusTCv20210807+URL\n* test set translations: opusTCv20210807+URL\n* test set scores: opusTCv20210807+URL\n\n\nBenchmarks\n----------### System Info:\n\n\n* hf\\_name: fi-en\n* source\\_languages: fin\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['fi', 'en']\n* src\\_constituents: ('Finnish', {'fin'})\n* tgt\\_constituents: ('English', {'eng'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: fin-eng\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: fin\n* tgt\\_alpha3: eng\n* chrF2\\_score: 0.7\n* bleu: 54.1\n* src\\_name: Finnish\n* tgt\\_name: English\n* train\\_date: 2021-08-25 00:00:00\n* src\\_alpha2: fi\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* short\\_pair: fi-en\n* helsinki\\_git\\_sha: 2ef219d5b67f0afb0c6b732cd07001d84181f002\n* transformers\\_git\\_sha: 12b4d66a80419db30a15e7b9d4208ceb9887c03b\n* port\\_machine: URL\n* port\\_time: 2021-11-04-21:36" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "fr", "it", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "fr", "it" ]

[ "### fr-it\n\n\n* source group: French\n* target group: Italian\n* OPUS readme: fra-ita\n* model: transformer-align\n* source language(s): fra\n* target language(s): ita\n* raw source language(s): fra\n* raw target language(s): ita\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------", "### System Info:\n\n\n* hf\\_name: fr-it\n* source\\_languages: fra\n* target\\_languages: ita\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['fr', 'it']\n* src\\_constituents: ('French', {'fra'})\n* tgt\\_constituents: ('Italian', {'ita'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: fra-ita\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: fra\n* tgt\\_alpha3: ita\n* chrF2\\_score: 0.737\n* bleu: 54.8\n* src\\_name: French\n* tgt\\_name: Italian\n* train\\_date: 2021-11-11 00:00:00\n* src\\_alpha2: fr\n* tgt\\_alpha2: it\n* prefer\\_old: False\n* short\\_pair: fr-it\n* helsinki\\_git\\_sha: 7ab0c987850187e0b10342bfc616cd47c027ba18\n* transformers\\_git\\_sha: df1f94eb4a18b1a27d27e32040b60a17410d516e\n* port\\_machine: URL\n* port\\_time: 2021-11-11-19:40" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #fr #it #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### fr-it\n\n\n* source group: French\n* target group: Italian\n* OPUS readme: fra-ita\n* model: transformer-align\n* source language(s): fra\n* target language(s): ita\n* raw source language(s): fra\n* raw target language(s): ita\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------", "### System Info:\n\n\n* hf\\_name: fr-it\n* source\\_languages: fra\n* target\\_languages: ita\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['fr', 'it']\n* src\\_constituents: ('French', {'fra'})\n* tgt\\_constituents: ('Italian', {'ita'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: fra-ita\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: fra\n* tgt\\_alpha3: ita\n* chrF2\\_score: 0.737\n* bleu: 54.8\n* src\\_name: French\n* tgt\\_name: Italian\n* train\\_date: 2021-11-11 00:00:00\n* src\\_alpha2: fr\n* tgt\\_alpha2: it\n* prefer\\_old: False\n* short\\_pair: fr-it\n* helsinki\\_git\\_sha: 7ab0c987850187e0b10342bfc616cd47c027ba18\n* transformers\\_git\\_sha: df1f94eb4a18b1a27d27e32040b60a17410d516e\n* port\\_machine: URL\n* port\\_time: 2021-11-11-19:40" ]

[ 51, 129, 390 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #fr #it #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### fr-it\n\n\n* source group: French\n* target group: Italian\n* OPUS readme: fra-ita\n* model: transformer-align\n* source language(s): fra\n* target language(s): ita\n* raw source language(s): fra\n* raw target language(s): ita\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------### System Info:\n\n\n* hf\\_name: fr-it\n* source\\_languages: fra\n* target\\_languages: ita\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['fr', 'it']\n* src\\_constituents: ('French', {'fra'})\n* tgt\\_constituents: ('Italian', {'ita'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: fra-ita\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: fra\n* tgt\\_alpha3: ita\n* chrF2\\_score: 0.737\n* bleu: 54.8\n* src\\_name: French\n* tgt\\_name: Italian\n* train\\_date: 2021-11-11 00:00:00\n* src\\_alpha2: fr\n* tgt\\_alpha2: it\n* prefer\\_old: False\n* short\\_pair: fr-it\n* helsinki\\_git\\_sha: 7ab0c987850187e0b10342bfc616cd47c027ba18\n* transformers\\_git\\_sha: df1f94eb4a18b1a27d27e32040b60a17410d516e\n* port\\_machine: URL\n* port\\_time: 2021-11-11-19:40" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "he", "fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "he", "fr" ]

[ "### he-fr\n\n\n* source group: Hebrew\n* target group: French\n* OPUS readme: heb-fra\n* model: transformer\n* source language(s): heb\n* target language(s): fra\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 47.3, chr-F: 0.644", "### System Info:\n\n\n* hf\\_name: he-fr\n* source\\_languages: heb\n* target\\_languages: fra\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['he', 'fr']\n* src\\_constituents: ('Hebrew', {'heb'})\n* tgt\\_constituents: ('French', {'fra'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: heb-fra\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: heb\n* tgt\\_alpha3: fra\n* chrF2\\_score: 0.644\n* bleu: 47.3\n* brevity\\_penalty: 0.9740000000000001\n* ref\\_len: 26123.0\n* src\\_name: Hebrew\n* tgt\\_name: French\n* train\\_date: 2020-12-10 00:00:00\n* src\\_alpha2: he\n* tgt\\_alpha2: fr\n* prefer\\_old: False\n* short\\_pair: he-fr\n* helsinki\\_git\\_sha: b317f78a3ec8a556a481b6a53dc70dc11769ca96\n* transformers\\_git\\_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de\n* port\\_machine: URL\n* port\\_time: 2020-12-11-16:03" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #he #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### he-fr\n\n\n* source group: Hebrew\n* target group: French\n* OPUS readme: heb-fra\n* model: transformer\n* source language(s): heb\n* target language(s): fra\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 47.3, chr-F: 0.644", "### System Info:\n\n\n* hf\\_name: he-fr\n* source\\_languages: heb\n* target\\_languages: fra\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['he', 'fr']\n* src\\_constituents: ('Hebrew', {'heb'})\n* tgt\\_constituents: ('French', {'fra'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: heb-fra\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: heb\n* tgt\\_alpha3: fra\n* chrF2\\_score: 0.644\n* bleu: 47.3\n* brevity\\_penalty: 0.9740000000000001\n* ref\\_len: 26123.0\n* src\\_name: Hebrew\n* tgt\\_name: French\n* train\\_date: 2020-12-10 00:00:00\n* src\\_alpha2: he\n* tgt\\_alpha2: fr\n* prefer\\_old: False\n* short\\_pair: he-fr\n* helsinki\\_git\\_sha: b317f78a3ec8a556a481b6a53dc70dc11769ca96\n* transformers\\_git\\_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de\n* port\\_machine: URL\n* port\\_time: 2020-12-11-16:03" ]

[ 51, 129, 420 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #he #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### he-fr\n\n\n* source group: Hebrew\n* target group: French\n* OPUS readme: heb-fra\n* model: transformer\n* source language(s): heb\n* target language(s): fra\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 47.3, chr-F: 0.644### System Info:\n\n\n* hf\\_name: he-fr\n* source\\_languages: heb\n* target\\_languages: fra\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['he', 'fr']\n* src\\_constituents: ('Hebrew', {'heb'})\n* tgt\\_constituents: ('French', {'fra'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: heb-fra\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: heb\n* tgt\\_alpha3: fra\n* chrF2\\_score: 0.644\n* bleu: 47.3\n* brevity\\_penalty: 0.9740000000000001\n* ref\\_len: 26123.0\n* src\\_name: Hebrew\n* tgt\\_name: French\n* train\\_date: 2020-12-10 00:00:00\n* src\\_alpha2: he\n* tgt\\_alpha2: fr\n* prefer\\_old: False\n* short\\_pair: he-fr\n* helsinki\\_git\\_sha: b317f78a3ec8a556a481b6a53dc70dc11769ca96\n* transformers\\_git\\_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de\n* port\\_machine: URL\n* port\\_time: 2020-12-11-16:03" ]

[ "transformers", "pytorch", "marian", "text2text-generation", "translation", "he", "it", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "he", "it" ]

[ "### he-it\n\n\n* source group: Hebrew\n* target group: Italian\n* OPUS readme: heb-ita\n* model: transformer\n* source language(s): heb\n* target language(s): ita\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 41.1, chr-F: 0.643", "### System Info:\n\n\n* hf\\_name: he-it\n* source\\_languages: heb\n* target\\_languages: ita\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['he', 'it']\n* src\\_constituents: ('Hebrew', {'heb'})\n* tgt\\_constituents: ('Italian', {'ita'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: heb-ita\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: heb\n* tgt\\_alpha3: ita\n* chrF2\\_score: 0.643\n* bleu: 41.1\n* brevity\\_penalty: 0.997\n* ref\\_len: 11464.0\n* src\\_name: Hebrew\n* tgt\\_name: Italian\n* train\\_date: 2020-12-10 00:00:00\n* src\\_alpha2: he\n* tgt\\_alpha2: it\n* prefer\\_old: False\n* short\\_pair: he-it\n* helsinki\\_git\\_sha: b317f78a3ec8a556a481b6a53dc70dc11769ca96\n* transformers\\_git\\_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de\n* port\\_machine: URL\n* port\\_time: 2020-12-11-16:01" ]

[ "TAGS\n#transformers #pytorch #marian #text2text-generation #translation #he #it #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### he-it\n\n\n* source group: Hebrew\n* target group: Italian\n* OPUS readme: heb-ita\n* model: transformer\n* source language(s): heb\n* target language(s): ita\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 41.1, chr-F: 0.643", "### System Info:\n\n\n* hf\\_name: he-it\n* source\\_languages: heb\n* target\\_languages: ita\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['he', 'it']\n* src\\_constituents: ('Hebrew', {'heb'})\n* tgt\\_constituents: ('Italian', {'ita'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: heb-ita\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: heb\n* tgt\\_alpha3: ita\n* chrF2\\_score: 0.643\n* bleu: 41.1\n* brevity\\_penalty: 0.997\n* ref\\_len: 11464.0\n* src\\_name: Hebrew\n* tgt\\_name: Italian\n* train\\_date: 2020-12-10 00:00:00\n* src\\_alpha2: he\n* tgt\\_alpha2: it\n* prefer\\_old: False\n* short\\_pair: he-it\n* helsinki\\_git\\_sha: b317f78a3ec8a556a481b6a53dc70dc11769ca96\n* transformers\\_git\\_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de\n* port\\_machine: URL\n* port\\_time: 2020-12-11-16:01" ]

[ 48, 131, 418 ]

[ "TAGS\n#transformers #pytorch #marian #text2text-generation #translation #he #it #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### he-it\n\n\n* source group: Hebrew\n* target group: Italian\n* OPUS readme: heb-ita\n* model: transformer\n* source language(s): heb\n* target language(s): ita\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 41.1, chr-F: 0.643### System Info:\n\n\n* hf\\_name: he-it\n* source\\_languages: heb\n* target\\_languages: ita\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['he', 'it']\n* src\\_constituents: ('Hebrew', {'heb'})\n* tgt\\_constituents: ('Italian', {'ita'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: heb-ita\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: heb\n* tgt\\_alpha3: ita\n* chrF2\\_score: 0.643\n* bleu: 41.1\n* brevity\\_penalty: 0.997\n* ref\\_len: 11464.0\n* src\\_name: Hebrew\n* tgt\\_name: Italian\n* train\\_date: 2020-12-10 00:00:00\n* src\\_alpha2: he\n* tgt\\_alpha2: it\n* prefer\\_old: False\n* short\\_pair: he-it\n* helsinki\\_git\\_sha: b317f78a3ec8a556a481b6a53dc70dc11769ca96\n* transformers\\_git\\_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de\n* port\\_machine: URL\n* port\\_time: 2020-12-11-16:01" ]

[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "it", "he", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "it", "he" ]

[ "### it-he\n\n\n* source group: Italian\n* target group: Hebrew\n* OPUS readme: ita-heb\n* model: transformer\n* source language(s): ita\n* target language(s): heb\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.5, chr-F: 0.593", "### System Info:\n\n\n* hf\\_name: it-he\n* source\\_languages: ita\n* target\\_languages: heb\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['it', 'he']\n* src\\_constituents: ('Italian', {'ita'})\n* tgt\\_constituents: ('Hebrew', {'heb'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: ita-heb\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ita\n* tgt\\_alpha3: heb\n* chrF2\\_score: 0.593\n* bleu: 38.5\n* brevity\\_penalty: 0.985\n* ref\\_len: 9796.0\n* src\\_name: Italian\n* tgt\\_name: Hebrew\n* train\\_date: 2020-12-10 00:00:00\n* src\\_alpha2: it\n* tgt\\_alpha2: he\n* prefer\\_old: False\n* short\\_pair: it-he\n* helsinki\\_git\\_sha: b317f78a3ec8a556a481b6a53dc70dc11769ca96\n* transformers\\_git\\_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de\n* port\\_machine: URL\n* port\\_time: 2020-12-11-16:02" ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #it #he #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### it-he\n\n\n* source group: Italian\n* target group: Hebrew\n* OPUS readme: ita-heb\n* model: transformer\n* source language(s): ita\n* target language(s): heb\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.5, chr-F: 0.593", "### System Info:\n\n\n* hf\\_name: it-he\n* source\\_languages: ita\n* target\\_languages: heb\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['it', 'he']\n* src\\_constituents: ('Italian', {'ita'})\n* tgt\\_constituents: ('Hebrew', {'heb'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: ita-heb\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ita\n* tgt\\_alpha3: heb\n* chrF2\\_score: 0.593\n* bleu: 38.5\n* brevity\\_penalty: 0.985\n* ref\\_len: 9796.0\n* src\\_name: Italian\n* tgt\\_name: Hebrew\n* train\\_date: 2020-12-10 00:00:00\n* src\\_alpha2: it\n* tgt\\_alpha2: he\n* prefer\\_old: False\n* short\\_pair: it-he\n* helsinki\\_git\\_sha: b317f78a3ec8a556a481b6a53dc70dc11769ca96\n* transformers\\_git\\_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de\n* port\\_machine: URL\n* port\\_time: 2020-12-11-16:02" ]

[ 51, 131, 419 ]

[ "TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #it #he #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### it-he\n\n\n* source group: Italian\n* target group: Hebrew\n* OPUS readme: ita-heb\n* model: transformer\n* source language(s): ita\n* target language(s): heb\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.5, chr-F: 0.593### System Info:\n\n\n* hf\\_name: it-he\n* source\\_languages: ita\n* target\\_languages: heb\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['it', 'he']\n* src\\_constituents: ('Italian', {'ita'})\n* tgt\\_constituents: ('Hebrew', {'heb'})\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* long\\_pair: ita-heb\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ita\n* tgt\\_alpha3: heb\n* chrF2\\_score: 0.593\n* bleu: 38.5\n* brevity\\_penalty: 0.985\n* ref\\_len: 9796.0\n* src\\_name: Italian\n* tgt\\_name: Hebrew\n* train\\_date: 2020-12-10 00:00:00\n* src\\_alpha2: it\n* tgt\\_alpha2: he\n* prefer\\_old: False\n* short\\_pair: it-he\n* helsinki\\_git\\_sha: b317f78a3ec8a556a481b6a53dc70dc11769ca96\n* transformers\\_git\\_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de\n* port\\_machine: URL\n* port\\_time: 2020-12-11-16:02" ]

[ "transformers", "pytorch", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n" ]

[ 32 ]

[ "TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n" ]

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ 54, 112, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "transformers", "pytorch", "bert", "text-classification", "classification", "zh", "dataset:Custom", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "zh" ]

[ "#### Sample Usage\n from transformers import BertTokenizer, BertForSequenceClassification\n \n device = URL(\"cuda\" if URL.is_available() else \"cpu\")\n checkpoint = \"Herais/pred_genre\"\n tokenizer = BertTokenizer.from_pretrained(checkpoint, \n problem_type=\"single_label_classification\")\n model = BertForSequenceClassification.from_pretrained(checkpoint).to(device)\n \n label2id_genre = {'涉案': 7, '都市': 10, '革命': 12, '农村': 4, '传奇': 0, \n '其它': 2, '传记': 1, '青少': 11, '军旅': 3, '武打': 6, \n '科幻': 9, '神话': 8, '宫廷': 5}\n\n id2label_genre = {7: '涉案', 10: '都市', 12: '革命', 4: '农村', 0: '传奇', \n 2: '其它', 1: '传记', 11: '青少', 3: '军旅', 6: '武打', \n 9: '科幻', 8: '神话', 5: '宫廷'}\n\n synopsis = \"\"\"加油吧！检察官。鲤州市安平区检察院检察官助理蔡晓与徐美津是两个刚入职场的“菜鸟”。\\\n 他们在老检察官冯昆的指导与鼓励下，凭借着自己的一腔热血与对检察事业的执著追求，克服工作上的种种困难，\\\n 成功办理电竞赌博、虚假诉讼、水产市场涉黑等一系列复杂案件，惩治了犯罪分子，维护了人民群众的合法权益，\\\n 为社会主义法治建设贡献了自己的一份力量。在这个过程中，蔡晓与徐美津不仅得到了业务能力上的提升，\\\n 也领悟了人生的真谛，学会真诚地面对家人与朋友，收获了亲情与友谊，成长为合格的员额检察官，\\\n 继续为检察事业贡献自己的青春。 \"\"\"\n \n inputs = tokenizer(synopsis, truncation=True, max_length=512, return_tensors='pt')\n URL()\n outputs = model(input)\n \n label_ids_pred = URL(URL, dim=1).to('cpu').numpy()\n labels_pred = [id2label_timeperiod[label] for label in labels_pred]\n \n print(labels_pred)\n # ['涉案']\n \n Citation\n TBA" ]

[ "TAGS\n#transformers #pytorch #bert #text-classification #classification #zh #dataset-Custom #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "#### Sample Usage\n from transformers import BertTokenizer, BertForSequenceClassification\n \n device = URL(\"cuda\" if URL.is_available() else \"cpu\")\n checkpoint = \"Herais/pred_genre\"\n tokenizer = BertTokenizer.from_pretrained(checkpoint, \n problem_type=\"single_label_classification\")\n model = BertForSequenceClassification.from_pretrained(checkpoint).to(device)\n \n label2id_genre = {'涉案': 7, '都市': 10, '革命': 12, '农村': 4, '传奇': 0, \n '其它': 2, '传记': 1, '青少': 11, '军旅': 3, '武打': 6, \n '科幻': 9, '神话': 8, '宫廷': 5}\n\n id2label_genre = {7: '涉案', 10: '都市', 12: '革命', 4: '农村', 0: '传奇', \n 2: '其它', 1: '传记', 11: '青少', 3: '军旅', 6: '武打', \n 9: '科幻', 8: '神话', 5: '宫廷'}\n\n synopsis = \"\"\"加油吧！检察官。鲤州市安平区检察院检察官助理蔡晓与徐美津是两个刚入职场的“菜鸟”。\\\n 他们在老检察官冯昆的指导与鼓励下，凭借着自己的一腔热血与对检察事业的执著追求，克服工作上的种种困难，\\\n 成功办理电竞赌博、虚假诉讼、水产市场涉黑等一系列复杂案件，惩治了犯罪分子，维护了人民群众的合法权益，\\\n 为社会主义法治建设贡献了自己的一份力量。在这个过程中，蔡晓与徐美津不仅得到了业务能力上的提升，\\\n 也领悟了人生的真谛，学会真诚地面对家人与朋友，收获了亲情与友谊，成长为合格的员额检察官，\\\n 继续为检察事业贡献自己的青春。 \"\"\"\n \n inputs = tokenizer(synopsis, truncation=True, max_length=512, return_tensors='pt')\n URL()\n outputs = model(input)\n \n label_ids_pred = URL(URL, dim=1).to('cpu').numpy()\n labels_pred = [id2label_timeperiod[label] for label in labels_pred]\n \n print(labels_pred)\n # ['涉案']\n \n Citation\n TBA" ]

[ 46, 677 ]

[ "TAGS\n#transformers #pytorch #bert #text-classification #classification #zh #dataset-Custom #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n#### Sample Usage\n from transformers import BertTokenizer, BertForSequenceClassification\n \n device = URL(\"cuda\" if URL.is_available() else \"cpu\")\n checkpoint = \"Herais/pred_genre\"\n tokenizer = BertTokenizer.from_pretrained(checkpoint, \n problem_type=\"single_label_classification\")\n model = BertForSequenceClassification.from_pretrained(checkpoint).to(device)\n \n label2id_genre = {'涉案': 7, '都市': 10, '革命': 12, '农村': 4, '传奇': 0, \n '其它': 2, '传记': 1, '青少': 11, '军旅': 3, '武打': 6, \n '科幻': 9, '神话': 8, '宫廷': 5}\n\n id2label_genre = {7: '涉案', 10: '都市', 12: '革命', 4: '农村', 0: '传奇', \n 2: '其它', 1: '传记', 11: '青少', 3: '军旅', 6: '武打', \n 9: '科幻', 8: '神话', 5: '宫廷'}\n\n synopsis = \"\"\"加油吧！检察官。鲤州市安平区检察院检察官助理蔡晓与徐美津是两个刚入职场的“菜鸟”。\\\n 他们在老检察官冯昆的指导与鼓励下，凭借着自己的一腔热血与对检察事业的执著追求，克服工作上的种种困难，\\\n 成功办理电竞赌博、虚假诉讼、水产市场涉黑等一系列复杂案件，惩治了犯罪分子，维护了人民群众的合法权益，\\\n 为社会主义法治建设贡献了自己的一份力量。在这个过程中，蔡晓与徐美津不仅得到了业务能力上的提升，\\\n 也领悟了人生的真谛，学会真诚地面对家人与朋友，收获了亲情与友谊，成长为合格的员额检察官，\\\n 继续为检察事业贡献自己的青春。 \"\"\"\n \n inputs = tokenizer(synopsis, truncation=True, max_length=512, return_tensors='pt')\n URL()\n outputs = model(input)\n \n label_ids_pred = URL(URL, dim=1).to('cpu').numpy()\n labels_pred = [id2label_timeperiod[label] for label in labels_pred]\n \n print(labels_pred)\n # ['涉案']\n \n Citation\n TBA" ]

[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "classification", "zh", "dataset:Custom", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "zh" ]

[ "#### Sample Usage\n from transformers import BertTokenizer, BertForSequenceClassification\n \n device = URL(\"cuda\" if URL.is_available() else \"cpu\")\n checkpoint = \"Herais/pred_timeperiod\"\n tokenizer = BertTokenizer.from_pretrained(checkpoint, \n problem_type=\"single_label_classification\")\n model = BertForSequenceClassification.from_pretrained(checkpoint).to(device)\n \n label2id_timeperiod = {'古代': 0, '当代': 1, '现代': 2, '近代': 3, '重大': 4}\n id2label_timeperiod = {0: '古代', 1: '当代', 2: '现代', 3: '近代', 4: '重大'}\n\n synopsis = \"\"\"加油吧！检察官。鲤州市安平区检察院检察官助理蔡晓与徐美津是两个刚入职场的“菜鸟”。\\\n 他们在老检察官冯昆的指导与鼓励下，凭借着自己的一腔热血与对检察事业的执著追求，克服工作上的种种困难，\\\n 成功办理电竞赌博、虚假诉讼、水产市场涉黑等一系列复杂案件，惩治了犯罪分子，维护了人民群众的合法权益，\\\n 为社会主义法治建设贡献了自己的一份力量。在这个过程中，蔡晓与徐美津不仅得到了业务能力上的提升，\\\n 也领悟了人生的真谛，学会真诚地面对家人与朋友，收获了亲情与友谊，成长为合格的员额检察官，\\\n 继续为检察事业贡献自己的青春。 \"\"\"\n \n inputs = tokenizer(synopsis, truncation=True, max_length=512, return_tensors='pt')\n URL()\n outputs = model(input)\n \n label_ids_pred = URL(URL, dim=1).to('cpu').numpy()\n labels_pred = [id2label_timeperiod[label] for label in labels_pred]\n \n print(labels_pred)\n # ['当代']\n \n Citation\n{}" ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #text-classification #classification #zh #dataset-Custom #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "#### Sample Usage\n from transformers import BertTokenizer, BertForSequenceClassification\n \n device = URL(\"cuda\" if URL.is_available() else \"cpu\")\n checkpoint = \"Herais/pred_timeperiod\"\n tokenizer = BertTokenizer.from_pretrained(checkpoint, \n problem_type=\"single_label_classification\")\n model = BertForSequenceClassification.from_pretrained(checkpoint).to(device)\n \n label2id_timeperiod = {'古代': 0, '当代': 1, '现代': 2, '近代': 3, '重大': 4}\n id2label_timeperiod = {0: '古代', 1: '当代', 2: '现代', 3: '近代', 4: '重大'}\n\n synopsis = \"\"\"加油吧！检察官。鲤州市安平区检察院检察官助理蔡晓与徐美津是两个刚入职场的“菜鸟”。\\\n 他们在老检察官冯昆的指导与鼓励下，凭借着自己的一腔热血与对检察事业的执著追求，克服工作上的种种困难，\\\n 成功办理电竞赌博、虚假诉讼、水产市场涉黑等一系列复杂案件，惩治了犯罪分子，维护了人民群众的合法权益，\\\n 为社会主义法治建设贡献了自己的一份力量。在这个过程中，蔡晓与徐美津不仅得到了业务能力上的提升，\\\n 也领悟了人生的真谛，学会真诚地面对家人与朋友，收获了亲情与友谊，成长为合格的员额检察官，\\\n 继续为检察事业贡献自己的青春。 \"\"\"\n \n inputs = tokenizer(synopsis, truncation=True, max_length=512, return_tensors='pt')\n URL()\n outputs = model(input)\n \n label_ids_pred = URL(URL, dim=1).to('cpu').numpy()\n labels_pred = [id2label_timeperiod[label] for label in labels_pred]\n \n print(labels_pred)\n # ['当代']\n \n Citation\n{}" ]

[ 50, 574 ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #text-classification #classification #zh #dataset-Custom #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n#### Sample Usage\n from transformers import BertTokenizer, BertForSequenceClassification\n \n device = URL(\"cuda\" if URL.is_available() else \"cpu\")\n checkpoint = \"Herais/pred_timeperiod\"\n tokenizer = BertTokenizer.from_pretrained(checkpoint, \n problem_type=\"single_label_classification\")\n model = BertForSequenceClassification.from_pretrained(checkpoint).to(device)\n \n label2id_timeperiod = {'古代': 0, '当代': 1, '现代': 2, '近代': 3, '重大': 4}\n id2label_timeperiod = {0: '古代', 1: '当代', 2: '现代', 3: '近代', 4: '重大'}\n\n synopsis = \"\"\"加油吧！检察官。鲤州市安平区检察院检察官助理蔡晓与徐美津是两个刚入职场的“菜鸟”。\\\n 他们在老检察官冯昆的指导与鼓励下，凭借着自己的一腔热血与对检察事业的执著追求，克服工作上的种种困难，\\\n 成功办理电竞赌博、虚假诉讼、水产市场涉黑等一系列复杂案件，惩治了犯罪分子，维护了人民群众的合法权益，\\\n 为社会主义法治建设贡献了自己的一份力量。在这个过程中，蔡晓与徐美津不仅得到了业务能力上的提升，\\\n 也领悟了人生的真谛，学会真诚地面对家人与朋友，收获了亲情与友谊，成长为合格的员额检察官，\\\n 继续为检察事业贡献自己的青春。 \"\"\"\n \n inputs = tokenizer(synopsis, truncation=True, max_length=512, return_tensors='pt')\n URL()\n outputs = model(input)\n \n label_ids_pred = URL(URL, dim=1).to('cpu').numpy()\n labels_pred = [id2label_timeperiod[label] for label in labels_pred]\n \n print(labels_pred)\n # ['当代']\n \n Citation\n{}" ]

[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 279, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* TensorFlow 2.7.0\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "TAGS\n#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 279, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* TensorFlow 2.7.0\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ 46, 195, 5, 38 ]

[ "TAGS\n#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 279, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* TensorFlow 2.7.0\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "transformers", "pytorch", "t5", "text2text-generation", "arxiv:2105.13626", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2105.13626" ]

[]

[ "## ByT5 Base Portuguese Product Reviews", "#### Model Description\nThis is a finetuned version from ByT5 Base by Google for Sentimental Analysis from Product Reviews in Portuguese.", "##### Paper: URL", "#### Training data\nIt was trained from products reviews from a URL. You can found the data here: URL", "#### Training Procedure\nIt was finetuned using the Trainer Class available on the Hugging Face library. For evaluation it was used accuracy, precision, recall and f1 score.", "##### Learning Rate: 1e-4", "##### Epochs: 1", "##### Colab for Finetuning: URL", "##### Colab for Metrics: URL", "#### Score:", "#### Goals\n\nMy true intention was totally educational, thus making available a this version of the model as a example for future proposes.\n\nHow to use" ]

[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2105.13626 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## ByT5 Base Portuguese Product Reviews", "#### Model Description\nThis is a finetuned version from ByT5 Base by Google for Sentimental Analysis from Product Reviews in Portuguese.", "##### Paper: URL", "#### Training data\nIt was trained from products reviews from a URL. You can found the data here: URL", "#### Training Procedure\nIt was finetuned using the Trainer Class available on the Hugging Face library. For evaluation it was used accuracy, precision, recall and f1 score.", "##### Learning Rate: 1e-4", "##### Epochs: 1", "##### Colab for Finetuning: URL", "##### Colab for Metrics: URL", "#### Score:", "#### Goals\n\nMy true intention was totally educational, thus making available a this version of the model as a example for future proposes.\n\nHow to use" ]

[ 47, 9, 29, 9, 26, 36, 12, 9, 14, 13, 6, 31 ]

[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2105.13626 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## ByT5 Base Portuguese Product Reviews#### Model Description\nThis is a finetuned version from ByT5 Base by Google for Sentimental Analysis from Product Reviews in Portuguese.##### Paper: URL#### Training data\nIt was trained from products reviews from a URL. You can found the data here: URL#### Training Procedure\nIt was finetuned using the Trainer Class available on the Hugging Face library. For evaluation it was used accuracy, precision, recall and f1 score.##### Learning Rate: 1e-4##### Epochs: 1##### Colab for Finetuning: URL##### Colab for Metrics: URL#### Score:#### Goals\n\nMy true intention was totally educational, thus making available a this version of the model as a example for future proposes.\n\nHow to use" ]

[ "transformers", "pytorch", "t5", "text2text-generation", "arxiv:2105.13626", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2105.13626" ]

[]

[ "## ByT5 Small Portuguese Product Reviews", "#### Model Description\nThis is a finetuned version from ByT5 Small by Google for Sentimental Analysis from Product Reviews in Portuguese.", "##### Paper: URL", "#### Training data\nIt was trained from products reviews from a URL. You can found the data here: URL", "#### Training Procedure\nIt was finetuned using the Trainer Class available on the Hugging Face library. For evaluation it was used accuracy, precision, recall and f1 score.", "##### Learning Rate: 1e-4", "##### Epochs: 1", "##### Colab for Finetuning: URL", "##### Colab for Metrics: URL", "#### Score:", "#### Goals\n\nMy true intention was totally educational, thus making available a this version of the model as a example for future proposes.\n\nHow to use" ]

[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2105.13626 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## ByT5 Small Portuguese Product Reviews", "#### Model Description\nThis is a finetuned version from ByT5 Small by Google for Sentimental Analysis from Product Reviews in Portuguese.", "##### Paper: URL", "#### Training data\nIt was trained from products reviews from a URL. You can found the data here: URL", "#### Training Procedure\nIt was finetuned using the Trainer Class available on the Hugging Face library. For evaluation it was used accuracy, precision, recall and f1 score.", "##### Learning Rate: 1e-4", "##### Epochs: 1", "##### Colab for Finetuning: URL", "##### Colab for Metrics: URL", "#### Score:", "#### Goals\n\nMy true intention was totally educational, thus making available a this version of the model as a example for future proposes.\n\nHow to use" ]

[ 47, 9, 29, 9, 26, 36, 12, 9, 14, 13, 6, 31 ]

[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2105.13626 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## ByT5 Small Portuguese Product Reviews#### Model Description\nThis is a finetuned version from ByT5 Small by Google for Sentimental Analysis from Product Reviews in Portuguese.##### Paper: URL#### Training data\nIt was trained from products reviews from a URL. You can found the data here: URL#### Training Procedure\nIt was finetuned using the Trainer Class available on the Hugging Face library. For evaluation it was used accuracy, precision, recall and f1 score.##### Learning Rate: 1e-4##### Epochs: 1##### Colab for Finetuning: URL##### Colab for Metrics: URL#### Score:#### Goals\n\nMy true intention was totally educational, thus making available a this version of the model as a example for future proposes.\n\nHow to use" ]

[ "transformers", "pytorch", "gpt_neo", "text-generation", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "## Emo Bot", "#### Model Description\nThis is a finetuned version from GPT-Neo-125M for Generating Music Lyrics by Emo Genre.", "#### Training data\nIt was trained with 2381 songs by 15 bands that were important to emo culture in the early 2000s, not necessary directly playing on the genre.", "#### Training Procedure\nIt was finetuned using the Trainer Class available on the Hugging Face library.", "##### Learning Rate: 2e-4", "##### Epochs: 40", "##### Colab for Finetuning: URL", "##### Colab for Testing: URL", "#### Goals\n\nMy true intention was totally educational, thus making available a this version of the model as a example for future proposes.\n\nHow to use" ]

[ "TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "## Emo Bot", "#### Model Description\nThis is a finetuned version from GPT-Neo-125M for Generating Music Lyrics by Emo Genre.", "#### Training data\nIt was trained with 2381 songs by 15 bands that were important to emo culture in the early 2000s, not necessary directly playing on the genre.", "#### Training Procedure\nIt was finetuned using the Trainer Class available on the Hugging Face library.", "##### Learning Rate: 2e-4", "##### Epochs: 40", "##### Colab for Finetuning: URL", "##### Colab for Testing: URL", "#### Goals\n\nMy true intention was totally educational, thus making available a this version of the model as a example for future proposes.\n\nHow to use" ]

[ 35, 5, 30, 36, 22, 12, 9, 14, 12, 31 ]

[ "TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n## Emo Bot#### Model Description\nThis is a finetuned version from GPT-Neo-125M for Generating Music Lyrics by Emo Genre.#### Training data\nIt was trained with 2381 songs by 15 bands that were important to emo culture in the early 2000s, not necessary directly playing on the genre.#### Training Procedure\nIt was finetuned using the Trainer Class available on the Hugging Face library.##### Learning Rate: 2e-4##### Epochs: 40##### Colab for Finetuning: URL##### Colab for Testing: URL#### Goals\n\nMy true intention was totally educational, thus making available a this version of the model as a example for future proposes.\n\nHow to use" ]

[ "transformers", "pytorch", "gpt_neo", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "## GPT-Neo Small Portuguese", "#### Model Description\nThis is a finetuned version from GPT-Neo 125M by EletheurAI to Portuguese language.", "#### Training data\nIt was trained from 227,382 selected texts from a PTWiki Dump. You can found all the data from here: URL", "#### Training Procedure\nEvery text was passed through a GPT2-Tokenizer with bos and eos tokens to separate them, with max sequence length that the GPT-Neo could support. It was finetuned using the default metrics of the Trainer Class, available on the Hugging Face library.", "##### Learning Rate: 2e-4", "##### Epochs: 1", "#### Goals\n\nMy true intention was totally educational, thus making available a Portuguese version of this model.\n\nHow to use" ]

[ "TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us \n", "## GPT-Neo Small Portuguese", "#### Model Description\nThis is a finetuned version from GPT-Neo 125M by EletheurAI to Portuguese language.", "#### Training data\nIt was trained from 227,382 selected texts from a PTWiki Dump. You can found all the data from here: URL", "#### Training Procedure\nEvery text was passed through a GPT2-Tokenizer with bos and eos tokens to separate them, with max sequence length that the GPT-Neo could support. It was finetuned using the default metrics of the Trainer Class, available on the Hugging Face library.", "##### Learning Rate: 2e-4", "##### Epochs: 1", "#### Goals\n\nMy true intention was totally educational, thus making available a Portuguese version of this model.\n\nHow to use" ]

[ 31, 8, 29, 34, 65, 12, 9, 25 ]

[ "TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us \n## GPT-Neo Small Portuguese#### Model Description\nThis is a finetuned version from GPT-Neo 125M by EletheurAI to Portuguese language.#### Training data\nIt was trained from 227,382 selected texts from a PTWiki Dump. You can found all the data from here: URL#### Training Procedure\nEvery text was passed through a GPT2-Tokenizer with bos and eos tokens to separate them, with max sequence length that the GPT-Neo could support. It was finetuned using the default metrics of the Trainer Class, available on the Hugging Face library.##### Learning Rate: 2e-4##### Epochs: 1#### Goals\n\nMy true intention was totally educational, thus making available a Portuguese version of this model.\n\nHow to use" ]

[ "region:us" ]

[]

[]

[ "# Convert Fairseq Wav2Vec2 to HF\n\nThis repo has two scripts that can show how to convert a fairseq checkpoint to HF Transformers.\n\nIt's important to always check in a forward pass that the two checkpoints are the same. The procedure should be as follows:\n\n1. Download original model\n2. Create HF version of the model:\n\n3. Convert the model\n\nThe \"0\" means that checkpoint is not a fine-tuned one.\n4. Verify that models are equal:\n\n\nCheck the scripts to better understand how they work or contact URL" ]

[ "TAGS\n#region-us \n", "# Convert Fairseq Wav2Vec2 to HF\n\nThis repo has two scripts that can show how to convert a fairseq checkpoint to HF Transformers.\n\nIt's important to always check in a forward pass that the two checkpoints are the same. The procedure should be as follows:\n\n1. Download original model\n2. Create HF version of the model:\n\n3. Convert the model\n\nThe \"0\" means that checkpoint is not a fine-tuned one.\n4. Verify that models are equal:\n\n\nCheck the scripts to better understand how they work or contact URL" ]

[ 5, 119 ]

[ "TAGS\n#region-us \n# Convert Fairseq Wav2Vec2 to HF\n\nThis repo has two scripts that can show how to convert a fairseq checkpoint to HF Transformers.\n\nIt's important to always check in a forward pass that the two checkpoints are the same. The procedure should be as follows:\n\n1. Download original model\n2. Create HF version of the model:\n\n3. Convert the model\n\nThe \"0\" means that checkpoint is not a fine-tuned one.\n4. Verify that models are equal:\n\n\nCheck the scripts to better understand how they work or contact URL" ]

[ "region:us" ]

[]

[]

[ "# Run any CTC model" ]

[ "TAGS\n#region-us \n", "# Run any CTC model" ]

[ 5, 6 ]

[ "TAGS\n#region-us \n# Run any CTC model" ]

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]

[ 49, 101, 5, 40 ]

[ "TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.10.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.10.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]

[ 47, 101, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.10.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ 36 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ "transformers", "pytorch", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 28 ]

[ "TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.", "#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\n=======", "#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------" ]

[ "TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n", "#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.", "#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\n=======", "#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------" ]

[ 31, 21, 52, 60 ]

[ "TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\n=======#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Harry Potter DialoGPT Model" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Harry Potter DialoGPT Model" ]

[ 39, 7 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Potter DialoGPT Model" ]

[ "transformers", "pytorch", "tf", "albert", "token-classification", "fa", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "fa" ]

[ "### Installing requirements", "### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsNER Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #albert #token-classification #fa #autotrain_compatible #endpoints_compatible #region-us \n", "### Installing requirements", "### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsNER Issues repo." ]

[ 33, 5, 34 ]

[ "TAGS\n#transformers #pytorch #tf #albert #token-classification #fa #autotrain_compatible #endpoints_compatible #region-us \n### Installing requirements### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsNER Issues repo." ]

[ "transformers", "pytorch", "tf", "albert", "fill-mask", "fa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "fa" ]

[ "# ALBERT-Persian\n\nA Lite BERT for Self-supervised Learning of Language Representations for the Persian Language\n\n> میتونی بهش بگی برت_کوچولو\n\n> Call it little_berty", "### BibTeX entry and citation info\n\nPlease cite in your publication as the following:", "## Questions?\nPost a Github issue on the ALBERT-Persian repo." ]

[ "TAGS\n#transformers #pytorch #tf #albert #fill-mask #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# ALBERT-Persian\n\nA Lite BERT for Self-supervised Learning of Language Representations for the Persian Language\n\n> میتونی بهش بگی برت_کوچولو\n\n> Call it little_berty", "### BibTeX entry and citation info\n\nPlease cite in your publication as the following:", "## Questions?\nPost a Github issue on the ALBERT-Persian repo." ]

[ 41, 50, 19, 18 ]

[ "TAGS\n#transformers #pytorch #tf #albert #fill-mask #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# ALBERT-Persian\n\nA Lite BERT for Self-supervised Learning of Language Representations for the Persian Language\n\n> میتونی بهش بگی برت_کوچولو\n\n> Call it little_berty### BibTeX entry and citation info\n\nPlease cite in your publication as the following:## Questions?\nPost a Github issue on the ALBERT-Persian repo." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "token-classification", "fa", "arxiv:2005.12515", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "2005.12515" ]

[ "fa" ]

[ "### ARMAN\n\n\nARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.\n\n\n1. Organization\n2. Location\n3. Facility\n4. Event\n5. Product\n6. Person\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------\n\n\n\nCite\n----\n\n\nPlease cite the following paper in your publication if you are using ParsBERT in your research:\n\n\nAcknowledgments\n---------------\n\n\nWe hereby, express our gratitude to the Tensorflow Research Cloud (TFRC) program for providing us with the necessary computation resources. We also thank Hooshvare Research Group for facilitating dataset gathering and scraping online text resources.\n\n\nContributors\n------------\n\n\n* Mehrdad Farahani: Linkedin, Twitter, Github\n* Mohammad Gharachorloo: Linkedin, Twitter, Github\n* Marzieh Farahani: Linkedin, Twitter, Github\n* Mohammad Manthouri: Linkedin, Twitter, Github\n* Hooshvare Team: Official Website, Linkedin, Twitter, Github, Instagram\n\n\n* And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: Linkedin, Behance, Instagram\n\n\nReleases\n--------", "### Release v0.1 (May 29, 2019)\n\n\nThis is the first version of our ParsBERT NER!" ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #fa #arxiv-2005.12515 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### ARMAN\n\n\nARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.\n\n\n1. Organization\n2. Location\n3. Facility\n4. Event\n5. Product\n6. Person\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------\n\n\n\nCite\n----\n\n\nPlease cite the following paper in your publication if you are using ParsBERT in your research:\n\n\nAcknowledgments\n---------------\n\n\nWe hereby, express our gratitude to the Tensorflow Research Cloud (TFRC) program for providing us with the necessary computation resources. We also thank Hooshvare Research Group for facilitating dataset gathering and scraping online text resources.\n\n\nContributors\n------------\n\n\n* Mehrdad Farahani: Linkedin, Twitter, Github\n* Mohammad Gharachorloo: Linkedin, Twitter, Github\n* Marzieh Farahani: Linkedin, Twitter, Github\n* Mohammad Manthouri: Linkedin, Twitter, Github\n* Hooshvare Team: Official Website, Linkedin, Twitter, Github, Instagram\n\n\n* And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: Linkedin, Behance, Instagram\n\n\nReleases\n--------", "### Release v0.1 (May 29, 2019)\n\n\nThis is the first version of our ParsBERT NER!" ]

[ 52, 336, 27 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #fa #arxiv-2005.12515 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### ARMAN\n\n\nARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.\n\n\n1. Organization\n2. Location\n3. Facility\n4. Event\n5. Product\n6. Person\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------\n\n\n\nCite\n----\n\n\nPlease cite the following paper in your publication if you are using ParsBERT in your research:\n\n\nAcknowledgments\n---------------\n\n\nWe hereby, express our gratitude to the Tensorflow Research Cloud (TFRC) program for providing us with the necessary computation resources. We also thank Hooshvare Research Group for facilitating dataset gathering and scraping online text resources.\n\n\nContributors\n------------\n\n\n* Mehrdad Farahani: Linkedin, Twitter, Github\n* Mohammad Gharachorloo: Linkedin, Twitter, Github\n* Marzieh Farahani: Linkedin, Twitter, Github\n* Mohammad Manthouri: Linkedin, Twitter, Github\n* Hooshvare Team: Official Website, Linkedin, Twitter, Github, Instagram\n\n\n* And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: Linkedin, Behance, Instagram\n\n\nReleases\n--------### Release v0.1 (May 29, 2019)\n\n\nThis is the first version of our ParsBERT NER!" ]

[ "transformers", "pytorch", "tf", "jax", "bert", "token-classification", "fa", "arxiv:2005.12515", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[ "2005.12515" ]

[ "fa" ]

[ "### PEYMA\n\n\nPEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.\n\n\n1. Organization\n2. Money\n3. Location\n4. Date\n5. Time\n6. Person\n7. Percent\n\n\n\nDownload\nYou can download the dataset from here\n\n\n\n\n---", "### ARMAN\n\n\nARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.\n\n\n1. Organization\n2. Location\n3. Facility\n4. Event\n5. Product\n6. Person\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------\n\n\n\nCite\n----\n\n\nPlease cite the following paper in your publication if you are using ParsBERT in your research:\n\n\nAcknowledgments\n---------------\n\n\nWe hereby, express our gratitude to the Tensorflow Research Cloud (TFRC) program for providing us with the necessary computation resources. We also thank Hooshvare Research Group for facilitating dataset gathering and scraping online text resources.\n\n\nContributors\n------------\n\n\n* Mehrdad Farahani: Linkedin, Twitter, Github\n* Mohammad Gharachorloo: Linkedin, Twitter, Github\n* Marzieh Farahani: Linkedin, Twitter, Github\n* Mohammad Manthouri: Linkedin, Twitter, Github\n* Hooshvare Team: Official Website, Linkedin, Twitter, Github, Instagram\n\n\n* And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: Linkedin, Behance, Instagram\n\n\nReleases\n--------", "### Release v0.1 (May 29, 2019)\n\n\nThis is the first version of our ParsBERT NER!" ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #fa #arxiv-2005.12515 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### PEYMA\n\n\nPEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.\n\n\n1. Organization\n2. Money\n3. Location\n4. Date\n5. Time\n6. Person\n7. Percent\n\n\n\nDownload\nYou can download the dataset from here\n\n\n\n\n---", "### ARMAN\n\n\nARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.\n\n\n1. Organization\n2. Location\n3. Facility\n4. Event\n5. Product\n6. Person\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------\n\n\n\nCite\n----\n\n\nPlease cite the following paper in your publication if you are using ParsBERT in your research:\n\n\nAcknowledgments\n---------------\n\n\nWe hereby, express our gratitude to the Tensorflow Research Cloud (TFRC) program for providing us with the necessary computation resources. We also thank Hooshvare Research Group for facilitating dataset gathering and scraping online text resources.\n\n\nContributors\n------------\n\n\n* Mehrdad Farahani: Linkedin, Twitter, Github\n* Mohammad Gharachorloo: Linkedin, Twitter, Github\n* Marzieh Farahani: Linkedin, Twitter, Github\n* Mohammad Manthouri: Linkedin, Twitter, Github\n* Hooshvare Team: Official Website, Linkedin, Twitter, Github, Instagram\n\n\n* And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: Linkedin, Behance, Instagram\n\n\nReleases\n--------", "### Release v0.1 (May 29, 2019)\n\n\nThis is the first version of our ParsBERT NER!" ]

[ 56, 72, 336, 27 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #fa #arxiv-2005.12515 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### PEYMA\n\n\nPEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.\n\n\n1. Organization\n2. Money\n3. Location\n4. Date\n5. Time\n6. Person\n7. Percent\n\n\n\nDownload\nYou can download the dataset from here\n\n\n\n\n---### ARMAN\n\n\nARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.\n\n\n1. Organization\n2. Location\n3. Facility\n4. Event\n5. Product\n6. Person\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------\n\n\n\nCite\n----\n\n\nPlease cite the following paper in your publication if you are using ParsBERT in your research:\n\n\nAcknowledgments\n---------------\n\n\nWe hereby, express our gratitude to the Tensorflow Research Cloud (TFRC) program for providing us with the necessary computation resources. We also thank Hooshvare Research Group for facilitating dataset gathering and scraping online text resources.\n\n\nContributors\n------------\n\n\n* Mehrdad Farahani: Linkedin, Twitter, Github\n* Mohammad Gharachorloo: Linkedin, Twitter, Github\n* Marzieh Farahani: Linkedin, Twitter, Github\n* Mohammad Manthouri: Linkedin, Twitter, Github\n* Hooshvare Team: Official Website, Linkedin, Twitter, Github, Instagram\n\n\n* And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: Linkedin, Behance, Instagram\n\n\nReleases\n--------### Release v0.1 (May 29, 2019)\n\n\nThis is the first version of our ParsBERT NER!" ]

[ "transformers", "pytorch", "tf", "jax", "bert", "token-classification", "fa", "arxiv:2005.12515", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "2005.12515" ]

[ "fa" ]

[ "### PEYMA\n\n\nPEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.\n\n\n1. Organization\n2. Money\n3. Location\n4. Date\n5. Time\n6. Person\n7. Percent\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------\n\n\n\nCite\n----\n\n\nPlease cite the following paper in your publication if you are using ParsBERT in your research:\n\n\nAcknowledgments\n---------------\n\n\nWe hereby, express our gratitude to the Tensorflow Research Cloud (TFRC) program for providing us with the necessary computation resources. We also thank Hooshvare Research Group for facilitating dataset gathering and scraping online text resources.\n\n\nContributors\n------------\n\n\n* Mehrdad Farahani: Linkedin, Twitter, Github\n* Mohammad Gharachorloo: Linkedin, Twitter, Github\n* Marzieh Farahani: Linkedin, Twitter, Github\n* Mohammad Manthouri: Linkedin, Twitter, Github\n* Hooshvare Team: Official Website, Linkedin, Twitter, Github, Instagram\n\n\n* And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: Linkedin, Behance, Instagram\n\n\nReleases\n--------", "### Release v0.1 (May 29, 2019)\n\n\nThis is the first version of our ParsBERT NER!" ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #fa #arxiv-2005.12515 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### PEYMA\n\n\nPEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.\n\n\n1. Organization\n2. Money\n3. Location\n4. Date\n5. Time\n6. Person\n7. Percent\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------\n\n\n\nCite\n----\n\n\nPlease cite the following paper in your publication if you are using ParsBERT in your research:\n\n\nAcknowledgments\n---------------\n\n\nWe hereby, express our gratitude to the Tensorflow Research Cloud (TFRC) program for providing us with the necessary computation resources. We also thank Hooshvare Research Group for facilitating dataset gathering and scraping online text resources.\n\n\nContributors\n------------\n\n\n* Mehrdad Farahani: Linkedin, Twitter, Github\n* Mohammad Gharachorloo: Linkedin, Twitter, Github\n* Marzieh Farahani: Linkedin, Twitter, Github\n* Mohammad Manthouri: Linkedin, Twitter, Github\n* Hooshvare Team: Official Website, Linkedin, Twitter, Github, Instagram\n\n\n* And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: Linkedin, Behance, Instagram\n\n\nReleases\n--------", "### Release v0.1 (May 29, 2019)\n\n\nThis is the first version of our ParsBERT NER!" ]

[ 52, 351, 27 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #fa #arxiv-2005.12515 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### PEYMA\n\n\nPEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.\n\n\n1. Organization\n2. Money\n3. Location\n4. Date\n5. Time\n6. Person\n7. Percent\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------\n\n\n\nCite\n----\n\n\nPlease cite the following paper in your publication if you are using ParsBERT in your research:\n\n\nAcknowledgments\n---------------\n\n\nWe hereby, express our gratitude to the Tensorflow Research Cloud (TFRC) program for providing us with the necessary computation resources. We also thank Hooshvare Research Group for facilitating dataset gathering and scraping online text resources.\n\n\nContributors\n------------\n\n\n* Mehrdad Farahani: Linkedin, Twitter, Github\n* Mohammad Gharachorloo: Linkedin, Twitter, Github\n* Marzieh Farahani: Linkedin, Twitter, Github\n* Mohammad Manthouri: Linkedin, Twitter, Github\n* Hooshvare Team: Official Website, Linkedin, Twitter, Github, Instagram\n\n\n* And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: Linkedin, Behance, Instagram\n\n\nReleases\n--------### Release v0.1 (May 29, 2019)\n\n\nThis is the first version of our ParsBERT NER!" ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "arxiv:2005.12515", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[ "2005.12515" ]

[]

[ "### Sentiment Analysis (SA) task", "### Text Classification (TC) task", "### Named Entity Recognition (NER) task\n\n\n\nIf you tested ParsBERT on a public dataset and you want to add your results to the table above, open a pull request or contact us. Also make sure to have your code available online so we can add it as a reference\n\n\nHow to use\n----------", "### TensorFlow 2.0", "### Pytorch\n\n\nNLP Tasks Tutorial\n------------------\n\n\nComing soon stay tuned\n\n\nCite\n----\n\n\nPlease cite the following paper in your publication if you are using ParsBERT in your research:\n\n\nAcknowledgments\n---------------\n\n\nWe hereby, express our gratitude to the Tensorflow Research Cloud (TFRC) program for providing us with the necessary computation resources. We also thank Hooshvare Research Group for facilitating dataset gathering and scraping online text resources.\n\n\nContributors\n------------\n\n\n* Mehrdad Farahani: Linkedin, Twitter, Github\n* Mohammad Gharachorloo: Linkedin, Twitter, Github\n* Marzieh Farahani: Linkedin, Twitter, Github\n* Mohammad Manthouri: Linkedin, Twitter, Github\n* Hooshvare Team: Official Website, Linkedin, Twitter, Github, Instagram\n\n\nReleases\n--------", "### Release v0.1 (May 27, 2019)\n\n\nThis is the first version of our ParsBERT based on BERTBASE" ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #arxiv-2005.12515 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### Sentiment Analysis (SA) task", "### Text Classification (TC) task", "### Named Entity Recognition (NER) task\n\n\n\nIf you tested ParsBERT on a public dataset and you want to add your results to the table above, open a pull request or contact us. Also make sure to have your code available online so we can add it as a reference\n\n\nHow to use\n----------", "### TensorFlow 2.0", "### Pytorch\n\n\nNLP Tasks Tutorial\n------------------\n\n\nComing soon stay tuned\n\n\nCite\n----\n\n\nPlease cite the following paper in your publication if you are using ParsBERT in your research:\n\n\nAcknowledgments\n---------------\n\n\nWe hereby, express our gratitude to the Tensorflow Research Cloud (TFRC) program for providing us with the necessary computation resources. We also thank Hooshvare Research Group for facilitating dataset gathering and scraping online text resources.\n\n\nContributors\n------------\n\n\n* Mehrdad Farahani: Linkedin, Twitter, Github\n* Mohammad Gharachorloo: Linkedin, Twitter, Github\n* Marzieh Farahani: Linkedin, Twitter, Github\n* Mohammad Manthouri: Linkedin, Twitter, Github\n* Hooshvare Team: Official Website, Linkedin, Twitter, Github, Instagram\n\n\nReleases\n--------", "### Release v0.1 (May 27, 2019)\n\n\nThis is the first version of our ParsBERT based on BERTBASE" ]

[ 46, 9, 9, 72, 8, 233, 28 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #arxiv-2005.12515 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Sentiment Analysis (SA) task### Text Classification (TC) task### Named Entity Recognition (NER) task\n\n\n\nIf you tested ParsBERT on a public dataset and you want to add your results to the table above, open a pull request or contact us. Also make sure to have your code available online so we can add it as a reference\n\n\nHow to use\n----------### TensorFlow 2.0### Pytorch\n\n\nNLP Tasks Tutorial\n------------------\n\n\nComing soon stay tuned\n\n\nCite\n----\n\n\nPlease cite the following paper in your publication if you are using ParsBERT in your research:\n\n\nAcknowledgments\n---------------\n\n\nWe hereby, express our gratitude to the Tensorflow Research Cloud (TFRC) program for providing us with the necessary computation resources. We also thank Hooshvare Research Group for facilitating dataset gathering and scraping online text resources.\n\n\nContributors\n------------\n\n\n* Mehrdad Farahani: Linkedin, Twitter, Github\n* Mohammad Gharachorloo: Linkedin, Twitter, Github\n* Marzieh Farahani: Linkedin, Twitter, Github\n* Mohammad Manthouri: Linkedin, Twitter, Github\n* Hooshvare Team: Official Website, Linkedin, Twitter, Github, Instagram\n\n\nReleases\n--------### Release v0.1 (May 27, 2019)\n\n\nThis is the first version of our ParsBERT based on BERTBASE" ]

[ "transformers", "pytorch", "tf", "jax", "bert", "text-classification", "fa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "fa" ]

[ "### DigiMag\n\n\nA total of 8,515 articles scraped from Digikala Online Magazine. This dataset includes seven different classes.\n\n\n1. Video Games\n2. Shopping Guide\n3. Health Beauty\n4. Science Technology\n5. General\n6. Art Cinema\n7. Books Literature\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### DigiMag\n\n\nA total of 8,515 articles scraped from Digikala Online Magazine. This dataset includes seven different classes.\n\n\n1. Video Games\n2. Shopping Guide\n3. Health Beauty\n4. Science Technology\n5. General\n6. Art Cinema\n7. Books Literature\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ 43, 120, 45 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### DigiMag\n\n\nA total of 8,515 articles scraped from Digikala Online Magazine. This dataset includes seven different classes.\n\n\n1. Video Games\n2. Shopping Guide\n3. Health Beauty\n4. Science Technology\n5. General\n6. Art Cinema\n7. Books Literature\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "text-classification", "fa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "fa" ]

[ "### Persian News\n\n\nA dataset of various news articles scraped from different online news agencies' websites. The total number of articles is 16,438, spread over eight different classes.\n\n\n1. Economic\n2. International\n3. Political\n4. Science Technology\n5. Cultural Art\n6. Sport\n7. Medical\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### Persian News\n\n\nA dataset of various news articles scraped from different online news agencies' websites. The total number of articles is 16,438, spread over eight different classes.\n\n\n1. Economic\n2. International\n3. Political\n4. Science Technology\n5. Cultural Art\n6. Sport\n7. Medical\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ 47, 124, 45 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Persian News\n\n\nA dataset of various news articles scraped from different online news agencies' websites. The total number of articles is 16,438, spread over eight different classes.\n\n\n1. Economic\n2. International\n3. Political\n4. Science Technology\n5. Cultural Art\n6. Sport\n7. Medical\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "token-classification", "fa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "fa" ]

[ "### ARMAN\n\n\nARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.\n\n\n1. Organization\n2. Location\n3. Facility\n4. Event\n5. Product\n6. Person\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### ARMAN\n\n\nARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.\n\n\n1. Organization\n2. Location\n3. Facility\n4. Event\n5. Product\n6. Person\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ 43, 108, 45 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### ARMAN\n\n\nARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.\n\n\n1. Organization\n2. Location\n3. Facility\n4. Event\n5. Product\n6. Person\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "token-classification", "fa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "fa" ]

[ "### PEYMA\n\n\nPEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.\n\n\n1. Organization\n2. Money\n3. Location\n4. Date\n5. Time\n6. Person\n7. Percent\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### PEYMA\n\n\nPEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.\n\n\n1. Organization\n2. Money\n3. Location\n4. Date\n5. Time\n6. Person\n7. Percent\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ 43, 123, 45 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### PEYMA\n\n\nPEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.\n\n\n1. Organization\n2. Money\n3. Location\n4. Date\n5. Time\n6. Person\n7. Percent\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "text-classification", "fa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "fa" ]

[ "### DeepSentiPers\n\n\nwhich is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classes; two positives (i.e., happy and delighted), two negatives (i.e., furious and angry) and one neutral class. Therefore, this dataset can be utilized for both multi-class and binary classification. In the case of binary classification, the neutral class and its corresponding sentences are removed from the dataset.\n\n\nBinary:\n\n\n1. Negative (Furious + Angry)\n2. Positive (Happy + Delighted)\n\n\nMulti\n\n\n1. Furious\n2. Angry\n3. Neutral\n4. Happy\n5. Delighted\n\n\n\nDownload\nYou can download the dataset from:\n\n\n* SentiPers\n* DeepSentiPers\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### DeepSentiPers\n\n\nwhich is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classes; two positives (i.e., happy and delighted), two negatives (i.e., furious and angry) and one neutral class. Therefore, this dataset can be utilized for both multi-class and binary classification. In the case of binary classification, the neutral class and its corresponding sentences are removed from the dataset.\n\n\nBinary:\n\n\n1. Negative (Furious + Angry)\n2. Positive (Happy + Delighted)\n\n\nMulti\n\n\n1. Furious\n2. Angry\n3. Neutral\n4. Happy\n5. Delighted\n\n\n\nDownload\nYou can download the dataset from:\n\n\n* SentiPers\n* DeepSentiPers\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ 43, 210, 45 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### DeepSentiPers\n\n\nwhich is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classes; two positives (i.e., happy and delighted), two negatives (i.e., furious and angry) and one neutral class. Therefore, this dataset can be utilized for both multi-class and binary classification. In the case of binary classification, the neutral class and its corresponding sentences are removed from the dataset.\n\n\nBinary:\n\n\n1. Negative (Furious + Angry)\n2. Positive (Happy + Delighted)\n\n\nMulti\n\n\n1. Furious\n2. Angry\n3. Neutral\n4. Happy\n5. Delighted\n\n\n\nDownload\nYou can download the dataset from:\n\n\n* SentiPers\n* DeepSentiPers\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "text-classification", "fa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "fa" ]

[ "### DeepSentiPers\n\n\nwhich is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classes; two positives (i.e., happy and delighted), two negatives (i.e., furious and angry) and one neutral class. Therefore, this dataset can be utilized for both multi-class and binary classification. In the case of binary classification, the neutral class and its corresponding sentences are removed from the dataset.\n\n\nBinary:\n\n\n1. Negative (Furious + Angry)\n2. Positive (Happy + Delighted)\n\n\nMulti\n\n\n1. Furious\n2. Angry\n3. Neutral\n4. Happy\n5. Delighted\n\n\n\nDownload\nYou can download the dataset from:\n\n\n* SentiPers\n* DeepSentiPers\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### DeepSentiPers\n\n\nwhich is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classes; two positives (i.e., happy and delighted), two negatives (i.e., furious and angry) and one neutral class. Therefore, this dataset can be utilized for both multi-class and binary classification. In the case of binary classification, the neutral class and its corresponding sentences are removed from the dataset.\n\n\nBinary:\n\n\n1. Negative (Furious + Angry)\n2. Positive (Happy + Delighted)\n\n\nMulti\n\n\n1. Furious\n2. Angry\n3. Neutral\n4. Happy\n5. Delighted\n\n\n\nDownload\nYou can download the dataset from:\n\n\n* SentiPers\n* DeepSentiPers\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ 43, 210, 45 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### DeepSentiPers\n\n\nwhich is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classes; two positives (i.e., happy and delighted), two negatives (i.e., furious and angry) and one neutral class. Therefore, this dataset can be utilized for both multi-class and binary classification. In the case of binary classification, the neutral class and its corresponding sentences are removed from the dataset.\n\n\nBinary:\n\n\n1. Negative (Furious + Angry)\n2. Positive (Happy + Delighted)\n\n\nMulti\n\n\n1. Furious\n2. Angry\n3. Neutral\n4. Happy\n5. Delighted\n\n\n\nDownload\nYou can download the dataset from:\n\n\n* SentiPers\n* DeepSentiPers\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "text-classification", "fa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "fa" ]

[ "### Digikala\n\n\nDigikala user comments provided by Open Data Mining Program (ODMP). This dataset contains 62,321 user comments with three labels:\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Digikala\n\n\nDigikala user comments provided by Open Data Mining Program (ODMP). This dataset contains 62,321 user comments with three labels:\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ 43, 99, 45 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Digikala\n\n\nDigikala user comments provided by Open Data Mining Program (ODMP). This dataset contains 62,321 user comments with three labels:\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "text-classification", "fa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "fa" ]

[ "### SnappFood\n\n\nSnappfood (an online food delivery company) user comments containing 70,000 comments with two labels (i.e. polarity classification):\n\n\n1. Happy\n2. Sad\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### SnappFood\n\n\nSnappfood (an online food delivery company) user comments containing 70,000 comments with two labels (i.e. polarity classification):\n\n\n1. Happy\n2. Sad\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ 43, 105, 45 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### SnappFood\n\n\nSnappfood (an online food delivery company) user comments containing 70,000 comments with two labels (i.e. polarity classification):\n\n\n1. Happy\n2. Sad\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.\n\n\n\nHow to use :hugs:\n-----------------### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "bert-fa", "bert-persian", "persian-lm", "fa", "arxiv:2005.12515", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[ "2005.12515" ]

[ "fa" ]

[ "### How to use", "#### TensorFlow 2.0", "#### Pytorch\n\n\nTraining\n--------\n\n\nParsBERT trained on a massive amount of public corpora (Persian Wikidumps, MirasText) and six other manually crawled text data from a various type of websites (BigBang Page 'scientific', Chetor 'lifestyle', Eligasht 'itinerary', Digikala 'digital magazine', Ted Talks 'general conversational', Books 'novels, storybooks, short stories from old to the contemporary era').\n\n\nAs a part of ParsBERT methodology, an extensive pre-processing combining POS tagging and WordPiece segmentation was carried out to bring the corpora into a proper format.\n\n\nGoals\n-----\n\n\nObjective goals during training are as below (after 300k steps).\n\n\nDerivative models\n-----------------", "### Base Config", "#### ParsBERT v2.0 Model\n\n\n* HooshvareLab/bert-fa-base-uncased", "#### ParsBERT v2.0 Sentiment Analysis\n\n\n* HooshvareLab/bert-fa-base-uncased-sentiment-digikala\n* HooshvareLab/bert-fa-base-uncased-sentiment-snappfood\n* HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary\n* HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi", "#### ParsBERT v2.0 Text Classification\n\n\n* HooshvareLab/bert-fa-base-uncased-clf-digimag\n* HooshvareLab/bert-fa-base-uncased-clf-persiannews", "#### ParsBERT v2.0 NER\n\n\n* HooshvareLab/bert-fa-base-uncased-ner-peyma\n* HooshvareLab/bert-fa-base-uncased-ner-arman\n\n\nEval results\n------------\n\n\nParsBERT is evaluated on three NLP downstream tasks: Sentiment Analysis (SA), Text Classification, and Named Entity Recognition (NER). For this matter and due to insufficient resources, two large datasets for SA and two for text classification were manually composed, which are available for public use and benchmarking. ParsBERT outperformed all other language models, including multilingual BERT and other hybrid deep learning models for all tasks, improving the state-of-the-art performance in Persian language modeling.", "### Sentiment Analysis (SA) Task", "### Text Classification (TC) Task", "### Named Entity Recognition (NER) Task", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #bert-fa #bert-persian #persian-lm #fa #arxiv-2005.12515 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### How to use", "#### TensorFlow 2.0", "#### Pytorch\n\n\nTraining\n--------\n\n\nParsBERT trained on a massive amount of public corpora (Persian Wikidumps, MirasText) and six other manually crawled text data from a various type of websites (BigBang Page 'scientific', Chetor 'lifestyle', Eligasht 'itinerary', Digikala 'digital magazine', Ted Talks 'general conversational', Books 'novels, storybooks, short stories from old to the contemporary era').\n\n\nAs a part of ParsBERT methodology, an extensive pre-processing combining POS tagging and WordPiece segmentation was carried out to bring the corpora into a proper format.\n\n\nGoals\n-----\n\n\nObjective goals during training are as below (after 300k steps).\n\n\nDerivative models\n-----------------", "### Base Config", "#### ParsBERT v2.0 Model\n\n\n* HooshvareLab/bert-fa-base-uncased", "#### ParsBERT v2.0 Sentiment Analysis\n\n\n* HooshvareLab/bert-fa-base-uncased-sentiment-digikala\n* HooshvareLab/bert-fa-base-uncased-sentiment-snappfood\n* HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary\n* HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi", "#### ParsBERT v2.0 Text Classification\n\n\n* HooshvareLab/bert-fa-base-uncased-clf-digimag\n* HooshvareLab/bert-fa-base-uncased-clf-persiannews", "#### ParsBERT v2.0 NER\n\n\n* HooshvareLab/bert-fa-base-uncased-ner-peyma\n* HooshvareLab/bert-fa-base-uncased-ner-arman\n\n\nEval results\n------------\n\n\nParsBERT is evaluated on three NLP downstream tasks: Sentiment Analysis (SA), Text Classification, and Named Entity Recognition (NER). For this matter and due to insufficient resources, two large datasets for SA and two for text classification were manually composed, which are available for public use and benchmarking. ParsBERT outperformed all other language models, including multilingual BERT and other hybrid deep learning models for all tasks, improving the state-of-the-art performance in Persian language modeling.", "### Sentiment Analysis (SA) Task", "### Text Classification (TC) Task", "### Named Entity Recognition (NER) Task", "### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ 69, 6, 9, 186, 7, 27, 103, 57, 178, 9, 9, 11, 45 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #bert-fa #bert-persian #persian-lm #fa #arxiv-2005.12515 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### How to use#### TensorFlow 2.0#### Pytorch\n\n\nTraining\n--------\n\n\nParsBERT trained on a massive amount of public corpora (Persian Wikidumps, MirasText) and six other manually crawled text data from a various type of websites (BigBang Page 'scientific', Chetor 'lifestyle', Eligasht 'itinerary', Digikala 'digital magazine', Ted Talks 'general conversational', Books 'novels, storybooks, short stories from old to the contemporary era').\n\n\nAs a part of ParsBERT methodology, an extensive pre-processing combining POS tagging and WordPiece segmentation was carried out to bring the corpora into a proper format.\n\n\nGoals\n-----\n\n\nObjective goals during training are as below (after 300k steps).\n\n\nDerivative models\n-----------------### Base Config#### ParsBERT v2.0 Model\n\n\n* HooshvareLab/bert-fa-base-uncased#### ParsBERT v2.0 Sentiment Analysis\n\n\n* HooshvareLab/bert-fa-base-uncased-sentiment-digikala\n* HooshvareLab/bert-fa-base-uncased-sentiment-snappfood\n* HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary\n* HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi#### ParsBERT v2.0 Text Classification\n\n\n* HooshvareLab/bert-fa-base-uncased-clf-digimag\n* HooshvareLab/bert-fa-base-uncased-clf-persiannews#### ParsBERT v2.0 NER\n\n\n* HooshvareLab/bert-fa-base-uncased-ner-peyma\n* HooshvareLab/bert-fa-base-uncased-ner-arman\n\n\nEval results\n------------\n\n\nParsBERT is evaluated on three NLP downstream tasks: Sentiment Analysis (SA), Text Classification, and Named Entity Recognition (NER). For this matter and due to insufficient resources, two large datasets for SA and two for text classification were manually composed, which are available for public use and benchmarking. ParsBERT outperformed all other language models, including multilingual BERT and other hybrid deep learning models for all tasks, improving the state-of-the-art performance in Persian language modeling.### Sentiment Analysis (SA) Task### Text Classification (TC) Task### Named Entity Recognition (NER) Task### BibTeX entry and citation info\n\n\nPlease cite in publications as the following:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsBERT Issues repo." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "token-classification", "fa", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "fa" ]

[ "### Installing requirements", "### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsNER Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #fa #autotrain_compatible #endpoints_compatible #region-us \n", "### Installing requirements", "### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsNER Issues repo." ]

[ 35, 5, 34 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #fa #autotrain_compatible #endpoints_compatible #region-us \n### Installing requirements### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsNER Issues repo." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "fa", "arxiv:2005.12515", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[ "2005.12515" ]

[ "fa" ]

[ "# ParsBERT (v3.0)\nA Transformer-based Model for Persian Language Understanding\n\nThe new version of BERT v3.0 for Persian is available today and can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.", "## Introduction\n\nParsBERT is a monolingual language model based on Google’s BERT architecture. This model is pre-trained on large Persian corpora with various writing styles from numerous subjects (e.g., scientific, novels, news).\n \nPaper presenting ParsBERT: arXiv:2005.12515", "### BibTeX entry and citation info\n\nPlease cite in publications as the following:", "## Questions?\nPost a Github issue on the ParsBERT Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #fa #arxiv-2005.12515 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# ParsBERT (v3.0)\nA Transformer-based Model for Persian Language Understanding\n\nThe new version of BERT v3.0 for Persian is available today and can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.", "## Introduction\n\nParsBERT is a monolingual language model based on Google’s BERT architecture. This model is pre-trained on large Persian corpora with various writing styles from numerous subjects (e.g., scientific, novels, news).\n \nPaper presenting ParsBERT: arXiv:2005.12515", "### BibTeX entry and citation info\n\nPlease cite in publications as the following:", "## Questions?\nPost a Github issue on the ParsBERT Issues repo." ]

[ 56, 70, 66, 18, 19 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #fa #arxiv-2005.12515 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# ParsBERT (v3.0)\nA Transformer-based Model for Persian Language Understanding\n\nThe new version of BERT v3.0 for Persian is available today and can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.## Introduction\n\nParsBERT is a monolingual language model based on Google’s BERT architecture. This model is pre-trained on large Persian corpora with various writing styles from numerous subjects (e.g., scientific, novels, news).\n \nPaper presenting ParsBERT: arXiv:2005.12515### BibTeX entry and citation info\n\nPlease cite in publications as the following:## Questions?\nPost a Github issue on the ParsBERT Issues repo." ]

[ "transformers", "pytorch", "tf", "distilbert", "token-classification", "fa", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "fa" ]

[ "### Installing requirements", "### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsNER Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #distilbert #token-classification #fa #autotrain_compatible #endpoints_compatible #region-us \n", "### Installing requirements", "### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsNER Issues repo." ]

[ 35, 5, 34 ]

[ "TAGS\n#transformers #pytorch #tf #distilbert #token-classification #fa #autotrain_compatible #endpoints_compatible #region-us \n### Installing requirements### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsNER Issues repo." ]

[ "transformers", "pytorch", "tf", "distilbert", "fill-mask", "fa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "fa" ]

[ "# DistilBERT\n\nThis model can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.", "## Questions?\nPost a Github issue on the ParsBERT Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #distilbert #fill-mask #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# DistilBERT\n\nThis model can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.", "## Questions?\nPost a Github issue on the ParsBERT Issues repo." ]

[ 43, 41, 19 ]

[ "TAGS\n#transformers #pytorch #tf #distilbert #fill-mask #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# DistilBERT\n\nThis model can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.## Questions?\nPost a Github issue on the ParsBERT Issues repo." ]

[ "transformers", "pytorch", "tf", "jax", "gpt2", "text-generation", "fa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "fa" ]

[ "# Persian Comment Generator \n\nThe model can generate comments based on your aspects, and the model was fine-tuned on persiannlp/parsinlu. Currently, the model only supports aspects in the food and movie scope. You can see the whole aspects in the following section.", "## Comments Aspects", "## Questions?\nPost a Github issue on the ParsGPT2 Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Persian Comment Generator \n\nThe model can generate comments based on your aspects, and the model was fine-tuned on persiannlp/parsinlu. Currently, the model only supports aspects in the food and movie scope. You can see the whole aspects in the following section.", "## Comments Aspects", "## Questions?\nPost a Github issue on the ParsGPT2 Issues repo." ]

[ 51, 55, 4, 20 ]

[ "TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Persian Comment Generator \n\nThe model can generate comments based on your aspects, and the model was fine-tuned on persiannlp/parsinlu. Currently, the model only supports aspects in the food and movie scope. You can see the whole aspects in the following section.## Comments Aspects## Questions?\nPost a Github issue on the ParsGPT2 Issues repo." ]

[ "transformers", "pytorch", "tf", "jax", "gpt2", "text-generation", "fa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "fa" ]

[ "# Persian Poet GPT2", "## Poets\nThe model can generate poetry based on your favorite poet, and you need to add one of the following lines as the input the box on the right side or follow the fine-tuning notebook.", "## Questions?\nPost a Github issue on the ParsGPT2 Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Persian Poet GPT2", "## Poets\nThe model can generate poetry based on your favorite poet, and you need to add one of the following lines as the input the box on the right side or follow the fine-tuning notebook.", "## Questions?\nPost a Github issue on the ParsGPT2 Issues repo." ]

[ 51, 6, 41, 20 ]

[ "TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Persian Poet GPT2## Poets\nThe model can generate poetry based on your favorite poet, and you need to add one of the following lines as the input the box on the right side or follow the fine-tuning notebook.## Questions?\nPost a Github issue on the ParsGPT2 Issues repo." ]

[ "transformers", "pytorch", "tf", "jax", "gpt2", "text-generation", "fa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]

[]

[ "fa" ]

[ "# ParsGPT2", "### BibTeX entry and citation info\n\nPlease cite in publications as the following:", "## Questions?\nPost a Github issue on the ParsGPT2 Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "# ParsGPT2", "### BibTeX entry and citation info\n\nPlease cite in publications as the following:", "## Questions?\nPost a Github issue on the ParsGPT2 Issues repo." ]

[ 55, 5, 18, 20 ]

[ "TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# ParsGPT2### BibTeX entry and citation info\n\nPlease cite in publications as the following:## Questions?\nPost a Github issue on the ParsGPT2 Issues repo." ]

[ "transformers", "pytorch", "tf", "jax", "roberta", "token-classification", "fa", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "fa" ]

[ "### Installing requirements", "### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsNER Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #jax #roberta #token-classification #fa #autotrain_compatible #endpoints_compatible #region-us \n", "### Installing requirements", "### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsNER Issues repo." ]

[ 35, 5, 34 ]

[ "TAGS\n#transformers #pytorch #tf #jax #roberta #token-classification #fa #autotrain_compatible #endpoints_compatible #region-us \n### Installing requirements### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsNER Issues repo." ]

[ "transformers", "pytorch", "tf", "jax", "roberta", "fill-mask", "fa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "fa" ]

[ "# Roberta\nThis model can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.", "## Questions?\nPost a Github issue on the ParsRoBERTa Issues repo." ]

[ "TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Roberta\nThis model can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.", "## Questions?\nPost a Github issue on the ParsRoBERTa Issues repo." ]

[ 43, 39, 20 ]

[ "TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Roberta\nThis model can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.## Questions?\nPost a Github issue on the ParsRoBERTa Issues repo." ]

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]

[ 56, 101, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# SpongeBob DialoGPT Model" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# SpongeBob DialoGPT Model" ]

[ 39, 8 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# SpongeBob DialoGPT Model" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# SpongeBob DialoGPT Model" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# SpongeBob DialoGPT Model" ]

[ 39, 8 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# SpongeBob DialoGPT Model" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ 39 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ "spacy", "token-classification", "en", "license:apache-2.0", "model-index", "region:us" ]

[]

[ "en" ]

[ "### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)", "### Accuracy" ]

[ "TAGS\n#spacy #token-classification #en #license-apache-2.0 #model-index #region-us \n", "### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)", "### Accuracy" ]

[ 26, 15, 4 ]

[ "TAGS\n#spacy #token-classification #en #license-apache-2.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)### Accuracy" ]

[ "spacy", "token-classification", "es", "license:apache-2.0", "model-index", "region:us" ]

[]

[ "es" ]

[ "### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)", "### Accuracy" ]

[ "TAGS\n#spacy #token-classification #es #license-apache-2.0 #model-index #region-us \n", "### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)", "### Accuracy" ]

[ 26, 15, 4 ]

[ "TAGS\n#spacy #token-classification #es #license-apache-2.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)### Accuracy" ]