Datasets:

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ftopal

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huggingface-datasets

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[ "license:openrail", "region:us" ]

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[ "TAGS\n#license-openrail #region-us \n" ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/flan-t5-large-stacked-samsum-1024-FP32-fin\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/flan-t5-large-stacked-samsum-1024-FP32-fin\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "region:us" ]

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[ "# Dataset Card for \"LLM_Description_Vocab_opt_facebook_opt_30b_downstream_tasks\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"LLM_Description_Vocab_opt_facebook_opt_30b_downstream_tasks\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"clinic-home\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"clinic-home\"\n\nMore Information needed" ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: facebook/bart-large-cnn\n* Dataset: gigaword\n* Config: default\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Xiaoci for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: facebook/bart-large-cnn\n* Dataset: gigaword\n* Config: default\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Xiaoci for evaluating this model." ]

[ "region:us" ]

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[ "TAGS\n#region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for \"clinic-kitchen_and_dining\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"clinic-kitchen_and_dining\"\n\nMore Information needed" ]

[ "license:cc-by-nc-sa-4.0", "region:us" ]

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[ "TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for \"fiszki-ocr-train\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"fiszki-ocr-train\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"bug-16718038814382\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"bug-16718038814382\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"bug-16718056078062\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"bug-16718056078062\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"tortas\"\n\nNote that when using PyTorch's transforms that these images are 4-channel images. The last channel is all 1's and can be ignored.\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"tortas\"\n\nNote that when using PyTorch's transforms that these images are 4-channel images. The last channel is all 1's and can be ignored.\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Mirror of billsum train split\n\nMirror with parquet files on hub, as downloading billsum data files from Google drive causes errors in distributed training." ]

[ "TAGS\n#region-us \n", "# Mirror of billsum train split\n\nMirror with parquet files on hub, as downloading billsum data files from Google drive causes errors in distributed training." ]

[ "region:us" ]

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[ "# Dataset Card for \"dreambooth-hackathon-images\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dreambooth-hackathon-images\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"dreambooth-hackathon-images\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dreambooth-hackathon-images\"\n\nMore Information needed" ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: florenceGundy/bert-finetuned-squad\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @florenceGundy for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: florenceGundy/bert-finetuned-squad\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @florenceGundy for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: florenceGundy/bert-finetuned-squad\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @florenceGundy for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: florenceGundy/bert-finetuned-squad\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @florenceGundy for evaluating this model." ]

[ "license:mit", "region:us" ]

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[ "TAGS\n#license-mit #region-us \n" ]

[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:cc-by-nc-sa-3.0", "region:us" ]

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[ "en" ]

[ "# Dataset Card for \"financial_news_sentiment_mixte_with_phrasebank_75\"\n\nThis is a customized version of the phrasebank dataset in which I kept only sentences validated by at least 75% annotators. \nIn addition I added ~2000 articles of Canadian news where sentiment was validated manually.\n\nThe dataset also include a column topic which contains one of the following value:\n* acquisition\n* other\n* quaterly financial release\n* appointment to new position\n* dividend\n* corporate update\n* drillings results\n* conference\n* share repurchase program\n* grant of stocks\n\nThis was generated automatically using a zero-shot classification model and was not reviewed manually.", "## References\nOriginal dataset is available here:\n[URL" ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-cc-by-nc-sa-3.0 #region-us \n", "# Dataset Card for \"financial_news_sentiment_mixte_with_phrasebank_75\"\n\nThis is a customized version of the phrasebank dataset in which I kept only sentences validated by at least 75% annotators. \nIn addition I added ~2000 articles of Canadian news where sentiment was validated manually.\n\nThe dataset also include a column topic which contains one of the following value:\n* acquisition\n* other\n* quaterly financial release\n* appointment to new position\n* dividend\n* corporate update\n* drillings results\n* conference\n* share repurchase program\n* grant of stocks\n\nThis was generated automatically using a zero-shot classification model and was not reviewed manually.", "## References\nOriginal dataset is available here:\n[URL" ]

[ "region:us" ]

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[ "TAGS\n#region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for \"LLM_Description_Vocab_opt_Multimodal_Fatima_opt_175b_downstream_tasks\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"LLM_Description_Vocab_opt_Multimodal_Fatima_opt_175b_downstream_tasks\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"ade20k-panoptic-demo-imagefolder\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"ade20k-panoptic-demo-imagefolder\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"common_voice_12.0_Augmented\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"common_voice_12.0_Augmented\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"dreambooth-hackathon-images-proteins\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dreambooth-hackathon-images-proteins\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"sobotta-anatomical-dataset\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"sobotta-anatomical-dataset\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"dreambooth-hackathon-images-protein2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dreambooth-hackathon-images-protein2\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"dreambooth-hackathon-images-protein3\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dreambooth-hackathon-images-protein3\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"20NG_train10.8k_test3.6K_valid3.6k\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"20NG_train10.8k_test3.6K_valid3.6k\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "TAGS\n#region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for \"legal_corpus\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"legal_corpus\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"clinic-credit_cards\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"clinic-credit_cards\"\n\nMore Information needed" ]

[ "license:apache-2.0", "region:us" ]

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[ "# Dataset Card for GPT-Negochat", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Fields\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations", "## Dataset Description\n\n- Repository: URL\n- Point of Contact: msamogh@URL", "### Dataset Summary\nhe GPT-Negochat corpus is a modified version of the original Negochat corpus (URL which contains negotiation dialogues between an Employer and a Candidate. The utterances in the original corpus were generated using a template-based NLG module and therefore, sound robotic and in general, do not sound convincingly real.\n\nGPT-Negochat is the result of using GPT-3 to modify this original corpus to make the dialogues resemble actual job-negotiation dialogues more closely while still retaining the original meaning of the utterances.\n\nIn addition to rephrasing the utterances, a small set of highly unrealistic dialogue segments have been removed in GPT-Negochat without affecting the coherence of the surrounding dialogue.", "### Supported Tasks and Leaderboards\n\n- Dialogue Act Classification\n- Offer Identification\n- Agreement Tracking", "### Languages\n\n- English", "## Dataset Structure", "### Data Fields\n\nBelow is an excerpt containing two consecutive turns from a dialogue. The 'input' field contains the utterance from the original Negochat corpus. The 'augmented_input' field contains the same utterance rephrased using GPT-3.", "## Dataset Creation", "### Curation Rationale\n\nThe original Negochat corpus is one of the only dialogue corpora with containing turn-level annotations for offers, acceptances, and rejects in a negotiation dialogue.\nHowever, the utterances in the corpus were generated using a template-based NLG system, which makes the dialogues unrealistic to the point of sounding robotic at times.\nWe wanted to make the utterances sound more like those from an actual negotiation dialogue in a job interview.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe original Negochat corpus can be found here: URL", "## Annotations\nSince each utterance in GPT-Negochat was generated by rephrasing the original without changing the underlying meaning, we simply transfer over the annotations from the original Negochat corpus." ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# Dataset Card for GPT-Negochat", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Fields\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations", "## Dataset Description\n\n- Repository: URL\n- Point of Contact: msamogh@URL", "### Dataset Summary\nhe GPT-Negochat corpus is a modified version of the original Negochat corpus (URL which contains negotiation dialogues between an Employer and a Candidate. The utterances in the original corpus were generated using a template-based NLG module and therefore, sound robotic and in general, do not sound convincingly real.\n\nGPT-Negochat is the result of using GPT-3 to modify this original corpus to make the dialogues resemble actual job-negotiation dialogues more closely while still retaining the original meaning of the utterances.\n\nIn addition to rephrasing the utterances, a small set of highly unrealistic dialogue segments have been removed in GPT-Negochat without affecting the coherence of the surrounding dialogue.", "### Supported Tasks and Leaderboards\n\n- Dialogue Act Classification\n- Offer Identification\n- Agreement Tracking", "### Languages\n\n- English", "## Dataset Structure", "### Data Fields\n\nBelow is an excerpt containing two consecutive turns from a dialogue. The 'input' field contains the utterance from the original Negochat corpus. The 'augmented_input' field contains the same utterance rephrased using GPT-3.", "## Dataset Creation", "### Curation Rationale\n\nThe original Negochat corpus is one of the only dialogue corpora with containing turn-level annotations for offers, acceptances, and rejects in a negotiation dialogue.\nHowever, the utterances in the corpus were generated using a template-based NLG system, which makes the dialogues unrealistic to the point of sounding robotic at times.\nWe wanted to make the utterances sound more like those from an actual negotiation dialogue in a job interview.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe original Negochat corpus can be found here: URL", "## Annotations\nSince each utterance in GPT-Negochat was generated by rephrasing the original without changing the underlying meaning, we simply transfer over the annotations from the original Negochat corpus." ]

[ "region:us" ]

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[ "# Summary \nDataset contains numbers in different formats:\n* Numbers (base 10)\n* Numbers as words\n* Roman numbers\n\nDataset range 1-4999" ]

[ "TAGS\n#region-us \n", "# Summary \nDataset contains numbers in different formats:\n* Numbers (base 10)\n* Numbers as words\n* Roman numbers\n\nDataset range 1-4999" ]

[ "region:us" ]

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[ "# Dataset Card for \"LLM_Description_Vocab_bloom_bigscience_bloom_downstream_tasks\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"LLM_Description_Vocab_bloom_bigscience_bloom_downstream_tasks\"\n\nMore Information needed" ]

[ "task_categories:question-answering", "task_categories:text-retrieval", "task_ids:open-domain-qa", "annotations_creators:no-annotation", "multilinguality:monolingual", "language:en", "license:cc-by-sa-3.0", "region:us" ]

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[ "en" ]

[ "# Dataset Card for Open-Domain Question Answering Wikipedia Corpora", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n- Dataset Structure\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Source Data", "## Dataset Description", "### Dataset Summary\n\nThe Wikipedia corpus variants provided can serve as knowledge sources for question-answering systems based on a retriever–reader pipeline. These corpus variants and their corresponding experiments are described further in the paper entitled: \n> Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering.", "## Dataset Structure", "### Data Fields\n\nThe dataset consists of passages that have been segmented from Wikipedia articles.\nFor each passage, the following fields are provided \n- : The passage id in the format of (X#Y) where passages from the same article share the same X, but Y denotes the segment id within the article \n- : The title of the article from where the passage comes\n- : The text content of the passage", "### Data Splits\n\nThere are 6 corpus variants in total\n- : The original DPR Wikipedia corpus with non-overlapping passages, each 100 words long, from Karpukhin et al.,\n> Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. Dense Passage Retrieval for Open-Domain Question Answering. _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_, pages 6769-6781, 2020.\n- : Our replication of the above corpus\n- : A corpus similar to above i.e. without tables, infoboxes, and lists. Segmentation is done differently, with a passage size of 6 sentences and a stride of 3 sentences. Note, this means that passages are overlapped.\n- : Like wiki-text-6-3, but with a passage size of 8 sentences and a stride of 4 sentences.\n- : A corpus with tables, infoboxes, and lists included with a passage size of 6 sentences and a stride of 3 sentences.\n- : Like wiki-all-6-3, but with a passage size of 8 sentences and a stride of 4 sentences.", "## Dataset Creation", "### Source Data", "#### Initial Data Collection and Normalization\n\nWe start with downloading the full December 20, 2018 Wikipedia XML dump: from the Internet Archive: URL This is then Pre-processed by WikiExtractor: URL (making sure to modify the code to include lists as desired and replacing any tables with the string \"TABLETOREPLACE\") and DrQA: URL (again making sure to modify the code to not remove lists as desired).\n\nWe then apply the pre-processing script) we make available in Pyserini to generate the different corpus variants." ]

[ "TAGS\n#task_categories-question-answering #task_categories-text-retrieval #task_ids-open-domain-qa #annotations_creators-no-annotation #multilinguality-monolingual #language-English #license-cc-by-sa-3.0 #region-us \n", "# Dataset Card for Open-Domain Question Answering Wikipedia Corpora", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n- Dataset Structure\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Source Data", "## Dataset Description", "### Dataset Summary\n\nThe Wikipedia corpus variants provided can serve as knowledge sources for question-answering systems based on a retriever–reader pipeline. These corpus variants and their corresponding experiments are described further in the paper entitled: \n> Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering.", "## Dataset Structure", "### Data Fields\n\nThe dataset consists of passages that have been segmented from Wikipedia articles.\nFor each passage, the following fields are provided \n- : The passage id in the format of (X#Y) where passages from the same article share the same X, but Y denotes the segment id within the article \n- : The title of the article from where the passage comes\n- : The text content of the passage", "### Data Splits\n\nThere are 6 corpus variants in total\n- : The original DPR Wikipedia corpus with non-overlapping passages, each 100 words long, from Karpukhin et al.,\n> Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. Dense Passage Retrieval for Open-Domain Question Answering. _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_, pages 6769-6781, 2020.\n- : Our replication of the above corpus\n- : A corpus similar to above i.e. without tables, infoboxes, and lists. Segmentation is done differently, with a passage size of 6 sentences and a stride of 3 sentences. Note, this means that passages are overlapped.\n- : Like wiki-text-6-3, but with a passage size of 8 sentences and a stride of 4 sentences.\n- : A corpus with tables, infoboxes, and lists included with a passage size of 6 sentences and a stride of 3 sentences.\n- : Like wiki-all-6-3, but with a passage size of 8 sentences and a stride of 4 sentences.", "## Dataset Creation", "### Source Data", "#### Initial Data Collection and Normalization\n\nWe start with downloading the full December 20, 2018 Wikipedia XML dump: from the Internet Archive: URL This is then Pre-processed by WikiExtractor: URL (making sure to modify the code to include lists as desired and replacing any tables with the string \"TABLETOREPLACE\") and DrQA: URL (again making sure to modify the code to not remove lists as desired).\n\nWe then apply the pre-processing script) we make available in Pyserini to generate the different corpus variants." ]

[ "region:us" ]

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[ "# Dataset Card for \"dreambooth-hackathon-images-mario-bg-1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dreambooth-hackathon-images-mario-bg-1\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"donut_check\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"donut_check\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"rvl_cdip_10_examples_per_class_donut\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"rvl_cdip_10_examples_per_class_donut\"\n\nMore Information needed" ]

[ "license:creativeml-openrail-m", "region:us" ]

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[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for \"coco-panoptic-val2017\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"coco-panoptic-val2017\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"dreambooth-hackathon-images\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dreambooth-hackathon-images\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"rulibrispeech\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"rulibrispeech\"\n\nMore Information needed" ]

[ "license:mit", "region:us" ]

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[ "TAGS\n#license-mit #region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed" ]

[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "license:mit", "bio", "bio-chem", "molnet", "molecule-net", "biophysics", "arxiv:1703.00564", "region:us" ]

[ "1703.00564" ]

[]

[ "# Dataset Card for tox21_srp53", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\n'tox21_srp53' is a dataset included in MoleculeNet. It is the p53 stress-response pathway activation (SR-p53) task from Tox21.", "## Dataset Structure", "### Data Fields\n\nEach split contains\n\n* 'smiles': the SMILES representation of a molecule\n* 'selfies': the SELFIES representation of a molecule\n* 'target': clinical trial toxicity (or absence of toxicity)", "### Data Splits\n\nThe dataset is split into an 80/10/10 train/valid/test split using scaffold split.", "### Source Data", "#### Initial Data Collection and Normalization\n\nData was originially generated by the Pande Group at Standford", "### Licensing Information\n\nThis dataset was originally released under an MIT license", "### Contributions\n\nThanks to @zanussbaum for adding this dataset." ]

[ "TAGS\n#task_categories-other #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #license-mit #bio #bio-chem #molnet #molecule-net #biophysics #arxiv-1703.00564 #region-us \n", "# Dataset Card for tox21_srp53", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\n'tox21_srp53' is a dataset included in MoleculeNet. It is the p53 stress-response pathway activation (SR-p53) task from Tox21.", "## Dataset Structure", "### Data Fields\n\nEach split contains\n\n* 'smiles': the SMILES representation of a molecule\n* 'selfies': the SELFIES representation of a molecule\n* 'target': clinical trial toxicity (or absence of toxicity)", "### Data Splits\n\nThe dataset is split into an 80/10/10 train/valid/test split using scaffold split.", "### Source Data", "#### Initial Data Collection and Normalization\n\nData was originially generated by the Pande Group at Standford", "### Licensing Information\n\nThis dataset was originally released under an MIT license", "### Contributions\n\nThanks to @zanussbaum for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"bkk-budget-ner-page\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"bkk-budget-ner-page\"\n\nMore Information needed" ]

[ "task_categories:image-classification", "task_ids:multi-label-image-classification", "annotations_creators:expert-generated", "language_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for ACLFig Dataset", "## Dataset Description\n\n- Paper:\n- Leaderboard:", "### Dataset Summary\n\n1758 total labelled images\n\nThe scientific figures dataset contains 1758 scientific figures extracted from 890 research papers(ACL). The scientific figures are in png format.\n\nThe dataset has been classified into 19 categories. These are \n- Algorithms \n- Architecture/Pipeline diagrams\n- Bar charts \n- Box Plots \n- Confusion Matrix\n- Graph \n- Line Chart \n- Maps \n- Natural Images \n- Neural Networks \n- NLP rules/grammar \n- Pie chart \n- Scatter Plot \n- Screenshots\n- Tables\n- Trees \n- Pareto chart \n- Venn Diagram \n- Word Cloud\n\n\nThe scientific figures are in the 'png' directory.\n\nThe 'metadata' directory contains metadata extracted from the pdf along with scientific figures in json format.\n\nFinally, the 'scientific_figures.csv' file contains following columns/fields:\n\n1. 'sci_fig' : Scientific figure name\n\n2. 'caption': Caption text\n\n3. 'inline_reference': Scientific figure contexts mentioned in the research paper\n\n4. 'metadata': metadata json filename\n\n5. 'label': One of the 19 categories as described above.\n\n6. 'acl_paper_id': Unique identifier assigned to each pdf by ACL", "### Supported Tasks and Leaderboards\n\nMulti-label classification", "## Dataset Creation\nThe dataset was created using papers in ACL Anthology.", "### Annotations", "#### Annotation process\n~2k images manually labelled\n\n\nTODO", "### Contributions\n\nThanks to @zebaKarishma, @shauryr and @KavyaPuranik for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-label-image-classification #annotations_creators-expert-generated #language_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for ACLFig Dataset", "## Dataset Description\n\n- Paper:\n- Leaderboard:", "### Dataset Summary\n\n1758 total labelled images\n\nThe scientific figures dataset contains 1758 scientific figures extracted from 890 research papers(ACL). The scientific figures are in png format.\n\nThe dataset has been classified into 19 categories. These are \n- Algorithms \n- Architecture/Pipeline diagrams\n- Bar charts \n- Box Plots \n- Confusion Matrix\n- Graph \n- Line Chart \n- Maps \n- Natural Images \n- Neural Networks \n- NLP rules/grammar \n- Pie chart \n- Scatter Plot \n- Screenshots\n- Tables\n- Trees \n- Pareto chart \n- Venn Diagram \n- Word Cloud\n\n\nThe scientific figures are in the 'png' directory.\n\nThe 'metadata' directory contains metadata extracted from the pdf along with scientific figures in json format.\n\nFinally, the 'scientific_figures.csv' file contains following columns/fields:\n\n1. 'sci_fig' : Scientific figure name\n\n2. 'caption': Caption text\n\n3. 'inline_reference': Scientific figure contexts mentioned in the research paper\n\n4. 'metadata': metadata json filename\n\n5. 'label': One of the 19 categories as described above.\n\n6. 'acl_paper_id': Unique identifier assigned to each pdf by ACL", "### Supported Tasks and Leaderboards\n\nMulti-label classification", "## Dataset Creation\nThe dataset was created using papers in ACL Anthology.", "### Annotations", "#### Annotation process\n~2k images manually labelled\n\n\nTODO", "### Contributions\n\nThanks to @zebaKarishma, @shauryr and @KavyaPuranik for adding this dataset." ]

[ "task_categories:text-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:extended|social_bias_frames", "language:en", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #source_datasets-extended|social_bias_frames #language-English #region-us \n" ]

[ "task_categories:text-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:extended", "language:en", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #source_datasets-extended #language-English #region-us \n" ]

[ "task_categories:text-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:extended", "language:en", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #source_datasets-extended #language-English #region-us \n" ]

[ "task_categories:text-classification", "annotations_creators:crowdsourced", "language_creators:find", "multilinguality:monolingual", "source_datasets:extended", "language:en", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #annotations_creators-crowdsourced #language_creators-find #multilinguality-monolingual #source_datasets-extended #language-English #region-us \n" ]

[ "task_categories:text-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:extended", "language:en", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #source_datasets-extended #language-English #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"c4-10k\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"c4-10k\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"c4-code-10k\"\n\n10K elements of C4 and 10K elements of code parrot clean (Python code).\n\nNote that these are the datasets used to train my interpretability-friendly models, but is *not* of the correct mixture. Those models were trained on 83% C4 and 17% Python Code (ish) by tokens. This dataset has 10K strings of each, and by tokens is about 22M of code and 5M of C4 (code is longer and harder to compress!)\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"c4-code-10k\"\n\n10K elements of C4 and 10K elements of code parrot clean (Python code).\n\nNote that these are the datasets used to train my interpretability-friendly models, but is *not* of the correct mixture. Those models were trained on 83% C4 and 17% Python Code (ish) by tokens. This dataset has 10K strings of each, and by tokens is about 22M of code and 5M of C4 (code is longer and harder to compress!)\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"wiki-10k\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"wiki-10k\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"code-10k\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"code-10k\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "multilinguality:monolingual", "language:he", "region:us" ]

[]

[ "he" ]

[ "# Dataset Card for \"HebrewStageAndLyricsWithNewLines\"\n\n* Contains poems and stories from \"New Stage\" (\"במה חדשה\")\n* Contains text lines from various Hebrew song lyrics\n* Data contains new-line characters\n* Generated from a text file in which different poems were seperated using a double new-line character\n* The script I made for converting the text file into a dataset is available here" ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #multilinguality-monolingual #language-Hebrew #region-us \n", "# Dataset Card for \"HebrewStageAndLyricsWithNewLines\"\n\n* Contains poems and stories from \"New Stage\" (\"במה חדשה\")\n* Contains text lines from various Hebrew song lyrics\n* Data contains new-line characters\n* Generated from a text file in which different poems were seperated using a double new-line character\n* The script I made for converting the text file into a dataset is available here" ]

[ "region:us" ]

[]

[]

[ "# Pick a Pic\n\n* We are periodically uploading (almost) all of the collected data from URL.\n* We have three different datasets:\n * Images dataset - includes the images that were created as part of Pick a Pic.\n * Rankings dataset - includes the rankings that users submitted in Pick a Pic.\n * Downloads dataset - includes the images that users downloaded in Pick a Pic.\n* Help us in creating the largest publicly available human-feedback for text-to-image dataset!\n* You can reach us on discord or by mail." ]

[ "TAGS\n#region-us \n", "# Pick a Pic\n\n* We are periodically uploading (almost) all of the collected data from URL.\n* We have three different datasets:\n * Images dataset - includes the images that were created as part of Pick a Pic.\n * Rankings dataset - includes the rankings that users submitted in Pick a Pic.\n * Downloads dataset - includes the images that users downloaded in Pick a Pic.\n* Help us in creating the largest publicly available human-feedback for text-to-image dataset!\n* You can reach us on discord or by mail." ]

[ "license:agpl-3.0", "region:us" ]

[]

[]

[ "## Description\n\nThe Pixiv Niji Journey dataset is a collection of 9766 images with accompanying metadata, scraped from the online art platform Pixiv. The images were collected using the 'gallery-dl' Python package, with the search term \"nijijourney\" on Pixiv. The collection period for the dataset was from November 6, 2022 to December 27, 2022.\n\nThe dataset is divided into two variants: 'raw' and 'preprocessed'. The 'raw' variant contains the pure dataset resulting from the scraping of Pixiv, while the 'preprocessed' variant contains the same dataset but with additional preprocessing steps applied. These preprocessing steps include converting the images from RGB to RGBA, labeling the dataset with captions using the BLIP tool, and providing Danbooru tags using the wd-v1-4-vit-tagger tool. The 'preprocessed' variant has also been carefully cleaned and filtered to remove any low quality or irrelevant images.\n\nThe images in the dataset are in JPG and PNG format, and the metadata is provided in JSON format, while the preprocessed metadata is provided in '.txt' and '.caption' format. The metadata includes information about the images such as their captions, tags, and other metadata provided by Pixiv. The structure of the raw and preprocessed variants of the dataset is described in the 'File Structure' section below.\n\nThe Pixiv Niji Journey dataset is primarily intended for use in machine learning tasks related to image classification and caption generation. It can also be used as a dataset for image generation models such as stable diffusion. However, users should be aware that the dataset may contain biases or limitations, such as the bias of the Pixiv platform or the specific search term used to collect the data.", "## File Structure\n\nThe structure of the raw files is as follows:\n\n\nwhile the structure of the preprocessed files is:", "## Usage \n\n- Access: the dataset is available for download from the Hugging Face dataset collection\n- Format: the dataset is provided in ZIP format, with images in PNG format and metadata in JSON format\n- Requirements: the dataset requires no specific requirements or dependencies for use", "## Data Quality\n\n- Number of images: 9766\n- Image sizes: vary, but all images are in PNG format\n- Class balance: the distribution of classes in the dataset is not known\n- Quality: the dataset has been carefully cleaned and filtered to remove low quality or irrelevant images", "## Limitations\n\nWhile the Pixiv Niji Journey dataset has been carefully cleaned and preprocessed to ensure high quality and consistency, it is important to be aware of certain limitations and biases that may be present in the dataset. Some potential limitations of the dataset include:\n\n- Bias of the Pixiv platform: Pixiv is an online art platform that may have its own biases in terms of the content that is available and the users who contribute to it. This could potentially introduce biases into the dataset.\n\n- Search term bias: The dataset was collected using the search term \"nijijourney\" on Pixiv, which may have introduced biases into the dataset depending on the popularity and prevalence of this term on the platform.\n\n- Limited scope: The dataset only includes images scraped from Pixiv, and therefore may not be representative of a wider range of images or artistic styles.\n\n- Potential errors or inconsistencies in the metadata: While every effort has been made to ensure the accuracy of the metadata, there may be errors or inconsistencies present in the data.\n\nIt is important to be aware of these limitations and to consider them when using the Pixiv Niji Journey dataset for research or other purposes.", "## License\n\nThe Pixiv Niji Journey dataset is made available under the terms of the AGPL-3.0 license. This license is a copyleft license that allows users to freely use, modify, and distribute the dataset, as long as any modified versions are also made available under the same terms.\n\nUnder the terms of the AGPL-3.0 license, users are allowed to:\n- Use the dataset for any purpose, commercial or non-commercial\n- Modify the dataset as needed for their purposes\n- Distribute copies of the dataset, either modified or unmodified\n\nHowever, users must also follow the following conditions:\n- Any modified versions of the dataset must be made available under the same AGPL-3.0 license\n- If the dataset is used to provide a service to others (such as through a website or API), the source code for the service must be made available to users under the AGPL-3.0 license\n\nIt is important to carefully review the terms of the AGPL-3.0 license and ensure that you understand your rights and obligations when using the Pixiv Niji Journey dataset. \n\nIf you use this dataset in your work, please cite it as follows:" ]

[ "TAGS\n#license-agpl-3.0 #region-us \n", "## Description\n\nThe Pixiv Niji Journey dataset is a collection of 9766 images with accompanying metadata, scraped from the online art platform Pixiv. The images were collected using the 'gallery-dl' Python package, with the search term \"nijijourney\" on Pixiv. The collection period for the dataset was from November 6, 2022 to December 27, 2022.\n\nThe dataset is divided into two variants: 'raw' and 'preprocessed'. The 'raw' variant contains the pure dataset resulting from the scraping of Pixiv, while the 'preprocessed' variant contains the same dataset but with additional preprocessing steps applied. These preprocessing steps include converting the images from RGB to RGBA, labeling the dataset with captions using the BLIP tool, and providing Danbooru tags using the wd-v1-4-vit-tagger tool. The 'preprocessed' variant has also been carefully cleaned and filtered to remove any low quality or irrelevant images.\n\nThe images in the dataset are in JPG and PNG format, and the metadata is provided in JSON format, while the preprocessed metadata is provided in '.txt' and '.caption' format. The metadata includes information about the images such as their captions, tags, and other metadata provided by Pixiv. The structure of the raw and preprocessed variants of the dataset is described in the 'File Structure' section below.\n\nThe Pixiv Niji Journey dataset is primarily intended for use in machine learning tasks related to image classification and caption generation. It can also be used as a dataset for image generation models such as stable diffusion. However, users should be aware that the dataset may contain biases or limitations, such as the bias of the Pixiv platform or the specific search term used to collect the data.", "## File Structure\n\nThe structure of the raw files is as follows:\n\n\nwhile the structure of the preprocessed files is:", "## Usage \n\n- Access: the dataset is available for download from the Hugging Face dataset collection\n- Format: the dataset is provided in ZIP format, with images in PNG format and metadata in JSON format\n- Requirements: the dataset requires no specific requirements or dependencies for use", "## Data Quality\n\n- Number of images: 9766\n- Image sizes: vary, but all images are in PNG format\n- Class balance: the distribution of classes in the dataset is not known\n- Quality: the dataset has been carefully cleaned and filtered to remove low quality or irrelevant images", "## Limitations\n\nWhile the Pixiv Niji Journey dataset has been carefully cleaned and preprocessed to ensure high quality and consistency, it is important to be aware of certain limitations and biases that may be present in the dataset. Some potential limitations of the dataset include:\n\n- Bias of the Pixiv platform: Pixiv is an online art platform that may have its own biases in terms of the content that is available and the users who contribute to it. This could potentially introduce biases into the dataset.\n\n- Search term bias: The dataset was collected using the search term \"nijijourney\" on Pixiv, which may have introduced biases into the dataset depending on the popularity and prevalence of this term on the platform.\n\n- Limited scope: The dataset only includes images scraped from Pixiv, and therefore may not be representative of a wider range of images or artistic styles.\n\n- Potential errors or inconsistencies in the metadata: While every effort has been made to ensure the accuracy of the metadata, there may be errors or inconsistencies present in the data.\n\nIt is important to be aware of these limitations and to consider them when using the Pixiv Niji Journey dataset for research or other purposes.", "## License\n\nThe Pixiv Niji Journey dataset is made available under the terms of the AGPL-3.0 license. This license is a copyleft license that allows users to freely use, modify, and distribute the dataset, as long as any modified versions are also made available under the same terms.\n\nUnder the terms of the AGPL-3.0 license, users are allowed to:\n- Use the dataset for any purpose, commercial or non-commercial\n- Modify the dataset as needed for their purposes\n- Distribute copies of the dataset, either modified or unmodified\n\nHowever, users must also follow the following conditions:\n- Any modified versions of the dataset must be made available under the same AGPL-3.0 license\n- If the dataset is used to provide a service to others (such as through a website or API), the source code for the service must be made available to users under the AGPL-3.0 license\n\nIt is important to carefully review the terms of the AGPL-3.0 license and ensure that you understand your rights and obligations when using the Pixiv Niji Journey dataset. \n\nIf you use this dataset in your work, please cite it as follows:" ]

[ "arxiv:2212.14746", "region:us" ]

[ "2212.14746" ]

[]

[ "# MindBigData 2022 A Large Dataset of Brain Signals\n> Supporting datasets for paper arXiv:2212.14746\n> There are 3 Main datasets with subdatasets:\n> \n1.- MindBigData MNIST of Brain Digits\n\n> based on URL\n> But all datasets splitted to 80% Train 20% Test (also proportional in the 11 classes)\n> EEG's Resampled to match original headsets sampling rate\n> Included headers.\n> and simplified to contain only label & EEG data as rows named in headers as ChannelName-SampleNum, ie for channel FP1 and MindWave will be FP1-0 FP1-1 ..... FP1-1023 since there are 1024 samples.\n> There are 4 subdatasets:\n> \n> For MindWave with 1 EEG Channel and 1024 samples x Channel\n> \n> For EPOC1 with 14 EEG Channels and 256 samples x Channel\n> \n> For Muse1 with 4 EEG Channels and 440 samples x Channel\n> \n> For Insight1 with 5 EEG Channels and 256 samples x Channel\n> \n1.1.- MindBigData MNIST of Brain digits MindWave1\nURL\n> \n1.2.- MindBigData MNIST of Brain digits EPOC1\nURL\n\n1.3.- MindBigData MNIST of Brain digits Muse1\nURL\n\n1.4.- MindBigData MNIST of Brain digits Insight1\nURL\n\n2.- MindBigData Imagenet of the Brain\n\n> based on URL\n> But all datasets splitted to 80% Train 20% Test (also proportional in all the classes)\n> EEG's Resampled to match original headsets sampling rate\n> Included headers.\n> contains label as the ILSVRC2013 category, and a hotencoded name lists, the RGB pixel values of the image seen resampled to 150pixels by 150 pixels & EEG data as rows named in headers as ChannelName-SampleNum, \n> There are 2 subdatasets:\n> \n> One with the Insight 1 EEG signals at 384 samples per channel (5 channels)\n> \n> One with the Spectrogram image 64x64px instead of the EEG as described in the paper\n> \n 2.1.- MindBigData Imagenet of the Brain Insight1 EEG\n URL\n \n 2.2.- MindBigData Imagenet of the Brain Insight1 Spectrogram\n URL\n\n3.- MindBigData Visual MNIST of Brain Digits\n\n> based on URL\n> But all datasets splitted to 80% Train 20% Test (also proportional in the 11 classes)\n> Included headers.\n> and simplified to contain only label, the original MNIST pixels of the digit seen 28x28pixels & EEG data as rows named in headers as ChannelName-SampleNum, ie for channel TP9 and Muse2 will be TP9-0 TP9-1 ..... TP9-511 since there are 512 samples.\n> There are 3 subdatasets:\n> \n> For Muse2 with 5 EEG Channels, 3 PPG Channels, 3 ACC Channels & 3 GYR Channels and 512 samples x Channel\n> \n> For Cap64 with 64 EEG Channels and 400 samples x Channel\n>\n> For Cap64 with 64 EEG Channels and 400 samples x Channel but with Morlet png images as EEG outputs\n> \n3.1.- MindBigData Visual MNIST of Brain digits Muse2\nURL\n\n3.2.- MindBigData Visual MNIST of Brain digits Cap64\nURL\n\n3.3.- MindBigData Visual MNIST of Brain digits Cap64 Morlet\nURL" ]

[ "TAGS\n#arxiv-2212.14746 #region-us \n", "# MindBigData 2022 A Large Dataset of Brain Signals\n> Supporting datasets for paper arXiv:2212.14746\n> There are 3 Main datasets with subdatasets:\n> \n1.- MindBigData MNIST of Brain Digits\n\n> based on URL\n> But all datasets splitted to 80% Train 20% Test (also proportional in the 11 classes)\n> EEG's Resampled to match original headsets sampling rate\n> Included headers.\n> and simplified to contain only label & EEG data as rows named in headers as ChannelName-SampleNum, ie for channel FP1 and MindWave will be FP1-0 FP1-1 ..... FP1-1023 since there are 1024 samples.\n> There are 4 subdatasets:\n> \n> For MindWave with 1 EEG Channel and 1024 samples x Channel\n> \n> For EPOC1 with 14 EEG Channels and 256 samples x Channel\n> \n> For Muse1 with 4 EEG Channels and 440 samples x Channel\n> \n> For Insight1 with 5 EEG Channels and 256 samples x Channel\n> \n1.1.- MindBigData MNIST of Brain digits MindWave1\nURL\n> \n1.2.- MindBigData MNIST of Brain digits EPOC1\nURL\n\n1.3.- MindBigData MNIST of Brain digits Muse1\nURL\n\n1.4.- MindBigData MNIST of Brain digits Insight1\nURL\n\n2.- MindBigData Imagenet of the Brain\n\n> based on URL\n> But all datasets splitted to 80% Train 20% Test (also proportional in all the classes)\n> EEG's Resampled to match original headsets sampling rate\n> Included headers.\n> contains label as the ILSVRC2013 category, and a hotencoded name lists, the RGB pixel values of the image seen resampled to 150pixels by 150 pixels & EEG data as rows named in headers as ChannelName-SampleNum, \n> There are 2 subdatasets:\n> \n> One with the Insight 1 EEG signals at 384 samples per channel (5 channels)\n> \n> One with the Spectrogram image 64x64px instead of the EEG as described in the paper\n> \n 2.1.- MindBigData Imagenet of the Brain Insight1 EEG\n URL\n \n 2.2.- MindBigData Imagenet of the Brain Insight1 Spectrogram\n URL\n\n3.- MindBigData Visual MNIST of Brain Digits\n\n> based on URL\n> But all datasets splitted to 80% Train 20% Test (also proportional in the 11 classes)\n> Included headers.\n> and simplified to contain only label, the original MNIST pixels of the digit seen 28x28pixels & EEG data as rows named in headers as ChannelName-SampleNum, ie for channel TP9 and Muse2 will be TP9-0 TP9-1 ..... TP9-511 since there are 512 samples.\n> There are 3 subdatasets:\n> \n> For Muse2 with 5 EEG Channels, 3 PPG Channels, 3 ACC Channels & 3 GYR Channels and 512 samples x Channel\n> \n> For Cap64 with 64 EEG Channels and 400 samples x Channel\n>\n> For Cap64 with 64 EEG Channels and 400 samples x Channel but with Morlet png images as EEG outputs\n> \n3.1.- MindBigData Visual MNIST of Brain digits Muse2\nURL\n\n3.2.- MindBigData Visual MNIST of Brain digits Cap64\nURL\n\n3.3.- MindBigData Visual MNIST of Brain digits Cap64 Morlet\nURL" ]

[ "license:mit", "arxiv:1908.11049", "region:us" ]

[ "1908.11049" ]

[]

[ "# Disclaimer\n*This is a hate speech dataset (in Arabic, French, and English).*\n\n*Offensive content that does not reflect the opinions of the authors.*", "# Dataset of our EMNLP 2019 Paper (Multilingual and Multi-Aspect Hate Speech Analysis)\nFor more details about our dataset, please check our paper:\n\n\t@inproceedings{ousidhoum-etal-multilingual-hate-speech-2019,\n \t\ttitle = \"Multilingual and Multi-Aspect Hate Speech Analysis\",\n \t\tauthor = \"Ousidhoum, Nedjma\n \t\tand Lin, Zizheng\n \t\tand Zhang, Hongming\n \t\tand Song, Yangqiu\n \t\tand Yeung, Dit-Yan\",\n \t\t\tbooktitle = \"Proceedings of EMNLP\",\n \t\tyear = \"2019\",\n \t\tpublisher =\t\"Association for Computational Linguistics\",\n\t}\t\n\n(You can preview our paper on URL", "## Clarification\nThe multi-labelled tasks are *the hostility type of the tweet* and the *annotator's sentiment*. (We kept labels on which at least two annotators agreed.)", "## Taxonomy\nIn further experiments that involved binary classification tasks of the hostility/hate/abuse type, we considered single-labelled *normal* instances to be *non-hate/non-toxic* and all the other instances to be *toxic*.", "## Dataset\nOur dataset is composed of three csv files sorted by language. They contain the tweets and the annotations described in our paper:\n\nthe hostility type *(column: tweet sentiment)* \n\nhostility directness *(column: directness)* \n\ntarget attribute *(column: target)*\n\ntarget group *(column: group)* \n\nannotator's sentiment *(column: annotator sentiment)*.", "## Experiments\n\nTo replicate our experiments, please see URL" ]

[ "TAGS\n#license-mit #arxiv-1908.11049 #region-us \n", "# Disclaimer\n*This is a hate speech dataset (in Arabic, French, and English).*\n\n*Offensive content that does not reflect the opinions of the authors.*", "# Dataset of our EMNLP 2019 Paper (Multilingual and Multi-Aspect Hate Speech Analysis)\nFor more details about our dataset, please check our paper:\n\n\t@inproceedings{ousidhoum-etal-multilingual-hate-speech-2019,\n \t\ttitle = \"Multilingual and Multi-Aspect Hate Speech Analysis\",\n \t\tauthor = \"Ousidhoum, Nedjma\n \t\tand Lin, Zizheng\n \t\tand Zhang, Hongming\n \t\tand Song, Yangqiu\n \t\tand Yeung, Dit-Yan\",\n \t\t\tbooktitle = \"Proceedings of EMNLP\",\n \t\tyear = \"2019\",\n \t\tpublisher =\t\"Association for Computational Linguistics\",\n\t}\t\n\n(You can preview our paper on URL", "## Clarification\nThe multi-labelled tasks are *the hostility type of the tweet* and the *annotator's sentiment*. (We kept labels on which at least two annotators agreed.)", "## Taxonomy\nIn further experiments that involved binary classification tasks of the hostility/hate/abuse type, we considered single-labelled *normal* instances to be *non-hate/non-toxic* and all the other instances to be *toxic*.", "## Dataset\nOur dataset is composed of three csv files sorted by language. They contain the tweets and the annotations described in our paper:\n\nthe hostility type *(column: tweet sentiment)* \n\nhostility directness *(column: directness)* \n\ntarget attribute *(column: target)*\n\ntarget group *(column: group)* \n\nannotator's sentiment *(column: annotator sentiment)*.", "## Experiments\n\nTo replicate our experiments, please see URL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"scalable_project\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"scalable_project\"\n\nMore Information needed" ]

[ "multilinguality:monolingual", "language:en", "license:cc0-1.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc0-1.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# TREC DL 2020 Query Variation" ]

[ "TAGS\n#region-us \n", "# TREC DL 2020 Query Variation" ]

[ "language:en", "license:creativeml-openrail-m", "stable-diffusion", "text-to-image", "image-to-image", "region:us" ]

[]

[ "en" ]

[ "# Albino Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nI trained the embedding two epochs until 6800 steps.\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #image-to-image #region-us \n", "# Albino Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nI trained the embedding two epochs until 6800 steps.\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "language:en", "license:creativeml-openrail-m", "stable-diffusion", "text-to-image", "image-to-image", "region:us" ]

[]

[ "en" ]

[ "# Barbosa Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nI trained the embedding two epochs until 8000 steps.\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #image-to-image #region-us \n", "# Barbosa Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nI trained the embedding two epochs until 8000 steps.\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "language:en", "license:creativeml-openrail-m", "stable-diffusion", "text-to-image", "image-to-image", "region:us" ]

[]

[ "en" ]

[ "# Cyberware Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, but this time I would use it just as it is.\n\nThe embedding itself is based on the dataset given by Eppinette: URL\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #image-to-image #region-us \n", "# Cyberware Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, but this time I would use it just as it is.\n\nThe embedding itself is based on the dataset given by Eppinette: URL\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"OxfordPets_facebook_opt_30b_LLM_Description_opt30b_downstream_tasks_ViT_L_14\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"OxfordPets_facebook_opt_30b_LLM_Description_opt30b_downstream_tasks_ViT_L_14\"\n\nMore Information needed" ]

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "language:en", "pretraining", "language modelling", "common crawl", "web", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for OLM November/December 2022 Common Crawl\n\nCleaned and deduplicated pretraining dataset, created with the OLM repo here from 15% of the November/December 2022 Common Crawl snapshot.\n\nNote: 'last_modified_timestamp' was parsed from whatever a website returned in it's 'Last-Modified' header; there are likely a small number of outliers that are incorrect, so we recommend removing the outliers before doing statistics with 'last_modified_timestamp'." ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #language-English #pretraining #language modelling #common crawl #web #region-us \n", "# Dataset Card for OLM November/December 2022 Common Crawl\n\nCleaned and deduplicated pretraining dataset, created with the OLM repo here from 15% of the November/December 2022 Common Crawl snapshot.\n\nNote: 'last_modified_timestamp' was parsed from whatever a website returned in it's 'Last-Modified' header; there are likely a small number of outliers that are incorrect, so we recommend removing the outliers before doing statistics with 'last_modified_timestamp'." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"dreambooth-hackathon-images-sbob\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dreambooth-hackathon-images-sbob\"\n\nMore Information needed" ]

[ "license:cc-by-nc-4.0", "not-for-all-audiences", "region:us" ]

[]

[]

[ "# Hagikora\n*Aka, Stripped photoshop.*", "## FAQ:\n\nQ: Can you remove the gated prompts? \nA: No. Personally I don't want any random person downloading the dataset and finding out it isn't suitable for them.\n\nQ: Can you make Zip file. \nA: Yes.\n\nQ: Filtering? \nA: No filtering done. All files are as is and untouched. You probably want to aesthetic filer on the images or something like that." ]

[ "TAGS\n#license-cc-by-nc-4.0 #not-for-all-audiences #region-us \n", "# Hagikora\n*Aka, Stripped photoshop.*", "## FAQ:\n\nQ: Can you remove the gated prompts? \nA: No. Personally I don't want any random person downloading the dataset and finding out it isn't suitable for them.\n\nQ: Can you make Zip file. \nA: Yes.\n\nQ: Filtering? \nA: No filtering done. All files are as is and untouched. You probably want to aesthetic filer on the images or something like that." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"base_code_review\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"base_code_review\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"dreambooth-hackathon-images-sbob-jpeg\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dreambooth-hackathon-images-sbob-jpeg\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"doge\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"doge\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"OxfordPets_Multimodal_Fatima_opt_175b_LLM_Description_opt175b_downstream_tasks_ViT_L_14\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"OxfordPets_Multimodal_Fatima_opt_175b_LLM_Description_opt175b_downstream_tasks_ViT_L_14\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"dreambooth-hackathon-images\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dreambooth-hackathon-images\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"olm-december-2022-tokenized-512\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"olm-december-2022-tokenized-512\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"olm-december-2022-tokenized-1024\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"olm-december-2022-tokenized-1024\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"dreambooth-hackathon-images-miko\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dreambooth-hackathon-images-miko\"\n\nMore Information needed" ]

[ "task_categories:token-classification", "language:ru", "region:us" ]

[]

[ "ru" ]

[]

[ "TAGS\n#task_categories-token-classification #language-Russian #region-us \n" ]

[ "task_categories:token-classification", "language:ru", "region:us" ]

[]

[ "ru" ]

[]

[ "TAGS\n#task_categories-token-classification #language-Russian #region-us \n" ]

[ "task_categories:summarization", "size_categories:1M<n<10M", "language:en", "license:cc-by-sa-3.0", "summarization", "Wikipedia", "arxiv:2212.09233", "region:us" ]

[ "2212.09233" ]

[ "en" ]

[ "### Data Instances\n\n\nFor each instance, there is a list of strings for the document, a list of strings for the summary, a string for the document title, a string for the aspect and a list of indices for the sentences in the corresponding section.\n\n\nThe average token count for the articles and the highlights are provided below:", "### Data Fields\n\n\n* 'title': a string, containing the original Wikipedia title.\n* 'document': a list of sentences, containing the original content in the Wikipedia sections except the first abstract section.\n* 'aspect': a string, containing the section name and its parent section names.\n* 'aspect\\_sents': a list of indices, representing the sentences in the 'aspect' section.\n* 'summary': a list of sentences, the corresponding aspect-based summary for the document.", "### Data Splits\n\n\nThe OASum dataset has 3 splits: *train*, *valid*, and *test*. Below are the statistics for the Version 1.0.0 of the dataset.\n\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe OASum Dataset version 1.0.0 is released under the CC-BY-SA-3.0 License" ]

[ "TAGS\n#task_categories-summarization #size_categories-1M<n<10M #language-English #license-cc-by-sa-3.0 #summarization #Wikipedia #arxiv-2212.09233 #region-us \n", "### Data Instances\n\n\nFor each instance, there is a list of strings for the document, a list of strings for the summary, a string for the document title, a string for the aspect and a list of indices for the sentences in the corresponding section.\n\n\nThe average token count for the articles and the highlights are provided below:", "### Data Fields\n\n\n* 'title': a string, containing the original Wikipedia title.\n* 'document': a list of sentences, containing the original content in the Wikipedia sections except the first abstract section.\n* 'aspect': a string, containing the section name and its parent section names.\n* 'aspect\\_sents': a list of indices, representing the sentences in the 'aspect' section.\n* 'summary': a list of sentences, the corresponding aspect-based summary for the document.", "### Data Splits\n\n\nThe OASum dataset has 3 splits: *train*, *valid*, and *test*. Below are the statistics for the Version 1.0.0 of the dataset.\n\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe OASum Dataset version 1.0.0 is released under the CC-BY-SA-3.0 License" ]

[ "language:en", "license:creativeml-openrail-m", "stable-diffusion", "text-to-image", "image-to-image", "region:us" ]

[]

[ "en" ]

[ "# Dpin Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #image-to-image #region-us \n", "# Dpin Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "language:en", "license:creativeml-openrail-m", "stable-diffusion", "text-to-image", "image-to-image", "region:us" ]

[]

[ "en" ]

[ "# Hurybone Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #image-to-image #region-us \n", "# Hurybone Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "language:en", "license:creativeml-openrail-m", "stable-diffusion", "text-to-image", "image-to-image", "region:us" ]

[]

[ "en" ]

[ "# Iskou Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #image-to-image #region-us \n", "# Iskou Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "language:en", "license:creativeml-openrail-m", "stable-diffusion", "text-to-image", "image-to-image", "region:us" ]

[]

[ "en" ]

[ "# Saska Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nI trained the embedding two epochs until 8000 steps.\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #image-to-image #region-us \n", "# Saska Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nI trained the embedding two epochs until 8000 steps.\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "language:en", "license:creativeml-openrail-m", "stable-diffusion", "text-to-image", "image-to-image", "region:us" ]

[]

[ "en" ]

[ "# Star Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nThis embedding can be used for characters aswell! Just use it with a strength of 0.6 or less!\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #image-to-image #region-us \n", "# Star Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nThis embedding can be used for characters aswell! Just use it with a strength of 0.6 or less!\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"4096_filtered_base_code_review\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"4096_filtered_base_code_review\"\n\nMore Information needed" ]

[ "task_categories:feature-extraction", "annotations_creators:machine-generated", "language_creators:machine-generated", "size_categories:10K<n<100K", "source_datasets:original", "license:gpl-3.0", "region:us" ]

[]

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[ "TAGS\n#task_categories-feature-extraction #annotations_creators-machine-generated #language_creators-machine-generated #size_categories-10K<n<100K #source_datasets-original #license-gpl-3.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"2048_has_code_filtered_base_code_review\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"2048_has_code_filtered_base_code_review\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "arxiv:2009.01325", "region:us" ]

[ "2009.01325" ]

[]

[ "# Dataset Card for Summarize from Feedback", "## Dataset Description\n\n\nIn the Learning to Summarize from Human Feedback paper, a reward model was trained from human feedback.\nThe reward model was then used to train a summarization model to align with human preferences. This is the dataset of human feedback that was released for reward modelling.\nThere are two parts of this dataset: 'comparisons' and 'axis'. In the 'comparisons' part, human annotators were asked to choose the best out of two summaries.\nIn the 'axis' part, human annotators gave scores on a likert scale for the quality of a summary.\nThe 'comparisons' part only has a train and validation split, and the 'axis' part only has a test and validation split.\n\nThe summaries used for training the reward model in the paper come from the TL;DR dataset.\nAdditional validation and test data come from the TL;DR dataset, CNN articles, and Daily Mail articles.\n\nFor more information, see the repo here.\n\n\n\nURL\n\n\n\nDataset added to the Hugging Face Hub with help from @Tristan" ]

[ "TAGS\n#arxiv-2009.01325 #region-us \n", "# Dataset Card for Summarize from Feedback", "## Dataset Description\n\n\nIn the Learning to Summarize from Human Feedback paper, a reward model was trained from human feedback.\nThe reward model was then used to train a summarization model to align with human preferences. This is the dataset of human feedback that was released for reward modelling.\nThere are two parts of this dataset: 'comparisons' and 'axis'. In the 'comparisons' part, human annotators were asked to choose the best out of two summaries.\nIn the 'axis' part, human annotators gave scores on a likert scale for the quality of a summary.\nThe 'comparisons' part only has a train and validation split, and the 'axis' part only has a test and validation split.\n\nThe summaries used for training the reward model in the paper come from the TL;DR dataset.\nAdditional validation and test data come from the TL;DR dataset, CNN articles, and Daily Mail articles.\n\nFor more information, see the repo here.\n\n\n\nURL\n\n\n\nDataset added to the Hugging Face Hub with help from @Tristan" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"dreambooth-hackathon-images-srkman\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dreambooth-hackathon-images-srkman\"\n\nMore Information needed" ]

[ "task_categories:object-detection", "roboflow", "roboflow2huggingface", "region:us" ]

[]

[]

[ "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on December 22, 2022 at 5:10 PM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 9898 images.\nPlanted are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Resize to 416x416 (Stretch)\n\nNo image augmentation techniques were applied." ]

[ "TAGS\n#task_categories-object-detection #roboflow #roboflow2huggingface #region-us \n", "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on December 22, 2022 at 5:10 PM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 9898 images.\nPlanted are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Resize to 416x416 (Stretch)\n\nNo image augmentation techniques were applied." ]

[ "task_categories:object-detection", "roboflow", "region:us" ]

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[ "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on December 26, 2022 at 10:13 PM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 4666 images.\nT are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 416x416 (Stretch)\n\nNo image augmentation techniques were applied." ]

[ "TAGS\n#task_categories-object-detection #roboflow #region-us \n", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on December 26, 2022 at 10:13 PM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 4666 images.\nT are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 416x416 (Stretch)\n\nNo image augmentation techniques were applied." ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"beats\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"beats\"\n\nMore Information needed" ]