Datasets:

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ftopal

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huggingface-datasets

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[ "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: Translation\n* Model: facebook/wmt19-en-de\n* Dataset: wmt19\n* Config: de-en\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @WillHeld 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: Translation\n* Model: facebook/wmt19-en-de\n* Dataset: wmt19\n* Config: de-en\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @WillHeld 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: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-125m_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten 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: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-125m_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten 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: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-1.3b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten 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: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-1.3b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten 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: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: WillHeld/stereoset_zero\n* Config: WillHeld--stereoset_zero\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @WillHeld 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: WillHeld/stereoset_zero\n* Config: WillHeld--stereoset_zero\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @WillHeld 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: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-7b1\n* Dataset: WillHeld/stereoset_zero\n* Config: WillHeld--stereoset_zero\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @WillHeld 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-7b1\n* Dataset: WillHeld/stereoset_zero\n* Config: WillHeld--stereoset_zero\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @WillHeld 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: WillHeld/stereoset_zero\n* Config: WillHeld--stereoset_zero\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @WillHeld 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: WillHeld/stereoset_zero\n* Config: WillHeld--stereoset_zero\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @WillHeld 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: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b7\n* Dataset: WillHeld/stereoset_zero\n* Config: WillHeld--stereoset_zero\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @WillHeld 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b7\n* Dataset: WillHeld/stereoset_zero\n* Config: WillHeld--stereoset_zero\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @WillHeld for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: WillHeld/stereoset_zero\n* Config: WillHeld--stereoset_zero\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @WillHeld 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: WillHeld/stereoset_zero\n* Config: WillHeld--stereoset_zero\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @WillHeld 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-7b1\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-7b1\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "license:mit", "depth-estimation", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-mit #depth-estimation #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: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b7\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b7\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-7b1\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-7b1\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b7\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b7\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:de", "language:fr", "language:lb", "language:nl", "language:la", "language:en", "license:cc0-1.0", "newspapers", "1800-1900", "lam", "region:us" ]

[]

[ "de", "fr", "lb", "nl", "la", "en" ]

[ "# Dataset Card for BnL Newspapers 1841-1879", "## Table of Contents\n- Dataset Card for bnl_newspapers1841-1879\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 - Dataset Creation\n - Curation Rationale\n - Source Data\n - Initial Data Collection and Normalization\n - Annotations\n - Annotation process\n - Who are the annotators?\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 - size of dataset\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper:\n- Leaderboard:\n- Point of Contact: opendata at URL", "### Dataset Summary\n\n630.709 articles from historical newspapers (1841-1879) along with metadata and the full text.\n\n21 newspaper titles\n24.415 newspaper issues\n99.957 scanned pages\nTranscribed using a variety of OCR engines and corrected using URL (95% threshold)\nPublic Domain, CC0 (See copyright notice)\n\nThe newspapers used are:\n- Der Arbeiter (1878)\n- L'Arlequin (1848-1848)\n- L'Avenir (1868-1871)\n- Courrier du Grand-Duché de Luxembourg (1844-1868)\n- Cäcilia (1863-1871)\n- Diekircher Wochenblatt (1841-1848)\n- Le Gratis luxembourgeois (1857-1858)\n- L'Indépendance luxembourgeoise (1871-1879)\n- Kirchlicher Anzeiger für die Diözese Luxemburg (1871-1879)\n- La Gazette du Grand-Duché de Luxembourg (1878)\n- Luxemburger Anzeiger (1856)\n- Luxemburger Bauernzeitung (1857)\n- Luxemburger Volks-Freund (1869-1876)\n- Luxemburger Wort (1848-1879)\n- Luxemburger Zeitung (1844-1845)\n- Luxemburger Zeitung = Journal de Luxembourg (1858-1859)\n- L'Union (1860-1871)\n- Das Vaterland (1869-1870)\n- Der Volksfreund (1848-1849)\n- Der Wächter an der Sauer (1849-1869)\n- D'Wäschfra (1868-1879)", "### Supported Tasks and Leaderboards", "### Languages\n\nGerman, French, Luxembourgish", "## Dataset Structure\n\nJSONL file zipped.", "### Data Instances", "### Data Fields\n\n- 'identifier' : unique and persistent identifier using ARK for the Article.\n- 'date' : publishing date of the document e.g \"1848-12-15\".\n- 'metsType' : set to \"newspaper\".\n- 'newpaperTitle' : title of the newspaper. It is transcribed as in the masthead of the individual issue and can thus change.\n- 'paperID' : local identifier for the newspaper title. It remains the same, even for short-term title changes.\n- 'publisher' : publisher of the document e.g. \"Verl. der St-Paulus-Druckerei\".\n- 'title' : main title of the article, section, advertisement, etc.\n- 'text' : full text of the entire article, section, advertisement etc. It includes any titles and subtitles as well. The content does not contain layout information, such as headings, paragraphs or lines.\n- 'creator' : author of the article, section, advertisement etc. Most articles do not have an associated author.\n- 'type' : type of the exported data e.g. ARTICLE, SECTION, ADVERTISEMENT, ...", "## Dataset Creation\n\nThe dataset was created by the National library of Luxembourg with the output of its newspaper digitisation program.", "### Curation Rationale\n\nThe selection of newspapers represent the current state of digitisation of the Luxembourg legal deposit collection of newspapers that are in the public domain. That means all newspapers printed in Luxembourg before and including 1879.", "### Source Data\n\nPrinted historical newspapers.", "#### Initial Data Collection and Normalization\n\nThe data was created through digitisation. The full digitisation specifications are available at URL", "### Annotations", "#### Annotation process\n\nDuring the digitisation process, newspaper pages were semi-automatically zoned into articles. This was done by external suppliers to the library according to the digitisation specifications.", "#### Who are the annotators?\n\nStaff at the external suppliers.", "### Personal and Sensitive Information\n\nThe dataset contains only data that was published in a newspaper. Since it contains only articles before 1879, no living person is expected to be included.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\nThe biases in the text represent the biases from newspaper editors and journalists at the time of the publication. In particular during the period from 1940/05/10 to 1944/09/10 the Nazi occupier controlled all information published.", "### Other Known Limitations\n\nThe OCR transcription is not perfect. It is estimated that the quality is 95% or better.", "## Additional Information", "### size of dataset\n\n500MB-2GB", "### Dataset Curators\n\nThis dataset is curated by the national library of Luxembourg (opendata at URL).", "### Licensing Information\n\nCreative Commons Public Domain Dedication and Certification", "### Contributions\n\nThanks to @ymaurer for adding this dataset." ]

[ "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-expert-generated #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-German #language-French #language-Luxembourgish #language-Dutch #language-Latin #language-English #license-cc0-1.0 #newspapers #1800-1900 #lam #region-us \n", "# Dataset Card for BnL Newspapers 1841-1879", "## Table of Contents\n- Dataset Card for bnl_newspapers1841-1879\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 - Dataset Creation\n - Curation Rationale\n - Source Data\n - Initial Data Collection and Normalization\n - Annotations\n - Annotation process\n - Who are the annotators?\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 - size of dataset\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper:\n- Leaderboard:\n- Point of Contact: opendata at URL", "### Dataset Summary\n\n630.709 articles from historical newspapers (1841-1879) along with metadata and the full text.\n\n21 newspaper titles\n24.415 newspaper issues\n99.957 scanned pages\nTranscribed using a variety of OCR engines and corrected using URL (95% threshold)\nPublic Domain, CC0 (See copyright notice)\n\nThe newspapers used are:\n- Der Arbeiter (1878)\n- L'Arlequin (1848-1848)\n- L'Avenir (1868-1871)\n- Courrier du Grand-Duché de Luxembourg (1844-1868)\n- Cäcilia (1863-1871)\n- Diekircher Wochenblatt (1841-1848)\n- Le Gratis luxembourgeois (1857-1858)\n- L'Indépendance luxembourgeoise (1871-1879)\n- Kirchlicher Anzeiger für die Diözese Luxemburg (1871-1879)\n- La Gazette du Grand-Duché de Luxembourg (1878)\n- Luxemburger Anzeiger (1856)\n- Luxemburger Bauernzeitung (1857)\n- Luxemburger Volks-Freund (1869-1876)\n- Luxemburger Wort (1848-1879)\n- Luxemburger Zeitung (1844-1845)\n- Luxemburger Zeitung = Journal de Luxembourg (1858-1859)\n- L'Union (1860-1871)\n- Das Vaterland (1869-1870)\n- Der Volksfreund (1848-1849)\n- Der Wächter an der Sauer (1849-1869)\n- D'Wäschfra (1868-1879)", "### Supported Tasks and Leaderboards", "### Languages\n\nGerman, French, Luxembourgish", "## Dataset Structure\n\nJSONL file zipped.", "### Data Instances", "### Data Fields\n\n- 'identifier' : unique and persistent identifier using ARK for the Article.\n- 'date' : publishing date of the document e.g \"1848-12-15\".\n- 'metsType' : set to \"newspaper\".\n- 'newpaperTitle' : title of the newspaper. It is transcribed as in the masthead of the individual issue and can thus change.\n- 'paperID' : local identifier for the newspaper title. It remains the same, even for short-term title changes.\n- 'publisher' : publisher of the document e.g. \"Verl. der St-Paulus-Druckerei\".\n- 'title' : main title of the article, section, advertisement, etc.\n- 'text' : full text of the entire article, section, advertisement etc. It includes any titles and subtitles as well. The content does not contain layout information, such as headings, paragraphs or lines.\n- 'creator' : author of the article, section, advertisement etc. Most articles do not have an associated author.\n- 'type' : type of the exported data e.g. ARTICLE, SECTION, ADVERTISEMENT, ...", "## Dataset Creation\n\nThe dataset was created by the National library of Luxembourg with the output of its newspaper digitisation program.", "### Curation Rationale\n\nThe selection of newspapers represent the current state of digitisation of the Luxembourg legal deposit collection of newspapers that are in the public domain. That means all newspapers printed in Luxembourg before and including 1879.", "### Source Data\n\nPrinted historical newspapers.", "#### Initial Data Collection and Normalization\n\nThe data was created through digitisation. The full digitisation specifications are available at URL", "### Annotations", "#### Annotation process\n\nDuring the digitisation process, newspaper pages were semi-automatically zoned into articles. This was done by external suppliers to the library according to the digitisation specifications.", "#### Who are the annotators?\n\nStaff at the external suppliers.", "### Personal and Sensitive Information\n\nThe dataset contains only data that was published in a newspaper. Since it contains only articles before 1879, no living person is expected to be included.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\nThe biases in the text represent the biases from newspaper editors and journalists at the time of the publication. In particular during the period from 1940/05/10 to 1944/09/10 the Nazi occupier controlled all information published.", "### Other Known Limitations\n\nThe OCR transcription is not perfect. It is estimated that the quality is 95% or better.", "## Additional Information", "### size of dataset\n\n500MB-2GB", "### Dataset Curators\n\nThis dataset is curated by the national library of Luxembourg (opendata at URL).", "### Licensing Information\n\nCreative Commons Public Domain Dedication and Certification", "### Contributions\n\nThanks to @ymaurer for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"ai4lam-demo\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"ai4lam-demo\"\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: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-7b1\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-7b1\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b7\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b7\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-3b\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-3b\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-7b1\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-7b1\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-1b1\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-1b1\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-1b7\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-1b7\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-560m\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-560m\n* Dataset: futin/guess\n* Config: en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-7b1\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-7b1\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-3b\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-3b\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-1b7\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-1b7\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-1b1\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-1b1\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-3b\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-3b\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-560m\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-560m\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-1b7\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-1b7\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-7b1\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-7b1\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloomz-1b1\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-1b1\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloomz-560m\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-560m\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-3b\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-3b\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-7b1\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-7b1\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-1b7\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-1b7\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloomz-1b1\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-1b1\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloomz-560m\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloomz-560m\n* Dataset: futin/guess\n* Config: vi_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

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

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[]

[ "# Telegram News (Farsi - Persian)", "## Updated 24 OCT 2022\n\n URL Total News: 139,275 Timespan: 2015-10-13 - 2022-10-24\n \n URL Total News: 241,346 Timespan: 2015-09-26 - 2022-10-24\n \n URL Total News: 134,023 Timespan: 2015-10-07 - 2022-10-24\n \n URL Total News: 137,459 Timespan: 2017-05-16 - 2022-10-24\n \n URL Total News: 178,395 Timespan: 2016-07-05 - 2022-10-24\n \n URL Total News: 384,922 Timespan: 2016-09-22 - 2022-10-24\n \n URL Total News: 102,122 Timespan: 2017-05-22 - 2022-10-24", "### Helper functions", "### Read the Files" ]

[ "TAGS\n#license-afl-3.0 #region-us \n", "# Telegram News (Farsi - Persian)", "## Updated 24 OCT 2022\n\n URL Total News: 139,275 Timespan: 2015-10-13 - 2022-10-24\n \n URL Total News: 241,346 Timespan: 2015-09-26 - 2022-10-24\n \n URL Total News: 134,023 Timespan: 2015-10-07 - 2022-10-24\n \n URL Total News: 137,459 Timespan: 2017-05-16 - 2022-10-24\n \n URL Total News: 178,395 Timespan: 2016-07-05 - 2022-10-24\n \n URL Total News: 384,922 Timespan: 2016-09-22 - 2022-10-24\n \n URL Total News: 102,122 Timespan: 2017-05-22 - 2022-10-24", "### Helper functions", "### Read the Files" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"legaltokenized1024\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"legaltokenized1024\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"legaltokenized256\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"legaltokenized256\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"wmt19-valid-only\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"wmt19-valid-only\"\n\nMore Information needed" ]

[ "license:openrail", "doi:10.57967/hf/0155", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-openrail #doi-10.57967/hf/0155 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"wmt19-valid-only-de_en\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"wmt19-valid-only-de_en\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"wmt19-valid-only-zh_en\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"wmt19-valid-only-zh_en\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"wmt19-valid-only-gu_en\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"wmt19-valid-only-gu_en\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"wmt19-valid-only-ru_en\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"wmt19-valid-only-ru_en\"\n\nMore Information needed" ]

[ "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:pt", "license:apache-2.0", "region:us" ]

[]

[ "pt" ]

[ "# Portuguese Legal Sentences\nCollection of Legal Sentences from the Portuguese Supreme Court of Justice\nThe goal of this dataset was to be used for MLM and TSDAE", "### Contributions\n@rufimelo99\n\n\nIf you use this work, please cite:" ]

[ "TAGS\n#annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #source_datasets-original #language-Portuguese #license-apache-2.0 #region-us \n", "# Portuguese Legal Sentences\nCollection of Legal Sentences from the Portuguese Supreme Court of Justice\nThe goal of this dataset was to be used for MLM and TSDAE", "### Contributions\n@rufimelo99\n\n\nIf you use this work, please cite:" ]

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

[]

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[]

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

[ "language:zh", "license:cc-by-4.0", "region:us" ]

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[ "zh" ]

[ "## Introduction\n\nChinese-C4 is a clean Chinese internet dataset based on Common Crawl. The dataset is 46.29GB and has undergone multiple cleaning strategies, including Chinese filtering, heuristic cleaning based on punctuation, line-based hashing for deduplication, and repetition removal.\n\nThe dataset is open source and free for commercial use, and you are welcome to use the data and the cleaning strategies provided and contribute your cleaning strategies.\n\nYou can find the cleaning script for the dataset on GitHub c4-dataset-script." ]

[ "TAGS\n#language-Chinese #license-cc-by-4.0 #region-us \n", "## Introduction\n\nChinese-C4 is a clean Chinese internet dataset based on Common Crawl. The dataset is 46.29GB and has undergone multiple cleaning strategies, including Chinese filtering, heuristic cleaning based on punctuation, line-based hashing for deduplication, and repetition removal.\n\nThe dataset is open source and free for commercial use, and you are welcome to use the data and the cleaning strategies provided and contribute your cleaning strategies.\n\nYou can find the cleaning script for the dataset on GitHub c4-dataset-script." ]

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

[]

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

[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:natural-language-inference", "annotations_creators:expert-generated", "language_creators:found", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:sl", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "sl" ]

[ "# Dataset Card for SI-NLI", "### Dataset Summary\n\nSI-NLI (Slovene Natural Language Inference Dataset) contains 5,937 human-created Slovene sentence pairs (premise and hypothesis) that are manually labeled with the labels \"entailment\", \"contradiction\", and \"neutral\". We created the dataset using sentences that appear in the Slovenian reference corpus ccKres. Annotators were tasked to modify the hypothesis in a candidate pair in a way that reflects one of the labels. The dataset is balanced since the annotators created three modifications (entailment, contradiction, neutral) for each candidate sentence pair. The dataset is split into train, validation, and test sets, with sizes of 4,392, 547, and 998. \n\nOnly the hypothesis and premise are given in the test set (i.e. no annotations) since SI-NLI is integrated into the Slovene evaluation framework SloBENCH. If you use the dataset to train your models, please consider submitting the test set predictions to SloBENCH to get the evaluation score and see how it compares to others. \n\nIf you have access to the private test set (with labels), you can load it instead of the public one via 'datasets.load_dataset(\"cjvt/si_nli\", \"private\", data_dir=\"<...>\")'.", "### Supported Tasks and Leaderboards\n\nNatural language inference.", "### Languages\n\nSlovenian.", "## Dataset Structure", "### Data Instances\n\nA sample instance from the dataset:", "### Data Fields\n\n- 'pair_id': string identifier of the pair ('\"\"' in the test set), \n- 'premise': premise sentence, \n- 'hypothesis': hypothesis sentence, \n- 'annotation1': the first annotation ('\"\"' if not available), \n- 'annotator1_id': anonymized identifier of the first annotator ('\"\"' if not available), \n- 'annotation2': the second annotation ('\"\"' if not available), \n- 'annotator2_id': anonymized identifier of the second annotator ('\"\"' if not available), \n- 'annotation3': the third annotation ('\"\"' if not available), \n- 'annotator3_id': anonymized identifier of the third annotator ('\"\"' if not available), \n- 'annotation_final': aggregated annotation where it could be unanimously determined ('\"\"' if not available or an unanimous agreement could not be reached), \n- 'label': aggregated annotation: either same as 'annotation_final' (in case of agreement), same as 'annotation1' (in case of disagreement), or '\"\"' (in the test set). Note that examples with disagreement are all put in the training set. This aggregation is just the most simple possibility and the user may instead do something more advanced based on the individual annotations (e.g., learning with disagreement). \n\n\\* A small number of examples did not go through the annotation process because they were constructed by the authors when writing the guidelines. The quality of these was therefore checked by the authors. Such examples do not have the individual annotations and the annotator IDs.", "## Additional Information", "### Dataset Curators\n\nMatej Klemen, Aleš Žagar, Jaka Čibej, Marko Robnik-Šikonja.", "### Licensing Information\n\nCC BY-NC-SA 4.0.", "### Contributions\n\nThanks to @matejklemen for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #task_ids-natural-language-inference #annotations_creators-expert-generated #language_creators-found #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #language-Slovenian #license-cc-by-nc-sa-4.0 #region-us \n", "# Dataset Card for SI-NLI", "### Dataset Summary\n\nSI-NLI (Slovene Natural Language Inference Dataset) contains 5,937 human-created Slovene sentence pairs (premise and hypothesis) that are manually labeled with the labels \"entailment\", \"contradiction\", and \"neutral\". We created the dataset using sentences that appear in the Slovenian reference corpus ccKres. Annotators were tasked to modify the hypothesis in a candidate pair in a way that reflects one of the labels. The dataset is balanced since the annotators created three modifications (entailment, contradiction, neutral) for each candidate sentence pair. The dataset is split into train, validation, and test sets, with sizes of 4,392, 547, and 998. \n\nOnly the hypothesis and premise are given in the test set (i.e. no annotations) since SI-NLI is integrated into the Slovene evaluation framework SloBENCH. If you use the dataset to train your models, please consider submitting the test set predictions to SloBENCH to get the evaluation score and see how it compares to others. \n\nIf you have access to the private test set (with labels), you can load it instead of the public one via 'datasets.load_dataset(\"cjvt/si_nli\", \"private\", data_dir=\"<...>\")'.", "### Supported Tasks and Leaderboards\n\nNatural language inference.", "### Languages\n\nSlovenian.", "## Dataset Structure", "### Data Instances\n\nA sample instance from the dataset:", "### Data Fields\n\n- 'pair_id': string identifier of the pair ('\"\"' in the test set), \n- 'premise': premise sentence, \n- 'hypothesis': hypothesis sentence, \n- 'annotation1': the first annotation ('\"\"' if not available), \n- 'annotator1_id': anonymized identifier of the first annotator ('\"\"' if not available), \n- 'annotation2': the second annotation ('\"\"' if not available), \n- 'annotator2_id': anonymized identifier of the second annotator ('\"\"' if not available), \n- 'annotation3': the third annotation ('\"\"' if not available), \n- 'annotator3_id': anonymized identifier of the third annotator ('\"\"' if not available), \n- 'annotation_final': aggregated annotation where it could be unanimously determined ('\"\"' if not available or an unanimous agreement could not be reached), \n- 'label': aggregated annotation: either same as 'annotation_final' (in case of agreement), same as 'annotation1' (in case of disagreement), or '\"\"' (in the test set). Note that examples with disagreement are all put in the training set. This aggregation is just the most simple possibility and the user may instead do something more advanced based on the individual annotations (e.g., learning with disagreement). \n\n\\* A small number of examples did not go through the annotation process because they were constructed by the authors when writing the guidelines. The quality of these was therefore checked by the authors. Such examples do not have the individual annotations and the annotator IDs.", "## Additional Information", "### Dataset Curators\n\nMatej Klemen, Aleš Žagar, Jaka Čibej, Marko Robnik-Šikonja.", "### Licensing Information\n\nCC BY-NC-SA 4.0.", "### Contributions\n\nThanks to @matejklemen for adding this dataset." ]

[ "region:us" ]

[]

[]

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

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

[ "task_categories:image-classification", "task_ids:multi-class-image-classification", "annotations_creators:expert-generated", "size_categories:10K<n<100K", "language:en", "license:cc-by-nc-sa-4.0", "maps", "historical", "National Library of Scotland", "heritage", "humanities", "lam", "region:us" ]

[]

[ "en" ]

[ "# Gold standards and outputs", "## Dataset Description\n\n- MapReader’s GitHub: URL \n- MapReader paper: URL\n- Zenodo link for gold standards and outputs: URL\n- Contacts: Katherine McDonough, The Alan Turing Institute, kmcdonough at URL; Kasra Hosseini, The Alan Turing Institute, k.hosseinizad at URL", "### Dataset Summary\n\nHere we share gold standard annotations and outputs from early experiments using MapReader. MapReader creates datasets for humanities research using historical map scans and metadata as inputs. \n\nUsing maps provided by the National Library of Scotland, these annotations and outputs reflect labeling tasks relevant to historical research on the Living with Machines project.\n\nData shared here is derived from maps printed in nineteenth-century Britain by the Ordnance Survey, Britain's state mapping agency. These maps cover England, Wales, and Scotland from 1888 to 1913.", "## Directory structure\n\nThe gold standards and outputs are stored on Zenodo. It contains the following directories/files:", "## annotations\n\nThe 'annotations' directory is as follows:", "### annotations/URL, URL and URL\n\nIn the 'MapReader_Data_SIGSPATIAL_2022/annotations' directory, there are three CSV files, namely 'URL', 'URL' and 'URL'. These files have two columns:\n\n\n\nin which:\n\n- 'image_id': path to each labelled patch. For example in 'slice_meters_100_100/train/patch-1390-3892-1529-4031-#map_101590193.png#.PNG':\n - 'slice_meters_100_100/train': directory where the patch is stored. (in this example, it is a patch used for training)\n - 'patch-1390-3892-1529-4031-#map_101590193.png#.PNG' has two parts itself: 'patch-1390-3892-1529-4031' is the patch ID, and the patch itself is extracted from 'map_101590193.png' map sheet.\n- 'label': label assigned to each patch by an annotator.\n - Labels: 0: no [building or railspace]; 1: railspace; 2: building; and 3: railspace and [non railspace] building.", "### annotations/slice_meters_100_100\n\nPatches used for training, validation, and test in PNG format.", "### annotations/maps\n\nMap sheets retrieved from the National Library of Scotland via webservers. These maps were later sliced into patches which can be found in 'annotations/slice_meters_100_100'.", "## outputs\n\nThe 'outputs' directory is as follows:", "### outputs/label_01_03\n\nStarting with:\n\n\n\nThe file 'pred_01_03_all.csv' contains the following columns:\n\n\n\n- center_lon: longitude of the patch center\n- center_lat: latitude of the patch center\n- pred: predicted label for the patch\n- conf: model confidence\n- mean_pixel_RGB: mean pixel intensities, using all three channels\n- std_pixel_RGB: standard deviations of pixel intensities, using all three channels\n- mean_pixel_A: mean pixel intensities of alpha channel\n- image_id: patch ID\n- parent_id: ID of the map sheet that the patch belongs to\n- pub_date: publication date of the map sheet that the patch belongs to\n- url: URL of the map sheet that the patch belongs to\n- x, y, z: to compute distances (using k-d tree)\n- opening_year_quicks: Date when the railway station first opened\n- closing_year_quicks: Date when the railway station last closed,\n- dist2quicks: distance to the closest StopsGB in meters.\n\nNB: See 'outputs/resources' below for description of the StopsGB (railway station) data and links to related publications.\n\n---\n\nThe other files in 'outputs/label_01_03' have the same columns as 'pred_01_03_all.csv' (described above). The difference is:\n\n- 'pred_01_03_all.csv': all patches predicted as labels 1 (railspace) or 3 (railspace and [non railspace] building).\n- 'pred_01_03_keep_01_0250.csv': similar to 'pred_01_03_all.csv' except that we removed those patches that had no other neighboring patches with the same label within a radius of 250 meters. Note 01 and 0250 in the name. 01 means one neighboring patch and 0250 means 250 meters.\n- 'pred_01_03_keep_05_0500.csv': similar to 'pred_01_03_all.csv' except that we removed those patches that had less than five neighboring patches with the same label within a radius of 500 meters.\n- 'pred_01_03_keep_10_1000.csv': similar to 'pred_01_03_all.csv' except that we removed those patches that had less than ten neighboring patches with the same label within a radius of 1000 meters.", "### outputs/label_02\n\nNext, these files:\n\n\n\nAre the same as the files described above for 'label_01_03' except for label 02 (i.e., building).", "### outputs/patches_all.csv\n\nAnd last:\n\n\n\nThe file 'patches_all.csv' has the following columns:\n\n️ this file contains the results for 30,490,411 patches used in the MapReader paper.\n\n\n\nin which:\n\n- center_lon: longitude of the patch center\n- center_lat: latitude of the patch center\n- pred: predicted label for the patch", "### outputs/percentage\n\nWe have added one file in 'outputs/percentage':\n\n\n\nThis file has the following columns:\n\n\n\nin which:\n\n- center_lon: longitude of the patch center\n- center_lat: latitude of the patch center\n- pred: predicted label for the patch\n- conf: model confidence\n- mean_pixel_RGB: mean pixel intensities, using all three channels\n- std_pixel_RGB: standard deviations of pixel intensities, using all three channels\n- mean_pixel_A: mean pixel intensities of alpha channel\n- image_id: patch ID\n- parent_id: ID of the map sheet that the patch belongs to\n- pub_date: publication date of the map sheet that the patch belongs to\n- url: URL of the map sheet that the patch belongs to\n- x, y, z: to compute distances (using k-d tree)\n- dist2rail: distance to the closest railspace patch (i.e., the patch that is classified as 1: railspace or 3: railspace and [non railspace] building)\n- dist2quicks: distance to the closest StopsGB station in meters.\n- dist2quicks_km: distance to the closest StopsGB station in km.\n- dist2rail_km: similar to dist2rail except in km.\n- dist2rail_minus_station: | dist2rail_km - dist2quicks_km |\n- dist2quicks_km_quantized: discrete version of dist2quicks_km, we used these intervals: 0. , 0.5), [0.5, 1.), [1., 1.5), ... , [4.5, 5.) and [5., inf).\n- dist2rail_km_quantized: discrete version of dist2rail_km, we used these intervals: [0. , 0.5), [0.5, 1.), [1., 1.5), ... , [4.5, 5.) and [5., inf).\n- dist2rail_minus_station_quantized: discrete version of dist2rail_minus_station, we used these intervals: [0. , 0.5), [0.5, 1.), [1., 1.5), ... , [4.5, 5.) and [5., inf).\n- perc_neigh_rails: what is the percentage of neighboring patches predicted as rail (labels 01 and 03).\n- perc_neigh_builds: what is the percentage of neighboring patches predicted as building (label 02).\n- harmonic_mean_rail_build: Harmonic mean of *perc_neigh_rails* and perc_neigh_builds.\n\nThese additional 'percentage' attributes shed light on the relationship between 'railspace' and stations, something we explore in further Living with Machines research.", "### outputs/resources\n\nFinally, we have the following files:\n\n\n\n- 'URL': this is a trimmed down version of StopsGB, a dataset documenting passenger railway stations in Great Britain (see [this link for the complete dataset). We filtered the stations as follows:\n - Keep only stations for which \"ghost_entry\" and \"cross_ref\" columns are \"False\". (These two fields help remove records in the StopsGB dataset that are not actually stations, but relics of the original publication formatting.)\n - \"Opening\" was NOT \"unknown\".\n - The map sheet was surveyed during a year when the station was operational (i.e., \"opening_year_quicks\" <= survey_date_of_map_sheet <= \"closing_year_quicks\").\n\nYou can learn more about the StopsGB dataset and how it was created from this paper:\n\n\n\n\n\n- 'six_inch4paper.json': similar to metadata_OS_Six_Inch_GB_WFS_light.json on MapReader's GitHub with some minor changes.", "## Dataset Creation", "### Curation Rationale\n\nThese annotations of map patches are part of a research project to develop humanistic methods for structuring visual information on digitized historical maps. Dividing thousands of nineteenth-century map sheets into 100m x 100m patches and labeling those patches with historically-meaningful concepts diverges from traditional methods for creating data from maps, both in terms of scale (the number of maps being examined), and of type (raster-style patches vs. pixel-level vector data). For more on the rationale for this approach, see the following paper:", "### Source Data", "#### Initial Data Access\n\nData was accessed via the National Library of Scotland's Historical Maps API: URL\n\nThe data shared here is derived from the six-inch to one mile sheets printed between 1888-1913: URL", "### Annotations and Outputs\n\nThe annotations and output datasets collected here are related to experiments to identify the 'footprint' of rail infrastructure in the UK, a concept we call 'railspace'. We also created a dataset to identify buildings on the maps.", "#### Annotation process\n\nThe custom annotation interface built into MapReader is designed specifically to assist researchers in labeling patches relevant to concepts of interest to their research questions. \n\nOur guidelines for the data shared here were:\n- for any non-null label (railspace, building, or railspace + building), if a patch contains any visual signal for that label (e.g. 'railspace'), it should be assigned the relevant label. For example, if it is possible for an annotator to see a railway track passing through the corner of a patch, that patch is labeled as 'railspace'.\n- the context around the patch should not be used as an aid in extreme cases where it is nearly impossible to determine whether a patch contains a non-null label\n- however, the patch context shown in the annotation interface can be used to quickly distinguish between different content types, particularly where the contiguity of a type across patches is useful in determining what label to assign\n- for 'railspace': use this label for any type of rail infrastructure as determined by expert labelers. This includes, for example, single-track mining railroads; larger double-track passenger routes; sidings and embankments; etc. It excludes urban trams.\n- for 'building': use this label for any size building\n- for 'building + railspace': use this label for patches combining these two types of content\n\nBecause 'none' (e.g. null) patches made up the vast majority of patches in the total dataset from these map sheets, we ordered patches to annotate based on their pixel intensity. This allowed us to focus first on patches containing more visual content printed on the map sheet, and later to move more quickly through the patches that captured parts of the map with little to no printed features.", "#### Who are the annotators?\n\nData shared here was annotated by Kasra Hosseini and Katherine McDonough.\n\nMembers of the Living with Machines research team contributed early annotations during the development of MapReader: Ruth Ahnert, Kaspar Beelen, Mariona Coll-Ardanuy, Emma Griffin, Tim Hobson, Jon Lawrence, Giorgia Tolfo, Daniel van Strien, Olivia Vane, and Daniel C.S. Wilson.", "## Credits and re-use terms", "### MapReader outputs\n\nThe files shared here (other than ) under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (URL (CC-BY-NC-SA) licence. \n\nIf you are interested in working with OS maps used to create these results, please also note the re-use terms of the original map images and metadata detailed below.", "### Digitized maps\n\nMapReader can retrieve maps from NLS (National Library of Scotland) via webservers. For all the digitized maps (retrieved or locally stored), please note the re-use terms:\n\nUse of the digitised maps for commercial purposes is currently restricted by contract. Use of these digitised maps for non-commercial purposes is permitted under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (URL (CC-BY-NC-SA) licence. Please refer to URL for details on copyright and re-use license.", "### Map metadata\n\nWe have provided some metadata files in on MapReader’s GitHub page (URL For all these file, please note the re-use terms:\n\nUse of the digitised maps for commercial purposes is currently restricted by contract. Use of these digitised maps for non-commercial purposes is permitted under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (URL (CC-BY-NC-SA) licence. Please refer to URL for details on copyright and re-use license.", "## Acknowledgements\n\nThis work was supported by Living with Machines (AHRC grant AH/S01179X/1) and The Alan Turing Institute (EPSRC grant EP/N510129/1). \nLiving with Machines, funded by the UK Research and Innovation (UKRI) Strategic Priority Fund, is a multidisciplinary collaboration delivered by the Arts and Humanities Research Council (AHRC), with The Alan Turing Institute, the British Library and the Universities of Cambridge, East Anglia, Exeter, and Queen Mary University of London." ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-class-image-classification #annotations_creators-expert-generated #size_categories-10K<n<100K #language-English #license-cc-by-nc-sa-4.0 #maps #historical #National Library of Scotland #heritage #humanities #lam #region-us \n", "# Gold standards and outputs", "## Dataset Description\n\n- MapReader’s GitHub: URL \n- MapReader paper: URL\n- Zenodo link for gold standards and outputs: URL\n- Contacts: Katherine McDonough, The Alan Turing Institute, kmcdonough at URL; Kasra Hosseini, The Alan Turing Institute, k.hosseinizad at URL", "### Dataset Summary\n\nHere we share gold standard annotations and outputs from early experiments using MapReader. MapReader creates datasets for humanities research using historical map scans and metadata as inputs. \n\nUsing maps provided by the National Library of Scotland, these annotations and outputs reflect labeling tasks relevant to historical research on the Living with Machines project.\n\nData shared here is derived from maps printed in nineteenth-century Britain by the Ordnance Survey, Britain's state mapping agency. These maps cover England, Wales, and Scotland from 1888 to 1913.", "## Directory structure\n\nThe gold standards and outputs are stored on Zenodo. It contains the following directories/files:", "## annotations\n\nThe 'annotations' directory is as follows:", "### annotations/URL, URL and URL\n\nIn the 'MapReader_Data_SIGSPATIAL_2022/annotations' directory, there are three CSV files, namely 'URL', 'URL' and 'URL'. These files have two columns:\n\n\n\nin which:\n\n- 'image_id': path to each labelled patch. For example in 'slice_meters_100_100/train/patch-1390-3892-1529-4031-#map_101590193.png#.PNG':\n - 'slice_meters_100_100/train': directory where the patch is stored. (in this example, it is a patch used for training)\n - 'patch-1390-3892-1529-4031-#map_101590193.png#.PNG' has two parts itself: 'patch-1390-3892-1529-4031' is the patch ID, and the patch itself is extracted from 'map_101590193.png' map sheet.\n- 'label': label assigned to each patch by an annotator.\n - Labels: 0: no [building or railspace]; 1: railspace; 2: building; and 3: railspace and [non railspace] building.", "### annotations/slice_meters_100_100\n\nPatches used for training, validation, and test in PNG format.", "### annotations/maps\n\nMap sheets retrieved from the National Library of Scotland via webservers. These maps were later sliced into patches which can be found in 'annotations/slice_meters_100_100'.", "## outputs\n\nThe 'outputs' directory is as follows:", "### outputs/label_01_03\n\nStarting with:\n\n\n\nThe file 'pred_01_03_all.csv' contains the following columns:\n\n\n\n- center_lon: longitude of the patch center\n- center_lat: latitude of the patch center\n- pred: predicted label for the patch\n- conf: model confidence\n- mean_pixel_RGB: mean pixel intensities, using all three channels\n- std_pixel_RGB: standard deviations of pixel intensities, using all three channels\n- mean_pixel_A: mean pixel intensities of alpha channel\n- image_id: patch ID\n- parent_id: ID of the map sheet that the patch belongs to\n- pub_date: publication date of the map sheet that the patch belongs to\n- url: URL of the map sheet that the patch belongs to\n- x, y, z: to compute distances (using k-d tree)\n- opening_year_quicks: Date when the railway station first opened\n- closing_year_quicks: Date when the railway station last closed,\n- dist2quicks: distance to the closest StopsGB in meters.\n\nNB: See 'outputs/resources' below for description of the StopsGB (railway station) data and links to related publications.\n\n---\n\nThe other files in 'outputs/label_01_03' have the same columns as 'pred_01_03_all.csv' (described above). The difference is:\n\n- 'pred_01_03_all.csv': all patches predicted as labels 1 (railspace) or 3 (railspace and [non railspace] building).\n- 'pred_01_03_keep_01_0250.csv': similar to 'pred_01_03_all.csv' except that we removed those patches that had no other neighboring patches with the same label within a radius of 250 meters. Note 01 and 0250 in the name. 01 means one neighboring patch and 0250 means 250 meters.\n- 'pred_01_03_keep_05_0500.csv': similar to 'pred_01_03_all.csv' except that we removed those patches that had less than five neighboring patches with the same label within a radius of 500 meters.\n- 'pred_01_03_keep_10_1000.csv': similar to 'pred_01_03_all.csv' except that we removed those patches that had less than ten neighboring patches with the same label within a radius of 1000 meters.", "### outputs/label_02\n\nNext, these files:\n\n\n\nAre the same as the files described above for 'label_01_03' except for label 02 (i.e., building).", "### outputs/patches_all.csv\n\nAnd last:\n\n\n\nThe file 'patches_all.csv' has the following columns:\n\n️ this file contains the results for 30,490,411 patches used in the MapReader paper.\n\n\n\nin which:\n\n- center_lon: longitude of the patch center\n- center_lat: latitude of the patch center\n- pred: predicted label for the patch", "### outputs/percentage\n\nWe have added one file in 'outputs/percentage':\n\n\n\nThis file has the following columns:\n\n\n\nin which:\n\n- center_lon: longitude of the patch center\n- center_lat: latitude of the patch center\n- pred: predicted label for the patch\n- conf: model confidence\n- mean_pixel_RGB: mean pixel intensities, using all three channels\n- std_pixel_RGB: standard deviations of pixel intensities, using all three channels\n- mean_pixel_A: mean pixel intensities of alpha channel\n- image_id: patch ID\n- parent_id: ID of the map sheet that the patch belongs to\n- pub_date: publication date of the map sheet that the patch belongs to\n- url: URL of the map sheet that the patch belongs to\n- x, y, z: to compute distances (using k-d tree)\n- dist2rail: distance to the closest railspace patch (i.e., the patch that is classified as 1: railspace or 3: railspace and [non railspace] building)\n- dist2quicks: distance to the closest StopsGB station in meters.\n- dist2quicks_km: distance to the closest StopsGB station in km.\n- dist2rail_km: similar to dist2rail except in km.\n- dist2rail_minus_station: | dist2rail_km - dist2quicks_km |\n- dist2quicks_km_quantized: discrete version of dist2quicks_km, we used these intervals: 0. , 0.5), [0.5, 1.), [1., 1.5), ... , [4.5, 5.) and [5., inf).\n- dist2rail_km_quantized: discrete version of dist2rail_km, we used these intervals: [0. , 0.5), [0.5, 1.), [1., 1.5), ... , [4.5, 5.) and [5., inf).\n- dist2rail_minus_station_quantized: discrete version of dist2rail_minus_station, we used these intervals: [0. , 0.5), [0.5, 1.), [1., 1.5), ... , [4.5, 5.) and [5., inf).\n- perc_neigh_rails: what is the percentage of neighboring patches predicted as rail (labels 01 and 03).\n- perc_neigh_builds: what is the percentage of neighboring patches predicted as building (label 02).\n- harmonic_mean_rail_build: Harmonic mean of *perc_neigh_rails* and perc_neigh_builds.\n\nThese additional 'percentage' attributes shed light on the relationship between 'railspace' and stations, something we explore in further Living with Machines research.", "### outputs/resources\n\nFinally, we have the following files:\n\n\n\n- 'URL': this is a trimmed down version of StopsGB, a dataset documenting passenger railway stations in Great Britain (see [this link for the complete dataset). We filtered the stations as follows:\n - Keep only stations for which \"ghost_entry\" and \"cross_ref\" columns are \"False\". (These two fields help remove records in the StopsGB dataset that are not actually stations, but relics of the original publication formatting.)\n - \"Opening\" was NOT \"unknown\".\n - The map sheet was surveyed during a year when the station was operational (i.e., \"opening_year_quicks\" <= survey_date_of_map_sheet <= \"closing_year_quicks\").\n\nYou can learn more about the StopsGB dataset and how it was created from this paper:\n\n\n\n\n\n- 'six_inch4paper.json': similar to metadata_OS_Six_Inch_GB_WFS_light.json on MapReader's GitHub with some minor changes.", "## Dataset Creation", "### Curation Rationale\n\nThese annotations of map patches are part of a research project to develop humanistic methods for structuring visual information on digitized historical maps. Dividing thousands of nineteenth-century map sheets into 100m x 100m patches and labeling those patches with historically-meaningful concepts diverges from traditional methods for creating data from maps, both in terms of scale (the number of maps being examined), and of type (raster-style patches vs. pixel-level vector data). For more on the rationale for this approach, see the following paper:", "### Source Data", "#### Initial Data Access\n\nData was accessed via the National Library of Scotland's Historical Maps API: URL\n\nThe data shared here is derived from the six-inch to one mile sheets printed between 1888-1913: URL", "### Annotations and Outputs\n\nThe annotations and output datasets collected here are related to experiments to identify the 'footprint' of rail infrastructure in the UK, a concept we call 'railspace'. We also created a dataset to identify buildings on the maps.", "#### Annotation process\n\nThe custom annotation interface built into MapReader is designed specifically to assist researchers in labeling patches relevant to concepts of interest to their research questions. \n\nOur guidelines for the data shared here were:\n- for any non-null label (railspace, building, or railspace + building), if a patch contains any visual signal for that label (e.g. 'railspace'), it should be assigned the relevant label. For example, if it is possible for an annotator to see a railway track passing through the corner of a patch, that patch is labeled as 'railspace'.\n- the context around the patch should not be used as an aid in extreme cases where it is nearly impossible to determine whether a patch contains a non-null label\n- however, the patch context shown in the annotation interface can be used to quickly distinguish between different content types, particularly where the contiguity of a type across patches is useful in determining what label to assign\n- for 'railspace': use this label for any type of rail infrastructure as determined by expert labelers. This includes, for example, single-track mining railroads; larger double-track passenger routes; sidings and embankments; etc. It excludes urban trams.\n- for 'building': use this label for any size building\n- for 'building + railspace': use this label for patches combining these two types of content\n\nBecause 'none' (e.g. null) patches made up the vast majority of patches in the total dataset from these map sheets, we ordered patches to annotate based on their pixel intensity. This allowed us to focus first on patches containing more visual content printed on the map sheet, and later to move more quickly through the patches that captured parts of the map with little to no printed features.", "#### Who are the annotators?\n\nData shared here was annotated by Kasra Hosseini and Katherine McDonough.\n\nMembers of the Living with Machines research team contributed early annotations during the development of MapReader: Ruth Ahnert, Kaspar Beelen, Mariona Coll-Ardanuy, Emma Griffin, Tim Hobson, Jon Lawrence, Giorgia Tolfo, Daniel van Strien, Olivia Vane, and Daniel C.S. Wilson.", "## Credits and re-use terms", "### MapReader outputs\n\nThe files shared here (other than ) under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (URL (CC-BY-NC-SA) licence. \n\nIf you are interested in working with OS maps used to create these results, please also note the re-use terms of the original map images and metadata detailed below.", "### Digitized maps\n\nMapReader can retrieve maps from NLS (National Library of Scotland) via webservers. For all the digitized maps (retrieved or locally stored), please note the re-use terms:\n\nUse of the digitised maps for commercial purposes is currently restricted by contract. Use of these digitised maps for non-commercial purposes is permitted under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (URL (CC-BY-NC-SA) licence. Please refer to URL for details on copyright and re-use license.", "### Map metadata\n\nWe have provided some metadata files in on MapReader’s GitHub page (URL For all these file, please note the re-use terms:\n\nUse of the digitised maps for commercial purposes is currently restricted by contract. Use of these digitised maps for non-commercial purposes is permitted under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (URL (CC-BY-NC-SA) licence. Please refer to URL for details on copyright and re-use license.", "## Acknowledgements\n\nThis work was supported by Living with Machines (AHRC grant AH/S01179X/1) and The Alan Turing Institute (EPSRC grant EP/N510129/1). \nLiving with Machines, funded by the UK Research and Innovation (UKRI) Strategic Priority Fund, is a multidisciplinary collaboration delivered by the Arts and Humanities Research Council (AHRC), with The Alan Turing Institute, the British Library and the Universities of Cambridge, East Anglia, Exeter, and Queen Mary University of London." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"malayalam-news-ds\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# artistas_brasileiros" ]

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

[ "task_categories:text-to-image", "annotations_creators:machine-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:n<1K", "language:en", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"RickAndMorty-HorizontalMirror-blip-captions\"" ]

[ "TAGS\n#task_categories-text-to-image #annotations_creators-machine-generated #language_creators-other #multilinguality-monolingual #size_categories-n<1K #language-English #license-cc-by-nc-sa-4.0 #region-us \n", "# Dataset Card for \"RickAndMorty-HorizontalMirror-blip-captions\"" ]

[ "multilinguality:monolingual", "language:fr", "license:cc-by-nc-sa-3.0", "region:us" ]

[]

[ "fr" ]

[ "# Dataset Card for IPC classification of French patents", "## Dataset Description\n\n- Homepage:\n- Repository: IPC Classification of French Patents\n- Paper: Patent Classification using Extreme Multi-label Learning: A Case Study of French Patents\n- Point of Contact: You Zuo", "### Dataset Summary\n\nINPI-CLS is a French Patents corpus extracted from the internal database of the INPI (National Institute of Industrial Property of France). It was initially designed for the patent classification task and consists of approximately 296k patent texts (including title, abstract, claims, and description) published between 2002 and 2021. Each patent in the corpus is annotated with labels ranging from sections to the IPC subgroup levels.", "### Languages\n\nFrench", "### Domain\n\nPatents (intellectual property).", "### Social Impact of Dataset\n\nThe purpose of this dataset is to help develop models that enable the classification of French patents in the International Patent Classification (IPC) system standard.\n\nThanks to the high integrity of the data, the INPI-CLS corpus can be utilized for various analytical studies concerning French language patents. Moreover, it serves as a valuable resource as a scientific corpus that comprehensively documents the technological inventions of the country." ]

[ "TAGS\n#multilinguality-monolingual #language-French #license-cc-by-nc-sa-3.0 #region-us \n", "# Dataset Card for IPC classification of French patents", "## Dataset Description\n\n- Homepage:\n- Repository: IPC Classification of French Patents\n- Paper: Patent Classification using Extreme Multi-label Learning: A Case Study of French Patents\n- Point of Contact: You Zuo", "### Dataset Summary\n\nINPI-CLS is a French Patents corpus extracted from the internal database of the INPI (National Institute of Industrial Property of France). It was initially designed for the patent classification task and consists of approximately 296k patent texts (including title, abstract, claims, and description) published between 2002 and 2021. Each patent in the corpus is annotated with labels ranging from sections to the IPC subgroup levels.", "### Languages\n\nFrench", "### Domain\n\nPatents (intellectual property).", "### Social Impact of Dataset\n\nThe purpose of this dataset is to help develop models that enable the classification of French patents in the International Patent Classification (IPC) system standard.\n\nThanks to the high integrity of the data, the INPI-CLS corpus can be utilized for various analytical studies concerning French language patents. Moreover, it serves as a valuable resource as a scientific corpus that comprehensively documents the technological inventions of the country." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"ai4lam-demo2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"ai4lam-demo2\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"github-issues\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "task_categories:token-classification", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "research papers", "acl", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nDataframe with extracted metadata (table below with details) and full text of the collection for analysis : size 489M", "### Languages\n\n\nen, zh and others\n\n\nDataset Structure\n-----------------\n\n\nDataframe", "### Data Instances\n\n\nEach row is a paper from ACL anthology", "### Data Fields\n\n\n\nDataset Creation\n----------------\n\n\nThe corpus has all the papers in ACL anthology - as of September'22", "### Source Data\n\n\n* ACL Anthology\n* Semantic Scholar\n\n\nAdditional Information\n======================", "### Licensing Information\n\n\nThe ACL OCL corpus is released under the CC BY-NC 4.0. By using this corpus, you are agreeing to its usage terms.\n\n\nIf you use this corpus in your research please use the following BibTeX entry:\n\n\n\n```\n@Misc{acl-ocl,\n author = {Shaurya Rohatgi, Yanxia Qin, Benjamin Aw, Niranjana Unnithan, Min-Yen Kan},\n title = {The ACL OCL Corpus: advancing Open science in Computational Linguistics},\n howpublished = {arXiv},\n year = {2022},\n url = {URL\n}\n\n```", "### Acknowledgements\n\n\nWe thank Semantic Scholar for providing access to the citation-related data in this corpus.", "### Contributions\n\n\nThanks to @shauryr, Yanxia Qin and Benjamin Aw for adding this dataset." ]

[ "TAGS\n#task_categories-token-classification #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #research papers #acl #region-us \n", "### Dataset Summary\n\n\nDataframe with extracted metadata (table below with details) and full text of the collection for analysis : size 489M", "### Languages\n\n\nen, zh and others\n\n\nDataset Structure\n-----------------\n\n\nDataframe", "### Data Instances\n\n\nEach row is a paper from ACL anthology", "### Data Fields\n\n\n\nDataset Creation\n----------------\n\n\nThe corpus has all the papers in ACL anthology - as of September'22", "### Source Data\n\n\n* ACL Anthology\n* Semantic Scholar\n\n\nAdditional Information\n======================", "### Licensing Information\n\n\nThe ACL OCL corpus is released under the CC BY-NC 4.0. By using this corpus, you are agreeing to its usage terms.\n\n\nIf you use this corpus in your research please use the following BibTeX entry:\n\n\n\n```\n@Misc{acl-ocl,\n author = {Shaurya Rohatgi, Yanxia Qin, Benjamin Aw, Niranjana Unnithan, Min-Yen Kan},\n title = {The ACL OCL Corpus: advancing Open science in Computational Linguistics},\n howpublished = {arXiv},\n year = {2022},\n url = {URL\n}\n\n```", "### Acknowledgements\n\n\nWe thank Semantic Scholar for providing access to the citation-related data in this corpus.", "### Contributions\n\n\nThanks to @shauryr, Yanxia Qin and Benjamin Aw for adding this dataset." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

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

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

[ "task_categories:automatic-speech-recognition", "whisper", "whispering", "base", "region:us" ]

[]

[]

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

[ "TAGS\n#task_categories-automatic-speech-recognition #whisper #whispering #base #region-us \n", "# Dataset Card for \"diana_uribe\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "task_categories:text-to-image", "annotations_creators:no-annotation", "language_creators:found", "source_datasets:original", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Teyvat BLIP captions\nDataset used to train Teyvat characters text to image model.\n\nBLIP generated captions for characters images from genshin-impact fandom wikiand biligame wiki for genshin impact.\n\nFor each row the dataset contains 'image' and 'text' keys. 'image' is a varying size PIL png, and 'text' is the accompanying text caption. Only a train split is provided.\n\nThe 'text' include the tag 'Teyvat', 'Name','Element', 'Weapon', 'Region', 'Model type', and 'Description', the 'Description' is captioned with the pre-trained BLIP model.", "## Examples\n\n<img src = \"URL title = \"Ganyu_001.png\" style=\"max-width: 20%;\" >\n\n> Teyvat, Name:Ganyu, Element:Cryo, Weapon:Bow, Region:Liyue, Model type:Medium Female, Description:an anime character with blue hair and blue eyes\n\n<img src = \"URL title = \"Ganyu_002.png\" style=\"max-width: 20%;\" >\n\n> Teyvat, Name:Ganyu, Element:Cryo, Weapon:Bow, Region:Liyue, Model type:Medium Female, Description:an anime character with blue hair and blue eyes\n\n<img src = \"URL title = \"Keqing_003.png\" style=\"max-width: 20%;\" >\n\n> Teyvat, Name:Keqing, Element:Electro, Weapon:Sword, Region:Liyue, Model type:Medium Female, Description:a anime girl with long white hair and blue eyes\n\n<img src = \"URL title = \"Keqing_004.png\" style=\"max-width: 20%;\" >\n\n> Teyvat, Name:Keqing, Element:Electro, Weapon:Sword, Region:Liyue, Model type:Medium Female, Description:an anime character wearing a purple dress and cat ears" ]

[ "TAGS\n#task_categories-text-to-image #annotations_creators-no-annotation #language_creators-found #source_datasets-original #language-English #license-unknown #region-us \n", "# Dataset Card for Teyvat BLIP captions\nDataset used to train Teyvat characters text to image model.\n\nBLIP generated captions for characters images from genshin-impact fandom wikiand biligame wiki for genshin impact.\n\nFor each row the dataset contains 'image' and 'text' keys. 'image' is a varying size PIL png, and 'text' is the accompanying text caption. Only a train split is provided.\n\nThe 'text' include the tag 'Teyvat', 'Name','Element', 'Weapon', 'Region', 'Model type', and 'Description', the 'Description' is captioned with the pre-trained BLIP model.", "## Examples\n\n<img src = \"URL title = \"Ganyu_001.png\" style=\"max-width: 20%;\" >\n\n> Teyvat, Name:Ganyu, Element:Cryo, Weapon:Bow, Region:Liyue, Model type:Medium Female, Description:an anime character with blue hair and blue eyes\n\n<img src = \"URL title = \"Ganyu_002.png\" style=\"max-width: 20%;\" >\n\n> Teyvat, Name:Ganyu, Element:Cryo, Weapon:Bow, Region:Liyue, Model type:Medium Female, Description:an anime character with blue hair and blue eyes\n\n<img src = \"URL title = \"Keqing_003.png\" style=\"max-width: 20%;\" >\n\n> Teyvat, Name:Keqing, Element:Electro, Weapon:Sword, Region:Liyue, Model type:Medium Female, Description:a anime girl with long white hair and blue eyes\n\n<img src = \"URL title = \"Keqing_004.png\" style=\"max-width: 20%;\" >\n\n> Teyvat, Name:Keqing, Element:Electro, Weapon:Sword, Region:Liyue, Model type:Medium Female, Description:an anime character wearing a purple dress and cat ears" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "TAGS\n#region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "task_categories:text2text-generation", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:ro", "license:apache-2.0", "region:us" ]

[]

[ "ro" ]

[ "# Romanian paraphrase dataset\nThis data set was created by me, special for paraphrase\n\nt5-small-paraphrase-ro\nt5-small-paraphrase-ro-v2\nt5-base-paraphrase-ro\nt5-base-paraphrase-ro-v2\n\nHere you can find ~100k examples of paraphrase." ]

[ "TAGS\n#task_categories-text2text-generation #multilinguality-monolingual #size_categories-10K<n<100K #language-Romanian #license-apache-2.0 #region-us \n", "# Romanian paraphrase dataset\nThis data set was created by me, special for paraphrase\n\nt5-small-paraphrase-ro\nt5-small-paraphrase-ro-v2\nt5-base-paraphrase-ro\nt5-base-paraphrase-ro-v2\n\nHere you can find ~100k examples of paraphrase." ]

[ "task_categories:text2text-generation", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:ro", "license:apache-2.0", "region:us" ]

[]

[ "ro" ]

[ "# Romanian grammar dataset\nThis data set was created by me, special for grammar \n\n\nHere you can find:\n~1600k examples of grammar (TRAIN).\n~220k examples of grammar (TEST)." ]

[ "TAGS\n#task_categories-text2text-generation #multilinguality-monolingual #size_categories-10K<n<100K #language-Romanian #license-apache-2.0 #region-us \n", "# Romanian grammar dataset\nThis data set was created by me, special for grammar \n\n\nHere you can find:\n~1600k examples of grammar (TRAIN).\n~220k examples of grammar (TEST)." ]

[ "task_categories:automatic-speech-recognition", "whisper", "whispering", "medium", "region:us" ]

[]

[]

[ "# Dataset Card for \"whisper-transcripts-linustechtips\"", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\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 - Contributions", "## Dataset Description\n\n- Homepage: Whispering-GPT\n- Repository: whisper_gpt_pipeline\n- Paper: whisper and gpt\n- Point of Contact: Whispering-GPT organization", "### Dataset Summary\n\nThis dataset is created by applying whisper to the videos of the Youtube channel Linus Tech Tips. The dataset was created a medium size whisper model.", "### Languages\n\n- Language: English", "## Dataset Structure\n\nThe dataset", "### Data Fields\n\nThe dataset is composed by:\n- id: Id of the youtube video.\n- channel: Name of the channel.\n- channel\\_id: Id of the youtube channel.\n- title: Title given to the video.\n- categories: Category of the video.\n- description: Description added by the author.\n- text: Whole transcript of the video.\n- segments: A list with the time and transcription of the video.\n - start: When started the trancription.\n - end: When the transcription ends.\n - text: The text of the transcription.", "### Data Splits\n\n- Train split.", "## Dataset Creation", "### Source Data\n\nThe transcriptions are from the videos of Linus Tech Tips Channel", "### Contributions\n\nThanks to Whispering-GPT organization for adding this dataset." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #whisper #whispering #medium #region-us \n", "# Dataset Card for \"whisper-transcripts-linustechtips\"", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\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 - Contributions", "## Dataset Description\n\n- Homepage: Whispering-GPT\n- Repository: whisper_gpt_pipeline\n- Paper: whisper and gpt\n- Point of Contact: Whispering-GPT organization", "### Dataset Summary\n\nThis dataset is created by applying whisper to the videos of the Youtube channel Linus Tech Tips. The dataset was created a medium size whisper model.", "### Languages\n\n- Language: English", "## Dataset Structure\n\nThe dataset", "### Data Fields\n\nThe dataset is composed by:\n- id: Id of the youtube video.\n- channel: Name of the channel.\n- channel\\_id: Id of the youtube channel.\n- title: Title given to the video.\n- categories: Category of the video.\n- description: Description added by the author.\n- text: Whole transcript of the video.\n- segments: A list with the time and transcription of the video.\n - start: When started the trancription.\n - end: When the transcription ends.\n - text: The text of the transcription.", "### Data Splits\n\n- Train split.", "## Dataset Creation", "### Source Data\n\nThe transcriptions are from the videos of Linus Tech Tips Channel", "### Contributions\n\nThanks to Whispering-GPT organization for adding this dataset." ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)\n - Table of Contents\n - Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n - Dataset Structure\n - Data Fields\n - Data Splits\n - Dataset Creation\n - Curation Rationale\n - Source Data\n - Initial Data Collection and Normalization\n - Who are the source language producers?\n - Annotations\n - Annotation process\n - Who are the annotators?\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- Homepage: URL\n- Repository:\n- Paper: ETPC - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and Negation\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\nWe present the Extended Paraphrase Typology (EPT) and the Extended Typology Paraphrase Corpus (ETPC). The EPT typology addresses several practical limitations of existing paraphrase typologies: it is the first typology that copes with the non-paraphrase pairs in the paraphrase identification corpora and distinguishes between contextual and habitual paraphrase types. ETPC is the largest corpus to date annotated with atomic paraphrase types. It is the first corpus with detailed annotation of both the paraphrase and the non-paraphrase pairs and the first corpus annotated with paraphrase and negation. Both new resources contribute to better understanding the paraphrase phenomenon, and allow for studying the relationship between paraphrasing and negation. To the developers of Paraphrase Identification systems ETPC corpus offers better means for evaluation and error analysis. Furthermore, the EPT typology and ETPC corpus emphasize the relationship with other areas of NLP such as Semantic Similarity, Textual Entailment, Summarization and Simplification.", "### Supported Tasks and Leaderboards\n- 'text-classification'", "### Languages\nThe text in the dataset is in English ('en').", "## Dataset Structure", "### Data Fields\n- 'idx': Monotonically increasing index ID.\n- 'sentence1': Complete sentence expressing an opinion about a film.\n- 'sentence2': Complete sentence expressing an opinion about a film.\n- 'etpc_label': Whether the text-pair is a paraphrase, either \"yes\" (1) or \"no\" (0) according to etpc annotation schema.\n- 'mrpc_label': Whether the text-pair is a paraphrase, either \"yes\" (1) or \"no\" (0) according to mrpc annotation schema.\n- 'negation': Whether on sentence is a negation of another, either \"yes\" (1) or \"no\" (0).", "### Data Splits\ntrain: 5801", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\nRotten Tomatoes reviewers.", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\nUnknown.", "### Contributions\nThanks to @jpwahle for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-unknown #region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)\n - Table of Contents\n - Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n - Dataset Structure\n - Data Fields\n - Data Splits\n - Dataset Creation\n - Curation Rationale\n - Source Data\n - Initial Data Collection and Normalization\n - Who are the source language producers?\n - Annotations\n - Annotation process\n - Who are the annotators?\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- Homepage: URL\n- Repository:\n- Paper: ETPC - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and Negation\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\nWe present the Extended Paraphrase Typology (EPT) and the Extended Typology Paraphrase Corpus (ETPC). The EPT typology addresses several practical limitations of existing paraphrase typologies: it is the first typology that copes with the non-paraphrase pairs in the paraphrase identification corpora and distinguishes between contextual and habitual paraphrase types. ETPC is the largest corpus to date annotated with atomic paraphrase types. It is the first corpus with detailed annotation of both the paraphrase and the non-paraphrase pairs and the first corpus annotated with paraphrase and negation. Both new resources contribute to better understanding the paraphrase phenomenon, and allow for studying the relationship between paraphrasing and negation. To the developers of Paraphrase Identification systems ETPC corpus offers better means for evaluation and error analysis. Furthermore, the EPT typology and ETPC corpus emphasize the relationship with other areas of NLP such as Semantic Similarity, Textual Entailment, Summarization and Simplification.", "### Supported Tasks and Leaderboards\n- 'text-classification'", "### Languages\nThe text in the dataset is in English ('en').", "## Dataset Structure", "### Data Fields\n- 'idx': Monotonically increasing index ID.\n- 'sentence1': Complete sentence expressing an opinion about a film.\n- 'sentence2': Complete sentence expressing an opinion about a film.\n- 'etpc_label': Whether the text-pair is a paraphrase, either \"yes\" (1) or \"no\" (0) according to etpc annotation schema.\n- 'mrpc_label': Whether the text-pair is a paraphrase, either \"yes\" (1) or \"no\" (0) according to mrpc annotation schema.\n- 'negation': Whether on sentence is a negation of another, either \"yes\" (1) or \"no\" (0).", "### Data Splits\ntrain: 5801", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\nRotten Tomatoes reviewers.", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\nUnknown.", "### Contributions\nThanks to @jpwahle for adding this dataset." ]

[ "task_categories:automatic-speech-recognition", "multilinguality:monolingual", "language:en", "audio", "automatic-speech-recognition", "en-atc", "en", "robust-speech-recognition", "noisy-speech-recognition", "speech-recognition", "arxiv:2203.16822", "region:us" ]

[ "2203.16822" ]

[ "en" ]

[ "# Dataset Card for ATCOSIM corpus", "## Table of Contents\n\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages and Other Details\n- Dataset Structure\n - Data Fields\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n- Homepage: ATCOSIM homepage\n- Repository: GitHub repository (used in research)\n- Paper: The ATCOSIM Corpus of Non-Prompted Clean Air Traffic Control Speech\n- Paper of this research: How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications", "### Dataset Summary\n\nThe ATCOSIM Air Traffic Control Simulation Speech corpus is a speech database of air traffic control (ATC) operator speech, provided by Graz University of Technology (TUG) and Eurocontrol Experimental Centre (EEC). It consists of ten hours of speech data, which were recorded during ATC real-time simulations using a close-talk headset microphone. The utterances are in English language and pronounced by ten non-native speakers. The database includes orthographic transcriptions and additional information on speakers and recording sessions. It was recorded and annotated by Konrad Hofbauer (description here).", "### Supported Tasks and Leaderboards\n\n- 'automatic-speech-recognition'. Already adapted/fine-tuned models are available here --> XLS-R-300m.", "### Languages and other details\nThe text and the recordings are in English. The participating controllers were all actively employed air traffic controllers and possessed professional experience in the simulated sectors. The six male and four female controllers were of either German or Swiss nationality and had German, Swiss German or Swiss French native tongue. The controllers had agreed to the recording of their voice for the purpose of language analysis as well as for research and development in speech technologies, and were asked to show their normal working behaviour.", "## Dataset Structure", "### Data Fields\n\n- 'id (string)': a string of recording identifier for each example, corresponding to its.\n- 'audio (audio)': audio data for the given ID\n- 'text (string)': transcript of the file already normalized. Follow these repositories for more details w2v2-air-traffic and bert-text-diarization-atc\n- 'segment_start_time (float32)': segment start time (normally 0)\n- 'segment_end_time (float32): segment end time\n- 'duration (float32)': duration of the recording, compute as segment_end_time - segment_start_time", "## Additional Information", "### Licensing Information\n\nThe licensing status of the dataset hinges on the legal status of the ATCOSIM corpus creators.\n\n\n\nContributors who prepared, processed, normalized and uploaded the dataset in HuggingFace:\n\n\n\n\nAuthors of the dataset:" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #multilinguality-monolingual #language-English #audio #automatic-speech-recognition #en-atc #en #robust-speech-recognition #noisy-speech-recognition #speech-recognition #arxiv-2203.16822 #region-us \n", "# Dataset Card for ATCOSIM corpus", "## Table of Contents\n\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages and Other Details\n- Dataset Structure\n - Data Fields\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n- Homepage: ATCOSIM homepage\n- Repository: GitHub repository (used in research)\n- Paper: The ATCOSIM Corpus of Non-Prompted Clean Air Traffic Control Speech\n- Paper of this research: How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications", "### Dataset Summary\n\nThe ATCOSIM Air Traffic Control Simulation Speech corpus is a speech database of air traffic control (ATC) operator speech, provided by Graz University of Technology (TUG) and Eurocontrol Experimental Centre (EEC). It consists of ten hours of speech data, which were recorded during ATC real-time simulations using a close-talk headset microphone. The utterances are in English language and pronounced by ten non-native speakers. The database includes orthographic transcriptions and additional information on speakers and recording sessions. It was recorded and annotated by Konrad Hofbauer (description here).", "### Supported Tasks and Leaderboards\n\n- 'automatic-speech-recognition'. Already adapted/fine-tuned models are available here --> XLS-R-300m.", "### Languages and other details\nThe text and the recordings are in English. The participating controllers were all actively employed air traffic controllers and possessed professional experience in the simulated sectors. The six male and four female controllers were of either German or Swiss nationality and had German, Swiss German or Swiss French native tongue. The controllers had agreed to the recording of their voice for the purpose of language analysis as well as for research and development in speech technologies, and were asked to show their normal working behaviour.", "## Dataset Structure", "### Data Fields\n\n- 'id (string)': a string of recording identifier for each example, corresponding to its.\n- 'audio (audio)': audio data for the given ID\n- 'text (string)': transcript of the file already normalized. Follow these repositories for more details w2v2-air-traffic and bert-text-diarization-atc\n- 'segment_start_time (float32)': segment start time (normally 0)\n- 'segment_end_time (float32): segment end time\n- 'duration (float32)': duration of the recording, compute as segment_end_time - segment_start_time", "## Additional Information", "### Licensing Information\n\nThe licensing status of the dataset hinges on the legal status of the ATCOSIM corpus creators.\n\n\n\nContributors who prepared, processed, normalized and uploaded the dataset in HuggingFace:\n\n\n\n\nAuthors of the dataset:" ]

[ "task_categories:image-to-text", "task_categories:zero-shot-image-classification", "task_categories:feature-extraction", "task_ids:image-captioning", "size_categories:100K<n<1M", "source_datasets:wikimedia/wit_base", "language:da", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "da" ]

[ "### Dataset Summary\n\n\nGoogle presented the Wikipedia Image Text (WIT) dataset in July\n2021, a dataset which contains\nscraped images from Wikipedia along with their descriptions. WikiMedia released\nWIT-Base in September\n2021,\nbeing a modified version of WIT where they have removed the images with empty\n\"reference descriptions\", as well as removing images where a person's face covers more\nthan 10% of the image surface, along with inappropriate images that are candidate for\ndeletion. This dataset is the Danish portion of the WIT-Base dataset, consisting of\nroughly 160,000 images with associated Danish descriptions. We release the dataset\nunder the CC BY-SA 4.0 license, in\naccordance with WIT-Base's identical\nlicense.", "### Supported Tasks and Leaderboards\n\n\nTraining machine learning models for caption generation, zero-shot image classification\nand text-image search are the intended tasks for this dataset. No leaderboard is active\nat this point.", "### Languages\n\n\nThe dataset is available in Danish ('da').\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n* Size of downloaded dataset files: 7.5 GB\n* Size of the generated dataset: 7.8 GB\n* Total amount of disk used: 15.3 GB\n\n\nAn example from the 'train' split looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'image': a 'dict' feature.\n* 'image\\_url': a 'str' feature.\n* 'embedding': a 'list' feature.\n* 'metadata\\_url': a 'str' feature.\n* 'original\\_height': an 'int' or 'NaN' feature.\n* 'original\\_width': an 'int' or 'NaN' feature.\n* 'mime\\_type': a 'str' or 'None' feature.\n* 'caption\\_attribution\\_description': a 'str' or 'None' feature.\n* 'page\\_url': a 'str' feature.\n* 'attribution\\_passes\\_lang\\_id': a 'bool' or 'None' feature.\n* 'caption\\_alt\\_text\\_description': a 'str' or 'None' feature.\n* 'caption\\_reference\\_description': a 'str' or 'None' feature.\n* 'caption\\_title\\_and\\_reference\\_description': a 'str' or 'None' feature.\n* 'context\\_page\\_description': a 'str' or 'None' feature.\n* 'context\\_section\\_description': a 'str' or 'None' feature.\n* 'hierarchical\\_section\\_title': a 'str' feature.\n* 'is\\_main\\_image': a 'bool' or 'None' feature.\n* 'page\\_changed\\_recently': a 'bool' or 'None' feature.\n* 'page\\_title': a 'str' feature.\n* 'section\\_title': a 'str' or 'None' feature.", "### Data Splits\n\n\nRoughly 2.60% of the WIT-Base dataset comes from the Danish Wikipedia. We have split\nthe resulting 168,740 samples into a training set, validation set and testing set of\nthe following sizes:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nIt is quite cumbersome to extract the Danish portion of the WIT-Base dataset,\nespecially as the dataset takes up 333 GB of disk space, so the curation of Danish-WIT\nis purely to make it easier to work with the Danish portion of it.", "### Source Data\n\n\nThe original data was collected from WikiMedia's\nWIT-Base dataset, which in turn\ncomes from Google's WIT dataset.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nDan Saattrup Nielsen from the The Alexandra\nInstitute curated this dataset.", "### Licensing Information\n\n\nThe dataset is licensed under the CC BY-SA 4.0\nlicense." ]

[ "TAGS\n#task_categories-image-to-text #task_categories-zero-shot-image-classification #task_categories-feature-extraction #task_ids-image-captioning #size_categories-100K<n<1M #source_datasets-wikimedia/wit_base #language-Danish #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nGoogle presented the Wikipedia Image Text (WIT) dataset in July\n2021, a dataset which contains\nscraped images from Wikipedia along with their descriptions. WikiMedia released\nWIT-Base in September\n2021,\nbeing a modified version of WIT where they have removed the images with empty\n\"reference descriptions\", as well as removing images where a person's face covers more\nthan 10% of the image surface, along with inappropriate images that are candidate for\ndeletion. This dataset is the Danish portion of the WIT-Base dataset, consisting of\nroughly 160,000 images with associated Danish descriptions. We release the dataset\nunder the CC BY-SA 4.0 license, in\naccordance with WIT-Base's identical\nlicense.", "### Supported Tasks and Leaderboards\n\n\nTraining machine learning models for caption generation, zero-shot image classification\nand text-image search are the intended tasks for this dataset. No leaderboard is active\nat this point.", "### Languages\n\n\nThe dataset is available in Danish ('da').\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n* Size of downloaded dataset files: 7.5 GB\n* Size of the generated dataset: 7.8 GB\n* Total amount of disk used: 15.3 GB\n\n\nAn example from the 'train' split looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'image': a 'dict' feature.\n* 'image\\_url': a 'str' feature.\n* 'embedding': a 'list' feature.\n* 'metadata\\_url': a 'str' feature.\n* 'original\\_height': an 'int' or 'NaN' feature.\n* 'original\\_width': an 'int' or 'NaN' feature.\n* 'mime\\_type': a 'str' or 'None' feature.\n* 'caption\\_attribution\\_description': a 'str' or 'None' feature.\n* 'page\\_url': a 'str' feature.\n* 'attribution\\_passes\\_lang\\_id': a 'bool' or 'None' feature.\n* 'caption\\_alt\\_text\\_description': a 'str' or 'None' feature.\n* 'caption\\_reference\\_description': a 'str' or 'None' feature.\n* 'caption\\_title\\_and\\_reference\\_description': a 'str' or 'None' feature.\n* 'context\\_page\\_description': a 'str' or 'None' feature.\n* 'context\\_section\\_description': a 'str' or 'None' feature.\n* 'hierarchical\\_section\\_title': a 'str' feature.\n* 'is\\_main\\_image': a 'bool' or 'None' feature.\n* 'page\\_changed\\_recently': a 'bool' or 'None' feature.\n* 'page\\_title': a 'str' feature.\n* 'section\\_title': a 'str' or 'None' feature.", "### Data Splits\n\n\nRoughly 2.60% of the WIT-Base dataset comes from the Danish Wikipedia. We have split\nthe resulting 168,740 samples into a training set, validation set and testing set of\nthe following sizes:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nIt is quite cumbersome to extract the Danish portion of the WIT-Base dataset,\nespecially as the dataset takes up 333 GB of disk space, so the curation of Danish-WIT\nis purely to make it easier to work with the Danish portion of it.", "### Source Data\n\n\nThe original data was collected from WikiMedia's\nWIT-Base dataset, which in turn\ncomes from Google's WIT dataset.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nDan Saattrup Nielsen from the The Alexandra\nInstitute curated this dataset.", "### Licensing Information\n\n\nThe dataset is licensed under the CC BY-SA 4.0\nlicense." ]

[ "region:us" ]

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[ "# Dataset Card for [Dataset Name]", "## 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:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#region-us \n", "# Dataset Card for [Dataset Name]", "## 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:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"binomial_3blue1brown_test\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"binomial_3blue1brown_test\"\n\nMore Information needed" ]

[ "region:us" ]

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

[ "TAGS\n#region-us \n", "# Dataset Card for \"test_push_og\"\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: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]