Datasets:

---

ftopal

/

huggingface-datasets

like 0

[ "task_categories:text-classification", "language:en", "region:us" ]

[]

[ "en" ]

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\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#task_categories-text-classification #language-English #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\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:" ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:image-classification", "annotations_creators:expert-generated", "size_categories:1K<n<10K", "license:cc0-1.0", "region:us" ]

[]

[]

[ "# Datastet card for Encyclopaedia Britannica Illustrated", "## Table of Contents\n- Dataset Card Creation Guide\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 - 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\n- Homepage: URL", "### 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#task_categories-image-classification #annotations_creators-expert-generated #size_categories-1K<n<10K #license-cc0-1.0 #region-us \n", "# Datastet card for Encyclopaedia Britannica Illustrated", "## Table of Contents\n- Dataset Card Creation Guide\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 - 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\n- Homepage: URL", "### 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." ]

[ "language_creators:found", "multilinguality:monolingual", "language:en", "license:mit", "laion", "stable-diffuson", "text2img", "region:us" ]

[]

[ "en" ]

[ "# LAION-Aesthetics :: CLIP → UMAP\n\nThis dataset is a CLIP (text) → UMAP embedding of the LAION-Aesthetics dataset - specifically the 'improved_aesthetics_6plus' version, which filters the full dataset to images with scores of > 6 under the \"aesthetic\" filtering model.\n\nThanks LAION for this amazing corpus!\n\n---\n\nThe dataset here includes coordinates for 3x separate UMAP fits using different values for the 'n_neighbors' parameter - '10', '30', and '60' - which are broken out as separate columns with different suffixes:\n\n- 'n_neighbors=10' → ('x_nn10', 'y_nn10')\n- 'n_neighbors=30' → ('x_nn30', 'y_nn30')\n- 'n_neighbors=60' → ('x_nn60', 'y_nn60')", "### 'nn10'\n\n!nn10", "### 'nn30'\n\n!nn30", "### 'nn60'\n\n(The version from Twitter.)\n\n!nn60", "## Pipeline\n\nThe script for producing this can be found here:\n\nURL\n\nAnd is very simple - just using the 'openai/clip-vit-base-patch32' model out-of-the-box to encode the text captions:\n\n\n\nThen using 'cuml.GaussianRandomProjection' to do an initial squeeze to 64d (which gets the embedding tensor small enough to fit onto a single GPU for the UMAP) -\n\n\n\nAnd then 'cuml.UMAP' to get from 64d -> 2d -" ]

[ "TAGS\n#language_creators-found #multilinguality-monolingual #language-English #license-mit #laion #stable-diffuson #text2img #region-us \n", "# LAION-Aesthetics :: CLIP → UMAP\n\nThis dataset is a CLIP (text) → UMAP embedding of the LAION-Aesthetics dataset - specifically the 'improved_aesthetics_6plus' version, which filters the full dataset to images with scores of > 6 under the \"aesthetic\" filtering model.\n\nThanks LAION for this amazing corpus!\n\n---\n\nThe dataset here includes coordinates for 3x separate UMAP fits using different values for the 'n_neighbors' parameter - '10', '30', and '60' - which are broken out as separate columns with different suffixes:\n\n- 'n_neighbors=10' → ('x_nn10', 'y_nn10')\n- 'n_neighbors=30' → ('x_nn30', 'y_nn30')\n- 'n_neighbors=60' → ('x_nn60', 'y_nn60')", "### 'nn10'\n\n!nn10", "### 'nn30'\n\n!nn30", "### 'nn60'\n\n(The version from Twitter.)\n\n!nn60", "## Pipeline\n\nThe script for producing this can be found here:\n\nURL\n\nAnd is very simple - just using the 'openai/clip-vit-base-patch32' model out-of-the-box to encode the text captions:\n\n\n\nThen using 'cuml.GaussianRandomProjection' to do an initial squeeze to 64d (which gets the embedding tensor small enough to fit onto a single GPU for the UMAP) -\n\n\n\nAnd then 'cuml.UMAP' to get from 64d -> 2d -" ]

[ "task_categories:text-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:es", "license:afl-3.0", "radiology", "biomedicine", "ICD-10", "region:us" ]

[]

[ "es" ]

[ "# CARES - A Corpus of Anonymised Radiological Evidences in Spanish \n\nCARES is a high-quality text resource manually labeled with ICD-10 codes and reviewed by radiologists. These types of resources are essential for developing automatic text classification tools as they are necessary for training and fine-tuning our computational systems. \n\nThe CARES corpus has been manually annotated using the ICD-10 ontology, which stands for for the 10th version of the International Classification of Diseases. For each radiological report, a minimum of one code and a maximum of 9 codes were assigned, while the average number of codes per text is 2.15 with the standard deviation of 1.12.\n\nThe corpus was additionally preprocessed in order to make its format coherent with the automatic text classification task. Considering the hierarchical structure of the ICD-10 ontology, each sub-code was mapped to its respective code and chapter, obtaining two new sets of labels for each report. The entire CARES collection contains 6,907 sub-code annotations among the 3,219 radiologic reports. There are 223 unique ICD-10 sub-codes within the annotations, which were mapped to 156 unique ICD-10 codes and 16 unique chapters of the cited ontology." ]

[ "TAGS\n#task_categories-text-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Spanish #license-afl-3.0 #radiology #biomedicine #ICD-10 #region-us \n", "# CARES - A Corpus of Anonymised Radiological Evidences in Spanish \n\nCARES is a high-quality text resource manually labeled with ICD-10 codes and reviewed by radiologists. These types of resources are essential for developing automatic text classification tools as they are necessary for training and fine-tuning our computational systems. \n\nThe CARES corpus has been manually annotated using the ICD-10 ontology, which stands for for the 10th version of the International Classification of Diseases. For each radiological report, a minimum of one code and a maximum of 9 codes were assigned, while the average number of codes per text is 2.15 with the standard deviation of 1.12.\n\nThe corpus was additionally preprocessed in order to make its format coherent with the automatic text classification task. Considering the hierarchical structure of the ICD-10 ontology, each sub-code was mapped to its respective code and chapter, obtaining two new sets of labels for each report. The entire CARES collection contains 6,907 sub-code annotations among the 3,219 radiologic reports. There are 223 unique ICD-10 sub-codes within the annotations, which were mapped to 156 unique ICD-10 codes and 16 unique chapters of the cited ontology." ]

[ "task_categories:image-classification", "task_ids:multi-class-image-classification", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:apache-2.0", "recycling", "image-classification", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for recycling-dataset", "### Dataset Summary\n\nThis is a recycling dataset that can be used for image classification. It has 11 categories:\n\n- aluminium\n- batteries\n- cardboard\n- disposable plates\n- glass\n- hard plastic\n- paper\n- paper towel\n- polystyrene\n- soft plastics\n- takeaway cups\n\nIt was scrapped from DuckDuckGo using this tool: URL" ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-class-image-classification #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-apache-2.0 #recycling #image-classification #region-us \n", "# Dataset Card for recycling-dataset", "### Dataset Summary\n\nThis is a recycling dataset that can be used for image classification. It has 11 categories:\n\n- aluminium\n- batteries\n- cardboard\n- disposable plates\n- glass\n- hard plastic\n- paper\n- paper towel\n- polystyrene\n- soft plastics\n- takeaway cups\n\nIt was scrapped from DuckDuckGo using this tool: URL" ]

[ "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: Multi-class Text Classification\n* Model: JNK789/distilbert-base-uncased-finetuned-emotion\n* Dataset: emotion\n* Config: default\n* Split: test\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: Multi-class Text Classification\n* Model: JNK789/distilbert-base-uncased-finetuned-emotion\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "task_categories:question-answering", "task_ids:closed-domain-qa", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2103.06268", "region:us" ]

[ "2103.06268" ]

[ "en" ]

[ "### Dataset Summary\n\n\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510 commercial legal contracts that have been manually labeled to identify 41 categories of important clauses that lawyers look for when reviewing contracts in connection with corporate transactions.\nCUAD is curated and maintained by The Atticus Project, Inc. to support NLP research and development in legal contract review. Analysis of CUAD can be found at URL Code for replicating the results and the trained model can be found at URL", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe dataset contains samples in English only.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\n* 'id': a 'string' feature.\n* 'title': a 'string' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.", "### Data Splits\n\n\nThis dataset is split into train/test set. Number of samples in each set is given below:\n\n\nTrain: CUAD, Test: 22450\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nA highly valuable specialized task without a public large-scale dataset is contract review, which costs humans substantial time, money, and attention. Many law firms spend approximately 50% of their time reviewing contracts (CEB, 2017). Due to the specialized training necessary to understand and interpret contracts, the billing rates for lawyers at large law firms are typically around $500-$900 per hour in the US. As a result, many transactions cost companies hundreds of thousands of dollars just so that lawyers can verify that there are no problematic obligations or requirements included in the contracts. Contract review can be a source of drudgery and, in comparison to other legal tasks, is widely considered to be especially boring.\nContract review costs also affect consumers. Since contract review costs are so prohibitive, contract review is not often performed outside corporate transactions. Small companies and individuals consequently often sign contracts without even reading them, which can result in predatory behavior that harms consumers. Automating contract review by openly releasing high-quality data and fine-tuned models can increase access to legal support for small businesses and individuals, so that legal support is not exclusively available to wealthy companies.\nTo reduce the disparate societal costs of contract review, and to study how well NLP models generalize to specialized domains, the authors introduced a new large-scale dataset for contract review. As part of The Atticus Project, a non-profit organization of legal experts, CUAD is introduced, the Contract Understanding Atticus Dataset. This dataset was created with a year-long effort pushed forward by dozens of law student annotators, lawyers, and machine learning researchers. The dataset includes more than 500 contracts and more than 13,000 expert annotations that span 41 label categories. For each of 41 different labels, models must learn to highlight the portions of a contract most salient to that label. This makes the task a matter of finding needles in a haystack.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe CUAD includes commercial contracts selected from 25 different types of contracts based on the contract names as shown below. Within each type, the creators randomly selected contracts based on the names of the filing companies across the alphabet.\nType of Contracts: # of Docs\nAffiliate Agreement: 10\nAgency Agreement: 13\nCollaboration/Cooperation Agreement: 26\nCo-Branding Agreement: 22\nConsulting Agreement: 11\nDevelopment Agreement: 29\nDistributor Agreement: 32\nEndorsement Agreement: 24\nFranchise Agreement: 15\nHosting Agreement: 20\nIP Agreement: 17\nJoint Venture Agreemen: 23\nLicense Agreement: 33\nMaintenance Agreement: 34\nManufacturing Agreement: 17\nMarketing Agreement: 17\nNon-Compete/No-Solicit/Non-Disparagement Agreement: 3\nOutsourcing Agreement: 18\nPromotion Agreement: 12\nReseller Agreement: 12\nService Agreement: 28\nSponsorship Agreement: 31\nSupply Agreement: 18\nStrategic Alliance Agreement: 32\nTransportation Agreement: 13\nTOTAL: 510", "#### Who are the source language producers?\n\n\nThe contracts were sourced from EDGAR, the Electronic Data Gathering, Analysis, and Retrieval system used at the U.S. Securities and Exchange Commission (SEC). Publicly traded companies in the United States are required to file certain contracts under the SEC rules. Access to these contracts is available to the public for free at URL Please read the Datasheet at URL for information on the intended use and limitations of the CUAD.", "### Annotations", "#### Annotation process\n\n\nThe labeling process included multiple steps to ensure accuracy:\n\n\n1. Law Student Training: law students attended training sessions on each of the categories that included a summary, video instructions by experienced attorneys, multiple quizzes and workshops. Students were then required to label sample contracts in eBrevia, an online contract review tool. The initial training took approximately 70-100 hours.\n2. Law Student Label: law students conducted manual contract review and labeling in eBrevia.\n3. Key Word Search: law students conducted keyword search in eBrevia to capture additional categories that have been missed during the “Student Label” step.\n4. Category-by-Category Report Review: law students exported the labeled clauses into reports, review each clause category-by-category and highlight clauses that they believe are mislabeled.\n5. Attorney Review: experienced attorneys reviewed the category-by-category report with students comments, provided comments and addressed student questions. When applicable, attorneys discussed such results with the students and reached consensus. Students made changes in eBrevia accordingly.\n6. eBrevia Extras Review. Attorneys and students used eBrevia to generate a list of “extras”, which are clauses that eBrevia AI tool identified as responsive to a category but not labeled by human annotators. Attorneys and students reviewed all of the “extras” and added the correct ones. The process is repeated until all or substantially all of the “extras” are incorrect labels.\n7. Final Report: The final report was exported into a CSV file. Volunteers manually added the “Yes/No” answer column to categories that do not contain an answer.", "#### Who are the annotators?\n\n\nAnswered in above section.", "### Personal and Sensitive Information\n\n\nSome clauses in the files are redacted because the party submitting these contracts redacted them to protect confidentiality. Such redaction may show up as asterisks (\\*\\*\\*) or underscores (\\_\\_\\_) or blank spaces. The dataset and the answers reflect such redactions. For example, the answer for “January \\_\\_ 2020” would be “1/[]/2020”).\nFor any categories that require an answer of “Yes/No”, annotators include full sentences as text context in a contract. To maintain consistency and minimize inter-annotator disagreement, annotators select text for the full sentence, under the instruction of “from period to period”.\nFor the other categories, annotators selected segments of the text in the contract that are responsive to each such category. One category in a contract may include multiple labels. For example, “Parties” may include 4-10 separate text strings that are not continuous in a contract. The answer is presented in the unified format separated by semicolons of “Party A Inc. (“Party A”); Party B Corp. (“Party B”)”.\nSome sentences in the files include confidential legends that are not part of the contracts. An example of such confidential legend is as follows:\nTHIS EXHIBIT HAS BEEN REDACTED AND IS THE SUBJECT OF A CONFIDENTIAL TREATMENT REQUEST. REDACTED MATERIAL IS MARKED WITH [\\* \\* \\*] AND HAS BEEN FILED SEPARATELY WITH THE SECURITIES AND EXCHANGE COMMISSION.\nSome sentences in the files contain irrelevant information such as footers or page numbers. Some sentences may not be relevant to the corresponding category. Some sentences may correspond to a different category. Because many legal clauses are very long and contain various sub-parts, sometimes only a sub-part of a sentence is responsive to a category.\nTo address the foregoing limitations, annotators manually deleted the portion that is not responsive, replacing it with the symbol \"\" to indicate that the two text segments do not appear immediately next to each other in the contracts. For example, if a “Termination for Convenience” clause starts with “Each Party may terminate this Agreement if” followed by three subparts “(a), (b) and (c)”, but only subpart (c) is responsive to this category, the authors manually deleted subparts (a) and (b) and replaced them with the symbol \"”. Another example is for “Effective Date”, the contract includes a sentence “This Agreement is effective as of the date written above” that appears after the date “January 1, 2010”. The annotation is as follows: “January 1, 2010 This Agreement is effective as of the date written above.”\nBecause the contracts were converted from PDF into TXT files, the converted TXT files may not stay true to the format of the original PDF files. For example, some contracts contain inconsistent spacing between words, sentences and paragraphs. Table format is not maintained in the TXT files.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nAttorney Advisors\nWei Chen, John Brockland, Kevin Chen, Jacky Fink, Spencer P. Goodson, Justin Haan, Alex Haskell, Kari Krusmark, Jenny Lin, Jonas Marson, Benjamin Petersen, Alexander Kwonji Rosenberg, William R. Sawyers, Brittany Schmeltz, Max Scott, Zhu Zhu\nLaw Student Leaders\nJohn Batoha, Daisy Beckner, Lovina Consunji, Gina Diaz, Chris Gronseth, Calvin Hannagan, Joseph Kroon, Sheetal Sharma Saran\nLaw Student Contributors\nScott Aronin, Bryan Burgoon, Jigar Desai, Imani Haynes, Jeongsoo Kim, Margaret Lynch, Allison Melville, Felix Mendez-Burgos, Nicole Mirkazemi, David Myers, Emily Rissberger, Behrang Seraj, Sarahginy Valcin\nTechnical Advisors & Contributors\nDan Hendrycks, Collin Burns, Spencer Ball, Anya Chen", "### Licensing Information\n\n\nCUAD is licensed under the Creative Commons Attribution 4.0 (CC BY 4.0) license and free to the public for commercial and non-commercial use.\nThe creators make no representations or warranties regarding the license status of the underlying contracts, which are publicly available and downloadable from EDGAR.\nPrivacy Policy & Disclaimers\nThe categories or the contracts included in the dataset are not comprehensive or representative. The authors encourage the public to help improve them by sending them your comments and suggestions to info@URL. Comments and suggestions will be reviewed by The Atticus Project at its discretion and will be included in future versions of Atticus categories once approved.\nThe use of CUAD is subject to their privacy policy URL and disclaimer URL", "### Contributions\n\n\nThanks to @bhavitvyamalik for adding the original CUAD dataset." ]

[ "TAGS\n#task_categories-question-answering #task_ids-closed-domain-qa #task_ids-extractive-qa #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-2103.06268 #region-us \n", "### Dataset Summary\n\n\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510 commercial legal contracts that have been manually labeled to identify 41 categories of important clauses that lawyers look for when reviewing contracts in connection with corporate transactions.\nCUAD is curated and maintained by The Atticus Project, Inc. to support NLP research and development in legal contract review. Analysis of CUAD can be found at URL Code for replicating the results and the trained model can be found at URL", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe dataset contains samples in English only.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\n* 'id': a 'string' feature.\n* 'title': a 'string' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.", "### Data Splits\n\n\nThis dataset is split into train/test set. Number of samples in each set is given below:\n\n\nTrain: CUAD, Test: 22450\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nA highly valuable specialized task without a public large-scale dataset is contract review, which costs humans substantial time, money, and attention. Many law firms spend approximately 50% of their time reviewing contracts (CEB, 2017). Due to the specialized training necessary to understand and interpret contracts, the billing rates for lawyers at large law firms are typically around $500-$900 per hour in the US. As a result, many transactions cost companies hundreds of thousands of dollars just so that lawyers can verify that there are no problematic obligations or requirements included in the contracts. Contract review can be a source of drudgery and, in comparison to other legal tasks, is widely considered to be especially boring.\nContract review costs also affect consumers. Since contract review costs are so prohibitive, contract review is not often performed outside corporate transactions. Small companies and individuals consequently often sign contracts without even reading them, which can result in predatory behavior that harms consumers. Automating contract review by openly releasing high-quality data and fine-tuned models can increase access to legal support for small businesses and individuals, so that legal support is not exclusively available to wealthy companies.\nTo reduce the disparate societal costs of contract review, and to study how well NLP models generalize to specialized domains, the authors introduced a new large-scale dataset for contract review. As part of The Atticus Project, a non-profit organization of legal experts, CUAD is introduced, the Contract Understanding Atticus Dataset. This dataset was created with a year-long effort pushed forward by dozens of law student annotators, lawyers, and machine learning researchers. The dataset includes more than 500 contracts and more than 13,000 expert annotations that span 41 label categories. For each of 41 different labels, models must learn to highlight the portions of a contract most salient to that label. This makes the task a matter of finding needles in a haystack.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe CUAD includes commercial contracts selected from 25 different types of contracts based on the contract names as shown below. Within each type, the creators randomly selected contracts based on the names of the filing companies across the alphabet.\nType of Contracts: # of Docs\nAffiliate Agreement: 10\nAgency Agreement: 13\nCollaboration/Cooperation Agreement: 26\nCo-Branding Agreement: 22\nConsulting Agreement: 11\nDevelopment Agreement: 29\nDistributor Agreement: 32\nEndorsement Agreement: 24\nFranchise Agreement: 15\nHosting Agreement: 20\nIP Agreement: 17\nJoint Venture Agreemen: 23\nLicense Agreement: 33\nMaintenance Agreement: 34\nManufacturing Agreement: 17\nMarketing Agreement: 17\nNon-Compete/No-Solicit/Non-Disparagement Agreement: 3\nOutsourcing Agreement: 18\nPromotion Agreement: 12\nReseller Agreement: 12\nService Agreement: 28\nSponsorship Agreement: 31\nSupply Agreement: 18\nStrategic Alliance Agreement: 32\nTransportation Agreement: 13\nTOTAL: 510", "#### Who are the source language producers?\n\n\nThe contracts were sourced from EDGAR, the Electronic Data Gathering, Analysis, and Retrieval system used at the U.S. Securities and Exchange Commission (SEC). Publicly traded companies in the United States are required to file certain contracts under the SEC rules. Access to these contracts is available to the public for free at URL Please read the Datasheet at URL for information on the intended use and limitations of the CUAD.", "### Annotations", "#### Annotation process\n\n\nThe labeling process included multiple steps to ensure accuracy:\n\n\n1. Law Student Training: law students attended training sessions on each of the categories that included a summary, video instructions by experienced attorneys, multiple quizzes and workshops. Students were then required to label sample contracts in eBrevia, an online contract review tool. The initial training took approximately 70-100 hours.\n2. Law Student Label: law students conducted manual contract review and labeling in eBrevia.\n3. Key Word Search: law students conducted keyword search in eBrevia to capture additional categories that have been missed during the “Student Label” step.\n4. Category-by-Category Report Review: law students exported the labeled clauses into reports, review each clause category-by-category and highlight clauses that they believe are mislabeled.\n5. Attorney Review: experienced attorneys reviewed the category-by-category report with students comments, provided comments and addressed student questions. When applicable, attorneys discussed such results with the students and reached consensus. Students made changes in eBrevia accordingly.\n6. eBrevia Extras Review. Attorneys and students used eBrevia to generate a list of “extras”, which are clauses that eBrevia AI tool identified as responsive to a category but not labeled by human annotators. Attorneys and students reviewed all of the “extras” and added the correct ones. The process is repeated until all or substantially all of the “extras” are incorrect labels.\n7. Final Report: The final report was exported into a CSV file. Volunteers manually added the “Yes/No” answer column to categories that do not contain an answer.", "#### Who are the annotators?\n\n\nAnswered in above section.", "### Personal and Sensitive Information\n\n\nSome clauses in the files are redacted because the party submitting these contracts redacted them to protect confidentiality. Such redaction may show up as asterisks (\\*\\*\\*) or underscores (\\_\\_\\_) or blank spaces. The dataset and the answers reflect such redactions. For example, the answer for “January \\_\\_ 2020” would be “1/[]/2020”).\nFor any categories that require an answer of “Yes/No”, annotators include full sentences as text context in a contract. To maintain consistency and minimize inter-annotator disagreement, annotators select text for the full sentence, under the instruction of “from period to period”.\nFor the other categories, annotators selected segments of the text in the contract that are responsive to each such category. One category in a contract may include multiple labels. For example, “Parties” may include 4-10 separate text strings that are not continuous in a contract. The answer is presented in the unified format separated by semicolons of “Party A Inc. (“Party A”); Party B Corp. (“Party B”)”.\nSome sentences in the files include confidential legends that are not part of the contracts. An example of such confidential legend is as follows:\nTHIS EXHIBIT HAS BEEN REDACTED AND IS THE SUBJECT OF A CONFIDENTIAL TREATMENT REQUEST. REDACTED MATERIAL IS MARKED WITH [\\* \\* \\*] AND HAS BEEN FILED SEPARATELY WITH THE SECURITIES AND EXCHANGE COMMISSION.\nSome sentences in the files contain irrelevant information such as footers or page numbers. Some sentences may not be relevant to the corresponding category. Some sentences may correspond to a different category. Because many legal clauses are very long and contain various sub-parts, sometimes only a sub-part of a sentence is responsive to a category.\nTo address the foregoing limitations, annotators manually deleted the portion that is not responsive, replacing it with the symbol \"\" to indicate that the two text segments do not appear immediately next to each other in the contracts. For example, if a “Termination for Convenience” clause starts with “Each Party may terminate this Agreement if” followed by three subparts “(a), (b) and (c)”, but only subpart (c) is responsive to this category, the authors manually deleted subparts (a) and (b) and replaced them with the symbol \"”. Another example is for “Effective Date”, the contract includes a sentence “This Agreement is effective as of the date written above” that appears after the date “January 1, 2010”. The annotation is as follows: “January 1, 2010 This Agreement is effective as of the date written above.”\nBecause the contracts were converted from PDF into TXT files, the converted TXT files may not stay true to the format of the original PDF files. For example, some contracts contain inconsistent spacing between words, sentences and paragraphs. Table format is not maintained in the TXT files.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nAttorney Advisors\nWei Chen, John Brockland, Kevin Chen, Jacky Fink, Spencer P. Goodson, Justin Haan, Alex Haskell, Kari Krusmark, Jenny Lin, Jonas Marson, Benjamin Petersen, Alexander Kwonji Rosenberg, William R. Sawyers, Brittany Schmeltz, Max Scott, Zhu Zhu\nLaw Student Leaders\nJohn Batoha, Daisy Beckner, Lovina Consunji, Gina Diaz, Chris Gronseth, Calvin Hannagan, Joseph Kroon, Sheetal Sharma Saran\nLaw Student Contributors\nScott Aronin, Bryan Burgoon, Jigar Desai, Imani Haynes, Jeongsoo Kim, Margaret Lynch, Allison Melville, Felix Mendez-Burgos, Nicole Mirkazemi, David Myers, Emily Rissberger, Behrang Seraj, Sarahginy Valcin\nTechnical Advisors & Contributors\nDan Hendrycks, Collin Burns, Spencer Ball, Anya Chen", "### Licensing Information\n\n\nCUAD is licensed under the Creative Commons Attribution 4.0 (CC BY 4.0) license and free to the public for commercial and non-commercial use.\nThe creators make no representations or warranties regarding the license status of the underlying contracts, which are publicly available and downloadable from EDGAR.\nPrivacy Policy & Disclaimers\nThe categories or the contracts included in the dataset are not comprehensive or representative. The authors encourage the public to help improve them by sending them your comments and suggestions to info@URL. Comments and suggestions will be reviewed by The Atticus Project at its discretion and will be included in future versions of Atticus categories once approved.\nThe use of CUAD is subject to their privacy policy URL and disclaimer URL", "### Contributions\n\n\nThanks to @bhavitvyamalik for adding the original CUAD dataset." ]

[ "task_categories:audio-classification", "task_categories:automatic-speech-recognition", "task_ids:speaker-identification", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:found", "language:ja", "license:cc", "speech-recognition", "region:us" ]

[]

[ "ja" ]

[ "# CABank Japanese Sakura Corpus\n\n- Susanne Miyata\n- Department of Medical Sciences\n- Aichi Shukotoku University\n- smiyata@URL\n- website: URL", "## Important\n\nThis data set is a copy from the original one located at URL", "## Details\n\n- Participants: 31\n- Type of Study: xxx\n- Location: Japan\n- Media type: audio\n- DOI: doi:10.21415/T5M90R\n\ninformation\n\nSome citation here.\nIn accordance with TalkBank rules, any use of data from this corpus must be accompanied by at least one of the above references.", "## Project Description\n\nThis corpus of 18 conversations is the product of six graduation theses on gender differences in students' group talk. Each conversation lasted between 12 and 35 minutes (avg. 25 minutes) resulting in an overall time of 7 hours and 30 minutes. 31 Students (19 female, 12 male) participated in the study (Table 1). The participants gathered in groups of 4 students, either of the same or the opposite sex (6 conversations with a group of 4 female students, 6 with 4 male students, and 6 conversations with 2 male and 2 female students), according to age (first and third year students) and affiliation (two academic departments). In addition, the participants of each conversation came from the same small-sized class and were well acquainted.\n\nThe participants were informed that their conversations may be transcribed and a video recorded for use in possible publication when recruited. Additionally, permission was asked once more after the transcription in cases where either private information had been displayed, or a misunderstanding concerning the nature and degree of the publication of the conversations became apparent during the conversation.\n\nThe recordings took place in a small conference room at the university between or after lectures. The participants were given a card with a conversation topic to start with, but were free to vary (topic 1 \"What do you expect from an opposite sex friend?\" [isee ni motomeru koto]; topic 2 \"Are you a dog lover or a cat lover?\" [inuha ka nekoha ka]; topic 3 \"About part-time work\" [arubaito ni tsuite]). The investigator was not present during the recording. The combination of participants, the topic, and the duration of the 18 conversations are given in Table 2.\n\nThe participants produced 15,449 utterances overall (female: 8,027 utterances, male: 7,422 utterances). All utterances were linked to video and transcribed in regular Japanese orthography and Latin script (Wakachi2002), and provided with morphological tags (JMOR04.1). Proper names were replaced by pseudonyms.", "## Acknowledgements\n\nAdditional contributors: Banno, Kyoko; Konishi, Saya; Matsui, Ayumi; Matsumoto, Shiori; Oogi, Rie; Takahashi, Akane; Muraki, Kyoko." ]

[ "TAGS\n#task_categories-audio-classification #task_categories-automatic-speech-recognition #task_ids-speaker-identification #annotations_creators-expert-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-found #language-Japanese #license-cc #speech-recognition #region-us \n", "# CABank Japanese Sakura Corpus\n\n- Susanne Miyata\n- Department of Medical Sciences\n- Aichi Shukotoku University\n- smiyata@URL\n- website: URL", "## Important\n\nThis data set is a copy from the original one located at URL", "## Details\n\n- Participants: 31\n- Type of Study: xxx\n- Location: Japan\n- Media type: audio\n- DOI: doi:10.21415/T5M90R\n\ninformation\n\nSome citation here.\nIn accordance with TalkBank rules, any use of data from this corpus must be accompanied by at least one of the above references.", "## Project Description\n\nThis corpus of 18 conversations is the product of six graduation theses on gender differences in students' group talk. Each conversation lasted between 12 and 35 minutes (avg. 25 minutes) resulting in an overall time of 7 hours and 30 minutes. 31 Students (19 female, 12 male) participated in the study (Table 1). The participants gathered in groups of 4 students, either of the same or the opposite sex (6 conversations with a group of 4 female students, 6 with 4 male students, and 6 conversations with 2 male and 2 female students), according to age (first and third year students) and affiliation (two academic departments). In addition, the participants of each conversation came from the same small-sized class and were well acquainted.\n\nThe participants were informed that their conversations may be transcribed and a video recorded for use in possible publication when recruited. Additionally, permission was asked once more after the transcription in cases where either private information had been displayed, or a misunderstanding concerning the nature and degree of the publication of the conversations became apparent during the conversation.\n\nThe recordings took place in a small conference room at the university between or after lectures. The participants were given a card with a conversation topic to start with, but were free to vary (topic 1 \"What do you expect from an opposite sex friend?\" [isee ni motomeru koto]; topic 2 \"Are you a dog lover or a cat lover?\" [inuha ka nekoha ka]; topic 3 \"About part-time work\" [arubaito ni tsuite]). The investigator was not present during the recording. The combination of participants, the topic, and the duration of the 18 conversations are given in Table 2.\n\nThe participants produced 15,449 utterances overall (female: 8,027 utterances, male: 7,422 utterances). All utterances were linked to video and transcribed in regular Japanese orthography and Latin script (Wakachi2002), and provided with morphological tags (JMOR04.1). Proper names were replaced by pseudonyms.", "## Acknowledgements\n\nAdditional contributors: Banno, Kyoko; Konishi, Saya; Matsui, Ayumi; Matsumoto, Shiori; Oogi, Rie; Takahashi, Akane; Muraki, Kyoko." ]

[ "task_categories:audio-classification", "task_categories:automatic-speech-recognition", "task_ids:speaker-identification", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:found", "language:ja", "license:cc", "speech-recognition", "region:us" ]

[]

[ "ja" ]

[ "# CABank Japanese CallHome Corpus\n\n- Participants: 120\n- Type of Study: phone call\n- Location: United States\n- Media type: audio\n- DOI: doi:10.21415/T5H59V\n\n- Web: URL\n\ninformation\n\nSome citation here.\nIn accordance with TalkBank rules, any use of data from this corpus must be accompanied by at least one of the above references.", "## Project Description\n\nThis is the Japanese portion of CallHome.\n\nSpeakers were solicited by the LDC to participate in this telephone speech collection effort via the internet, publications (advertisements), and personal contacts. A total of 200 call originators were found, each of whom placed a telephone call via a toll-free robot operator maintained by the LDC. Access to the robot operator was possible via a unique Personal Identification Number (PIN) issued by the recruiting staff at the LDC when the caller enrolled in the project. The participants were made aware that their telephone call would be recorded, as were the call recipients. The call was allowed only if both parties agreed to being recorded. Each caller was allowed to talk up to 30 minutes. Upon successful completion of the call, the caller was paid $20 (in addition to making a free long-distance telephone call). Each caller was allowed to place only one telephone call.\n\nAlthough the goal of the call collection effort was to have unique speakers in all calls, a handful of repeat speakers are included in the corpus. In all, 200 calls were transcribed. Of these, 80 have been designated as training calls, 20 as development test calls, and 100 as evaluation test calls. For each of the training and development test calls, a contiguous 10-minute region was selected for transcription; for the evaluation test calls, a 5-minute region was transcribed. For the present publication, only 20 of the evaluation test calls are being released; the remaining 80 test calls are being held in reserve for future LVCSR benchmark tests.\n\nAfter a successful call was completed, a human audit of each telephone call was conducted to verify that the proper language was spoken, to check the quality of the recording, and to select and describe the region to be transcribed. The description of the transcribed region provides information about channel quality, number of speakers, their gender, and other attributes.", "## Acknowledgements\n\nAndrew Yankes reformatted this corpus into accord with current versions of CHAT." ]

[ "TAGS\n#task_categories-audio-classification #task_categories-automatic-speech-recognition #task_ids-speaker-identification #annotations_creators-expert-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-found #language-Japanese #license-cc #speech-recognition #region-us \n", "# CABank Japanese CallHome Corpus\n\n- Participants: 120\n- Type of Study: phone call\n- Location: United States\n- Media type: audio\n- DOI: doi:10.21415/T5H59V\n\n- Web: URL\n\ninformation\n\nSome citation here.\nIn accordance with TalkBank rules, any use of data from this corpus must be accompanied by at least one of the above references.", "## Project Description\n\nThis is the Japanese portion of CallHome.\n\nSpeakers were solicited by the LDC to participate in this telephone speech collection effort via the internet, publications (advertisements), and personal contacts. A total of 200 call originators were found, each of whom placed a telephone call via a toll-free robot operator maintained by the LDC. Access to the robot operator was possible via a unique Personal Identification Number (PIN) issued by the recruiting staff at the LDC when the caller enrolled in the project. The participants were made aware that their telephone call would be recorded, as were the call recipients. The call was allowed only if both parties agreed to being recorded. Each caller was allowed to talk up to 30 minutes. Upon successful completion of the call, the caller was paid $20 (in addition to making a free long-distance telephone call). Each caller was allowed to place only one telephone call.\n\nAlthough the goal of the call collection effort was to have unique speakers in all calls, a handful of repeat speakers are included in the corpus. In all, 200 calls were transcribed. Of these, 80 have been designated as training calls, 20 as development test calls, and 100 as evaluation test calls. For each of the training and development test calls, a contiguous 10-minute region was selected for transcription; for the evaluation test calls, a 5-minute region was transcribed. For the present publication, only 20 of the evaluation test calls are being released; the remaining 80 test calls are being held in reserve for future LVCSR benchmark tests.\n\nAfter a successful call was completed, a human audit of each telephone call was conducted to verify that the proper language was spoken, to check the quality of the recording, and to select and describe the region to be transcribed. The description of the transcribed region provides information about channel quality, number of speakers, their gender, and other attributes.", "## Acknowledgements\n\nAndrew Yankes reformatted this corpus into accord with current versions of CHAT." ]

[ "arxiv:2104.15046", "region:us" ]

[ "2104.15046" ]

[]

[ "# URL Slate Dataset for Recommender Systems\n> Data and helper functions for URL slate dataset containing both viewed items and clicks from the URL second hand marketplace.\n\nNote: The dataset is originally hosted at URL and this is a copy of the readme until this repo is properly created \"huggingface-style\".\n\nWe release the *URL slate dataset* to improve recommender systems research.\nThe dataset includes both search and recommendation interactions between users and the platform over a 30 day period.\nThe dataset has logged both exposures and clicks, *including interactions where the user did not click on any of the items in the slate*.\nTo our knowledge there exists no such large-scale dataset, and we hope this contribution can help researchers constructing improved models and improve offline evaluation metrics.\n\n!A visualization of a presented slate to the user on the frontpage of URL\n\nFor each user u and interaction step t we recorded all items in the visible slate !equ ) (up to the scroll length !equ), and the user's click response !equ.\nThe dataset consists of 37.4 million interactions, |U| ≈ 2.3) million users and |I| ≈ 1.3 million items that belong to one of G = 290 item groups. For a detailed description of the data please see the paper.\n\n!A visualization of a presented slate to the user on the frontpage of URL\n\nURL is the leading marketplace in the Norwegian classifieds market and provides users with a platform to buy and sell general merchandise, cars, real estate, as well as house rentals and job offerings.\nFor questions, email URL@URL or file an issue.", "## Install\n\n'pip install recsys_slates_dataset'", "## How to use\n\nTo download the generic numpy data files:\n\n\n\nDownload and prepare data into ready-to-use PyTorch dataloaders:", "## Organization\nThe repository is organized as follows:\n- The dataset is placed in 'data/' and stored using git-lfs. We also provide an automatic download function in the pip package (preferred usage).\n- The code open sourced from the article \"Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling\" is found in ('code_eide_et_al21/'). However, we are in the process of making the data more generally available which makes the code incompatible with the current (newer) version of the data. Please use the v1.0 release of the repository for a compatible version of the code and dataset.", "## Quickstart dataset  which includes all necessary steps above.\nIt gives a quick introduction to how to use the dataset.", "## Example training scripts\nWe provide an example training jupyter notebook that implements a matrix factorization model with categorical loss that can be found in 'examples/'.\nIt is also runnable using Google Colab: ![matrix_factorization.ipynb](URL \nThere is ongoing work in progress to build additional examples and use them as benchmarks for the dataset.", "### Dataset files\nThe dataset 'URL' contains the following fields:\n- userId: The unique identifier of the user.\n- click: The items the user clicked on in each of the 20 presented slates.\n- click_idx: The index the clicked item was on in each of the 20 presented slates.\n- slate_lengths: The length of the 20 presented slates.\n- slate: All the items in each of the 20 presented slates.\n- interaction_type: The recommendation slate can be the result of a search query (1), a recommendation (2) or can be undefined (0).\n\nThe dataset 'URL' contains the categories ranging from 0 to 290. Corresponding with the 290 unique groups that the items belong to. These 290 unique groups are constructed using a combination of categorical information and the geographical location. \n\nThe dataset 'URL' contains the mapping between the indices and the values of the categories (e.g. '\"287\": \"JOB, Rogaland\"') and interaction types (e.g. '\"1\": \"search\"'). \ns\nThis repository accompanies the paper \"Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling\" by Simen Eide, David S. Leslie and Arnoldo Frigessi.\nThe article is under review, and the preprint can be obtained here.\n\nIf you use either the code, data or paper, please consider citing the paper.\n\n\n\n---\nlicense: apache-2.0\n---" ]

[ "TAGS\n#arxiv-2104.15046 #region-us \n", "# URL Slate Dataset for Recommender Systems\n> Data and helper functions for URL slate dataset containing both viewed items and clicks from the URL second hand marketplace.\n\nNote: The dataset is originally hosted at URL and this is a copy of the readme until this repo is properly created \"huggingface-style\".\n\nWe release the *URL slate dataset* to improve recommender systems research.\nThe dataset includes both search and recommendation interactions between users and the platform over a 30 day period.\nThe dataset has logged both exposures and clicks, *including interactions where the user did not click on any of the items in the slate*.\nTo our knowledge there exists no such large-scale dataset, and we hope this contribution can help researchers constructing improved models and improve offline evaluation metrics.\n\n!A visualization of a presented slate to the user on the frontpage of URL\n\nFor each user u and interaction step t we recorded all items in the visible slate !equ ) (up to the scroll length !equ), and the user's click response !equ.\nThe dataset consists of 37.4 million interactions, |U| ≈ 2.3) million users and |I| ≈ 1.3 million items that belong to one of G = 290 item groups. For a detailed description of the data please see the paper.\n\n!A visualization of a presented slate to the user on the frontpage of URL\n\nURL is the leading marketplace in the Norwegian classifieds market and provides users with a platform to buy and sell general merchandise, cars, real estate, as well as house rentals and job offerings.\nFor questions, email URL@URL or file an issue.", "## Install\n\n'pip install recsys_slates_dataset'", "## How to use\n\nTo download the generic numpy data files:\n\n\n\nDownload and prepare data into ready-to-use PyTorch dataloaders:", "## Organization\nThe repository is organized as follows:\n- The dataset is placed in 'data/' and stored using git-lfs. We also provide an automatic download function in the pip package (preferred usage).\n- The code open sourced from the article \"Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling\" is found in ('code_eide_et_al21/'). However, we are in the process of making the data more generally available which makes the code incompatible with the current (newer) version of the data. Please use the v1.0 release of the repository for a compatible version of the code and dataset.", "## Quickstart dataset  which includes all necessary steps above.\nIt gives a quick introduction to how to use the dataset.", "## Example training scripts\nWe provide an example training jupyter notebook that implements a matrix factorization model with categorical loss that can be found in 'examples/'.\nIt is also runnable using Google Colab: ![matrix_factorization.ipynb](URL \nThere is ongoing work in progress to build additional examples and use them as benchmarks for the dataset.", "### Dataset files\nThe dataset 'URL' contains the following fields:\n- userId: The unique identifier of the user.\n- click: The items the user clicked on in each of the 20 presented slates.\n- click_idx: The index the clicked item was on in each of the 20 presented slates.\n- slate_lengths: The length of the 20 presented slates.\n- slate: All the items in each of the 20 presented slates.\n- interaction_type: The recommendation slate can be the result of a search query (1), a recommendation (2) or can be undefined (0).\n\nThe dataset 'URL' contains the categories ranging from 0 to 290. Corresponding with the 290 unique groups that the items belong to. These 290 unique groups are constructed using a combination of categorical information and the geographical location. \n\nThe dataset 'URL' contains the mapping between the indices and the values of the categories (e.g. '\"287\": \"JOB, Rogaland\"') and interaction types (e.g. '\"1\": \"search\"'). \ns\nThis repository accompanies the paper \"Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling\" by Simen Eide, David S. Leslie and Arnoldo Frigessi.\nThe article is under review, and the preprint can be obtained here.\n\nIf you use either the code, data or paper, please consider citing the paper.\n\n\n\n---\nlicense: apache-2.0\n---" ]

[ "license:apache-2.0", "arxiv:1902.09707", "region:us" ]

[ "1902.09707" ]

[]

[ "# MFQEv2 Dataset\n\nFor some video enhancement/restoration tasks, lossless reference videos are necessary.\n\nWe open-source the dataset used in our MFQEv2 paper, which includes 108 lossless YUV videos for training and 18 test videos recommended by ITU-T.", "## 1. Content\n\n- 108 lossless YUV videos for training.\n- 18 lossless YUV videos for test, recommended by ITU-T.\n- An HEVC compression tool box.\n\n43.1 GB in total.", "## 2. Download Raw Videos\n\n[[Dropbox]](URL\n\nor [[百度网盘 (key: mfqe)]](URL", "## 3. Compress Videos\n\nWe compress both training and test videos by HM 16.5 at low delay P (LDP) mode with QP=37. The video compression toolbox is provided at the dataset folder.\n\nWe will get:", "### Ubuntu\n\n1. 'cd video_compression/'\n2. Edit 'URL'.\n3. 'chmod +x TAppEncoderStatic'\n4. 'python unzip_n_compress.py'", "### Windows\n\n1. Unzip 'train_108.zip' and 'test_18.zip' manually!\n2. 'cd video_compression\\'\n3. Edit 'URL' (e.g., 'system: windows').\n4. 'python unzip_n_compress.py'", "## 4. Citation\n\nIf you find this helpful, please star and cite:" ]

[ "TAGS\n#license-apache-2.0 #arxiv-1902.09707 #region-us \n", "# MFQEv2 Dataset\n\nFor some video enhancement/restoration tasks, lossless reference videos are necessary.\n\nWe open-source the dataset used in our MFQEv2 paper, which includes 108 lossless YUV videos for training and 18 test videos recommended by ITU-T.", "## 1. Content\n\n- 108 lossless YUV videos for training.\n- 18 lossless YUV videos for test, recommended by ITU-T.\n- An HEVC compression tool box.\n\n43.1 GB in total.", "## 2. Download Raw Videos\n\n[[Dropbox]](URL\n\nor [[百度网盘 (key: mfqe)]](URL", "## 3. Compress Videos\n\nWe compress both training and test videos by HM 16.5 at low delay P (LDP) mode with QP=37. The video compression toolbox is provided at the dataset folder.\n\nWe will get:", "### Ubuntu\n\n1. 'cd video_compression/'\n2. Edit 'URL'.\n3. 'chmod +x TAppEncoderStatic'\n4. 'python unzip_n_compress.py'", "### Windows\n\n1. Unzip 'train_108.zip' and 'test_18.zip' manually!\n2. 'cd video_compression\\'\n3. Edit 'URL' (e.g., 'system: windows').\n4. 'python unzip_n_compress.py'", "## 4. Citation\n\nIf you find this helpful, please star and cite:" ]

[ "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: Multi-class Text Classification\n* Model: Emanuel/twitter-emotion-deberta-v3-base\n* Dataset: emotion\n* Config: default\n* Split: test\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: Multi-class Text Classification\n* Model: Emanuel/twitter-emotion-deberta-v3-base\n* Dataset: emotion\n* Config: default\n* Split: test\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" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: ARTeLab/it5-summarization-fanpage\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ehahaha for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: ARTeLab/it5-summarization-fanpage\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ehahaha for evaluating this model." ]

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

[]

[ "en" ]

[ "# Dataset Card for Pokémon BLIP captions\n\n_Dataset used to train Pokémon text to image model_\n\nBLIP generated captions for Pokémon images from Few Shot Pokémon dataset introduced by _Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis_ (FastGAN). Original images were obtained from FastGAN-pytorch and captioned with the pre-trained BLIP model.\n\nFor each row the dataset contains 'image' and 'text' keys. 'image' is a varying size PIL jpeg, and 'text' is the accompanying text caption. Only a train split is provided.", "## Examples\n\n\n!URL\n> a drawing of a green pokemon with red eyes\n\n!URL\n> a green and yellow toy with a red nose\n\n!URL\n> a red and white ball with an angry look on its face\n\nIf you use this dataset, please cite it as:" ]

[ "TAGS\n#task_categories-text-to-image #annotations_creators-machine-generated #language_creators-other #multilinguality-monolingual #size_categories-n<1K #source_datasets-huggan/few-shot-pokemon #language-English #license-cc-by-nc-sa-4.0 #region-us \n", "# Dataset Card for Pokémon BLIP captions\n\n_Dataset used to train Pokémon text to image model_\n\nBLIP generated captions for Pokémon images from Few Shot Pokémon dataset introduced by _Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis_ (FastGAN). Original images were obtained from FastGAN-pytorch and captioned with the pre-trained BLIP model.\n\nFor each row the dataset contains 'image' and 'text' keys. 'image' is a varying size PIL jpeg, and 'text' is the accompanying text caption. Only a train split is provided.", "## Examples\n\n\n!URL\n> a drawing of a green pokemon with red eyes\n\n!URL\n> a green and yellow toy with a red nose\n\n!URL\n> a red and white ball with an angry look on its face\n\nIf you use this dataset, please cite it as:" ]

[ "task_categories:summarization", "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|other-MS^2", "source_datasets:extended|other-Cochrane", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-summarization #task_categories-text2text-generation #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other-MS^2 #source_datasets-extended|other-Cochrane #language-English #license-apache-2.0 #region-us \n" ]

[ "task_categories:summarization", "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|other-MS^2", "source_datasets:extended|other-Cochrane", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-summarization #task_categories-text2text-generation #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other-MS^2 #source_datasets-extended|other-Cochrane #language-English #license-apache-2.0 #region-us \n" ]

[ "task_categories:summarization", "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|other-MS^2", "source_datasets:extended|other-Cochrane", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-summarization #task_categories-text2text-generation #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other-MS^2 #source_datasets-extended|other-Cochrane #language-English #license-apache-2.0 #region-us \n" ]

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

[]

[]

[ "# Trojan Detection", "##### Detect and Analyze Trojan attacks on deep neural networks that are designed to be difficult to detect.", "### Overview\n\nNeural Trojans are a growing concern for the security of ML systems, but little is known about the fundamental offense-defense balance of Trojan detection. Early work suggests that standard Trojan attacks may be easy to detect, but recently it has been shown that in simple cases one can design practically undetectable Trojans.\n\nThis repository contains code for the Trojan Detection Challenge (TDC) NeurIPS 2022 competition.\n\nThere are 3 main tracks for this competition:\n- Trojan Detection Track: Given a dataset of Trojaned and clean networks spanning multiple data sources, build a Trojan detector that classifies a test set of networks with held-out labels (Trojan, clean). For more information, see here.\n\n- Trojan Analysis Track: Given a dataset of Trojaned networks spanning multiple data sources, predict various properties of Trojaned networks on a test set with held-out labels. This track has two subtracks: (1) target label prediction, (2) trigger synthesis. For more information, see here.\n\n- Evasive Trojans Track: Given a dataset of clean networks and a list of attack specifications, train a small set of Trojaned networks meeting the specifications and upload them to the evaluation server. The server will verify that the attack specifications are met, then train and evaluate a baseline Trojan detector using held-out clean networks and the submitted Trojaned networks. The task is to create Trojaned networks that are hard to detect. For more information, see here.\n\nThe competition has two rounds: In the primary round, participants will compete on the three main tracks. In the final round, the solution of the first-place team in the Evasive Trojans track will be used to train a new set of hard-to-detect Trojans, and participants will compete to detect these networks. For more information on the final round, see here.", "### Contents\n\nThere are four folders corresponding to different tracks and subtracks: 1) Trojan Detection, 2) Trojan Analysis (Target Label Prediction), 3) Trojan Analysis (Trigger Synthesis), and 4) Evasive Trojans. We provide starter code for submitting baselines in under each folder. The folder is expected to be under the same parent directory as . The datasets are available here. You can download them from the Zenodo website or by running .\n\nThe file contains helper functions for loading new models, generating new attack specifications, and training clean/Trojaned networks. This is primarily used for the Evasive Trojans Track starter kit. It also contains the load_data function for loading data sources (CIFAR-10/100, GTSRB, MNIST), which may be of general use. To load GTSRB images, unzip in the data folder (NOTE: This folder is only for storing data sources. The network datasets are stored in tdc_datasets, which must be downloaded from Zenodo). You may need to adjust the paths in the load_data function depending on your working directory. The file contains the definition of the Wide Residual Network class used for CIFAR-10 and CIFAR-100 models. When loading networks from the competition datasets, must be in your path. See the example submission notebooks for details.", "### Data\n\nUnlike standard machine learning tasks, the datasets consist of neural networks. That is, rather than making predictions on input images, goal will be identifying hidden functionality in neural networks. Networks are trained on four standard data sources: MNIST, CIFAR-10, CIFAR-100, and GTSRB. Variants of two standard Trojan attacks are used that are modified to be harder to detect. For the Detection Track, the training, validation, and test sets have 1,000 neural networks each. Networks are split evenly across all four data sources. Half of the networks are Trojaned, and there is a 50/50 split between the two attack types.", "## How to Use\n\nClone this repository, download the competition datasets from my HuggingFace repository and unzip adjacent to the repository. Ensure that Jupyter version is up-to-date (fairly recent). To avoid errors with model incompatibility, please use PyTorch version 1.11.0. Run one of the example notebooks or start building your own submission.", "### Additional Information", "#### Model Architectures and Data Sources\n\nNetworks have been trained on four standard data sources: MNIST, CIFAR-10, CIFAR-100, and GTSRB. GTSRB images are resized to 32x32.\n\nFor MNIST, convolutional networks have been used. For CIFAR-10 and CIFAR-100, Wide Residual Networks have been used. For GTSRB, Vision Transformers have been used.", "#### Trojan Attacks\n\nTrojaned networks have been trained with patch and whole-image attacks. These attacks are variants of the foundational BadNets and blended attacks modified to be harder to detect. These modified attacks use a simple change to the standard Trojan training procedure. Instead of training Trojaned networks from scratch, they were fine-tuned from the starting parameters of clean networks and regularize them with various similarity losses such that they are similar to the distribution of clean networks. Additionally, the networks have been trained to have high specificity for the particular trigger pattern associated with the attack. In extensive experiments, baseline detectors have been verified obtain substantially lower performance on these hard-to-detect Trojans.\n\nAll patch attacks in datasets use random trigger patterns sampled from an independent Bernoulli 0/1 distribution for each pixel and color channel (for Trojan detection and target label prediction, patches are black-and-white; for trigger synthesis, patches are colored). Each patch attack uses a different location and size for its trigger mask. All blended attacks in our datasets use random trigger trigger patterns sampled from an independent Uniform(0,1) distribution for each pixel and color channel. All attacks are all-to-one with a random target label. For more details, please see the starter kit. \n\nMNTD, Neural Cleanse, and ABS has been used as baseline Trojan detectors for participants to improve upon. These are well-known Trojan detectors from the academic literature, each with a distinct approach to Trojan detection. Also a specificity-based detector has been used as a baseline, since Trojan attacks with low specificity can be highly susceptible to such a detector. The specificity detector applies random triggers to inputs from a given data source, then runs these triggered inputs through the network in question. The negative entropy of the average posterior is used as a detection score. This leverages the fact that Trojan attacks without specificity are activated quite frequently by randomly sampled triggers." ]

[ "TAGS\n#license-mit #region-us \n", "# Trojan Detection", "##### Detect and Analyze Trojan attacks on deep neural networks that are designed to be difficult to detect.", "### Overview\n\nNeural Trojans are a growing concern for the security of ML systems, but little is known about the fundamental offense-defense balance of Trojan detection. Early work suggests that standard Trojan attacks may be easy to detect, but recently it has been shown that in simple cases one can design practically undetectable Trojans.\n\nThis repository contains code for the Trojan Detection Challenge (TDC) NeurIPS 2022 competition.\n\nThere are 3 main tracks for this competition:\n- Trojan Detection Track: Given a dataset of Trojaned and clean networks spanning multiple data sources, build a Trojan detector that classifies a test set of networks with held-out labels (Trojan, clean). For more information, see here.\n\n- Trojan Analysis Track: Given a dataset of Trojaned networks spanning multiple data sources, predict various properties of Trojaned networks on a test set with held-out labels. This track has two subtracks: (1) target label prediction, (2) trigger synthesis. For more information, see here.\n\n- Evasive Trojans Track: Given a dataset of clean networks and a list of attack specifications, train a small set of Trojaned networks meeting the specifications and upload them to the evaluation server. The server will verify that the attack specifications are met, then train and evaluate a baseline Trojan detector using held-out clean networks and the submitted Trojaned networks. The task is to create Trojaned networks that are hard to detect. For more information, see here.\n\nThe competition has two rounds: In the primary round, participants will compete on the three main tracks. In the final round, the solution of the first-place team in the Evasive Trojans track will be used to train a new set of hard-to-detect Trojans, and participants will compete to detect these networks. For more information on the final round, see here.", "### Contents\n\nThere are four folders corresponding to different tracks and subtracks: 1) Trojan Detection, 2) Trojan Analysis (Target Label Prediction), 3) Trojan Analysis (Trigger Synthesis), and 4) Evasive Trojans. We provide starter code for submitting baselines in under each folder. The folder is expected to be under the same parent directory as . The datasets are available here. You can download them from the Zenodo website or by running .\n\nThe file contains helper functions for loading new models, generating new attack specifications, and training clean/Trojaned networks. This is primarily used for the Evasive Trojans Track starter kit. It also contains the load_data function for loading data sources (CIFAR-10/100, GTSRB, MNIST), which may be of general use. To load GTSRB images, unzip in the data folder (NOTE: This folder is only for storing data sources. The network datasets are stored in tdc_datasets, which must be downloaded from Zenodo). You may need to adjust the paths in the load_data function depending on your working directory. The file contains the definition of the Wide Residual Network class used for CIFAR-10 and CIFAR-100 models. When loading networks from the competition datasets, must be in your path. See the example submission notebooks for details.", "### Data\n\nUnlike standard machine learning tasks, the datasets consist of neural networks. That is, rather than making predictions on input images, goal will be identifying hidden functionality in neural networks. Networks are trained on four standard data sources: MNIST, CIFAR-10, CIFAR-100, and GTSRB. Variants of two standard Trojan attacks are used that are modified to be harder to detect. For the Detection Track, the training, validation, and test sets have 1,000 neural networks each. Networks are split evenly across all four data sources. Half of the networks are Trojaned, and there is a 50/50 split between the two attack types.", "## How to Use\n\nClone this repository, download the competition datasets from my HuggingFace repository and unzip adjacent to the repository. Ensure that Jupyter version is up-to-date (fairly recent). To avoid errors with model incompatibility, please use PyTorch version 1.11.0. Run one of the example notebooks or start building your own submission.", "### Additional Information", "#### Model Architectures and Data Sources\n\nNetworks have been trained on four standard data sources: MNIST, CIFAR-10, CIFAR-100, and GTSRB. GTSRB images are resized to 32x32.\n\nFor MNIST, convolutional networks have been used. For CIFAR-10 and CIFAR-100, Wide Residual Networks have been used. For GTSRB, Vision Transformers have been used.", "#### Trojan Attacks\n\nTrojaned networks have been trained with patch and whole-image attacks. These attacks are variants of the foundational BadNets and blended attacks modified to be harder to detect. These modified attacks use a simple change to the standard Trojan training procedure. Instead of training Trojaned networks from scratch, they were fine-tuned from the starting parameters of clean networks and regularize them with various similarity losses such that they are similar to the distribution of clean networks. Additionally, the networks have been trained to have high specificity for the particular trigger pattern associated with the attack. In extensive experiments, baseline detectors have been verified obtain substantially lower performance on these hard-to-detect Trojans.\n\nAll patch attacks in datasets use random trigger patterns sampled from an independent Bernoulli 0/1 distribution for each pixel and color channel (for Trojan detection and target label prediction, patches are black-and-white; for trigger synthesis, patches are colored). Each patch attack uses a different location and size for its trigger mask. All blended attacks in our datasets use random trigger trigger patterns sampled from an independent Uniform(0,1) distribution for each pixel and color channel. All attacks are all-to-one with a random target label. For more details, please see the starter kit. \n\nMNTD, Neural Cleanse, and ABS has been used as baseline Trojan detectors for participants to improve upon. These are well-known Trojan detectors from the academic literature, each with a distinct approach to Trojan detection. Also a specificity-based detector has been used as a baseline, since Trojan attacks with low specificity can be highly susceptible to such a detector. The specificity detector applies random triggers to inputs from a given data source, then runs these triggered inputs through the network in question. The negative entropy of the average posterior is used as a detection score. This leverages the fact that Trojan attacks without specificity are activated quite frequently by randomly sampled triggers." ]

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

[]

[]

[]

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

[ "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: Summarization\n* Model: t5-base\n* Dataset: HadiPourmousa/TextSummarization\n* Config: HadiPourmousa--TextSummarization\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @marcmaxmeister for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: t5-base\n* Dataset: HadiPourmousa/TextSummarization\n* Config: HadiPourmousa--TextSummarization\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @marcmaxmeister 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: Summarization\n* Model: shivaniNK8/t5-small-finetuned-cnn-news\n* Dataset: HadiPourmousa/TextSummarization\n* Config: HadiPourmousa--TextSummarization\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @marcmaxmeister for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: shivaniNK8/t5-small-finetuned-cnn-news\n* Dataset: HadiPourmousa/TextSummarization\n* Config: HadiPourmousa--TextSummarization\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @marcmaxmeister for evaluating this model." ]

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:lad", "license:cc-by-4.0", "arxiv:2205.15599", "region:us" ]

[ "2205.15599" ]

[ "lad" ]

[ "# Şalom Ladino articles text corpus \n\nText corpus compiled from 397 articles from the Judeo-Espanyol section of Şalom newspaper. Original sentences and articles belong to Şalom. \n\nSize: 176,843 words\n\nOffical link\n\nPaper on ArXiv\n\nCitation:\n\n\n\nThis dataset is created as part of project \"Judeo-Spanish: Connecting the two ends of the Mediterranean\" carried out by Col·lectivaT and Sephardic Center of Istanbul within the framework of the “Grant Scheme for Common Cultural Heritage: Preservation and Dialogue between Turkey and the EU–II (CCH-II)” implemented by the Ministry of Culture and Tourism of the Republic of Turkey with the financial support of the European Union. The content of this website is the sole responsibility of Col·lectivaT and does not necessarily reflect the views of the European Union." ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Ladino #license-cc-by-4.0 #arxiv-2205.15599 #region-us \n", "# Şalom Ladino articles text corpus \n\nText corpus compiled from 397 articles from the Judeo-Espanyol section of Şalom newspaper. Original sentences and articles belong to Şalom. \n\nSize: 176,843 words\n\nOffical link\n\nPaper on ArXiv\n\nCitation:\n\n\n\nThis dataset is created as part of project \"Judeo-Spanish: Connecting the two ends of the Mediterranean\" carried out by Col·lectivaT and Sephardic Center of Istanbul within the framework of the “Grant Scheme for Common Cultural Heritage: Preservation and Dialogue between Turkey and the EU–II (CCH-II)” implemented by the Ministry of Culture and Tourism of the Republic of Turkey with the financial support of the European Union. The content of this website is the sole responsibility of Col·lectivaT and does not necessarily reflect the views of the European Union." ]

[ "task_categories:text-generation", "task_categories:translation", "task_ids:language-modeling", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:lad", "language:es", "language:tr", "language:en", "license:cc-by-4.0", "arxiv:2205.15599", "region:us" ]

[ "2205.15599" ]

[ "lad", "es", "tr", "en" ]

[ "# Una fraza al diya\n\nLadino language learning sentences prepared by Karen Sarhon of Sephardic Center of Istanbul. Each sentence has translations in Turkish, English, Spanish. Includes audio and image. 307 sentences in total.\n\nSource: URL\n\nImages and audio: URL \n\nOffical link on Ladino Data Hub\n\nPaper on ArXiv\n\nCitation:\n\n\n\nThis dataset is created as part of project \"Judeo-Spanish: Connecting the two ends of the Mediterranean\" carried out by Col·lectivaT and Sephardic Center of Istanbul within the framework of the “Grant Scheme for Common Cultural Heritage: Preservation and Dialogue between Turkey and the EU–II (CCH-II)” implemented by the Ministry of Culture and Tourism of the Republic of Turkey with the financial support of the European Union. The content of this website is the sole responsibility of Col·lectivaT and does not necessarily reflect the views of the European Union." ]

[ "TAGS\n#task_categories-text-generation #task_categories-translation #task_ids-language-modeling #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-Ladino #language-Spanish #language-Turkish #language-English #license-cc-by-4.0 #arxiv-2205.15599 #region-us \n", "# Una fraza al diya\n\nLadino language learning sentences prepared by Karen Sarhon of Sephardic Center of Istanbul. Each sentence has translations in Turkish, English, Spanish. Includes audio and image. 307 sentences in total.\n\nSource: URL\n\nImages and audio: URL \n\nOffical link on Ladino Data Hub\n\nPaper on ArXiv\n\nCitation:\n\n\n\nThis dataset is created as part of project \"Judeo-Spanish: Connecting the two ends of the Mediterranean\" carried out by Col·lectivaT and Sephardic Center of Istanbul within the framework of the “Grant Scheme for Common Cultural Heritage: Preservation and Dialogue between Turkey and the EU–II (CCH-II)” implemented by the Ministry of Culture and Tourism of the Republic of Turkey with the financial support of the European Union. The content of this website is the sole responsibility of Col·lectivaT and does not necessarily reflect the views of the European Union." ]

[ "task_categories:text2text-generation", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:code", "license:mit", "code-generation", "arxiv:1805.08949", "region:us" ]

[ "1805.08949" ]

[ "code" ]

[ "### Dataset Summary\n\n\nCoNaLa is a benchmark of code and natural language pairs, for the evaluation of code generation tasks. The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators, split into 2,379 training and 500 test examples. The automatically mined dataset is also available with almost 600k examples.", "### Supported Tasks and Leaderboards\n\n\nThis dataset is used to evaluate code generations.", "### Languages\n\n\nEnglish - Python code.\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### CoNaLa - curated\n\n\nThis is the curated dataset by annotators", "#### CoNaLa - mined\n\n\nThis is the automatically mined dataset before curation", "### Data Fields\n\n\nCurated:\n\n\nField: question\\_id, Type: int64, Description: Id of the Stack Overflow question\nField: intent, Type: string, Description: Natural Language intent (i.e., the title of a Stack Overflow question)\nField: rewritten\\_intent, Type: string, Description: Crowdsourced revised intents that try to better reflect the full meaning of the code\nField: snippet, Type: string, Description: Code snippet that implements the intent\n\n\nMined:\n\n\nField: question\\_id, Type: int64, Description: Id of the Stack Overflow question\nField: parent\\_answer\\_post\\_id, Type: int64, Description: Id of the answer post from which the candidate snippet is extracted\nField: intent, Type: string, Description: Natural Language intent (i.e., the title of a Stack Overflow question)\nField: snippet, Type: string, Description: Code snippet that implements the intent\nField: id, Type: string, Description: Unique id for this intent/snippet pair\nField: prob, Type: float64, Description: Probability given by the mining model", "### Data Splits\n\n\nThere are two version of the dataset (curated and mined), mined only has a train split and curated has two splits: train and test.\n\n\nDataset Creation\n----------------\n\n\nThe dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators. For more details, please refer to the original paper" ]

[ "TAGS\n#task_categories-text2text-generation #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-code #license-mit #code-generation #arxiv-1805.08949 #region-us \n", "### Dataset Summary\n\n\nCoNaLa is a benchmark of code and natural language pairs, for the evaluation of code generation tasks. The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators, split into 2,379 training and 500 test examples. The automatically mined dataset is also available with almost 600k examples.", "### Supported Tasks and Leaderboards\n\n\nThis dataset is used to evaluate code generations.", "### Languages\n\n\nEnglish - Python code.\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### CoNaLa - curated\n\n\nThis is the curated dataset by annotators", "#### CoNaLa - mined\n\n\nThis is the automatically mined dataset before curation", "### Data Fields\n\n\nCurated:\n\n\nField: question\\_id, Type: int64, Description: Id of the Stack Overflow question\nField: intent, Type: string, Description: Natural Language intent (i.e., the title of a Stack Overflow question)\nField: rewritten\\_intent, Type: string, Description: Crowdsourced revised intents that try to better reflect the full meaning of the code\nField: snippet, Type: string, Description: Code snippet that implements the intent\n\n\nMined:\n\n\nField: question\\_id, Type: int64, Description: Id of the Stack Overflow question\nField: parent\\_answer\\_post\\_id, Type: int64, Description: Id of the answer post from which the candidate snippet is extracted\nField: intent, Type: string, Description: Natural Language intent (i.e., the title of a Stack Overflow question)\nField: snippet, Type: string, Description: Code snippet that implements the intent\nField: id, Type: string, Description: Unique id for this intent/snippet pair\nField: prob, Type: float64, Description: Probability given by the mining model", "### Data Splits\n\n\nThere are two version of the dataset (curated and mined), mined only has a train split and curated has two splits: train and test.\n\n\nDataset Creation\n----------------\n\n\nThe dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators. For more details, please refer to the original paper" ]

[ "task_categories:summarization", "task_ids:news-articles-summarization", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-summarization #task_ids-news-articles-summarization #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-other #region-us \n" ]

[ "task_categories:summarization", "task_ids:news-articles-summarization", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-summarization #task_ids-news-articles-summarization #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-other #region-us \n" ]

[ "task_categories:summarization", "task_ids:news-articles-summarization", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-summarization #task_ids-news-articles-summarization #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-other #region-us \n" ]

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

[]

[]

[]

[ "TAGS\n#license-apache-2.0 #region-us \n" ]

[ "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: Summarization\n* Model: SamuelAllen123/t5-efficient-large-nl36_sum\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @samuelfipps123 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: SamuelAllen123/t5-efficient-large-nl36_sum\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @samuelfipps123 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: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V12\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V12\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "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: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V12\n* Dataset: big_patent\n* Config: y\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V12\n* Dataset: big_patent\n* Config: y\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "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: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V12\n* Dataset: billsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V12\n* Dataset: billsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "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: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V12\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V12\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "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: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V12\n* Dataset: launch/gov_report\n* Config: plain_text\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V12\n* Dataset: launch/gov_report\n* Config: plain_text\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "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: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V12\n* Dataset: Blaise-g/PubMed_summ\n* Config: Blaise-g--PubMed_summ\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V12\n* Dataset: Blaise-g/PubMed_summ\n* Config: Blaise-g--PubMed_summ\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "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: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V12\n* Dataset: ccdv/arxiv-summarization\n* Config: document\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V12\n* Dataset: ccdv/arxiv-summarization\n* Config: document\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "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: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V12\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V12\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "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: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "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: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP11\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP11\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "task_categories:question-answering", "task_ids:closed-domain-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:n<1K", "source_datasets:original", "language:as", "language:bn", "language:gu", "language:hi", "language:kn", "language:ml", "language:mr", "language:or", "language:pa", "language:ta", "language:te", "license:cc-by-4.0", "region:us" ]

[]

[ "as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te" ]

[ "# 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#task_categories-question-answering #task_ids-closed-domain-qa #annotations_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-n<1K #source_datasets-original #language-Assamese #language-Bengali #language-Gujarati #language-Hindi #language-Kannada #language-Malayalam #language-Marathi #language-Oriya (macrolanguage) #language-Panjabi #language-Tamil #language-Telugu #license-cc-by-4.0 #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." ]

[ "multilinguality:monolingual", "language:es", "license:mit", "region:us" ]

[]

[ "es" ]

[ "# Dataset Card for Unannotated Spanish 3 Billion Words Corpora", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n - Source Data\n - Data Subset\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n* Number of lines: 300904000 (300M)\n* Number of tokens: 2996016962 (3B)\n* Number of chars: 18431160978 (18.4B)", "### Languages\n* Spanish", "### Source Data\n* Available to download here: Zenodo", "### Data Subset\n* Spanish Wikis: Wich include Wikipedia, Wikinews, Wikiquotes and more. These were first processed with wikiextractor (URL using the wikis dump of 20/04/2019.\n* ParaCrawl: Spanish portion of ParaCrawl (URL\n* EUBookshop: Spanish portion of EUBookshop (URL\n* MultiUN: Spanish portion of MultiUN (URL\n* OpenSubtitles: Spanish portion of OpenSubtitles2018 (URL\n* DGC: Spanish portion of DGT (URL\n* DOGC: Spanish portion of DOGC (URL\n* ECB: Spanish portion of ECB (URL\n* EMEA: Spanish portion of EMEA (URL\n* Europarl: Spanish portion of Europarl (URL\n* GlobalVoices: Spanish portion of GlobalVoices (URL\n* JRC: Spanish portion of JRC (URL\n* News-Commentary11: Spanish portion of NCv11 (URL\n* TED: Spanish portion of TED (URL\n* UN: Spanish portion of UN (URL", "## Additional Information", "### Licensing Information\n\n* MIT Licence" ]

[ "TAGS\n#multilinguality-monolingual #language-Spanish #license-mit #region-us \n", "# Dataset Card for Unannotated Spanish 3 Billion Words Corpora", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n - Source Data\n - Data Subset\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n* Number of lines: 300904000 (300M)\n* Number of tokens: 2996016962 (3B)\n* Number of chars: 18431160978 (18.4B)", "### Languages\n* Spanish", "### Source Data\n* Available to download here: Zenodo", "### Data Subset\n* Spanish Wikis: Wich include Wikipedia, Wikinews, Wikiquotes and more. These were first processed with wikiextractor (URL using the wikis dump of 20/04/2019.\n* ParaCrawl: Spanish portion of ParaCrawl (URL\n* EUBookshop: Spanish portion of EUBookshop (URL\n* MultiUN: Spanish portion of MultiUN (URL\n* OpenSubtitles: Spanish portion of OpenSubtitles2018 (URL\n* DGC: Spanish portion of DGT (URL\n* DOGC: Spanish portion of DOGC (URL\n* ECB: Spanish portion of ECB (URL\n* EMEA: Spanish portion of EMEA (URL\n* Europarl: Spanish portion of Europarl (URL\n* GlobalVoices: Spanish portion of GlobalVoices (URL\n* JRC: Spanish portion of JRC (URL\n* News-Commentary11: Spanish portion of NCv11 (URL\n* TED: Spanish portion of TED (URL\n* UN: Spanish portion of UN (URL", "## Additional Information", "### Licensing Information\n\n* MIT Licence" ]

[ "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: Question Answering\n* Model: Adrian/distilbert-base-uncased-finetuned-squad-colab\n* Dataset: squad_v2\n* Config: squad_v2\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: Question Answering\n* Model: Adrian/distilbert-base-uncased-finetuned-squad-colab\n* Dataset: squad_v2\n* Config: squad_v2\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" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: 21iridescent/distilbert-base-uncased-finetuned-squad\n* Dataset: squad_v2\n* Config: squad_v2\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: Question Answering\n* Model: 21iridescent/distilbert-base-uncased-finetuned-squad\n* Dataset: squad_v2\n* Config: squad_v2\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" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: Akari/albert-base-v2-finetuned-squad\n* Dataset: squad_v2\n* Config: squad_v2\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: Question Answering\n* Model: Akari/albert-base-v2-finetuned-squad\n* Dataset: squad_v2\n* Config: squad_v2\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" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: 21iridescent/distilroberta-base-finetuned-squad2-lwt\n* Dataset: squad_v2\n* Config: squad_v2\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: Question Answering\n* Model: 21iridescent/distilroberta-base-finetuned-squad2-lwt\n* Dataset: squad_v2\n* Config: squad_v2\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" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: Akihiro2/bert-finetuned-squad\n* Dataset: squad_v2\n* Config: squad_v2\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: Question Answering\n* Model: Akihiro2/bert-finetuned-squad\n* Dataset: squad_v2\n* Config: squad_v2\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" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: AyushPJ/test-squad-trained-finetuned-squad\n* Dataset: squad_v2\n* Config: squad_v2\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: Question Answering\n* Model: AyushPJ/test-squad-trained-finetuned-squad\n* Dataset: squad_v2\n* Config: squad_v2\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" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: haritzpuerto/MiniLM-L12-H384-uncased-squad\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @timbmg for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: haritzpuerto/MiniLM-L12-H384-uncased-squad\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @timbmg 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: Question Answering\n* Model: mrm8488/longformer-base-4096-finetuned-squadv2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Liam-Scott-Russell for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: mrm8488/longformer-base-4096-finetuned-squadv2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Liam-Scott-Russell for evaluating this model." ]

[ "ANDDigest", "ANDSystem", "PubMed", "arxiv:2007.15779", "region:us" ]

[ "2007.15779" ]

[]

[]

[ "TAGS\n#ANDDigest #ANDSystem #PubMed #arxiv-2007.15779 #region-us \n" ]

[ "ANDDigest", "ANDSystem", "PubMed", "arxiv:2007.15779", "region:us" ]

[ "2007.15779" ]

[]

[]

[ "TAGS\n#ANDDigest #ANDSystem #PubMed #arxiv-2007.15779 #region-us \n" ]

[ "ANDDigest", "ANDSystem", "PubMed", "arxiv:2007.15779", "region:us" ]

[ "2007.15779" ]

[]

[]

[ "TAGS\n#ANDDigest #ANDSystem #PubMed #arxiv-2007.15779 #region-us \n" ]

[ "ANDDigest", "ANDSystem", "PubMed", "arxiv:2007.15779", "region:us" ]

[ "2007.15779" ]

[]

[]

[ "TAGS\n#ANDDigest #ANDSystem #PubMed #arxiv-2007.15779 #region-us \n" ]

[ "ANDDigest", "ANDSystem", "PubMed", "arxiv:2007.15779", "region:us" ]

[ "2007.15779" ]

[]

[]

[ "TAGS\n#ANDDigest #ANDSystem #PubMed #arxiv-2007.15779 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Data card for Internet Archive historic book pages unlabelled.\n\n\n- '10,844,387' unlabelled pages from historical books from the internet archive.\n- Intended to be used for:\n - pre-training computer vision models in an unsupervised manner\n - using weak supervision to generate labels" ]

[ "TAGS\n#region-us \n", "# Data card for Internet Archive historic book pages unlabelled.\n\n\n- '10,844,387' unlabelled pages from historical books from the internet archive.\n- Intended to be used for:\n - pre-training computer vision models in an unsupervised manner\n - using weak supervision to generate labels" ]

[ "region:us" ]

[]

[]

[]

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

[ "language:en", "region:us" ]

[]

[ "en" ]

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\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:\n\n\n\nGitHub Link to this project : Telegram Trade Msg Backtest ML\n============================================================\n\n\nNeed custom model for your application? : Place a order on URL : Custom Token Classification or Named Entity Recognition (NER) model as in Natural Language Processing (NLP) Machine Learning\n=============================================================================================================================================================================================\n\n\nWhat this repository contains? :\n--------------------------------\n\n\n1. Label data using LabelStudio NER(Named Entity Recognition or Token Classification) tool.\n!Screenshot from 2022-09-30 12-28-50 convert to !Screenshot from 2022-09-30 18-59-14\n2. Convert LabelStudio CSV or JSON to HuggingFace-autoTrain dataset conversion script\n!Screenshot from 2022-10-01 10-36-03\n3. Train NER model on Hugginface-autoTrain.\n!Screenshot from 2022-10-01 10-38-24\n4. Use Hugginface-autoTrain model to predict labels on new data in LabelStudio using LabelStudio-ML-Backend.\n!Screenshot from 2022-10-01 10-41-07\n!Screenshot from 2022-10-01 10-42-36\n!Screenshot from 2022-10-01 10-44-56\n5. Define python function to predict labels using Hugginface-autoTrain model.\n!Screenshot from 2022-10-01 10-47-08\n!Screenshot from 2022-10-01 10-47-25\n6. Only label new data from newly predicted-labels-dataset that has falsified labels.\n!Screenshot from 2022-09-30 22-47-23\n7. Backtest Truely labelled dataset against real historical data of the stock using zerodha kiteconnect and jugaad\\_trader.\n!Screenshot from 2022-10-01 00-05-55\n8. Evaluate total gained percentage since inception summation-wise and compounded and plot.\n!Screenshot from 2022-10-01 00-06-59\n9. Listen to telegram channel for new LIVE messages using telegram API for algotrading.\n!Screenshot from 2022-10-01 00-09-29\n10. Serve the app as flask web API for web request and respond to it as labelled tokens.\n!Screenshot from 2022-10-01 00-12-12\n11. Outperforming or underperforming results of the telegram channel tips against exchange index by percentage.\n!Screenshot from 2022-10-01 11-16-27\n\n\nPlace a custom order on URL : URL\n\n\n\n\n---", "### Contact us\n\n\nMobile : +917016525813\nWhatsapp & Telegram : +919409077371\n\n\nEmail : hemangjoshi37a@URL\n\n\nPlace a custom order on URL : URL\n\n\nPlease contribute your suggestions and corections to support our efforts.\n\n\nThank you.\n\n\nBuy us a coffee for $5 on PayPal ?\n\n\n![paypal](URL\n\n\n\n\n---", "### Checkout Our Other Repositories\n\n\n* pyPortMan\n* transformers\\_stock\\_prediction\n* TrendMaster\n* hjAlgos\\_notebooks\n* AutoCut\n* My\\_Projects\n* Cool Arduino and ESP8266 or NodeMCU Projects\n* Telegram Trade Msg Backtest ML", "### Checkout Our Other Products\n\n\n* WiFi IoT LED Matrix Display\n* SWiBoard WiFi Switch Board IoT Device\n* Electric Bicycle\n* Product 3D Design Service with Solidworks\n* AutoCut : Automatic Wire Cutter Machine\n* Custom AlgoTrading Software Coding Services\n* SWiBoard :Tasmota MQTT Control App\n* Custom Token Classification or Named Entity Recognition (NER) model as in Natural Language Processing (NLP) Machine Learning\n\n\nSome Cool Arduino and ESP8266 (or NodeMCU) IoT projects:\n--------------------------------------------------------\n\n\n* IoT\\_LED\\_over\\_ESP8266\\_NodeMCU : Turn LED on and off using web server hosted on a nodemcu or esp8266\n* ESP8266\\_NodeMCU\\_BasicOTA : Simple OTA (Over The Air) upload code from Arduino IDE using WiFi to NodeMCU or ESP8266\n* IoT\\_CSV\\_SD : Read analog value of Voltage and Current and write it to SD Card in CSV format for Arduino, ESP8266, NodeMCU etc\n* Honeywell\\_I2C\\_Datalogger : Log data in A SD Card from a Honeywell I2C HIH8000 or HIH6000 series sensor having external I2C RTC clock\n* IoT\\_Load\\_Cell\\_using\\_ESP8266\\_NodeMC : Read ADC value from High Precision 12bit ADS1015 ADC Sensor and Display on SSD1306 SPI Display as progress bar for Arduino or ESP8266 or NodeMCU\n* IoT\\_SSD1306\\_ESP8266\\_NodeMCU : Read from High Precision 12bit ADC seonsor ADS1015 and display to SSD1306 SPI as progress bar in ESP8266 or NodeMCU or Arduino\n\n\nCheckout Our Awesome 3D GrabCAD Models:\n---------------------------------------\n\n\n* AutoCut : Automatic Wire Cutter Machine\n* ESP Matrix Display 5mm Acrylic Box\n* Arcylic Bending Machine w/ Hot Air Gun\n* Automatic Wire Cutter/Stripper\n\n\nOur HuggingFace Models :\n------------------------\n\n\n* hemangjoshi37a/autotrain-ratnakar\\_1000\\_sample\\_curated-1474454086 : Stock tip message NER(Named Entity Recognition or Token Classification) using HUggingFace-AutoTrain and LabelStudio and Ratnakar Securities Pvt. Ltd.\n\n\nOur HuggingFace Datasets :\n--------------------------\n\n\n* hemangjoshi37a/autotrain-data-ratnakar\\_1000\\_sample\\_curated : Stock tip message NER(Named Entity Recognition or Token Classification) using HUggingFace-AutoTrain and LabelStudio and Ratnakar Securities Pvt. Ltd.\n\n\nWe sell Gigs on Fiverr :\n------------------------\n\n\n* code android and ios app for you using flutter firebase software stack\n* code custom algotrading software for zerodha or angel broking\n\n\nAwesome Fiverr. Gigs:\n---------------------\n\n\n* develop machine learning ner model as in nlp using python\n* train custom chatgpt question answering model\n* build algotrading, backtesting and stock monitoring tools using python\n* tutor you in your science problems\n* make apps for you crossplatform" ]

[ "TAGS\n#language-English #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\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:\n\n\n\nGitHub Link to this project : Telegram Trade Msg Backtest ML\n============================================================\n\n\nNeed custom model for your application? : Place a order on URL : Custom Token Classification or Named Entity Recognition (NER) model as in Natural Language Processing (NLP) Machine Learning\n=============================================================================================================================================================================================\n\n\nWhat this repository contains? :\n--------------------------------\n\n\n1. Label data using LabelStudio NER(Named Entity Recognition or Token Classification) tool.\n!Screenshot from 2022-09-30 12-28-50 convert to !Screenshot from 2022-09-30 18-59-14\n2. Convert LabelStudio CSV or JSON to HuggingFace-autoTrain dataset conversion script\n!Screenshot from 2022-10-01 10-36-03\n3. Train NER model on Hugginface-autoTrain.\n!Screenshot from 2022-10-01 10-38-24\n4. Use Hugginface-autoTrain model to predict labels on new data in LabelStudio using LabelStudio-ML-Backend.\n!Screenshot from 2022-10-01 10-41-07\n!Screenshot from 2022-10-01 10-42-36\n!Screenshot from 2022-10-01 10-44-56\n5. Define python function to predict labels using Hugginface-autoTrain model.\n!Screenshot from 2022-10-01 10-47-08\n!Screenshot from 2022-10-01 10-47-25\n6. Only label new data from newly predicted-labels-dataset that has falsified labels.\n!Screenshot from 2022-09-30 22-47-23\n7. Backtest Truely labelled dataset against real historical data of the stock using zerodha kiteconnect and jugaad\\_trader.\n!Screenshot from 2022-10-01 00-05-55\n8. Evaluate total gained percentage since inception summation-wise and compounded and plot.\n!Screenshot from 2022-10-01 00-06-59\n9. Listen to telegram channel for new LIVE messages using telegram API for algotrading.\n!Screenshot from 2022-10-01 00-09-29\n10. Serve the app as flask web API for web request and respond to it as labelled tokens.\n!Screenshot from 2022-10-01 00-12-12\n11. Outperforming or underperforming results of the telegram channel tips against exchange index by percentage.\n!Screenshot from 2022-10-01 11-16-27\n\n\nPlace a custom order on URL : URL\n\n\n\n\n---", "### Contact us\n\n\nMobile : +917016525813\nWhatsapp & Telegram : +919409077371\n\n\nEmail : hemangjoshi37a@URL\n\n\nPlace a custom order on URL : URL\n\n\nPlease contribute your suggestions and corections to support our efforts.\n\n\nThank you.\n\n\nBuy us a coffee for $5 on PayPal ?\n\n\n![paypal](URL\n\n\n\n\n---", "### Checkout Our Other Repositories\n\n\n* pyPortMan\n* transformers\\_stock\\_prediction\n* TrendMaster\n* hjAlgos\\_notebooks\n* AutoCut\n* My\\_Projects\n* Cool Arduino and ESP8266 or NodeMCU Projects\n* Telegram Trade Msg Backtest ML", "### Checkout Our Other Products\n\n\n* WiFi IoT LED Matrix Display\n* SWiBoard WiFi Switch Board IoT Device\n* Electric Bicycle\n* Product 3D Design Service with Solidworks\n* AutoCut : Automatic Wire Cutter Machine\n* Custom AlgoTrading Software Coding Services\n* SWiBoard :Tasmota MQTT Control App\n* Custom Token Classification or Named Entity Recognition (NER) model as in Natural Language Processing (NLP) Machine Learning\n\n\nSome Cool Arduino and ESP8266 (or NodeMCU) IoT projects:\n--------------------------------------------------------\n\n\n* IoT\\_LED\\_over\\_ESP8266\\_NodeMCU : Turn LED on and off using web server hosted on a nodemcu or esp8266\n* ESP8266\\_NodeMCU\\_BasicOTA : Simple OTA (Over The Air) upload code from Arduino IDE using WiFi to NodeMCU or ESP8266\n* IoT\\_CSV\\_SD : Read analog value of Voltage and Current and write it to SD Card in CSV format for Arduino, ESP8266, NodeMCU etc\n* Honeywell\\_I2C\\_Datalogger : Log data in A SD Card from a Honeywell I2C HIH8000 or HIH6000 series sensor having external I2C RTC clock\n* IoT\\_Load\\_Cell\\_using\\_ESP8266\\_NodeMC : Read ADC value from High Precision 12bit ADS1015 ADC Sensor and Display on SSD1306 SPI Display as progress bar for Arduino or ESP8266 or NodeMCU\n* IoT\\_SSD1306\\_ESP8266\\_NodeMCU : Read from High Precision 12bit ADC seonsor ADS1015 and display to SSD1306 SPI as progress bar in ESP8266 or NodeMCU or Arduino\n\n\nCheckout Our Awesome 3D GrabCAD Models:\n---------------------------------------\n\n\n* AutoCut : Automatic Wire Cutter Machine\n* ESP Matrix Display 5mm Acrylic Box\n* Arcylic Bending Machine w/ Hot Air Gun\n* Automatic Wire Cutter/Stripper\n\n\nOur HuggingFace Models :\n------------------------\n\n\n* hemangjoshi37a/autotrain-ratnakar\\_1000\\_sample\\_curated-1474454086 : Stock tip message NER(Named Entity Recognition or Token Classification) using HUggingFace-AutoTrain and LabelStudio and Ratnakar Securities Pvt. Ltd.\n\n\nOur HuggingFace Datasets :\n--------------------------\n\n\n* hemangjoshi37a/autotrain-data-ratnakar\\_1000\\_sample\\_curated : Stock tip message NER(Named Entity Recognition or Token Classification) using HUggingFace-AutoTrain and LabelStudio and Ratnakar Securities Pvt. Ltd.\n\n\nWe sell Gigs on Fiverr :\n------------------------\n\n\n* code android and ios app for you using flutter firebase software stack\n* code custom algotrading software for zerodha or angel broking\n\n\nAwesome Fiverr. Gigs:\n---------------------\n\n\n* develop machine learning ner model as in nlp using python\n* train custom chatgpt question answering model\n* build algotrading, backtesting and stock monitoring tools using python\n* tutor you in your science problems\n* make apps for you crossplatform" ]

[ "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: autoevaluate/zero-shot-classification\n* Dataset: autoevaluate/zero-shot-classification-sample\n* Config: autoevaluate--zero-shot-classification-sample\n* Split: test\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: Zero-Shot Text Classification\n* Model: autoevaluate/zero-shot-classification\n* Dataset: autoevaluate/zero-shot-classification-sample\n* Config: autoevaluate--zero-shot-classification-sample\n* Split: test\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" ]

[]

[]

[ "# 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: autoevaluate/zero-shot-classification\n* Dataset: Tristan/zero_shot_classification_test\n* Config: Tristan--zero_shot_classification_test\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Tristan 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: autoevaluate/zero-shot-classification\n* Dataset: Tristan/zero_shot_classification_test\n* Config: Tristan--zero_shot_classification_test\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Tristan 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:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:extended|openwebtext", "language:en", "license:cc0-1.0", "region:us" ]

[]

[ "en" ]

[ "# openwebtext_20p", "## Dataset Description\n- Origin: openwebtext\n- Download Size 4.60 GiB\n- Generated Size 7.48 GiB\n- Total Size 12.08 GiB\n\nfirst 20% of openwebtext" ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-extended|openwebtext #language-English #license-cc0-1.0 #region-us \n", "# openwebtext_20p", "## Dataset Description\n- Origin: openwebtext\n- Download Size 4.60 GiB\n- Generated Size 7.48 GiB\n- Total Size 12.08 GiB\n\nfirst 20% of openwebtext" ]

[ "task_categories:text-classification", "task_ids:text-scoring", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:sv", "license:cc-by-4.0", "lexicon", "swedish", "CEFR", "region:us" ]

[]

[ "sv" ]

[ "# Dataset Card for Kelly\n\nKeywords for Language Learning for Young and adults alike", "## 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- Additional Information\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Paper: URL", "### Dataset Summary\n\nThe Swedish Kelly list is a freely available frequency-based vocabulary list\nthat comprises general-purpose language of modern Swedish. The list was\ngenerated from a large web-acquired corpus (SweWaC) of 114 million words\ndating from the 2010s. It is adapted to the needs of language learners and\ncontains 8,425 most frequent lemmas that cover 80% of SweWaC.", "### Languages\n\nSwedish (sv-SE)", "## Dataset Structure", "### Data Instances\n\nHere is a sample of the data:\n\n\n\nThis can be understood as:\n\n> The common noun \"dag\" (\"day\") has a rank of 190 in the list. It was used 117,835\ntimes in SweWaC, meaning it occured 1033.61 times per million words. This word\nis among the most important vocabulary words for Swedish language learners and\nshould be learned at the A1 CEFR level. An example usage of this word is the\nphrase \"god dag\" (\"good day\").", "### Data Fields\n\n- 'id': The row number for the data entry, starting at 1. Generally corresponds\n to the rank of the word.\n- 'raw_frequency': The raw frequency of the word.\n- 'relative_frequency': The relative frequency of the word measured in\n number of occurences per million words.\n- 'cefr_level': The CEFR level (A1, A2, B1, B2, C1, C2) of the word.\n- 'source': Whether the word came from SweWaC, translation lists (T2), or\n was manually added (manual).\n- 'marker': The grammatical marker of the word, if any, such as an article or\n infinitive marker.\n- 'lemma': The lemma of the word, sometimes provided with its spelling or\n stylistic variants.\n- 'pos': The word's part-of-speech.\n- 'examples': Usage examples and comments. Only available for some of the words.\n\nManual entries were prepended to the list, giving them a higher rank than they\nmight otherwise have had. For example, the manual entry \"Göteborg (\"Gothenberg\")\nhas a rank of 20, while the first non-manual entry \"och\" (\"and\") has a rank of\n87. However, a conjunction and common stopword is far more likely to occur than\nthe name of a city.", "### Data Splits\n\nThere is a single split, 'train'.", "## Dataset Creation\n\nPlease refer to the article Corpus-based approaches for the creation of a frequency\nbased vocabulary list in the EU project KELLY – issues on reliability, validity and\ncoverage for information about how\nthe original dataset was created and considerations for using the data.\n\nThe following changes have been made to the original dataset:\n\n- Changed header names.\n- Normalized the large web-acquired corpus name to \"SweWac\" in the 'source' field.\n- Set the relative frequency of manual entries to null rather than 1000000.", "## Additional Information", "### Licensing Information\n\nCC BY 4.0\n\n\n\nPlease cite the authors if you use this dataset in your work:", "### Contributions\n\nThanks to @spraakbanken for creating this dataset\nand to @codesue for adding it." ]

[ "TAGS\n#task_categories-text-classification #task_ids-text-scoring #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #language-Swedish #license-cc-by-4.0 #lexicon #swedish #CEFR #region-us \n", "# Dataset Card for Kelly\n\nKeywords for Language Learning for Young and adults alike", "## 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- Additional Information\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Paper: URL", "### Dataset Summary\n\nThe Swedish Kelly list is a freely available frequency-based vocabulary list\nthat comprises general-purpose language of modern Swedish. The list was\ngenerated from a large web-acquired corpus (SweWaC) of 114 million words\ndating from the 2010s. It is adapted to the needs of language learners and\ncontains 8,425 most frequent lemmas that cover 80% of SweWaC.", "### Languages\n\nSwedish (sv-SE)", "## Dataset Structure", "### Data Instances\n\nHere is a sample of the data:\n\n\n\nThis can be understood as:\n\n> The common noun \"dag\" (\"day\") has a rank of 190 in the list. It was used 117,835\ntimes in SweWaC, meaning it occured 1033.61 times per million words. This word\nis among the most important vocabulary words for Swedish language learners and\nshould be learned at the A1 CEFR level. An example usage of this word is the\nphrase \"god dag\" (\"good day\").", "### Data Fields\n\n- 'id': The row number for the data entry, starting at 1. Generally corresponds\n to the rank of the word.\n- 'raw_frequency': The raw frequency of the word.\n- 'relative_frequency': The relative frequency of the word measured in\n number of occurences per million words.\n- 'cefr_level': The CEFR level (A1, A2, B1, B2, C1, C2) of the word.\n- 'source': Whether the word came from SweWaC, translation lists (T2), or\n was manually added (manual).\n- 'marker': The grammatical marker of the word, if any, such as an article or\n infinitive marker.\n- 'lemma': The lemma of the word, sometimes provided with its spelling or\n stylistic variants.\n- 'pos': The word's part-of-speech.\n- 'examples': Usage examples and comments. Only available for some of the words.\n\nManual entries were prepended to the list, giving them a higher rank than they\nmight otherwise have had. For example, the manual entry \"Göteborg (\"Gothenberg\")\nhas a rank of 20, while the first non-manual entry \"och\" (\"and\") has a rank of\n87. However, a conjunction and common stopword is far more likely to occur than\nthe name of a city.", "### Data Splits\n\nThere is a single split, 'train'.", "## Dataset Creation\n\nPlease refer to the article Corpus-based approaches for the creation of a frequency\nbased vocabulary list in the EU project KELLY – issues on reliability, validity and\ncoverage for information about how\nthe original dataset was created and considerations for using the data.\n\nThe following changes have been made to the original dataset:\n\n- Changed header names.\n- Normalized the large web-acquired corpus name to \"SweWac\" in the 'source' field.\n- Set the relative frequency of manual entries to null rather than 1000000.", "## Additional Information", "### Licensing Information\n\nCC BY 4.0\n\n\n\nPlease cite the authors if you use this dataset in your work:", "### Contributions\n\nThanks to @spraakbanken for creating this dataset\nand to @codesue for adding it." ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "multilinguality:monolingual", "size_categories:100K<n<200K", "size_categories:300K<n<400K", "language:en", "license:mit", "pii-detection", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nA synthetic PII dataset generated using Privy, a tool which parses OpenAPI specifications and generates synthetic request payloads, searching for keywords in API schema definitions to select appropriate data providers. Generated API payloads are converted to various protocol trace formats like JSON and SQL to approximate the data developers might encounter while debugging applications.\n\n\nThis labelled PII dataset consists of protocol traces (JSON, SQL (PostgreSQL, MySQL), HTML, and XML) generated from OpenAPI specifications and includes 60+ PII types.", "### Supported Tasks and Leaderboards\n\n\nNamed Entity Recognition (NER) and PII classification.", "### Label Scheme\n\n\n\nView label scheme (26 labels for 60 PII data providers)", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample:\n\n\nDataset Creation\n----------------", "### 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\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #size_categories-100K<n<200K #size_categories-300K<n<400K #language-English #license-mit #pii-detection #region-us \n", "### Dataset Summary\n\n\nA synthetic PII dataset generated using Privy, a tool which parses OpenAPI specifications and generates synthetic request payloads, searching for keywords in API schema definitions to select appropriate data providers. Generated API payloads are converted to various protocol trace formats like JSON and SQL to approximate the data developers might encounter while debugging applications.\n\n\nThis labelled PII dataset consists of protocol traces (JSON, SQL (PostgreSQL, MySQL), HTML, and XML) generated from OpenAPI specifications and includes 60+ PII types.", "### Supported Tasks and Leaderboards\n\n\nNamed Entity Recognition (NER) and PII classification.", "### Label Scheme\n\n\n\nView label scheme (26 labels for 60 PII data providers)", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample:\n\n\nDataset Creation\n----------------", "### 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\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "task_categories:automatic-speech-recognition", "language:zh", "region:us" ]

[]

[ "zh" ]

[ "# Dataset Card for Nexdata/Mandarin_Spontaneous_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe data were recorded by 700 Mandarin speakers, 65% of whom were women. There is no pre-made text, and speakers makes phone calls in a natural way while recording the contents of the calls. This data mainly labels the near-end speech, and the speech content is naturally colloquial.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nMandarin", "## 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\n\nCommercial License", "### Contributions" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #language-Chinese #region-us \n", "# Dataset Card for Nexdata/Mandarin_Spontaneous_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe data were recorded by 700 Mandarin speakers, 65% of whom were women. There is no pre-made text, and speakers makes phone calls in a natural way while recording the contents of the calls. This data mainly labels the near-end speech, and the speech content is naturally colloquial.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nMandarin", "## 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\n\nCommercial License", "### Contributions" ]

[ "task_categories:conversational", "language:ko", "region:us" ]

[]

[ "ko" ]

[ "# Dataset Card for Nexdata/Korean_Conversational_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nAbout 700 Korean speakers participated in the recording, and conducted face-to-face communication in a natural way. They had free discussion on a number of given topics, with a wide range of fields; the voice was natural and fluent, in line with the actual dialogue scene. Text is transferred manually, with high accuracy. \n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nKorean", "## 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\n\nCommercial License", "### Contributions" ]

[ "TAGS\n#task_categories-conversational #language-Korean #region-us \n", "# Dataset Card for Nexdata/Korean_Conversational_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nAbout 700 Korean speakers participated in the recording, and conducted face-to-face communication in a natural way. They had free discussion on a number of given topics, with a wide range of fields; the voice was natural and fluent, in line with the actual dialogue scene. Text is transferred manually, with high accuracy. \n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nKorean", "## 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\n\nCommercial License", "### Contributions" ]

[ "task_categories:conversational", "language:ja", "region:us" ]

[]

[ "ja" ]

[ "# Dataset Card for Nexdata/Japanese_Conversational_Speech_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nAbout 1000 speakers participated in the recording, and conducted face-to-face communication in a natural way. They had free discussion on a number of given topics, with a wide range of fields; the voice was natural and fluent, in line with the actual dialogue scene. Text is transferred manually, with high accuracy. \n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nJapanese", "## 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\n\nCommercial License", "### Contributions" ]

[ "TAGS\n#task_categories-conversational #language-Japanese #region-us \n", "# Dataset Card for Nexdata/Japanese_Conversational_Speech_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nAbout 1000 speakers participated in the recording, and conducted face-to-face communication in a natural way. They had free discussion on a number of given topics, with a wide range of fields; the voice was natural and fluent, in line with the actual dialogue scene. Text is transferred manually, with high accuracy. \n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nJapanese", "## 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\n\nCommercial License", "### Contributions" ]

[ "task_categories:conversational", "language:it", "region:us" ]

[]

[ "it" ]

[ "# Dataset Card for Nexdata/Italian_Conversational_Speech_Data_by_Mobile_Phone", "## Description\nAbout 700 speakers participated in the recording, and conducted face-to-face communication in a natural way. They had free discussion on a number of given topics, with a wide range of fields; the voice was natural and fluent, in line with the actual dialogue scene. Text is transferred manually, with high accuracy.\n\nFor more details, please refer to the link: URL", "## Format\n16kHz, 16bit, uncompressed wav, mono channel;", "## Recording Environment\nquiet indoor environment, without echo;", "## Recording content\ndozens of topics are specified, and the speakers make dialogue under those topics while the recording is performed;", "## Demographics\nAbout 700 people.", "## Annotation\nannotating for the transcription text, speaker identification and gender", "## Device\nAndroid mobile phone, iPhone;", "## Language\nItalian", "## Application scenarios\nspeech recognition; voiceprint recognition;", "## Accuracy rate\nthe word accuracy rate is not less than 98%", "# Licensing Information\nCommercial License" ]

[ "TAGS\n#task_categories-conversational #language-Italian #region-us \n", "# Dataset Card for Nexdata/Italian_Conversational_Speech_Data_by_Mobile_Phone", "## Description\nAbout 700 speakers participated in the recording, and conducted face-to-face communication in a natural way. They had free discussion on a number of given topics, with a wide range of fields; the voice was natural and fluent, in line with the actual dialogue scene. Text is transferred manually, with high accuracy.\n\nFor more details, please refer to the link: URL", "## Format\n16kHz, 16bit, uncompressed wav, mono channel;", "## Recording Environment\nquiet indoor environment, without echo;", "## Recording content\ndozens of topics are specified, and the speakers make dialogue under those topics while the recording is performed;", "## Demographics\nAbout 700 people.", "## Annotation\nannotating for the transcription text, speaker identification and gender", "## Device\nAndroid mobile phone, iPhone;", "## Language\nItalian", "## Application scenarios\nspeech recognition; voiceprint recognition;", "## Accuracy rate\nthe word accuracy rate is not less than 98%", "# Licensing Information\nCommercial License" ]

[ "task_categories:text-classification", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ko", "region:us" ]

[]

[ "ko" ]

[]

[ "TAGS\n#task_categories-text-classification #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Korean #region-us \n" ]

[ "task_categories:text-classification", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ko", "region:us" ]

[]

[ "ko" ]

[]

[ "TAGS\n#task_categories-text-classification #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Korean #region-us \n" ]

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

[]

[]

[]

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

[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|glue", "language:es", "license:cc-by-4.0", "region:us" ]

[]

[ "es" ]

[ "# WNLI-es", "## 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- Website: URL\n- Point of Contact: Carlos Rodríguez-Penagos and Carme Armentano-Oller", "### Dataset Summary\n\n\"A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its resolution. The schema takes its name from Terry Winograd.\" Source: The Winograd Schema Challenge.\n\nThe Winograd NLI dataset presents 855 sentence pairs, in which the first sentence contains an ambiguity and the second one a possible interpretation of it. The label indicates if the interpretation is correct (1) or not (0).\n\nThis dataset is a professional translation into Spanish of Winograd NLI dataset as published in GLUE Benchmark.\n\nBoth the original dataset and this translation are licenced under a Creative Commons Attribution 4.0 International License.", "### Supported Tasks and Leaderboards\n\nTextual entailment, Text classification, Language Model.", "### Languages\n\n* Spanish (es)", "## Dataset Structure", "### Data Instances\n\nThree tsv files.", "### Data Fields\n\n- index\n- sentence 1: first sentence of the pair\n- sentence 2: second sentence of the pair\n- label: relation between the two sentences:\n * 0: the second sentence does not entail a correct interpretation of the first one (neutral)\n * 1: the second sentence entails a correct interpretation of the first one (entailment)", "### Data Splits\n\n- URL: 636 sentence pairs\n- URL: 72 sentence pairs\n- URL: 147 sentence pairs", "## Dataset Creation", "### Curation Rationale\n\nWe translated this dataset to contribute to the development of language models in Spanish.", "### Source Data\n\n- GLUE Benchmark site", "#### Initial Data Collection and Normalization\n\nThis is a professional translation of WNLI dataset into Spanish, commissioned by BSC TeMU within the the framework of the Plan-TL.\n\nFor more information on how the Winograd NLI dataset was created, visit the webpage The Winograd Schema Challenge.", "#### Who are the source language producers?\n\nFor more information on how the Winograd NLI dataset was created, visit the webpage The Winograd Schema Challenge.", "### Annotations", "#### Annotation process\n\nWe comissioned a professional translation of WNLI dataset into Spanish.", "#### Who are the annotators?\n\nTranslation was commisioned to a professional translation agency.", "### Personal and Sensitive Information\n\nNo personal or sensitive information included.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThis dataset contributes to the development of language models in Spanish.", "### Discussion of Biases\n\n[N/A]", "### Other Known Limitations\n\n[N/A]", "## Additional Information", "### Dataset Curators \nText Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL). \n\nFor further information, send an email to (plantl-gob-es@URL).\n\nThis work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.", "### Licensing information\nThis work is licensed under CC Attribution 4.0 International License.\n\nCopyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)", "### Contributions\n[N/A]" ]

[ "TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-unknown #source_datasets-extended|glue #language-Spanish #license-cc-by-4.0 #region-us \n", "# WNLI-es", "## 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- Website: URL\n- Point of Contact: Carlos Rodríguez-Penagos and Carme Armentano-Oller", "### Dataset Summary\n\n\"A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its resolution. The schema takes its name from Terry Winograd.\" Source: The Winograd Schema Challenge.\n\nThe Winograd NLI dataset presents 855 sentence pairs, in which the first sentence contains an ambiguity and the second one a possible interpretation of it. The label indicates if the interpretation is correct (1) or not (0).\n\nThis dataset is a professional translation into Spanish of Winograd NLI dataset as published in GLUE Benchmark.\n\nBoth the original dataset and this translation are licenced under a Creative Commons Attribution 4.0 International License.", "### Supported Tasks and Leaderboards\n\nTextual entailment, Text classification, Language Model.", "### Languages\n\n* Spanish (es)", "## Dataset Structure", "### Data Instances\n\nThree tsv files.", "### Data Fields\n\n- index\n- sentence 1: first sentence of the pair\n- sentence 2: second sentence of the pair\n- label: relation between the two sentences:\n * 0: the second sentence does not entail a correct interpretation of the first one (neutral)\n * 1: the second sentence entails a correct interpretation of the first one (entailment)", "### Data Splits\n\n- URL: 636 sentence pairs\n- URL: 72 sentence pairs\n- URL: 147 sentence pairs", "## Dataset Creation", "### Curation Rationale\n\nWe translated this dataset to contribute to the development of language models in Spanish.", "### Source Data\n\n- GLUE Benchmark site", "#### Initial Data Collection and Normalization\n\nThis is a professional translation of WNLI dataset into Spanish, commissioned by BSC TeMU within the the framework of the Plan-TL.\n\nFor more information on how the Winograd NLI dataset was created, visit the webpage The Winograd Schema Challenge.", "#### Who are the source language producers?\n\nFor more information on how the Winograd NLI dataset was created, visit the webpage The Winograd Schema Challenge.", "### Annotations", "#### Annotation process\n\nWe comissioned a professional translation of WNLI dataset into Spanish.", "#### Who are the annotators?\n\nTranslation was commisioned to a professional translation agency.", "### Personal and Sensitive Information\n\nNo personal or sensitive information included.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThis dataset contributes to the development of language models in Spanish.", "### Discussion of Biases\n\n[N/A]", "### Other Known Limitations\n\n[N/A]", "## Additional Information", "### Dataset Curators \nText Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL). \n\nFor further information, send an email to (plantl-gob-es@URL).\n\nThis work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.", "### Licensing information\nThis work is licensed under CC Attribution 4.0 International License.\n\nCopyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)", "### Contributions\n[N/A]" ]

[ "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: Question Answering\n* Model: Jiqing/bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad\n* Dataset: squad_v2\n* Config: squad_v2\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: Question Answering\n* Model: Jiqing/bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad\n* Dataset: squad_v2\n* Config: squad_v2\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" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: MYX4567/distilbert-base-uncased-finetuned-squad\n* Dataset: squad_v2\n* Config: squad_v2\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: Question Answering\n* Model: MYX4567/distilbert-base-uncased-finetuned-squad\n* Dataset: squad_v2\n* Config: squad_v2\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" ]

[]

[]

[ "# 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: mathemakitten/opt-125m\n* Dataset: autoevaluate/zero-shot-classification-sample\n* Config: autoevaluate--zero-shot-classification-sample\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: mathemakitten/opt-125m\n* Dataset: autoevaluate/zero-shot-classification-sample\n* Config: autoevaluate--zero-shot-classification-sample\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" ]

[]

[]

[ "# 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: mathemakitten/opt-125m\n* Dataset: autoevaluate/zero-shot-classification-sample\n* Config: autoevaluate--zero-shot-classification-sample\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: mathemakitten/opt-125m\n* Dataset: autoevaluate/zero-shot-classification-sample\n* Config: autoevaluate--zero-shot-classification-sample\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" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: chandrasutrisnotjhong/bert-finetuned-ner\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\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: Token Classification\n* Model: chandrasutrisnotjhong/bert-finetuned-ner\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\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" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: baptiste/deberta-finetuned-ner\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\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: Token Classification\n* Model: baptiste/deberta-finetuned-ner\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\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" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: mariolinml/roberta_large-ner-conll2003_0818_v0\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\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: Token Classification\n* Model: mariolinml/roberta_large-ner-conll2003_0818_v0\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\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: Token Classification\n* Model: jjglilleberg/bert-finetuned-ner\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\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: Token Classification\n* Model: jjglilleberg/bert-finetuned-ner\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\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" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Yv/bert-finetuned-ner\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\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: Token Classification\n* Model: Yv/bert-finetuned-ner\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\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" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: armandnlp/distilbert-base-uncased-finetuned-emotion\n* Dataset: emotion\n* Config: default\n* Split: test\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: Multi-class Text Classification\n* Model: armandnlp/distilbert-base-uncased-finetuned-emotion\n* Dataset: emotion\n* Config: default\n* Split: test\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" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: andreaschandra/distilbert-base-uncased-finetuned-emotion\n* Dataset: emotion\n* Config: default\n* Split: test\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: Multi-class Text Classification\n* Model: andreaschandra/distilbert-base-uncased-finetuned-emotion\n* Dataset: emotion\n* Config: default\n* Split: test\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" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: bousejin/distilbert-base-uncased-finetuned-emotion\n* Dataset: emotion\n* Config: default\n* Split: test\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: Multi-class Text Classification\n* Model: bousejin/distilbert-base-uncased-finetuned-emotion\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "task_categories:text-classification", "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#task_categories-text-classification #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:" ]

[ "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: autoevaluate/zero-shot-classification\n* Dataset: Tristan/zero-shot-classification-large-test\n* Config: Tristan--zero-shot-classification-large-test\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Tristan 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: autoevaluate/zero-shot-classification\n* Dataset: Tristan/zero-shot-classification-large-test\n* Config: Tristan--zero-shot-classification-large-test\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Tristan 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: Summarization\n* Model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @samuelfipps123 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @samuelfipps123 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: Summarization\n* Model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum\n* Dataset: samsum\n* Config: samsum\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @samuelallen123 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum\n* Dataset: samsum\n* Config: samsum\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @samuelallen123 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: Summarization\n* Model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum\n* Dataset: samsum\n* Config: samsum\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @samuelallen123 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum\n* Dataset: samsum\n* Config: samsum\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @samuelallen123 for evaluating this model." ]

[ "task_categories:translation", "language:en", "language:zh", "region:us" ]

[]

[ "en", "zh" ]

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en2zh.\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#task_categories-translation #language-English #language-Chinese #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en2zh.\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:translation", "annotations_creators:found", "annotations_creators:machine-generated", "language_creators:found", "language_creators:machine-generated", "multilinguality:translation", "size_categories:10K<n<100K", "source_datasets:original", "language:myv", "language:ru", "license:cc-by-sa-4.0", "erzya", "mordovian", "arxiv:2209.09368", "region:us" ]

[ "2209.09368" ]

[ "myv", "ru" ]

[ "### Dataset Summary\n\n\nThis is a corpus of parallel Erzya-Russian words, phrases and sentences, collected in the paper The first neural machine translation system for the Erzya language.\n\n\nErzya ('myv') is a language from the Uralic family. It is spoken primarily in the Republic of Mordovia and some other regions of Russia and other post-Soviet countries. We use the Cyrillic version of its script.\n\n\nThe corpus consists of the following parts:\n\n\nname: train, size: 74503, composition: parallel words, phrases and sentences, mined from dictionaries, books and web texts\nname: dev, size: 1500, composition: parallel sentences mined from books and web texts\nname: test, size: 1500, composition: parallel sentences mined from books and web texts\nname: mono, size: 333651, composition: Erzya sentences mined from books and web texts, translated to Russian by a neural model\n\n\nThe dev and test splits contain sentences from the following sources\n\n\nname: wiki, size: 600, description: Aligned sentences from linked Erzya and Russian Wikipedia articles\nname: bible, size: 400, description: Paired verses from the Bible (URL)\nname: games, size: 250, description: Aligned sentences from the book *\"Сказовые формы мордовской литературы\", И.И. Шеянова, 2017, НИИ гуманитарых наук при Правительстве Республики Мордовия, Саранск*\nname: tales, size: 100, description: Aligned sentences from the book *\"Мордовские народные игры\", В.С. Брыжинский, 2009, Мордовское книжное издательство, Саранск*\nname: fiction, size: 100, description: Aligned sentences from modern Erzya prose and poetry (URL\nname: constitution, size: 50, description: Aligned sentences from the Soviet 1938 constitution\n\n\nTo load the first three parts (train, validation and test), use the code:\n\n\nTo load all four parts (included the back-translated data), please specify the data files explicitly:", "### Supported Tasks and Leaderboards\n\n\n* 'translation': the dataset may be used to train 'ru-myv' translation models. There are no specific leaderboards for it yet, but if you feel like discussing it, welcome to the comments!", "### Languages\n\n\nThe main part of the dataset ('train', 'dev' and 'test') consists of \"natural\" Erzya (Cyrillic) and Russian sentences, translated to the other language by humans. There is also a larger Erzya-only part of the corpus ('mono'), translated to Russian automatically.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAll data instances have three string fields: 'myv', 'ru' and 'src' (the last one is currently meaningful only for dev and test splits), for example:", "### Data Fields\n\n\n* 'myv': the Erzya text (word, phrase, or sentence)\n* 'ru': the corresponding Russian text\n* 'src': the source of data (only for dev and test splits)", "### Data Splits\n\n\n* train: parallel sentences, words and phrases, collected from various sources. Most of them are aligned automatically. Noisy.\n* dev: 1500 parallel sentences, selected from the 6 most reliable and diverse sources.\n* test: same as dev.\n* mono: Erzya sentences collected from various sources, with the Russian counterpart generated by a neural machine translation model.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis is, as far as we know, the first publicly available parallel Russian-Erzya corpus, and the first medium-sized translation corpus for Erzya.\nWe hope that it sets a meaningful baseline for Erzya machine translation.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe dataset was collected from various sources (see below).\n\n\nThe texts were spit in sentences using the razdel package.\nFor some sources, sentences were filtered by language using the slone/fastText-LID-323 model.\nFor most of the sources, 'myv' and 'ru' sentences were aligned automatically using the slone/LaBSE-en-ru-myv-v1 sentence encoder\nand the code from the paper repository.", "#### Who are the source language producers?\n\n\nThe dataset comprises parallel 'myv-ru' and monolingual 'myv' texts from diverse sources:\n\n\n* 12K parallel sentences from the Bible (URL);\n* 3K parallel Wikimedia sentences from OPUS;\n* 42K parallel words or short phrases collected from various online dictionaries ();\n* the Erzya Wikipedia and the corresponding articles from the Russian Wikipedia;\n* 18 books, including 3 books with Erzya-Russian bitexts (URL);\n* Soviet-time books and periodicals (URL);\n* The Erzya part of Wikisource (URL\n* Short texts by modern Erzya authors (URL\n* News articles from the Erzya Pravda website (URL);\n* Texts found in LiveJournal (URL) by searching with the 100 most frequent Erzya words.", "### Annotations\n\n\nNo human annotation was involved in the data collection.", "### Personal and Sensitive Information\n\n\nAll data was collected from public sources, so no sensitive information is expected in them.\nHowever, some sentences collected, for example, from news articles or LiveJournal posts, can contain personal data.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nPublication of this dataset may attract some attention to the endangered Erzya language.", "### Discussion of Biases\n\n\nMost of the dataset has been collected by automatical means, so it may contain errors and noise.\nSome types of these errors are systemic: for example, the words for \"Erzya\" and \"Russian\" are often aligned together,\nbecause they appear in the corresponding Wikipedias on similar positions.", "### Other Known Limitations\n\n\nThe dataset is noisy: some texts in it may be ungrammatical, in a wrong language, or poorly aligned.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe data was collected by David Dale (URL", "### Licensing Information\n\n\nThe status of the dataset is not final, but after we check everything, we hope to be able to distribute it under the CC-BY-SA license.\n\n\n[TBD]" ]

[ "TAGS\n#task_categories-translation #annotations_creators-found #annotations_creators-machine-generated #language_creators-found #language_creators-machine-generated #multilinguality-translation #size_categories-10K<n<100K #source_datasets-original #language-Erzya #language-Russian #license-cc-by-sa-4.0 #erzya #mordovian #arxiv-2209.09368 #region-us \n", "### Dataset Summary\n\n\nThis is a corpus of parallel Erzya-Russian words, phrases and sentences, collected in the paper The first neural machine translation system for the Erzya language.\n\n\nErzya ('myv') is a language from the Uralic family. It is spoken primarily in the Republic of Mordovia and some other regions of Russia and other post-Soviet countries. We use the Cyrillic version of its script.\n\n\nThe corpus consists of the following parts:\n\n\nname: train, size: 74503, composition: parallel words, phrases and sentences, mined from dictionaries, books and web texts\nname: dev, size: 1500, composition: parallel sentences mined from books and web texts\nname: test, size: 1500, composition: parallel sentences mined from books and web texts\nname: mono, size: 333651, composition: Erzya sentences mined from books and web texts, translated to Russian by a neural model\n\n\nThe dev and test splits contain sentences from the following sources\n\n\nname: wiki, size: 600, description: Aligned sentences from linked Erzya and Russian Wikipedia articles\nname: bible, size: 400, description: Paired verses from the Bible (URL)\nname: games, size: 250, description: Aligned sentences from the book *\"Сказовые формы мордовской литературы\", И.И. Шеянова, 2017, НИИ гуманитарых наук при Правительстве Республики Мордовия, Саранск*\nname: tales, size: 100, description: Aligned sentences from the book *\"Мордовские народные игры\", В.С. Брыжинский, 2009, Мордовское книжное издательство, Саранск*\nname: fiction, size: 100, description: Aligned sentences from modern Erzya prose and poetry (URL\nname: constitution, size: 50, description: Aligned sentences from the Soviet 1938 constitution\n\n\nTo load the first three parts (train, validation and test), use the code:\n\n\nTo load all four parts (included the back-translated data), please specify the data files explicitly:", "### Supported Tasks and Leaderboards\n\n\n* 'translation': the dataset may be used to train 'ru-myv' translation models. There are no specific leaderboards for it yet, but if you feel like discussing it, welcome to the comments!", "### Languages\n\n\nThe main part of the dataset ('train', 'dev' and 'test') consists of \"natural\" Erzya (Cyrillic) and Russian sentences, translated to the other language by humans. There is also a larger Erzya-only part of the corpus ('mono'), translated to Russian automatically.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAll data instances have three string fields: 'myv', 'ru' and 'src' (the last one is currently meaningful only for dev and test splits), for example:", "### Data Fields\n\n\n* 'myv': the Erzya text (word, phrase, or sentence)\n* 'ru': the corresponding Russian text\n* 'src': the source of data (only for dev and test splits)", "### Data Splits\n\n\n* train: parallel sentences, words and phrases, collected from various sources. Most of them are aligned automatically. Noisy.\n* dev: 1500 parallel sentences, selected from the 6 most reliable and diverse sources.\n* test: same as dev.\n* mono: Erzya sentences collected from various sources, with the Russian counterpart generated by a neural machine translation model.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis is, as far as we know, the first publicly available parallel Russian-Erzya corpus, and the first medium-sized translation corpus for Erzya.\nWe hope that it sets a meaningful baseline for Erzya machine translation.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe dataset was collected from various sources (see below).\n\n\nThe texts were spit in sentences using the razdel package.\nFor some sources, sentences were filtered by language using the slone/fastText-LID-323 model.\nFor most of the sources, 'myv' and 'ru' sentences were aligned automatically using the slone/LaBSE-en-ru-myv-v1 sentence encoder\nand the code from the paper repository.", "#### Who are the source language producers?\n\n\nThe dataset comprises parallel 'myv-ru' and monolingual 'myv' texts from diverse sources:\n\n\n* 12K parallel sentences from the Bible (URL);\n* 3K parallel Wikimedia sentences from OPUS;\n* 42K parallel words or short phrases collected from various online dictionaries ();\n* the Erzya Wikipedia and the corresponding articles from the Russian Wikipedia;\n* 18 books, including 3 books with Erzya-Russian bitexts (URL);\n* Soviet-time books and periodicals (URL);\n* The Erzya part of Wikisource (URL\n* Short texts by modern Erzya authors (URL\n* News articles from the Erzya Pravda website (URL);\n* Texts found in LiveJournal (URL) by searching with the 100 most frequent Erzya words.", "### Annotations\n\n\nNo human annotation was involved in the data collection.", "### Personal and Sensitive Information\n\n\nAll data was collected from public sources, so no sensitive information is expected in them.\nHowever, some sentences collected, for example, from news articles or LiveJournal posts, can contain personal data.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nPublication of this dataset may attract some attention to the endangered Erzya language.", "### Discussion of Biases\n\n\nMost of the dataset has been collected by automatical means, so it may contain errors and noise.\nSome types of these errors are systemic: for example, the words for \"Erzya\" and \"Russian\" are often aligned together,\nbecause they appear in the corresponding Wikipedias on similar positions.", "### Other Known Limitations\n\n\nThe dataset is noisy: some texts in it may be ungrammatical, in a wrong language, or poorly aligned.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe data was collected by David Dale (URL", "### Licensing Information\n\n\nThe status of the dataset is not final, but after we check everything, we hope to be able to distribute it under the CC-BY-SA license.\n\n\n[TBD]" ]

[ "task_categories:image-to-image", "size_categories:10K<n<100K", "audio", "spectrograms", "region:us" ]

[]

[]

[]

[ "TAGS\n#task_categories-image-to-image #size_categories-10K<n<100K #audio #spectrograms #region-us \n" ]

[ "language:fr", "language:br", "license:unknown", "region:us" ]

[]

[ "fr", "br" ]

[ "# Korpus-frazennou-brezhonek \n\nCorpus de 4532 phrases bilingues (français-breton) alignées et libres de droits provenant de l'Office Public de la Langue Bretonne.\nPlus d'informations ici", "# Usage" ]

[ "TAGS\n#language-French #language-Breton #license-unknown #region-us \n", "# Korpus-frazennou-brezhonek \n\nCorpus de 4532 phrases bilingues (français-breton) alignées et libres de droits provenant de l'Office Public de la Langue Bretonne.\nPlus d'informations ici", "# Usage" ]

[ "task_categories:unconditional-image-generation", "size_categories:100K<n<1M", "source_datasets:original", "license:cc0-1.0", "region:us" ]

[]

[]

[ "## Dataset Description\n\nFBAnimeHQ is a dataset with high-quality full-body anime girl images in a resolution of 1024 × 512.", "### Dataset Summary\n\nThe dataset contains 112,806 images.\n\nAll images are on white background", "### Collection Method", "#### v1.0\nCollect from danbooru website.\n\nUse yolov5 to detect and clip image.\n\nUse anime-segmentation to remove background.\n\nUse deepdanbooru to filter image.\n\nFinally clean the dataset manually.", "#### v2.0\n\nBase on v1.0, use Novelai image-to-image to enhance and expand the dataset.", "### Contributions\n\nThanks to @SkyTNT for adding this dataset." ]

[ "TAGS\n#task_categories-unconditional-image-generation #size_categories-100K<n<1M #source_datasets-original #license-cc0-1.0 #region-us \n", "## Dataset Description\n\nFBAnimeHQ is a dataset with high-quality full-body anime girl images in a resolution of 1024 × 512.", "### Dataset Summary\n\nThe dataset contains 112,806 images.\n\nAll images are on white background", "### Collection Method", "#### v1.0\nCollect from danbooru website.\n\nUse yolov5 to detect and clip image.\n\nUse anime-segmentation to remove background.\n\nUse deepdanbooru to filter image.\n\nFinally clean the dataset manually.", "#### v2.0\n\nBase on v1.0, use Novelai image-to-image to enhance and expand the dataset.", "### Contributions\n\nThanks to @SkyTNT for adding this dataset." ]

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

[]

[]

[ "# Please cite as" ]

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

[ "region:us" ]

[]

[]

[]

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

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

[]

[ "en" ]

[ "# Stable Diffusion Dataset\n\nThis is a set of about 80,000 prompts filtered and extracted from the image finder for Stable Diffusion: \"URL\". It was a little difficult to extract the data, since the search engine still doesn't have a public API without being protected by cloudflare.\n\nIf you want to test the model with a demo, you can go to: \"spaces/Gustavosta/MagicPrompt-Stable-Diffusion\".\n\nIf you want to see the model, go to: \"Gustavosta/MagicPrompt-Stable-Diffusion\"." ]

[ "TAGS\n#annotations_creators-no-annotation #language_creators-found #source_datasets-original #language-English #license-unknown #region-us \n", "# Stable Diffusion Dataset\n\nThis is a set of about 80,000 prompts filtered and extracted from the image finder for Stable Diffusion: \"URL\". It was a little difficult to extract the data, since the search engine still doesn't have a public API without being protected by cloudflare.\n\nIf you want to test the model with a demo, you can go to: \"spaces/Gustavosta/MagicPrompt-Stable-Diffusion\".\n\nIf you want to see the model, go to: \"Gustavosta/MagicPrompt-Stable-Diffusion\"." ]

[ "task_categories:translation", "multilinguality:translation", "size_categories:100K<n<1M", "language:en", "language:ro", "region:us" ]

[]

[ "en", "ro" ]

[]

[ "TAGS\n#task_categories-translation #multilinguality-translation #size_categories-100K<n<1M #language-English #language-Romanian #region-us \n" ]

[ "task_categories:time-series-forecasting", "region:us" ]

[]

[]

[]

[ "TAGS\n#task_categories-time-series-forecasting #region-us \n" ]

[ "task_categories:other", "task_categories:object-detection", "task_categories:text-retrieval", "task_categories:token-classification", "task_categories:text-generation", "multilinguality:multilingual", "size_categories:10M<n<100M", "source_datasets:original", "language:multilingual", "license:cc-by-nc-sa-4.0", "text-mining", "region:us" ]

[]

[ "multilingual" ]

[ "### How to use it\n\n\nThe Mario Maker 2 levels dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of 'datasets'. You can load and iterate through the dataset with the following code:\n\n\nLevel data is a binary blob describing the actual level and is equivalent to the level format Nintendo uses in-game. It is gzip compressed and needs to be decompressed to be read. To read it you only need to use the provided 'URL' kaitai struct file and install the kaitai struct runtime to parse it into an object:\n\n\nRendering the level data into an image can be done using Toost if desired.\n\n\nYou can also download the full dataset. Note that this will download ~100GB:\n\n\nData Structure\n--------------", "### Data Instances", "### Data Fields\n\n\nField: data\\_id, Type: int, Description: Data IDs are unique identifiers, gaps in the table are due to levels deleted by users or Nintendo\nField: name, Type: string, Description: Course name\nField: description, Type: string, Description: Course description\nField: uploaded, Type: int, Description: UTC timestamp for when the level was uploaded\nField: created, Type: int, Description: Local timestamp for when the level was created\nField: gamestyle, Type: int, Description: Gamestyle, enum below\nField: theme, Type: int, Description: Theme, enum below\nField: difficulty, Type: int, Description: Difficulty, enum below\nField: tag1, Type: int, Description: The first tag, if it exists, enum below\nField: tag2, Type: int, Description: The second tag, if it exists, enum below\nField: game\\_version, Type: int, Description: The version of the game this level was made on\nField: world\\_record, Type: int, Description: The world record in milliseconds\nField: upload\\_time, Type: int, Description: The upload time in milliseconds\nField: upload\\_attempts, Type: int, Description: The number of attempts it took the uploader to upload\nField: num\\_comments, Type: int, Description: Number of comments, may not reflect the archived comments if there were more than 1000 comments\nField: clear\\_condition, Type: int, Description: Clear condition, enum below\nField: clear\\_condition\\_magnitude, Type: int, Description: If applicable, the magnitude of the clear condition\nField: timer, Type: int, Description: The timer of the level\nField: autoscroll\\_speed, Type: int, Description: A unit of how fast the configured autoscroll speed is for the level\nField: clears, Type: int, Description: Course clears\nField: attempts, Type: int, Description: Course attempts\nField: clear\\_rate, Type: float, Description: Course clear rate as a float between 0 and 1\nField: plays, Type: int, Description: Course plays, or \"footprints\"\nField: versus\\_matches, Type: int, Description: Course versus matches\nField: coop\\_matches, Type: int, Description: Course coop matches\nField: likes, Type: int, Description: Course likes\nField: boos, Type: int, Description: Course boos\nField: unique\\_players\\_and\\_versus, Type: int, Description: All unique players that have ever played this level, including the number of versus matches\nField: weekly\\_likes, Type: int, Description: The weekly likes on this course\nField: weekly\\_plays, Type: int, Description: The weekly plays on this course\nField: uploader\\_pid, Type: string, Description: The player ID of the uploader\nField: first\\_completer\\_pid, Type: string, Description: The player ID of the user who first cleared this course\nField: record\\_holder\\_pid, Type: string, Description: The player ID of the user who held the world record at time of archival\nField: level\\_data, Type: bytes, Description: The GZIP compressed decrypted level data, kaitai struct file is provided for reading\nField: unk2, Type: int, Description: Unknown\nField: unk3, Type: bytes, Description: Unknown\nField: unk9, Type: int, Description: Unknown\nField: unk10, Type: int, Description: Unknown\nField: unk11, Type: int, Description: Unknown\nField: unk12, Type: int, Description: Unknown", "### Data Splits\n\n\nThe dataset only contains a train split.\n\n\nEnums\n-----\n\n\nThe dataset contains some enum integer fields. This can be used to convert back to their string equivalents:\n\n\nDataset Creation\n----------------\n\n\nThe dataset was created over a little more than a month in Febuary 2022 using the self hosted Mario Maker 2 api. As requests made to Nintendo's servers require authentication the process had to be done with upmost care and limiting download speed as to not overload the API and risk a ban. There are no intentions to create an updated release of this dataset.\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nThe dataset consists of levels from many different Mario Maker 2 players globally and as such their titles and descriptions could contain harmful language. Harmful depictions could also be present in the level data, should you choose to render it." ]

[ "TAGS\n#task_categories-other #task_categories-object-detection #task_categories-text-retrieval #task_categories-token-classification #task_categories-text-generation #multilinguality-multilingual #size_categories-10M<n<100M #source_datasets-original #language-multilingual #license-cc-by-nc-sa-4.0 #text-mining #region-us \n", "### How to use it\n\n\nThe Mario Maker 2 levels dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of 'datasets'. You can load and iterate through the dataset with the following code:\n\n\nLevel data is a binary blob describing the actual level and is equivalent to the level format Nintendo uses in-game. It is gzip compressed and needs to be decompressed to be read. To read it you only need to use the provided 'URL' kaitai struct file and install the kaitai struct runtime to parse it into an object:\n\n\nRendering the level data into an image can be done using Toost if desired.\n\n\nYou can also download the full dataset. Note that this will download ~100GB:\n\n\nData Structure\n--------------", "### Data Instances", "### Data Fields\n\n\nField: data\\_id, Type: int, Description: Data IDs are unique identifiers, gaps in the table are due to levels deleted by users or Nintendo\nField: name, Type: string, Description: Course name\nField: description, Type: string, Description: Course description\nField: uploaded, Type: int, Description: UTC timestamp for when the level was uploaded\nField: created, Type: int, Description: Local timestamp for when the level was created\nField: gamestyle, Type: int, Description: Gamestyle, enum below\nField: theme, Type: int, Description: Theme, enum below\nField: difficulty, Type: int, Description: Difficulty, enum below\nField: tag1, Type: int, Description: The first tag, if it exists, enum below\nField: tag2, Type: int, Description: The second tag, if it exists, enum below\nField: game\\_version, Type: int, Description: The version of the game this level was made on\nField: world\\_record, Type: int, Description: The world record in milliseconds\nField: upload\\_time, Type: int, Description: The upload time in milliseconds\nField: upload\\_attempts, Type: int, Description: The number of attempts it took the uploader to upload\nField: num\\_comments, Type: int, Description: Number of comments, may not reflect the archived comments if there were more than 1000 comments\nField: clear\\_condition, Type: int, Description: Clear condition, enum below\nField: clear\\_condition\\_magnitude, Type: int, Description: If applicable, the magnitude of the clear condition\nField: timer, Type: int, Description: The timer of the level\nField: autoscroll\\_speed, Type: int, Description: A unit of how fast the configured autoscroll speed is for the level\nField: clears, Type: int, Description: Course clears\nField: attempts, Type: int, Description: Course attempts\nField: clear\\_rate, Type: float, Description: Course clear rate as a float between 0 and 1\nField: plays, Type: int, Description: Course plays, or \"footprints\"\nField: versus\\_matches, Type: int, Description: Course versus matches\nField: coop\\_matches, Type: int, Description: Course coop matches\nField: likes, Type: int, Description: Course likes\nField: boos, Type: int, Description: Course boos\nField: unique\\_players\\_and\\_versus, Type: int, Description: All unique players that have ever played this level, including the number of versus matches\nField: weekly\\_likes, Type: int, Description: The weekly likes on this course\nField: weekly\\_plays, Type: int, Description: The weekly plays on this course\nField: uploader\\_pid, Type: string, Description: The player ID of the uploader\nField: first\\_completer\\_pid, Type: string, Description: The player ID of the user who first cleared this course\nField: record\\_holder\\_pid, Type: string, Description: The player ID of the user who held the world record at time of archival\nField: level\\_data, Type: bytes, Description: The GZIP compressed decrypted level data, kaitai struct file is provided for reading\nField: unk2, Type: int, Description: Unknown\nField: unk3, Type: bytes, Description: Unknown\nField: unk9, Type: int, Description: Unknown\nField: unk10, Type: int, Description: Unknown\nField: unk11, Type: int, Description: Unknown\nField: unk12, Type: int, Description: Unknown", "### Data Splits\n\n\nThe dataset only contains a train split.\n\n\nEnums\n-----\n\n\nThe dataset contains some enum integer fields. This can be used to convert back to their string equivalents:\n\n\nDataset Creation\n----------------\n\n\nThe dataset was created over a little more than a month in Febuary 2022 using the self hosted Mario Maker 2 api. As requests made to Nintendo's servers require authentication the process had to be done with upmost care and limiting download speed as to not overload the API and risk a ban. There are no intentions to create an updated release of this dataset.\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nThe dataset consists of levels from many different Mario Maker 2 players globally and as such their titles and descriptions could contain harmful language. Harmful depictions could also be present in the level data, should you choose to render it." ]

[ "task_categories:other", "task_categories:object-detection", "task_categories:text-retrieval", "task_categories:token-classification", "task_categories:text-generation", "multilinguality:multilingual", "size_categories:10M<n<100M", "source_datasets:original", "language:multilingual", "license:cc-by-nc-sa-4.0", "text-mining", "region:us" ]

[]

[ "multilingual" ]

[ "### How to use it\n\n\nThe Mario Maker 2 level comment dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of 'datasets'. You can load and iterate through the dataset with the following code:\n\n\nComments can be one of three types: text, reaction image or custom image. 'type' can be used with the enum below to identify different kinds of comments. Custom images are binary PNGs.\n\n\nYou can also download the full dataset. Note that this will download ~20GB:\n\n\nData Structure\n--------------", "### Data Instances", "### Data Fields\n\n\nField: data\\_id, Type: int, Description: The data ID of the level this comment appears on\nField: comment\\_id, Type: string, Description: Comment ID\nField: type, Type: int, Description: Type of comment, enum below\nField: pid, Type: string, Description: Player ID of the comment creator\nField: posted, Type: int, Description: UTC timestamp of when this comment was created\nField: clear\\_required, Type: bool, Description: Whether this comment requires a clear to view\nField: text, Type: string, Description: If the comment type is text, the text of the comment\nField: reaction\\_image\\_id, Type: int, Description: If this comment is a reaction image, the id of the reaction image, enum below\nField: custom\\_image, Type: bytes, Description: If this comment is a custom drawing, the custom drawing as a PNG binary\nField: has\\_beaten, Type: int, Description: Whether the user had beaten the level when they created the comment\nField: x, Type: int, Description: The X position of the comment in game\nField: y, Type: int, Description: The Y position of the comment in game\nField: reaction\\_face, Type: int, Description: The reaction face of the mii of this user, enum below\nField: unk8, Type: int, Description: Unknown\nField: unk10, Type: int, Description: Unknown\nField: unk12, Type: int, Description: Unknown\nField: unk14, Type: bytes, Description: Unknown\nField: unk17, Type: int, Description: Unknown", "### Data Splits\n\n\nThe dataset only contains a train split.\n\n\nEnums\n-----\n\n\nThe dataset contains some enum integer fields. This can be used to convert back to their string equivalents:\n\n\nDataset Creation\n----------------\n\n\nThe dataset was created over a little more than a month in Febuary 2022 using the self hosted Mario Maker 2 api. As requests made to Nintendo's servers require authentication the process had to be done with upmost care and limiting download speed as to not overload the API and risk a ban. There are no intentions to create an updated release of this dataset.\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nThe dataset consists of comments from many different Mario Maker 2 players globally and as such their text could contain harmful language. Harmful depictions could also be present in the custom images." ]

[ "TAGS\n#task_categories-other #task_categories-object-detection #task_categories-text-retrieval #task_categories-token-classification #task_categories-text-generation #multilinguality-multilingual #size_categories-10M<n<100M #source_datasets-original #language-multilingual #license-cc-by-nc-sa-4.0 #text-mining #region-us \n", "### How to use it\n\n\nThe Mario Maker 2 level comment dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of 'datasets'. You can load and iterate through the dataset with the following code:\n\n\nComments can be one of three types: text, reaction image or custom image. 'type' can be used with the enum below to identify different kinds of comments. Custom images are binary PNGs.\n\n\nYou can also download the full dataset. Note that this will download ~20GB:\n\n\nData Structure\n--------------", "### Data Instances", "### Data Fields\n\n\nField: data\\_id, Type: int, Description: The data ID of the level this comment appears on\nField: comment\\_id, Type: string, Description: Comment ID\nField: type, Type: int, Description: Type of comment, enum below\nField: pid, Type: string, Description: Player ID of the comment creator\nField: posted, Type: int, Description: UTC timestamp of when this comment was created\nField: clear\\_required, Type: bool, Description: Whether this comment requires a clear to view\nField: text, Type: string, Description: If the comment type is text, the text of the comment\nField: reaction\\_image\\_id, Type: int, Description: If this comment is a reaction image, the id of the reaction image, enum below\nField: custom\\_image, Type: bytes, Description: If this comment is a custom drawing, the custom drawing as a PNG binary\nField: has\\_beaten, Type: int, Description: Whether the user had beaten the level when they created the comment\nField: x, Type: int, Description: The X position of the comment in game\nField: y, Type: int, Description: The Y position of the comment in game\nField: reaction\\_face, Type: int, Description: The reaction face of the mii of this user, enum below\nField: unk8, Type: int, Description: Unknown\nField: unk10, Type: int, Description: Unknown\nField: unk12, Type: int, Description: Unknown\nField: unk14, Type: bytes, Description: Unknown\nField: unk17, Type: int, Description: Unknown", "### Data Splits\n\n\nThe dataset only contains a train split.\n\n\nEnums\n-----\n\n\nThe dataset contains some enum integer fields. This can be used to convert back to their string equivalents:\n\n\nDataset Creation\n----------------\n\n\nThe dataset was created over a little more than a month in Febuary 2022 using the self hosted Mario Maker 2 api. As requests made to Nintendo's servers require authentication the process had to be done with upmost care and limiting download speed as to not overload the API and risk a ban. There are no intentions to create an updated release of this dataset.\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nThe dataset consists of comments from many different Mario Maker 2 players globally and as such their text could contain harmful language. Harmful depictions could also be present in the custom images." ]