Datasets:

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ftopal

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huggingface-datasets

like 0

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

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[ "en" ]

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[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #region-us \n" ]

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

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

[ "task_categories:text-classification", "task_ids:acceptability-classification", "task_ids:natural-language-inference", "task_ids:semantic-similarity-scoring", "task_ids:sentiment-classification", "task_ids:text-scoring", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "qa-nli", "coreference-nli", "paraphrase-identification", "doi:10.57967/hf/0065", "region:us" ]

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[ "en" ]

[ "### Dataset Summary\n\n\nGLUE, the General Language Understanding Evaluation benchmark (URL is a collection of resources for training, evaluating, and analyzing natural language understanding systems.", "### Supported Tasks and Leaderboards\n\n\nThe leaderboard for the GLUE benchmark can be found at this address. It comprises the following tasks:", "#### ax\n\n\nA manually-curated evaluation dataset for fine-grained analysis of system performance on a broad range of linguistic phenomena. This dataset evaluates sentence understanding through Natural Language Inference (NLI) problems. Use a model trained on MulitNLI to produce predictions for this dataset.", "#### cola\n\n\nThe Corpus of Linguistic Acceptability consists of English acceptability judgments drawn from books and journal articles on linguistic theory. Each example is a sequence of words annotated with whether it is a grammatical English sentence.", "#### mnli\n\n\nThe Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data.", "#### mnli\\_matched\n\n\nThe matched validation and test splits from MNLI. See the \"mnli\" BuilderConfig for additional information.", "#### mnli\\_mismatched\n\n\nThe mismatched validation and test splits from MNLI. See the \"mnli\" BuilderConfig for additional information.", "#### mrpc\n\n\nThe Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.", "#### qnli\n\n\nThe Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The authors of the benchmark convert the task into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue.", "#### qqp\n\n\nThe Quora Question Pairs2 dataset is a collection of question pairs from the community question-answering website Quora. The task is to determine whether a pair of questions are semantically equivalent.", "#### rte\n\n\nThe Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges. The authors of the benchmark combined the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009). Examples are constructed based on news and Wikipedia text. The authors of the benchmark convert all datasets to a two-class split, where for three-class datasets they collapse neutral and contradiction into not entailment, for consistency.", "#### sst2\n\n\nThe Stanford Sentiment Treebank consists of sentences from movie reviews and human annotations of their sentiment. The task is to predict the sentiment of a given sentence. It uses the two-way (positive/negative) class split, with only sentence-level labels.", "#### stsb\n\n\nThe Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data. Each pair is human-annotated with a similarity score from 1 to 5.", "#### wnli\n\n\nThe Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, the authors of the benchmark construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. They use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. The authors of the benchmark call converted dataset WNLI (Winograd NLI).", "### Languages\n\n\nThe language data in GLUE is in English (BCP-47 'en')\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### ax\n\n\n* Size of downloaded dataset files: 0.21 MB\n* Size of the generated dataset: 0.23 MB\n* Total amount of disk used: 0.44 MB\n\n\nAn example of 'test' looks as follows.", "#### cola\n\n\n* Size of downloaded dataset files: 0.36 MB\n* Size of the generated dataset: 0.58 MB\n* Total amount of disk used: 0.94 MB\n\n\nAn example of 'train' looks as follows.", "#### mnli\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 78.65 MB\n* Total amount of disk used: 376.95 MB\n\n\nAn example of 'train' looks as follows.", "#### mnli\\_matched\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 3.52 MB\n* Total amount of disk used: 301.82 MB\n\n\nAn example of 'test' looks as follows.", "#### mnli\\_mismatched\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 3.73 MB\n* Total amount of disk used: 302.02 MB\n\n\nAn example of 'test' looks as follows.", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### ax\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### cola\n\n\n* 'sentence': a 'string' feature.\n* 'label': a classification label, with possible values including 'unacceptable' (0), 'acceptable' (1).\n* 'idx': a 'int32' feature.", "#### mnli\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mnli\\_matched\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mnli\\_mismatched\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli", "### Data Splits", "#### ax", "#### cola", "#### mnli", "#### mnli\\_matched", "#### mnli\\_mismatched", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli\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\n\n\nThanks to @patpizio, @jeswan, @thomwolf, @patrickvonplaten, @mariamabarham for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-acceptability-classification #task_ids-natural-language-inference #task_ids-semantic-similarity-scoring #task_ids-sentiment-classification #task_ids-text-scoring #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #qa-nli #coreference-nli #paraphrase-identification #doi-10.57967/hf/0065 #region-us \n", "### Dataset Summary\n\n\nGLUE, the General Language Understanding Evaluation benchmark (URL is a collection of resources for training, evaluating, and analyzing natural language understanding systems.", "### Supported Tasks and Leaderboards\n\n\nThe leaderboard for the GLUE benchmark can be found at this address. It comprises the following tasks:", "#### ax\n\n\nA manually-curated evaluation dataset for fine-grained analysis of system performance on a broad range of linguistic phenomena. This dataset evaluates sentence understanding through Natural Language Inference (NLI) problems. Use a model trained on MulitNLI to produce predictions for this dataset.", "#### cola\n\n\nThe Corpus of Linguistic Acceptability consists of English acceptability judgments drawn from books and journal articles on linguistic theory. Each example is a sequence of words annotated with whether it is a grammatical English sentence.", "#### mnli\n\n\nThe Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data.", "#### mnli\\_matched\n\n\nThe matched validation and test splits from MNLI. See the \"mnli\" BuilderConfig for additional information.", "#### mnli\\_mismatched\n\n\nThe mismatched validation and test splits from MNLI. See the \"mnli\" BuilderConfig for additional information.", "#### mrpc\n\n\nThe Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.", "#### qnli\n\n\nThe Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The authors of the benchmark convert the task into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue.", "#### qqp\n\n\nThe Quora Question Pairs2 dataset is a collection of question pairs from the community question-answering website Quora. The task is to determine whether a pair of questions are semantically equivalent.", "#### rte\n\n\nThe Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges. The authors of the benchmark combined the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009). Examples are constructed based on news and Wikipedia text. The authors of the benchmark convert all datasets to a two-class split, where for three-class datasets they collapse neutral and contradiction into not entailment, for consistency.", "#### sst2\n\n\nThe Stanford Sentiment Treebank consists of sentences from movie reviews and human annotations of their sentiment. The task is to predict the sentiment of a given sentence. It uses the two-way (positive/negative) class split, with only sentence-level labels.", "#### stsb\n\n\nThe Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data. Each pair is human-annotated with a similarity score from 1 to 5.", "#### wnli\n\n\nThe Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, the authors of the benchmark construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. They use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. The authors of the benchmark call converted dataset WNLI (Winograd NLI).", "### Languages\n\n\nThe language data in GLUE is in English (BCP-47 'en')\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### ax\n\n\n* Size of downloaded dataset files: 0.21 MB\n* Size of the generated dataset: 0.23 MB\n* Total amount of disk used: 0.44 MB\n\n\nAn example of 'test' looks as follows.", "#### cola\n\n\n* Size of downloaded dataset files: 0.36 MB\n* Size of the generated dataset: 0.58 MB\n* Total amount of disk used: 0.94 MB\n\n\nAn example of 'train' looks as follows.", "#### mnli\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 78.65 MB\n* Total amount of disk used: 376.95 MB\n\n\nAn example of 'train' looks as follows.", "#### mnli\\_matched\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 3.52 MB\n* Total amount of disk used: 301.82 MB\n\n\nAn example of 'test' looks as follows.", "#### mnli\\_mismatched\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 3.73 MB\n* Total amount of disk used: 302.02 MB\n\n\nAn example of 'test' looks as follows.", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### ax\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### cola\n\n\n* 'sentence': a 'string' feature.\n* 'label': a classification label, with possible values including 'unacceptable' (0), 'acceptable' (1).\n* 'idx': a 'int32' feature.", "#### mnli\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mnli\\_matched\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mnli\\_mismatched\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli", "### Data Splits", "#### ax", "#### cola", "#### mnli", "#### mnli\\_matched", "#### mnli\\_mismatched", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli\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\n\n\nThanks to @patpizio, @jeswan, @thomwolf, @patrickvonplaten, @mariamabarham for adding this dataset." ]

[ "task_categories:image-classification", "annotations_creators:expert-generated", "license:other", "region:us" ]

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[ "# Dataset Card for V4Design Europeana style dataset", "## 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\n\nThis dataset contains:\n> 1614 paintings belonging to the categories Baroque, Rococo, and Other. The images were obtained using the Europeana Search API, selecting open objects from the art thematic collection. 24k images were obtained, from which the current dataset was derived. The labels were added by the V4Design team, using a custom annotation tool. As described in the project documentation, other categories were used besides Baroque and Rococo. But for the sake of training a machine learning model we have retained only the categories with a significant number of annotations source\n\nThis version of the dataset is generated using the CSV file hosted on Zenodo. This CSV file contains the labels with URLs for the relevant images. Some of these URLs no longer resolve to an image. For consitency with the original dataset and if these URLs become valid again, these rows of the data are preserved here. If you want only successfully loaded images in your dataset, you can filter out the missing images as follows.", "### Supported Tasks and Leaderboards\n\nThis dataset is primarily intended for 'image-classification'.", "### 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 @davanstrien for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #annotations_creators-expert-generated #license-other #region-us \n", "# Dataset Card for V4Design Europeana style dataset", "## 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\n\nThis dataset contains:\n> 1614 paintings belonging to the categories Baroque, Rococo, and Other. The images were obtained using the Europeana Search API, selecting open objects from the art thematic collection. 24k images were obtained, from which the current dataset was derived. The labels were added by the V4Design team, using a custom annotation tool. As described in the project documentation, other categories were used besides Baroque and Rococo. But for the sake of training a machine learning model we have retained only the categories with a significant number of annotations source\n\nThis version of the dataset is generated using the CSV file hosted on Zenodo. This CSV file contains the labels with URLs for the relevant images. Some of these URLs no longer resolve to an image. For consitency with the original dataset and if these URLs become valid again, these rows of the data are preserved here. If you want only successfully loaded images in your dataset, you can filter out the missing images as follows.", "### Supported Tasks and Leaderboards\n\nThis dataset is primarily intended for 'image-classification'.", "### 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 @davanstrien for adding this dataset." ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:image-classification", "task_ids:multi-label-image-classification", "annotations_creators:expert-generated", "size_categories:10K<n<100K", "license:cc-by-nc-sa-4.0", "region:us" ]

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[]

[ "# Dataset Card for Early Printed Books Font Detection Dataset", "## 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:URL\n- Paper:: URL\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n> This dataset is composed of photos of various resolution of 35'623 pages of printed books dating from the 15th to the 18th century. Each page has been attributed by experts from one to five labels corresponding to the font groups used in the text, with two extra-classes for non-textual content and fonts not present in the following list: Antiqua, Bastaπrda, Fraktur, Gotico Antiqua, Greek, Hebrew, Italic, Rotunda, Schwabacher, and Textura.", "### Supported Tasks and Leaderboards\n\nThe primary use case for this datasets is\n- 'multi-label-image-classification': This dataset can be used to train a model for multi label image classification where each image can have one, or more labels. \n- 'image-classification': This dataset could also be adapted to only predict a single label for each image", "### Languages\n\nThe dataset includes books from a range of libraries (see below for further details). The paper doesn't provide a detailed overview of language breakdown. However, the books are from the 15th-18th century and appear to be dominated by European languages from that time period. The dataset also includes Hebrew.", "## Dataset Structure\n\nThis dataset has a single configuration.", "### Data Instances\n\nAn example instance from this dataset:", "### Data Fields\n\nThis dataset contains two fields:\n\n- 'image': the image of the book page\n- 'labels': one or more labels for the font used in the book page depicted in the 'image'", "### Data Splits\n\nThe dataset is broken into a train and test split with the following breakdown of number of examples: \n\n- train: 24,866 \n- test: 10,757", "## Dataset Creation", "### Curation Rationale\n\nThe dataset was created to help train and evaluate automatic methods for font detection. The paper describing the paper also states that:\n\n>data was cherry-picked, thus it is not statistically representative of what can be found in libraries. For example, as we had a small amount of Textura at the start, we specifically looked for more pages containing this font group, so we can expect that less than 3.6 % of randomly selected pages from libraries would contain Textura.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe images in this dataset are from books held by the British Library (London), Bayerische Staatsbibliothek München, Staatsbibliothek zu Berlin, Universitätsbibliothek Erlangen, Universitätsbibliothek Heidelberg, Staats- und Universitäatsbibliothek Göttingen, Stadt- und Universitätsbibliothek Köln, Württembergische Landesbibliothek Stuttgart and Herzog August Bibliothek Wolfenbüttel.", "#### 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 #task_ids-multi-label-image-classification #annotations_creators-expert-generated #size_categories-10K<n<100K #license-cc-by-nc-sa-4.0 #region-us \n", "# Dataset Card for Early Printed Books Font Detection Dataset", "## 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:URL\n- Paper:: URL\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n> This dataset is composed of photos of various resolution of 35'623 pages of printed books dating from the 15th to the 18th century. Each page has been attributed by experts from one to five labels corresponding to the font groups used in the text, with two extra-classes for non-textual content and fonts not present in the following list: Antiqua, Bastaπrda, Fraktur, Gotico Antiqua, Greek, Hebrew, Italic, Rotunda, Schwabacher, and Textura.", "### Supported Tasks and Leaderboards\n\nThe primary use case for this datasets is\n- 'multi-label-image-classification': This dataset can be used to train a model for multi label image classification where each image can have one, or more labels. \n- 'image-classification': This dataset could also be adapted to only predict a single label for each image", "### Languages\n\nThe dataset includes books from a range of libraries (see below for further details). The paper doesn't provide a detailed overview of language breakdown. However, the books are from the 15th-18th century and appear to be dominated by European languages from that time period. The dataset also includes Hebrew.", "## Dataset Structure\n\nThis dataset has a single configuration.", "### Data Instances\n\nAn example instance from this dataset:", "### Data Fields\n\nThis dataset contains two fields:\n\n- 'image': the image of the book page\n- 'labels': one or more labels for the font used in the book page depicted in the 'image'", "### Data Splits\n\nThe dataset is broken into a train and test split with the following breakdown of number of examples: \n\n- train: 24,866 \n- test: 10,757", "## Dataset Creation", "### Curation Rationale\n\nThe dataset was created to help train and evaluate automatic methods for font detection. The paper describing the paper also states that:\n\n>data was cherry-picked, thus it is not statistically representative of what can be found in libraries. For example, as we had a small amount of Textura at the start, we specifically looked for more pages containing this font group, so we can expect that less than 3.6 % of randomly selected pages from libraries would contain Textura.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe images in this dataset are from books held by the British Library (London), Bayerische Staatsbibliothek München, Staatsbibliothek zu Berlin, Universitätsbibliothek Erlangen, Universitätsbibliothek Heidelberg, Staats- und Universitäatsbibliothek Göttingen, Stadt- und Universitätsbibliothek Köln, Württembergische Landesbibliothek Stuttgart and Herzog August Bibliothek Wolfenbüttel.", "#### 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." ]

[ "task_categories:tabular-classification", "task_categories:tabular-regression", "region:us" ]

[]

[]

[ "### Dataset Summary\n\n\nBenchmark made of curation of various tabular data learning tasks, including:\n\n\n* Regression from Numerical and Categorical Features\n* Regression from Numerical Features\n* Classification from Numerical and Categorical Features\n* Classification from Numerical Features", "### Supported Tasks and Leaderboards\n\n\n* 'tabular-regression'\n* 'tabular-classification'\n\n\nDataset Structure\n-----------------", "### Data Splits\n\n\nThis dataset consists of four splits (folders) based on tasks and datasets included in tasks.\n\n\n* reg\\_num: Task identifier for regression on numerical features.\n* reg\\_cat: Task identifier for regression on numerical and categorical features.\n* clf\\_num: Task identifier for classification on numerical features.\n* clf\\_cat: Task identifier for classification on categorical features.\n\n\nDepending on the dataset you want to load, you can load the dataset by passing 'task\\_name/dataset\\_name' to 'data\\_files' argument of 'load\\_dataset' like below:\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis dataset is curated to benchmark performance of tree based models against neural networks. The process of picking the datasets for curation is mentioned in the paper as below:\n\n\n* Heterogeneous columns. Columns should correspond to features of different nature. This excludes\nimages or signal datasets where each column corresponds to the same signal on different sensors.\n* Not high dimensional. We only keep datasets with a d/n ratio below 1/10.\n* Undocumented datasets We remove datasets where too little information is available. We did keep\ndatasets with hidden column names if it was clear that the features were heterogeneous.\n* I.I.D. data. We remove stream-like datasets or time series.\n* Real-world data. We remove artificial datasets but keep some simulated datasets. The difference is\nsubtle, but we try to keep simulated datasets if learning these datasets are of practical importance\n(like the Higgs dataset), and not just a toy example to test specific model capabilities.\n* Not too small. We remove datasets with too few features (< 4) and too few samples (< 3 000). For\nbenchmarks on numerical features only, we remove categorical features before checking if enough\nfeatures and samples are remaining.\n* Not too easy. We remove datasets which are too easy. Specifically, we remove a dataset if a simple model (max of a single tree and a regression, logistic or OLS)\nreaches a score whose relative difference with the score of both a default Resnet (from Gorishniy et al. [2021]) and a default HistGradientBoosting model (from scikit learn)\nis below 5%. Other benchmarks use different metrics to remove too easy datasets, like removing datasets perfectly separated by a single decision classifier [Bischl et al., 2021],\nbut this ignores varying Bayes rate across datasets. As tree ensembles are superior to simple trees and logistic regresison [Fernández-Delgado et al., 2014],\na close score for the simple and powerful models suggests that we are already close to the best achievable score.\n* Not deterministic. We remove datasets where the target is a deterministic function of the data. This\nmostly means removing datasets on games like poker and chess. Indeed, we believe that these\ndatasets are very different from most real-world tabular datasets, and should be studied separately", "### Source Data\n\n\nNumerical Classification\n\n\n\nCategorical Classification\n\n\n\nNumerical Regression\n\n\n\nCategorical Regression", "### Dataset Curators\n\n\nLéo Grinsztajn, Edouard Oyallon, Gaël Varoquaux.", "### Licensing Information\n\n\nLéo Grinsztajn, Edouard Oyallon, Gaël Varoquaux. Why do tree-based models still outperform deep\nlearning on typical tabular data?. NeurIPS 2022 Datasets and Benchmarks Track, Nov 2022, New\nOrleans, United States. ffhal-03723551v2f" ]

[ "TAGS\n#task_categories-tabular-classification #task_categories-tabular-regression #region-us \n", "### Dataset Summary\n\n\nBenchmark made of curation of various tabular data learning tasks, including:\n\n\n* Regression from Numerical and Categorical Features\n* Regression from Numerical Features\n* Classification from Numerical and Categorical Features\n* Classification from Numerical Features", "### Supported Tasks and Leaderboards\n\n\n* 'tabular-regression'\n* 'tabular-classification'\n\n\nDataset Structure\n-----------------", "### Data Splits\n\n\nThis dataset consists of four splits (folders) based on tasks and datasets included in tasks.\n\n\n* reg\\_num: Task identifier for regression on numerical features.\n* reg\\_cat: Task identifier for regression on numerical and categorical features.\n* clf\\_num: Task identifier for classification on numerical features.\n* clf\\_cat: Task identifier for classification on categorical features.\n\n\nDepending on the dataset you want to load, you can load the dataset by passing 'task\\_name/dataset\\_name' to 'data\\_files' argument of 'load\\_dataset' like below:\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis dataset is curated to benchmark performance of tree based models against neural networks. The process of picking the datasets for curation is mentioned in the paper as below:\n\n\n* Heterogeneous columns. Columns should correspond to features of different nature. This excludes\nimages or signal datasets where each column corresponds to the same signal on different sensors.\n* Not high dimensional. We only keep datasets with a d/n ratio below 1/10.\n* Undocumented datasets We remove datasets where too little information is available. We did keep\ndatasets with hidden column names if it was clear that the features were heterogeneous.\n* I.I.D. data. We remove stream-like datasets or time series.\n* Real-world data. We remove artificial datasets but keep some simulated datasets. The difference is\nsubtle, but we try to keep simulated datasets if learning these datasets are of practical importance\n(like the Higgs dataset), and not just a toy example to test specific model capabilities.\n* Not too small. We remove datasets with too few features (< 4) and too few samples (< 3 000). For\nbenchmarks on numerical features only, we remove categorical features before checking if enough\nfeatures and samples are remaining.\n* Not too easy. We remove datasets which are too easy. Specifically, we remove a dataset if a simple model (max of a single tree and a regression, logistic or OLS)\nreaches a score whose relative difference with the score of both a default Resnet (from Gorishniy et al. [2021]) and a default HistGradientBoosting model (from scikit learn)\nis below 5%. Other benchmarks use different metrics to remove too easy datasets, like removing datasets perfectly separated by a single decision classifier [Bischl et al., 2021],\nbut this ignores varying Bayes rate across datasets. As tree ensembles are superior to simple trees and logistic regresison [Fernández-Delgado et al., 2014],\na close score for the simple and powerful models suggests that we are already close to the best achievable score.\n* Not deterministic. We remove datasets where the target is a deterministic function of the data. This\nmostly means removing datasets on games like poker and chess. Indeed, we believe that these\ndatasets are very different from most real-world tabular datasets, and should be studied separately", "### Source Data\n\n\nNumerical Classification\n\n\n\nCategorical Classification\n\n\n\nNumerical Regression\n\n\n\nCategorical Regression", "### Dataset Curators\n\n\nLéo Grinsztajn, Edouard Oyallon, Gaël Varoquaux.", "### Licensing Information\n\n\nLéo Grinsztajn, Edouard Oyallon, Gaël Varoquaux. Why do tree-based models still outperform deep\nlearning on typical tabular data?. NeurIPS 2022 Datasets and Benchmarks Track, Nov 2022, New\nOrleans, United States. ffhal-03723551v2f" ]

[ "task_categories:translation", "language:en", "language:jbo", "license:mit", "region:us" ]

[]

[ "en", "jbo" ]

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en2jb.\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-Lojban #license-mit #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en2jb.\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:" ]

[ "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#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" ]

[]

[]

[ "# Dataset Card for Product Reviews\n\nCustomer reviews of Amazon products, categorised by the number of stars assigned to each product. The dataset consists of several thousand reviews in English, German, and French.", "## Licensing information\n\nThis datasets is based on the 'amazon_reviews_multi' dataset." ]

[ "TAGS\n#region-us \n", "# Dataset Card for Product Reviews\n\nCustomer reviews of Amazon products, categorised by the number of stars assigned to each product. The dataset consists of several thousand reviews in English, German, and French.", "## Licensing information\n\nThis datasets is based on the 'amazon_reviews_multi' dataset." ]

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

[]

[ "en" ]

[]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #region-us \n" ]

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

[]

[ "en" ]

[]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #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: ARTeLab/it5-summarization-ilpost\n* Dataset: ARTeLab/ilpost\n* Config: ARTeLab--ilpost\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @morenolq 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-ilpost\n* Dataset: ARTeLab/ilpost\n* Config: ARTeLab--ilpost\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @morenolq 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: Summarization\n* Model: ARTeLab/it5-summarization-fanpage\n* Dataset: ARTeLab/fanpage\n* Config: ARTeLab--fanpage\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @morenolq 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: ARTeLab/fanpage\n* Config: ARTeLab--fanpage\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @morenolq 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-mlsum\n* Dataset: ARTeLab/mlsum-it\n* Config: ARTeLab--mlsum-it\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @morenolq 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-mlsum\n* Dataset: ARTeLab/mlsum-it\n* Config: ARTeLab--mlsum-it\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @morenolq for evaluating this model." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"hotel-reviews\"\n\nMore Information needed\n\nData was obtained from here" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"hotel-reviews\"\n\nMore Information needed\n\nData was obtained from here" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Naruto BLIP captions\n\n_Dataset used to train TBD._\n\nThe original images were obtained from URL 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.", "## Example stable diffusion outputs\n\n!URL\n> \"Bill Gates with a hoodie\", \"John Oliver with Naruto style\", \"Hello Kitty with Naruto style\", \"Lebron James with a hat\", \"Mickael Jackson as a ninja\", \"Banksy Street art of ninja\"\n\nIf you use this dataset, please cite it as:" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Naruto BLIP captions\n\n_Dataset used to train TBD._\n\nThe original images were obtained from URL 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.", "## Example stable diffusion outputs\n\n!URL\n> \"Bill Gates with a hoodie\", \"John Oliver with Naruto style\", \"Hello Kitty with Naruto style\", \"Lebron James with a hat\", \"Mickael Jackson as a ninja\", \"Banksy Street art of ninja\"\n\nIf you use this dataset, please cite it as:" ]

[ "region:us" ]

[]

[]

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

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

[ "task_categories:question-answering", "task_ids:open-domain-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:ar", "multilinguality:de", "multilinguality:ja", "multilinguality:hi", "multilinguality:pt", "multilinguality:en", "multilinguality:es", "multilinguality:it", "multilinguality:fr", "size_categories:100K<n<1M", "source_datasets:original", "license:cc-by-4.0", "region:us" ]

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[ "# Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering", "## 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- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Point of Contact: GitHub", "### Dataset Summary\n\nMintaka is a complex, natural, and multilingual question answering (QA) dataset composed of 20,000 question-answer pairs elicited from MTurk workers and annotated with Wikidata question and answer entities. Full details on the Mintaka dataset can be found in our paper: URL\n\nTo build Mintaka, we explicitly collected questions in 8 complexity types, as well as generic questions:\n\n- Count (e.g., Q: How many astronauts have been elected to Congress? A: 4)\n- Comparative (e.g., Q: Is Mont Blanc taller than Mount Rainier? A: Yes)\n- Superlative (e.g., Q: Who was the youngest tribute in the Hunger Games? A: Rue)\n- Ordinal (e.g., Q: Who was the last Ptolemaic ruler of Egypt? A: Cleopatra)\n- Multi-hop (e.g., Q: Who was the quarterback of the team that won Super Bowl 50? A: Peyton Manning)\n- Intersection (e.g., Q: Which movie was directed by Denis Villeneuve and stars Timothee Chalamet? A: Dune)\n- Difference (e.g., Q: Which Mario Kart game did Yoshi not appear in? A: Mario Kart Live: Home Circuit)\n- Yes/No (e.g., Q: Has Lady Gaga ever made a song with Ariana Grande? A: Yes.)\n- Generic (e.g., Q: Where was Michael Phelps born? A: Baltimore, Maryland)\n- We collected questions about 8 categories: Movies, Music, Sports, Books, Geography, Politics, Video Games, and History\n\nMintaka is one of the first large-scale complex, natural, and multilingual datasets that can be used for end-to-end question-answering models.", "### Supported Tasks and Leaderboards\n\nThe dataset can be used to train a model for question answering.\nTo ensure comparability, please refer to our evaluation script here: URL", "### Languages\n\nAll questions were written in English and translated into 8 additional languages: Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish.", "## Dataset Structure", "### Data Instances\n\nAn example of 'train' looks as follows.", "### Data Fields\n\nThe data fields are the same among all splits.\n\n'id': a unique ID for the given sample.\n\n'lang': the language of the question. \n\n'question': the original question elicited in the corresponding language.\n\n'answerText': the original answer text elicited in English.\n\n'category': the category of the question. Options are: geography, movies, history, books, politics, music, videogames, or sports\n\n'complexityType': the complexity type of the question. Options are: ordinal, intersection, count, superlative, yesno comparative, multihop, difference, or generic\n\n'questionEntity': a list of annotated question entities identified by crowd workers.\n\n'answerEntity': a list of annotated answer entities identified by crowd workers.", "### Data Splits\n\nFor each language, we split into train (14,000 samples), dev (2,000 samples), and test (4,000 samples) sets.", "### Personal and Sensitive Information\n\nThe corpora is free of personal or sensitive information.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nAmazon Alexa AI.", "### Licensing Information\n\nThis project is licensed under the CC-BY-4.0 License.\n\n\n\nPlease cite the following papers when using this dataset.", "### Contributions\n\nThanks to @afaji for adding this dataset." ]

[ "TAGS\n#task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-expert-generated #language_creators-found #multilinguality-ar #multilinguality-de #multilinguality-ja #multilinguality-hi #multilinguality-pt #multilinguality-en #multilinguality-es #multilinguality-it #multilinguality-fr #size_categories-100K<n<1M #source_datasets-original #license-cc-by-4.0 #region-us \n", "# Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering", "## 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- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Point of Contact: GitHub", "### Dataset Summary\n\nMintaka is a complex, natural, and multilingual question answering (QA) dataset composed of 20,000 question-answer pairs elicited from MTurk workers and annotated with Wikidata question and answer entities. Full details on the Mintaka dataset can be found in our paper: URL\n\nTo build Mintaka, we explicitly collected questions in 8 complexity types, as well as generic questions:\n\n- Count (e.g., Q: How many astronauts have been elected to Congress? A: 4)\n- Comparative (e.g., Q: Is Mont Blanc taller than Mount Rainier? A: Yes)\n- Superlative (e.g., Q: Who was the youngest tribute in the Hunger Games? A: Rue)\n- Ordinal (e.g., Q: Who was the last Ptolemaic ruler of Egypt? A: Cleopatra)\n- Multi-hop (e.g., Q: Who was the quarterback of the team that won Super Bowl 50? A: Peyton Manning)\n- Intersection (e.g., Q: Which movie was directed by Denis Villeneuve and stars Timothee Chalamet? A: Dune)\n- Difference (e.g., Q: Which Mario Kart game did Yoshi not appear in? A: Mario Kart Live: Home Circuit)\n- Yes/No (e.g., Q: Has Lady Gaga ever made a song with Ariana Grande? A: Yes.)\n- Generic (e.g., Q: Where was Michael Phelps born? A: Baltimore, Maryland)\n- We collected questions about 8 categories: Movies, Music, Sports, Books, Geography, Politics, Video Games, and History\n\nMintaka is one of the first large-scale complex, natural, and multilingual datasets that can be used for end-to-end question-answering models.", "### Supported Tasks and Leaderboards\n\nThe dataset can be used to train a model for question answering.\nTo ensure comparability, please refer to our evaluation script here: URL", "### Languages\n\nAll questions were written in English and translated into 8 additional languages: Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish.", "## Dataset Structure", "### Data Instances\n\nAn example of 'train' looks as follows.", "### Data Fields\n\nThe data fields are the same among all splits.\n\n'id': a unique ID for the given sample.\n\n'lang': the language of the question. \n\n'question': the original question elicited in the corresponding language.\n\n'answerText': the original answer text elicited in English.\n\n'category': the category of the question. Options are: geography, movies, history, books, politics, music, videogames, or sports\n\n'complexityType': the complexity type of the question. Options are: ordinal, intersection, count, superlative, yesno comparative, multihop, difference, or generic\n\n'questionEntity': a list of annotated question entities identified by crowd workers.\n\n'answerEntity': a list of annotated answer entities identified by crowd workers.", "### Data Splits\n\nFor each language, we split into train (14,000 samples), dev (2,000 samples), and test (4,000 samples) sets.", "### Personal and Sensitive Information\n\nThe corpora is free of personal or sensitive information.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nAmazon Alexa AI.", "### Licensing Information\n\nThis project is licensed under the CC-BY-4.0 License.\n\n\n\nPlease cite the following papers when using this dataset.", "### Contributions\n\nThanks to @afaji for adding this dataset." ]

[ "region:us" ]

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

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

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

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[ "#### Disclosure\n\n\n While its not perfect i hope that you are able to create some nice pieces with it, i am working on improving for the next embedding coming soon, if you have any suggestions or issues please let me know", "#### Usage\n\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder\nTo use it in a prompt add\n*art by skeleton slime* \n\n\nadd **[ ]** around it to reduce its weight.", "#### Included Files\n\n\n* 6500 steps *Usage: art by skeleton slime- 6500*\n* 10,000 steps *Usage: art by skeleton slime-10000*\n* 15,000 steps *Usage: art by skeleton slime*\n\n\ncheers \n\nWipeout", "#### Example Pictures", "#### prompt comparison\n\n\n[<img height=\"100%\" width=\"100%\" src=\"https://i.URL\n<h4> Licence \nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: \n\n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content\n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\n<a rel=\"noopener nofollow\" href=\"URL read the full license here</a>](https://i.URL target=)" ]

[ "TAGS\n#license-creativeml-openrail-m #region-us \n", "#### Disclosure\n\n\n While its not perfect i hope that you are able to create some nice pieces with it, i am working on improving for the next embedding coming soon, if you have any suggestions or issues please let me know", "#### Usage\n\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder\nTo use it in a prompt add\n*art by skeleton slime* \n\n\nadd **[ ]** around it to reduce its weight.", "#### Included Files\n\n\n* 6500 steps *Usage: art by skeleton slime- 6500*\n* 10,000 steps *Usage: art by skeleton slime-10000*\n* 15,000 steps *Usage: art by skeleton slime*\n\n\ncheers \n\nWipeout", "#### Example Pictures", "#### prompt comparison\n\n\n[<img height=\"100%\" width=\"100%\" src=\"https://i.URL\n<h4> Licence \nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: \n\n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content\n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\n<a rel=\"noopener nofollow\" href=\"URL read the full license here</a>](https://i.URL target=)" ]

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

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

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-6.7b\n* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1\n* Config: mathemakitten--winobias_antistereotype_test_cot_v1\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: facebook/opt-6.7b\n* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1\n* Config: mathemakitten--winobias_antistereotype_test_cot_v1\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

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

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-66b\n* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1\n* Config: mathemakitten--winobias_antistereotype_test_cot_v1\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: facebook/opt-66b\n* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1\n* Config: mathemakitten--winobias_antistereotype_test_cot_v1\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

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

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: ArthurZ/opt-350m\n* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1\n* Config: mathemakitten--winobias_antistereotype_test_cot_v1\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: ArthurZ/opt-350m\n* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1\n* Config: mathemakitten--winobias_antistereotype_test_cot_v1\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

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

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-30b\n* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1\n* Config: mathemakitten--winobias_antistereotype_test_cot_v1\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: facebook/opt-30b\n* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1\n* Config: mathemakitten--winobias_antistereotype_test_cot_v1\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

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

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: ArthurZ/opt-125m\n* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1\n* Config: mathemakitten--winobias_antistereotype_test_cot_v1\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: ArthurZ/opt-125m\n* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1\n* Config: mathemakitten--winobias_antistereotype_test_cot_v1\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." ]

[ "license:odc-by", "region:us" ]

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[ "# Dataset Card for SyNLI\nA synthetic NLI datasets from open domain sentences using T5 as data synthesizer. The data can be used to train sentence embedding models.", "## Data Fields\nThe data have several fields:\n- 'sent0': premise as a string\n- 'sent1': entailment hypothesis as a string\n- 'hard_neg': contradiction hypothesis as a string" ]

[ "TAGS\n#license-odc-by #region-us \n", "# Dataset Card for SyNLI\nA synthetic NLI datasets from open domain sentences using T5 as data synthesizer. The data can be used to train sentence embedding models.", "## Data Fields\nThe data have several fields:\n- 'sent0': premise as a string\n- 'sent1': entailment hypothesis as a string\n- 'hard_neg': contradiction hypothesis as a string" ]

[ "region:us" ]

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[ "# Dataset Card for \"Romance-cleaned-2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Romance-cleaned-2\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"latent_lsun_church_256px\"\n\nThis is derived from URL\n\nEach image is cropped to 256px square and encoded to a 4x32x32 latent representation using the same VAE as that employed by Stable Diffusion\n\nDecoding" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"latent_lsun_church_256px\"\n\nThis is derived from URL\n\nEach image is cropped to 256px square and encoded to a 4x32x32 latent representation using the same VAE as that employed by Stable Diffusion\n\nDecoding" ]

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"latent_afhqv2_256px\"\n\nEach image is cropped to 256px square and encoded to a 4x32x32 latent representation using the same VAE as that employed by Stable Diffusion\n\nDecoding" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"latent_afhqv2_256px\"\n\nEach image is cropped to 256px square and encoded to a 4x32x32 latent representation using the same VAE as that employed by Stable Diffusion\n\nDecoding" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:afl-3.0", "acl", "sciBERT", "sci", "11711", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for [naacl2022]", "## 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\n\nThis is a named entity recognition dataset annotated for the science entity recognition task, a project from the CMU 11-711 course.", "### Supported Tasks and Leaderboards\n\nNER task.", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\nA sample of the dataset\n{'id': '0',\n'tokens': ['We', 'sample', '50', 'negative', 'cases', 'from', 'T5LARGE', '+', 'GenMC', 'for', 'each', 'dataset'],\n'ner_tags':['O', 'O', 'O', 'O', 'O', 'O', 'B-MethodName', 'O', 'B-MethodName', 'O', 'O', 'O']}", "### Data Fields\n\nid,tokens,ner_tags\n\n- 'id': a 'string' feature give the sample index.\n- 'tokens': a 'list' of 'string' features give the sequence.\n- 'ner_tags': a 'list' of classification labels for each token in the sentence, with possible values including \n'O' (0), 'B-MethodName' (1), 'I-MethodName' (2), 'B-HyperparameterName' (3),'I-HyperparameterName' (4),'B-HyperparameterValue' (5),'I-HyperparameterValue' (6),'B-MetricName' (7),'I-MetricName' (8),'B-MetricValue' (9),'I-MetricValue' (10),'B-TaskName' (11),'I-TaskName' (12),'B-DatasetName' (13),'I-DatasetName' (14).", "### Data Splits\n\nData split into\nURL\nURL\nURL", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\nThe data is annotated by using labelstudio, the papers are collected from TACL and ACL 2022 conferences.", "#### Who are the annotators?\n\nXiaoyue Cui and Haotian Teng annotated the datasets.", "### 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 @xcui297; @haotianteng for adding this dataset." ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-afl-3.0 #acl #sciBERT #sci #11711 #region-us \n", "# Dataset Card for [naacl2022]", "## 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\n\nThis is a named entity recognition dataset annotated for the science entity recognition task, a project from the CMU 11-711 course.", "### Supported Tasks and Leaderboards\n\nNER task.", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\nA sample of the dataset\n{'id': '0',\n'tokens': ['We', 'sample', '50', 'negative', 'cases', 'from', 'T5LARGE', '+', 'GenMC', 'for', 'each', 'dataset'],\n'ner_tags':['O', 'O', 'O', 'O', 'O', 'O', 'B-MethodName', 'O', 'B-MethodName', 'O', 'O', 'O']}", "### Data Fields\n\nid,tokens,ner_tags\n\n- 'id': a 'string' feature give the sample index.\n- 'tokens': a 'list' of 'string' features give the sequence.\n- 'ner_tags': a 'list' of classification labels for each token in the sentence, with possible values including \n'O' (0), 'B-MethodName' (1), 'I-MethodName' (2), 'B-HyperparameterName' (3),'I-HyperparameterName' (4),'B-HyperparameterValue' (5),'I-HyperparameterValue' (6),'B-MetricName' (7),'I-MetricName' (8),'B-MetricValue' (9),'I-MetricValue' (10),'B-TaskName' (11),'I-TaskName' (12),'B-DatasetName' (13),'I-DatasetName' (14).", "### Data Splits\n\nData split into\nURL\nURL\nURL", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\nThe data is annotated by using labelstudio, the papers are collected from TACL and ACL 2022 conferences.", "#### Who are the annotators?\n\nXiaoyue Cui and Haotian Teng annotated the datasets.", "### 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 @xcui297; @haotianteng for adding this dataset." ]

[ "region:us" ]

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[ "# Dataset Card for \"latent_afhqv2_512px\"\n\nEach image is cropped to 512px square and encoded to a 4x64x64 latent representation using the same VAE as that employed by Stable Diffusion\n\nDecoding" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"latent_afhqv2_512px\"\n\nEach image is cropped to 512px square and encoded to a 4x64x64 latent representation using the same VAE as that employed by Stable Diffusion\n\nDecoding" ]

[ "task_categories:token-classification", "task_ids:part-of-speech", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "language:es", "license:cc-by-4.0", "region:us" ]

[]

[ "es" ]

[ "# UD_Spanish-AnCora", "## 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: Daniel Zeman", "### Dataset Summary\n\nThis dataset is composed of the annotations from the AnCora corpus, projected on the Universal Dependencies treebank. We use the POS annotations of this corpus as part of the EvalEs Spanish language benchmark.", "### Supported Tasks and Leaderboards\n\nPOS tagging", "### Languages\n\nThe dataset is in Spanish ('es-ES')", "## Dataset Structure", "### Data Instances\n\nThree conllu files.\n\nAnnotations are encoded in plain text files (UTF-8, normalized to NFC, using only the LF character as line break, including an LF character at the end of file) with three types of lines:\n\n1) Word lines containing the annotation of a word/token in 10 fields separated by single tab characters (see below).\n2) Blank lines marking sentence boundaries.\n3) Comment lines starting with hash (#).", "### Data Fields\nWord lines contain the following fields:\n\n1) ID: Word index, integer starting at 1 for each new sentence; may be a range for multiword tokens; may be a decimal number for empty nodes (decimal numbers can be lower than 1 but must be greater than 0).\n2) FORM: Word form or punctuation symbol.\n3) LEMMA: Lemma or stem of word form.\n4) UPOS: Universal part-of-speech tag.\n5) XPOS: Language-specific part-of-speech tag; underscore if not available.\n6) FEATS: List of morphological features from the universal feature inventory or from a defined language-specific extension; underscore if not available.\n7) HEAD: Head of the current word, which is either a value of ID or zero (0).\n8) DEPREL: Universal dependency relation to the HEAD (root iff HEAD = 0) or a defined language-specific subtype of one.\n9) DEPS: Enhanced dependency graph in the form of a list of head-deprel pairs.\n10) MISC: Any other annotation.\n \nFrom: URL", "### Data Splits\n\n- es_ancora-URL\n- es_ancora-URL\n- es_ancora-URL", "## Dataset Creation", "### Curation Rationale\n[N/A]", "### Source Data\n\nUD_Spanish-AnCora", "#### Initial Data Collection and Normalization\n\nThe original annotation was done in a constituency framework as a part of the AnCora project at the University of Barcelona. It was converted to dependencies by the Universal Dependencies team and used in the CoNLL 2009 shared task. The CoNLL 2009 version was later converted to HamleDT and to Universal Dependencies.\n\nFor more information on the AnCora project, visit the AnCora site.\n\nTo learn about the Universal Dependences, visit the webpage URL", "#### Who are the source language producers?\n\nFor more information on the AnCora corpus and its sources, visit the AnCora site.", "### Annotations", "#### Annotation process\n\nFor more information on the first AnCora annotation, visit the AnCora site.", "#### Who are the annotators?\n\nFor more information on the AnCora annotation team, visit the AnCora site.", "### 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\n\n[N/A]", "### Licensing Information\n\nThis work is licensed under a <a rel=\"license\" href=\"URL Attribution 4.0 International License</a>.\n\n\n\nThe following paper must be cited when using this corpus:\n\nTaulé, M., M.A. Martí, M. Recasens (2008) 'Ancora: Multilevel Annotated Corpora for Catalan and Spanish', Proceedings of 6th International Conference on Language Resources and Evaluation. Marrakesh (Morocco).\n\nTo cite the Universal Dependencies project:\n\nRueter, J. (Creator), Erina, O. (Contributor), Klementeva, J. (Contributor), Ryabov, I. (Contributor), Tyers, F. M. (Contributor), Zeman, D. (Contributor), Nivre, J. (Creator) (15 Nov 2020). Universal Dependencies version 2.7 Erzya JR. Universal Dependencies Consortium.", "### Contributions\n\n[N/A]" ]

[ "TAGS\n#task_categories-token-classification #task_ids-part-of-speech #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #language-Spanish #license-cc-by-4.0 #region-us \n", "# UD_Spanish-AnCora", "## 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: Daniel Zeman", "### Dataset Summary\n\nThis dataset is composed of the annotations from the AnCora corpus, projected on the Universal Dependencies treebank. We use the POS annotations of this corpus as part of the EvalEs Spanish language benchmark.", "### Supported Tasks and Leaderboards\n\nPOS tagging", "### Languages\n\nThe dataset is in Spanish ('es-ES')", "## Dataset Structure", "### Data Instances\n\nThree conllu files.\n\nAnnotations are encoded in plain text files (UTF-8, normalized to NFC, using only the LF character as line break, including an LF character at the end of file) with three types of lines:\n\n1) Word lines containing the annotation of a word/token in 10 fields separated by single tab characters (see below).\n2) Blank lines marking sentence boundaries.\n3) Comment lines starting with hash (#).", "### Data Fields\nWord lines contain the following fields:\n\n1) ID: Word index, integer starting at 1 for each new sentence; may be a range for multiword tokens; may be a decimal number for empty nodes (decimal numbers can be lower than 1 but must be greater than 0).\n2) FORM: Word form or punctuation symbol.\n3) LEMMA: Lemma or stem of word form.\n4) UPOS: Universal part-of-speech tag.\n5) XPOS: Language-specific part-of-speech tag; underscore if not available.\n6) FEATS: List of morphological features from the universal feature inventory or from a defined language-specific extension; underscore if not available.\n7) HEAD: Head of the current word, which is either a value of ID or zero (0).\n8) DEPREL: Universal dependency relation to the HEAD (root iff HEAD = 0) or a defined language-specific subtype of one.\n9) DEPS: Enhanced dependency graph in the form of a list of head-deprel pairs.\n10) MISC: Any other annotation.\n \nFrom: URL", "### Data Splits\n\n- es_ancora-URL\n- es_ancora-URL\n- es_ancora-URL", "## Dataset Creation", "### Curation Rationale\n[N/A]", "### Source Data\n\nUD_Spanish-AnCora", "#### Initial Data Collection and Normalization\n\nThe original annotation was done in a constituency framework as a part of the AnCora project at the University of Barcelona. It was converted to dependencies by the Universal Dependencies team and used in the CoNLL 2009 shared task. The CoNLL 2009 version was later converted to HamleDT and to Universal Dependencies.\n\nFor more information on the AnCora project, visit the AnCora site.\n\nTo learn about the Universal Dependences, visit the webpage URL", "#### Who are the source language producers?\n\nFor more information on the AnCora corpus and its sources, visit the AnCora site.", "### Annotations", "#### Annotation process\n\nFor more information on the first AnCora annotation, visit the AnCora site.", "#### Who are the annotators?\n\nFor more information on the AnCora annotation team, visit the AnCora site.", "### 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\n\n[N/A]", "### Licensing Information\n\nThis work is licensed under a <a rel=\"license\" href=\"URL Attribution 4.0 International License</a>.\n\n\n\nThe following paper must be cited when using this corpus:\n\nTaulé, M., M.A. Martí, M. Recasens (2008) 'Ancora: Multilevel Annotated Corpora for Catalan and Spanish', Proceedings of 6th International Conference on Language Resources and Evaluation. Marrakesh (Morocco).\n\nTo cite the Universal Dependencies project:\n\nRueter, J. (Creator), Erina, O. (Contributor), Klementeva, J. (Contributor), Ryabov, I. (Contributor), Tyers, F. M. (Contributor), Zeman, D. (Contributor), Nivre, J. (Creator) (15 Nov 2020). Universal Dependencies version 2.7 Erzya JR. Universal Dependencies Consortium.", "### Contributions\n\n[N/A]" ]

[ "task_categories:token-classification", "task_ids:part-of-speech", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "language:es", "region:us" ]

[]

[ "es" ]

[ "# CoNLL-NERC-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: Xavier Carreras", "### Dataset Summary\n\nCoNLL-NERC is the Spanish dataset of the CoNLL-2002 Shared Task (Tjong Kim Sang, 2002). The dataset is annotated with four types of named entities --persons, locations, organizations, and other miscellaneous entities-- formatted in the standard Beginning-Inside-Outside (BIO) format. The corpus consists of 8,324 train sentences with 19,400 named entities, 1,916 development sentences with 4,568 named entities, and 1,518 test sentences with 3,644 named entities.\n\nWe use this corpus as part of the EvalEs Spanish language benchmark.", "### Supported Tasks and Leaderboards\n\nNamed Entity Recognition and Classification", "### Languages\n\nThe dataset is in Spanish ('es-ES')", "## Dataset Structure", "### Data Instances\n\n<pre>\nEl DA O\nAbogado NC B-PER\nGeneral AQ I-PER\ndel SP I-PER\nEstado NC I-PER\n, Fc O\nDaryl VMI B-PER\nWilliams NC I-PER\n, Fc O\nsubrayó VMI O\nhoy RG O\nla DA O\nnecesidad NC O\nde SP O\ntomar VMN O\nmedidas NC O\npara SP O\nproteger VMN O\nal SP O\nsistema NC O\njudicial AQ O\naustraliano AQ O\nfrente RG O\na SP O\nuna DI O\npágina NC O\nde SP O\ninternet NC O\nque PR O\nimposibilita VMI O\nel DA O\ncumplimiento NC O\nde SP O\nlos DA O\nprincipios NC O\nbásicos AQ O\nde SP O\nla DA O\nLey NC B-MISC\n. Fp O\n</pre>", "### Data Fields\n\nEvery file has two columns, with the word form or punctuation symbol in the first one and the corresponding IOB tag in the second one. The different files are separated by an empty line.", "### Data Splits\n\n- URL: 273037 lines\n- URL: 54837 lines (used as dev)\n- URL: 53049 lines (used as test)", "## Dataset Creation", "### Curation Rationale\n[N/A]", "### Source Data\n\nThe data is a collection of news wire articles made available by the Spanish EFE News Agency. The articles are from May 2000.", "#### Initial Data Collection and Normalization\n\nFor more information visit the paper from the CoNLL-2002 Shared Task (Tjong Kim Sang, 2002).", "#### Who are the source language producers?\n\nFor more information visit the paper from the CoNLL-2002 Shared Task (Tjong Kim Sang, 2002).", "### Annotations", "#### Annotation process\n\nFor more information visit the paper from the CoNLL-2002 Shared Task (Tjong Kim Sang, 2002).", "#### Who are the annotators?\n\nThe annotation was carried out by the TALP Research Center2 of the Technical University of Catalonia (UPC) and the Center of Language and Computation (CLiC3 ) of the University of Barcelona (UB), and funded by the European Commission through the NAMIC pro ject (IST-1999-12392).\n\nFor more information visit the paper from the CoNLL-2002 Shared Task (Tjong Kim Sang, 2002).", "### Personal and Sensitive Information\n\n[N/A]", "## 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", "### Licensing information\n\n\n\n\nThe following paper must be cited when using this corpus:\n\nErik F. Tjong Kim Sang. 2002. Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition. In COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002).", "### Contributions\n\n[N/A]" ]

[ "TAGS\n#task_categories-token-classification #task_ids-part-of-speech #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #language-Spanish #region-us \n", "# CoNLL-NERC-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: Xavier Carreras", "### Dataset Summary\n\nCoNLL-NERC is the Spanish dataset of the CoNLL-2002 Shared Task (Tjong Kim Sang, 2002). The dataset is annotated with four types of named entities --persons, locations, organizations, and other miscellaneous entities-- formatted in the standard Beginning-Inside-Outside (BIO) format. The corpus consists of 8,324 train sentences with 19,400 named entities, 1,916 development sentences with 4,568 named entities, and 1,518 test sentences with 3,644 named entities.\n\nWe use this corpus as part of the EvalEs Spanish language benchmark.", "### Supported Tasks and Leaderboards\n\nNamed Entity Recognition and Classification", "### Languages\n\nThe dataset is in Spanish ('es-ES')", "## Dataset Structure", "### Data Instances\n\n<pre>\nEl DA O\nAbogado NC B-PER\nGeneral AQ I-PER\ndel SP I-PER\nEstado NC I-PER\n, Fc O\nDaryl VMI B-PER\nWilliams NC I-PER\n, Fc O\nsubrayó VMI O\nhoy RG O\nla DA O\nnecesidad NC O\nde SP O\ntomar VMN O\nmedidas NC O\npara SP O\nproteger VMN O\nal SP O\nsistema NC O\njudicial AQ O\naustraliano AQ O\nfrente RG O\na SP O\nuna DI O\npágina NC O\nde SP O\ninternet NC O\nque PR O\nimposibilita VMI O\nel DA O\ncumplimiento NC O\nde SP O\nlos DA O\nprincipios NC O\nbásicos AQ O\nde SP O\nla DA O\nLey NC B-MISC\n. Fp O\n</pre>", "### Data Fields\n\nEvery file has two columns, with the word form or punctuation symbol in the first one and the corresponding IOB tag in the second one. The different files are separated by an empty line.", "### Data Splits\n\n- URL: 273037 lines\n- URL: 54837 lines (used as dev)\n- URL: 53049 lines (used as test)", "## Dataset Creation", "### Curation Rationale\n[N/A]", "### Source Data\n\nThe data is a collection of news wire articles made available by the Spanish EFE News Agency. The articles are from May 2000.", "#### Initial Data Collection and Normalization\n\nFor more information visit the paper from the CoNLL-2002 Shared Task (Tjong Kim Sang, 2002).", "#### Who are the source language producers?\n\nFor more information visit the paper from the CoNLL-2002 Shared Task (Tjong Kim Sang, 2002).", "### Annotations", "#### Annotation process\n\nFor more information visit the paper from the CoNLL-2002 Shared Task (Tjong Kim Sang, 2002).", "#### Who are the annotators?\n\nThe annotation was carried out by the TALP Research Center2 of the Technical University of Catalonia (UPC) and the Center of Language and Computation (CLiC3 ) of the University of Barcelona (UB), and funded by the European Commission through the NAMIC pro ject (IST-1999-12392).\n\nFor more information visit the paper from the CoNLL-2002 Shared Task (Tjong Kim Sang, 2002).", "### Personal and Sensitive Information\n\n[N/A]", "## 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", "### Licensing information\n\n\n\n\nThe following paper must be cited when using this corpus:\n\nErik F. Tjong Kim Sang. 2002. Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition. In COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002).", "### Contributions\n\n[N/A]" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"latent_lsun_church_128px\"\n\nEach image is cropped to 128px square and encoded to a 4x16x16 latent representation using the same VAE as that employed by Stable Diffusion\n\nDecoding" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"latent_lsun_church_128px\"\n\nEach image is cropped to 128px square and encoded to a 4x16x16 latent representation using the same VAE as that employed by Stable Diffusion\n\nDecoding" ]

[ "task_categories:fill-mask", "task_ids:masked-language-modeling", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:amh", "language:orm", "language:lin", "language:hau", "language:ibo", "language:kin", "language:lug", "language:luo", "language:pcm", "language:swa", "language:wol", "language:yor", "language:bam", "language:bbj", "language:ewe", "language:fon", "language:mos", "language:nya", "language:sna", "language:tsn", "language:twi", "language:xho", "language:zul", "license:cc-by-4.0", "afrolm", "active learning", "language modeling", "research papers", "natural language processing", "self-active learning", "arxiv:2211.03263", "region:us" ]

[ "2211.03263" ]

[ "amh", "orm", "lin", "hau", "ibo", "kin", "lug", "luo", "pcm", "swa", "wol", "yor", "bam", "bbj", "ewe", "fon", "mos", "nya", "sna", "tsn", "twi", "xho", "zul" ]

[]

[ "TAGS\n#task_categories-fill-mask #task_ids-masked-language-modeling #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-Amharic #language-Oromo #language-Lingala #language-Hausa #language-Igbo #language-Kinyarwanda #language-Ganda #language-Luo (Kenya and Tanzania) #language-Nigerian Pidgin #language-Swahili (macrolanguage) #language-Wolof #language-Yoruba #language-Bambara #language-Ghomálá' #language-Ewe #language-Fon #language-Mossi #language-Nyanja #language-Shona #language-Tswana #language-Twi #language-Xhosa #language-Zulu #license-cc-by-4.0 #afrolm #active learning #language modeling #research papers #natural language processing #self-active learning #arxiv-2211.03263 #region-us \n" ]

[ "task_categories:text-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:multilingual", "language:es", "license:cc-by-nc-4.0", "region:us" ]

[]

[ "es" ]

[ "# MLDoc", "## 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", "### Dataset Summary\n\nFor document classification, we use the Multilingual Document Classification Corpus (MLDoc) (Schwenk and Li, 2018), a cross-lingual document classification dataset covering 8 languages. We use the Spanish portion to evaluate our models on monolingual classification as part of the EvalEs Spanish language benchmark. The corpus consists of 14,458 news articles from Reuters classified in four categories: Corporate/Industrial, Economics, Government/Social and Markets.\n\nThis dataset can't be downloaded straight from HuggingFace as it requires signing specific agreements. The detailed instructions on how to download it can be found in this repository.", "### Supported Tasks and Leaderboards\n\nText Classification", "### Languages\n\nThe dataset is in English, German, French, Spanish, Italian, Russian, Japanese and Chinese.", "## Dataset Structure", "### Data Instances\n\n<pre>\nMCAT\tb' FRANCFORT, 17 feb (Reuter) - La Bolsa de Francfort abri\\xc3\\xb3 la sesi\\xc3\\xb3n de corros con baja por la ca\\xc3\\xadda del viernes en Wall Street y una toma de beneficios. El d\\xc3\\xb3lar ayudaba a apuntalar al mercado, que pronto podr\\xc3\\xada reanudar su tendencia alcista. Volkswagen bajaba por los da\\xc3\\xb1os ocasionados por la huelga de camioneros en Espa\\xc3\\xb1a. Preussag participaba en un joint venture de exploraci\\xc3\\xb3n petrol\\xc3\\xadfera en Filipinas con Atlantic Richfield Co. A las 0951 GMT, el Dax 30 bajaba 10,49 puntos, un 0,32 pct, a 3.237,69 tras abrir a un m\\xc3\\xa1ximo de 3.237,69. (c) Reuters Limited 1997. '\n</pre>", "### Data Fields\n\n- Label: CCAT (Corporate/Industrial), ECAT (Economics), GCAT (Government/Social) and MCAT (Markets)\n- Text", "### Data Splits\n\n- URL: 9,458 lines\n- URL: 1,000 lines \n- URL: 4,000 lines", "## Dataset Creation", "### Curation Rationale\n\n[N/A]", "### Source Data\n\nThe source data is from the Reuters Corpus. In 2000, Reuters Ltd made available a large collection of Reuters News stories for use in research and development of natural language processing, information retrieval, and machine learning systems. This corpus, known as \"Reuters Corpus, Volume 1\" or RCV1, is significantly larger than the older, well-known Reuters-21578 collection heavily used in the text classification community.\n\nFor more information visit the paper (Lewis et al., 2004).", "#### Initial Data Collection and Normalization\n\nFor more information visit the paper (Lewis et al., 2004).", "#### Who are the source language producers?\n\nFor more information visit the paper (Lewis et al., 2004).", "### Annotations", "#### Annotation process\n\nFor more information visit the paper (Schwenk and Li, 2018; Lewis et al., 2004).", "#### Who are the annotators?\n\nFor more information visit the paper (Schwenk and Li, 2018; Lewis et al., 2004).", "### Personal and Sensitive Information\n\n[N/A]", "## 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\n\n[N/A]", "### Licensing Information\n\nAccess to the actual news stories of the Reuters Corpus (both RCV1 and RCV2) requires a NIST agreement. The stories in the Reuters Corpus are under the copyright of Reuters Ltd and/or Thomson Reuters, and their use is governed by the following agreements:\n- Organizational agreement: This agreement must be signed by the person responsible for the data at your organization, and sent to NIST.\n- Individual agreement: This agreement must be signed by all researchers using the Reuters Corpus at your organization, and kept on file at your organization. \n\nFor more information about the agreement see here\n\n\n\nThe following paper must be cited when using this corpus:" ]

[ "TAGS\n#task_categories-text-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-multilingual #language-Spanish #license-cc-by-nc-4.0 #region-us \n", "# MLDoc", "## 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", "### Dataset Summary\n\nFor document classification, we use the Multilingual Document Classification Corpus (MLDoc) (Schwenk and Li, 2018), a cross-lingual document classification dataset covering 8 languages. We use the Spanish portion to evaluate our models on monolingual classification as part of the EvalEs Spanish language benchmark. The corpus consists of 14,458 news articles from Reuters classified in four categories: Corporate/Industrial, Economics, Government/Social and Markets.\n\nThis dataset can't be downloaded straight from HuggingFace as it requires signing specific agreements. The detailed instructions on how to download it can be found in this repository.", "### Supported Tasks and Leaderboards\n\nText Classification", "### Languages\n\nThe dataset is in English, German, French, Spanish, Italian, Russian, Japanese and Chinese.", "## Dataset Structure", "### Data Instances\n\n<pre>\nMCAT\tb' FRANCFORT, 17 feb (Reuter) - La Bolsa de Francfort abri\\xc3\\xb3 la sesi\\xc3\\xb3n de corros con baja por la ca\\xc3\\xadda del viernes en Wall Street y una toma de beneficios. El d\\xc3\\xb3lar ayudaba a apuntalar al mercado, que pronto podr\\xc3\\xada reanudar su tendencia alcista. Volkswagen bajaba por los da\\xc3\\xb1os ocasionados por la huelga de camioneros en Espa\\xc3\\xb1a. Preussag participaba en un joint venture de exploraci\\xc3\\xb3n petrol\\xc3\\xadfera en Filipinas con Atlantic Richfield Co. A las 0951 GMT, el Dax 30 bajaba 10,49 puntos, un 0,32 pct, a 3.237,69 tras abrir a un m\\xc3\\xa1ximo de 3.237,69. (c) Reuters Limited 1997. '\n</pre>", "### Data Fields\n\n- Label: CCAT (Corporate/Industrial), ECAT (Economics), GCAT (Government/Social) and MCAT (Markets)\n- Text", "### Data Splits\n\n- URL: 9,458 lines\n- URL: 1,000 lines \n- URL: 4,000 lines", "## Dataset Creation", "### Curation Rationale\n\n[N/A]", "### Source Data\n\nThe source data is from the Reuters Corpus. In 2000, Reuters Ltd made available a large collection of Reuters News stories for use in research and development of natural language processing, information retrieval, and machine learning systems. This corpus, known as \"Reuters Corpus, Volume 1\" or RCV1, is significantly larger than the older, well-known Reuters-21578 collection heavily used in the text classification community.\n\nFor more information visit the paper (Lewis et al., 2004).", "#### Initial Data Collection and Normalization\n\nFor more information visit the paper (Lewis et al., 2004).", "#### Who are the source language producers?\n\nFor more information visit the paper (Lewis et al., 2004).", "### Annotations", "#### Annotation process\n\nFor more information visit the paper (Schwenk and Li, 2018; Lewis et al., 2004).", "#### Who are the annotators?\n\nFor more information visit the paper (Schwenk and Li, 2018; Lewis et al., 2004).", "### Personal and Sensitive Information\n\n[N/A]", "## 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\n\n[N/A]", "### Licensing Information\n\nAccess to the actual news stories of the Reuters Corpus (both RCV1 and RCV2) requires a NIST agreement. The stories in the Reuters Corpus are under the copyright of Reuters Ltd and/or Thomson Reuters, and their use is governed by the following agreements:\n- Organizational agreement: This agreement must be signed by the person responsible for the data at your organization, and sent to NIST.\n- Individual agreement: This agreement must be signed by all researchers using the Reuters Corpus at your organization, and kept on file at your organization. \n\nFor more information about the agreement see here\n\n\n\nThe following paper must be cited when using this corpus:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"latent_celebA_256px\"\n\nEach image is cropped to 256px square and encoded to a 4x32x32 latent representation using the same VAE as that employed by Stable Diffusion\n\nDecoding" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"latent_celebA_256px\"\n\nEach image is cropped to 256px square and encoded to a 4x32x32 latent representation using the same VAE as that employed by Stable Diffusion\n\nDecoding" ]

[ "task_categories:translation", "size_categories:unknown", "language:it", "license:unknown", "conditional-text-generation", "region:us" ]

[]

[ "it" ]

[ "# Dataset Card for mt_en_it", "## Table of Contents\n\n- Dataset Card for mt_en_it\n - Table of Contents\n - Dataset Summary\n - Languages\n - Dataset Structure\n - Data Instances\n - Data Fields\n - Dataset Creation", "### Dataset Summary\nThis dataset comprises traditional Neapolitan songs from napoligrafia translated into Italian.", "### Languages\n- italian-to-neapolitan", "### Data Instances\nA sample from the dataset.\n\nThe text is provided without further preprocessing or tokenization.", "### Data Fields\n- 'url': source URL.\n- 'napoletano': Neapolitan text.\n- 'italiano': Italian text.", "### Dataset Creation\nThe dataset was created by scraping napoligrafia songs." ]

[ "TAGS\n#task_categories-translation #size_categories-unknown #language-Italian #license-unknown #conditional-text-generation #region-us \n", "# Dataset Card for mt_en_it", "## Table of Contents\n\n- Dataset Card for mt_en_it\n - Table of Contents\n - Dataset Summary\n - Languages\n - Dataset Structure\n - Data Instances\n - Data Fields\n - Dataset Creation", "### Dataset Summary\nThis dataset comprises traditional Neapolitan songs from napoligrafia translated into Italian.", "### Languages\n- italian-to-neapolitan", "### Data Instances\nA sample from the dataset.\n\nThe text is provided without further preprocessing or tokenization.", "### Data Fields\n- 'url': source URL.\n- 'napoletano': Neapolitan text.\n- 'italiano': Italian text.", "### Dataset Creation\nThe dataset was created by scraping napoligrafia songs." ]

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

[]

[]

[ "#### Disclosure\n\n\n While its not perfect i hope that you are able to create some nice pictures, i am working on improving for the next embedding coming soon, if you have any suggestions or issues please let me know", "#### Usage\n\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder\nTo use it in a prompt add\n*art by spectral\\_wind* \n\n\nadd **[ ]** around it to reduce its weight.", "#### Included Files\n\n\n* 6500 steps *Usage: art by spectral\\_wind- 6500*\n* 10,000 steps *Usage: art by spectral\\_wind-10000*\n* 15,000 steps *Usage: art by spectral\\_wind*\n\n\ncheers \n\nWipeout", "#### Example Pictures", "#### prompt comparison\n\n\n[<img height=\"100%\" width=\"100%\" src=\"https://i.URL\n<h4> Licence \nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: \n\n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content\n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\n<a rel=\"noopener nofollow\" href=\"URL read the full license here</a>](https://i.URL target=)" ]

[ "TAGS\n#license-creativeml-openrail-m #region-us \n", "#### Disclosure\n\n\n While its not perfect i hope that you are able to create some nice pictures, i am working on improving for the next embedding coming soon, if you have any suggestions or issues please let me know", "#### Usage\n\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder\nTo use it in a prompt add\n*art by spectral\\_wind* \n\n\nadd **[ ]** around it to reduce its weight.", "#### Included Files\n\n\n* 6500 steps *Usage: art by spectral\\_wind- 6500*\n* 10,000 steps *Usage: art by spectral\\_wind-10000*\n* 15,000 steps *Usage: art by spectral\\_wind*\n\n\ncheers \n\nWipeout", "#### Example Pictures", "#### prompt comparison\n\n\n[<img height=\"100%\" width=\"100%\" src=\"https://i.URL\n<h4> Licence \nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: \n\n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content\n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\n<a rel=\"noopener nofollow\" href=\"URL read the full license here</a>](https://i.URL target=)" ]

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

[]

[]

[ "# Media Dataset for IRAN Protests\n\nFollowing recent protests in Iran corresponding to __Mahsa Amini__'s death, her name has been a trend on social media like Twitter( #MahsaAmini , #مهسا_امینی).\nUntile Octore 15, 2022, there have been 300+ million tweets on Twitter and among them, there are many posts including media files like images and videos.\n\nIt will be helpful for Media Companies, Developers or whoever is interested in reviewing and assessing these files. Our data has been collected since September 14, 2022.\nMore than __3.1M records__ (including(unique) 2.5M images and 600 thousands videos) is available in current dataset.", "### Dataset:\n1. created_at: datetime when the tweet posted\n2. md_url: URL of the media\n3. md_type: show media type (image or video)\n4. tw_id: tweet id", "## Disclaimer:\nThe dataset includes any type of media based on what is published by users on Twitter. So, there will be no accusation against the publisher of this dataset.\n\n\n\nFor more information about dataset and the way that able to download the read media files, please refer to Github." ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# Media Dataset for IRAN Protests\n\nFollowing recent protests in Iran corresponding to __Mahsa Amini__'s death, her name has been a trend on social media like Twitter( #MahsaAmini , #مهسا_امینی).\nUntile Octore 15, 2022, there have been 300+ million tweets on Twitter and among them, there are many posts including media files like images and videos.\n\nIt will be helpful for Media Companies, Developers or whoever is interested in reviewing and assessing these files. Our data has been collected since September 14, 2022.\nMore than __3.1M records__ (including(unique) 2.5M images and 600 thousands videos) is available in current dataset.", "### Dataset:\n1. created_at: datetime when the tweet posted\n2. md_url: URL of the media\n3. md_type: show media type (image or video)\n4. tw_id: tweet id", "## Disclaimer:\nThe dataset includes any type of media based on what is published by users on Twitter. So, there will be no accusation against the publisher of this dataset.\n\n\n\nFor more information about dataset and the way that able to download the read media files, please refer to Github." ]

[ "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: inverse-scaling/opt-1.3b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1\n* Config: mathemakitten--winobias_antistereotype_test_cot_v1\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-1.3b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1\n* Config: mathemakitten--winobias_antistereotype_test_cot_v1\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: inverse-scaling/opt-2.7b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1\n* Config: mathemakitten--winobias_antistereotype_test_cot_v1\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-2.7b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1\n* Config: mathemakitten--winobias_antistereotype_test_cot_v1\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: inverse-scaling/opt-13b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1\n* Config: mathemakitten--winobias_antistereotype_test_cot_v1\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-13b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1\n* Config: mathemakitten--winobias_antistereotype_test_cot_v1\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." ]

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

[]

[]

[ "# Maccha style embedding", "## Samples\n\n<img alt=\"Samples\" src=\"URL style=\"max-height: 80vh\"/>\n<img alt=\"Comparsion\" src=\"URL style=\"max-height: 80vh\"/>", "## About\n\nUse this Stable Diffusion embedding to achieve style of Matcha_ / maccha_(mochancc) Pixiv", "## Usage\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder \nTo use it in a prompt add __art by maccha-*__ \n\nAdd ( :1.0) around it to modify its weight", "## Included Files\n- 8000 steps Usage: art by maccha-8000\n- 15000 steps Usage: art by maccha-15000", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:\n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content\n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here" ]

[ "TAGS\n#license-creativeml-openrail-m #region-us \n", "# Maccha style embedding", "## Samples\n\n<img alt=\"Samples\" src=\"URL style=\"max-height: 80vh\"/>\n<img alt=\"Comparsion\" src=\"URL style=\"max-height: 80vh\"/>", "## About\n\nUse this Stable Diffusion embedding to achieve style of Matcha_ / maccha_(mochancc) Pixiv", "## Usage\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder \nTo use it in a prompt add __art by maccha-*__ \n\nAdd ( :1.0) around it to modify its weight", "## Included Files\n- 8000 steps Usage: art by maccha-8000\n- 15000 steps Usage: art by maccha-15000", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:\n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content\n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here" ]

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

[]

[]

[ "# Yuki Miku 2017 embedding", "## Samples\n\n<img alt=\"Samples\" src=\"URL style=\"max-height: 80vh\"/>\n<img alt=\"Comparsion\" src=\"URL style=\"max-height: 80vh\"/>", "## About\n\nUse this Stable Diffusion embedding to achieve the Hatsune Miku Yuki Style 2017 outfit", "## Usage\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder \nTo use it in a prompt add __yuki_miku_2017-*__ \n\nAdd ( :1.0) around it to modify its weight", "## Included Files\n- 8000 steps Usage: yuki_miku_2017-8000\n- 10000 steps Usage: yuki_miku_2017-10000\n- 15000 steps Usage: yuki_miku_2017-15000", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:\n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content\n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here" ]

[ "TAGS\n#license-creativeml-openrail-m #region-us \n", "# Yuki Miku 2017 embedding", "## Samples\n\n<img alt=\"Samples\" src=\"URL style=\"max-height: 80vh\"/>\n<img alt=\"Comparsion\" src=\"URL style=\"max-height: 80vh\"/>", "## About\n\nUse this Stable Diffusion embedding to achieve the Hatsune Miku Yuki Style 2017 outfit", "## Usage\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder \nTo use it in a prompt add __yuki_miku_2017-*__ \n\nAdd ( :1.0) around it to modify its weight", "## Included Files\n- 8000 steps Usage: yuki_miku_2017-8000\n- 10000 steps Usage: yuki_miku_2017-10000\n- 15000 steps Usage: yuki_miku_2017-15000", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:\n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content\n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here" ]

[ "region:us" ]

[]

[]

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

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

[ "task_categories:text-classification", "task_ids:acceptability-classification", "task_ids:natural-language-inference", "task_ids:semantic-similarity-scoring", "task_ids:sentiment-classification", "task_ids:text-scoring", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "qa-nli", "coreference-nli", "paraphrase-identification", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nGLUE, the General Language Understanding Evaluation benchmark (URL is a collection of resources for training, evaluating, and analyzing natural language understanding systems.", "### Supported Tasks and Leaderboards\n\n\nThe leaderboard for the GLUE benchmark can be found at this address. It comprises the following tasks:", "#### ax\n\n\nA manually-curated evaluation dataset for fine-grained analysis of system performance on a broad range of linguistic phenomena. This dataset evaluates sentence understanding through Natural Language Inference (NLI) problems. Use a model trained on MulitNLI to produce predictions for this dataset.", "#### cola\n\n\nThe Corpus of Linguistic Acceptability consists of English acceptability judgments drawn from books and journal articles on linguistic theory. Each example is a sequence of words annotated with whether it is a grammatical English sentence.", "#### mnli\n\n\nThe Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data.", "#### mnli\\_matched\n\n\nThe matched validation and test splits from MNLI. See the \"mnli\" BuilderConfig for additional information.", "#### mnli\\_mismatched\n\n\nThe mismatched validation and test splits from MNLI. See the \"mnli\" BuilderConfig for additional information.", "#### mrpc\n\n\nThe Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.", "#### qnli\n\n\nThe Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The authors of the benchmark convert the task into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue.", "#### qqp\n\n\nThe Quora Question Pairs2 dataset is a collection of question pairs from the community question-answering website Quora. The task is to determine whether a pair of questions are semantically equivalent.", "#### rte\n\n\nThe Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges. The authors of the benchmark combined the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009). Examples are constructed based on news and Wikipedia text. The authors of the benchmark convert all datasets to a two-class split, where for three-class datasets they collapse neutral and contradiction into not entailment, for consistency.", "#### sst2\n\n\nThe Stanford Sentiment Treebank consists of sentences from movie reviews and human annotations of their sentiment. The task is to predict the sentiment of a given sentence. It uses the two-way (positive/negative) class split, with only sentence-level labels.", "#### stsb\n\n\nThe Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data. Each pair is human-annotated with a similarity score from 1 to 5.", "#### wnli\n\n\nThe Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, the authors of the benchmark construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. They use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. The authors of the benchmark call converted dataset WNLI (Winograd NLI).", "### Languages\n\n\nThe language data in GLUE is in English (BCP-47 'en')\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### ax\n\n\n* Size of downloaded dataset files: 0.21 MB\n* Size of the generated dataset: 0.23 MB\n* Total amount of disk used: 0.44 MB\n\n\nAn example of 'test' looks as follows.", "#### cola\n\n\n* Size of downloaded dataset files: 0.36 MB\n* Size of the generated dataset: 0.58 MB\n* Total amount of disk used: 0.94 MB\n\n\nAn example of 'train' looks as follows.", "#### mnli\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 78.65 MB\n* Total amount of disk used: 376.95 MB\n\n\nAn example of 'train' looks as follows.", "#### mnli\\_matched\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 3.52 MB\n* Total amount of disk used: 301.82 MB\n\n\nAn example of 'test' looks as follows.", "#### mnli\\_mismatched\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 3.73 MB\n* Total amount of disk used: 302.02 MB\n\n\nAn example of 'test' looks as follows.", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### ax\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### cola\n\n\n* 'sentence': a 'string' feature.\n* 'label': a classification label, with possible values including 'unacceptable' (0), 'acceptable' (1).\n* 'idx': a 'int32' feature.", "#### mnli\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mnli\\_matched\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mnli\\_mismatched\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli", "### Data Splits", "#### ax", "#### cola", "#### mnli", "#### mnli\\_matched", "#### mnli\\_mismatched", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli\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\n\n\nThanks to @patpizio, @jeswan, @thomwolf, @patrickvonplaten, @mariamabarham for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-acceptability-classification #task_ids-natural-language-inference #task_ids-semantic-similarity-scoring #task_ids-sentiment-classification #task_ids-text-scoring #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #qa-nli #coreference-nli #paraphrase-identification #region-us \n", "### Dataset Summary\n\n\nGLUE, the General Language Understanding Evaluation benchmark (URL is a collection of resources for training, evaluating, and analyzing natural language understanding systems.", "### Supported Tasks and Leaderboards\n\n\nThe leaderboard for the GLUE benchmark can be found at this address. It comprises the following tasks:", "#### ax\n\n\nA manually-curated evaluation dataset for fine-grained analysis of system performance on a broad range of linguistic phenomena. This dataset evaluates sentence understanding through Natural Language Inference (NLI) problems. Use a model trained on MulitNLI to produce predictions for this dataset.", "#### cola\n\n\nThe Corpus of Linguistic Acceptability consists of English acceptability judgments drawn from books and journal articles on linguistic theory. Each example is a sequence of words annotated with whether it is a grammatical English sentence.", "#### mnli\n\n\nThe Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data.", "#### mnli\\_matched\n\n\nThe matched validation and test splits from MNLI. See the \"mnli\" BuilderConfig for additional information.", "#### mnli\\_mismatched\n\n\nThe mismatched validation and test splits from MNLI. See the \"mnli\" BuilderConfig for additional information.", "#### mrpc\n\n\nThe Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.", "#### qnli\n\n\nThe Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The authors of the benchmark convert the task into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue.", "#### qqp\n\n\nThe Quora Question Pairs2 dataset is a collection of question pairs from the community question-answering website Quora. The task is to determine whether a pair of questions are semantically equivalent.", "#### rte\n\n\nThe Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges. The authors of the benchmark combined the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009). Examples are constructed based on news and Wikipedia text. The authors of the benchmark convert all datasets to a two-class split, where for three-class datasets they collapse neutral and contradiction into not entailment, for consistency.", "#### sst2\n\n\nThe Stanford Sentiment Treebank consists of sentences from movie reviews and human annotations of their sentiment. The task is to predict the sentiment of a given sentence. It uses the two-way (positive/negative) class split, with only sentence-level labels.", "#### stsb\n\n\nThe Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data. Each pair is human-annotated with a similarity score from 1 to 5.", "#### wnli\n\n\nThe Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, the authors of the benchmark construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. They use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. The authors of the benchmark call converted dataset WNLI (Winograd NLI).", "### Languages\n\n\nThe language data in GLUE is in English (BCP-47 'en')\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### ax\n\n\n* Size of downloaded dataset files: 0.21 MB\n* Size of the generated dataset: 0.23 MB\n* Total amount of disk used: 0.44 MB\n\n\nAn example of 'test' looks as follows.", "#### cola\n\n\n* Size of downloaded dataset files: 0.36 MB\n* Size of the generated dataset: 0.58 MB\n* Total amount of disk used: 0.94 MB\n\n\nAn example of 'train' looks as follows.", "#### mnli\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 78.65 MB\n* Total amount of disk used: 376.95 MB\n\n\nAn example of 'train' looks as follows.", "#### mnli\\_matched\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 3.52 MB\n* Total amount of disk used: 301.82 MB\n\n\nAn example of 'test' looks as follows.", "#### mnli\\_mismatched\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 3.73 MB\n* Total amount of disk used: 302.02 MB\n\n\nAn example of 'test' looks as follows.", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### ax\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### cola\n\n\n* 'sentence': a 'string' feature.\n* 'label': a classification label, with possible values including 'unacceptable' (0), 'acceptable' (1).\n* 'idx': a 'int32' feature.", "#### mnli\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mnli\\_matched\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mnli\\_mismatched\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli", "### Data Splits", "#### ax", "#### cola", "#### mnli", "#### mnli\\_matched", "#### mnli\\_mismatched", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli\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\n\n\nThanks to @patpizio, @jeswan, @thomwolf, @patrickvonplaten, @mariamabarham for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Romance-cleaned-3\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Romance-cleaned-3\"\n\nMore Information needed" ]

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

[]

[ "en", "ja" ]

[ "# Dataset Card for Pokémon BLIP captions with English and Japanese.\n\nDataset used to train Pokémon text to image model, add a Japanese Column of Pokémon BLIP captions\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 en_text (caption in English) and ja_text (caption in Japanese) keys. image is a varying size PIL jpeg, and text is the accompanying text caption. Only a train split is provided.\n\nThe Japanese captions are translated by Deepl" ]

[ "TAGS\n#task_categories-text-to-image #annotations_creators-machine-generated #language_creators-other #multilinguality-multilingual #size_categories-n<1K #source_datasets-huggan/few-shot-pokemon #language-English #language-Japanese #license-cc-by-nc-sa-4.0 #region-us \n", "# Dataset Card for Pokémon BLIP captions with English and Japanese.\n\nDataset used to train Pokémon text to image model, add a Japanese Column of Pokémon BLIP captions\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 en_text (caption in English) and ja_text (caption in Japanese) keys. image is a varying size PIL jpeg, and text is the accompanying text caption. Only a train split is provided.\n\nThe Japanese captions are translated by Deepl" ]

[ "region:us" ]

[]

[]

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

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

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

[]

[ "en" ]

[]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #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: patrickvonplaten/bert2bert_cnn_daily_mail\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 @Jomon07 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: patrickvonplaten/bert2bert_cnn_daily_mail\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 @Jomon07 for evaluating this model." ]

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

[]

[]

[ "#### Disclosure\n\n\n I hope that you are able to create some nice pictures,, if you have any embedding suggestions or issues please let me know", "#### Usage\n\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder\nTo use it in a prompt add\n*art by insane\\_style* \n\n\nadd **[ ]** around it to reduce its weight.", "#### Included Files\n\n\n* 6500 steps *Usage: art by insane\\_style-6500*\n* 10,000 steps *Usage: art by insane\\_style-10000*\n* 15,000 steps *Usage: art by insane\\_style*\n\n\ncheers \n\nWipeout", "#### Example Pictures", "#### prompt comparison\n\n\n *click the image to enlarge*\n[<img height=\"50%\" width=\"50%\" src=\"https://i.URL](https://i.URL target=)" ]

[ "TAGS\n#license-creativeml-openrail-m #region-us \n", "#### Disclosure\n\n\n I hope that you are able to create some nice pictures,, if you have any embedding suggestions or issues please let me know", "#### Usage\n\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder\nTo use it in a prompt add\n*art by insane\\_style* \n\n\nadd **[ ]** around it to reduce its weight.", "#### Included Files\n\n\n* 6500 steps *Usage: art by insane\\_style-6500*\n* 10,000 steps *Usage: art by insane\\_style-10000*\n* 15,000 steps *Usage: art by insane\\_style*\n\n\ncheers \n\nWipeout", "#### Example Pictures", "#### prompt comparison\n\n\n *click the image to enlarge*\n[<img height=\"50%\" width=\"50%\" src=\"https://i.URL](https://i.URL target=)" ]

[ "task_categories:text-to-image", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:n<10K", "source_datasets:FS-COCO", "language:en", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Sketch Scene Descriptions\n\n_Dataset used to train [Sketch Scene text to image model]()_\n\nWe advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey well scene content but can be sketched within a few minutes by a person with any sketching skills. Our dataset comprises around 10,000 freehand scene vector sketches with per-point space-time information by 100 non-expert individuals, offering both object- and scene-level abstraction. Each sketch is augmented with its text description.\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.\n\n\nIf you use this dataset, please cite it as:" ]

[ "TAGS\n#task_categories-text-to-image #language_creators-machine-generated #multilinguality-monolingual #size_categories-n<10K #source_datasets-FS-COCO #language-English #license-cc-by-nc-sa-4.0 #region-us \n", "# Dataset Card for Sketch Scene Descriptions\n\n_Dataset used to train [Sketch Scene text to image model]()_\n\nWe advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey well scene content but can be sketched within a few minutes by a person with any sketching skills. Our dataset comprises around 10,000 freehand scene vector sketches with per-point space-time information by 100 non-expert individuals, offering both object- and scene-level abstraction. Each sketch is augmented with its text description.\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.\n\n\nIf you use this dataset, please cite it as:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"turkishReviews-ds-mini\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[]

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

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

[]

[ "en", "nl" ]

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en2nl.\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-Dutch #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en2nl.\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:" ]

[ "license:bigscience-bloom-rail-1.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-bigscience-bloom-rail-1.0 #region-us \n" ]

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

[]

[ "en" ]

[ "# Dataset Description", "## Structure\n\n- Consists of 5 fields\n- Each row corresponds to a policy - sequence of actions, given an initial '<START>' state, and corresponding rewards at each step.", "## Fields\n\n'steps', 'step_attn_masks', 'rewards', 'actions', 'dones'", "## Field descriptions\n\n- 'steps' (List of lists of 'Int's) - tokenized step tokens of all the steps in the policy sequence (here we use the 'roberta-base' tokenizer, as 'roberta-base' would be used to encode each step of a recipe)\n- 'step_attn_masks' (List of lists of 'Int's) - Attention masks corresponding to 'steps'\n- 'rewards' (List of 'Float's) - Sequence of rewards (normalized b/w 0 and 1) assigned per step.\n- 'actions' (List of lists of 'Int's) - Sequence of actions (one-hot encoded, as the action space is discrete). There are '33' different actions possible (we consider the maximum number of steps per recipe = '16', so the action can vary from '-16' to '+16'; The class label is got by adding 16 to the actual action value)\n- 'dones' (List of 'Bool') - Sequence of flags, conveying if the work is completed when that step is reached, or not.", "## Dataset Size\n\n- Number of rows = '2255673'\n- Maximum number of steps per row = '16'" ]

[ "TAGS\n#multilinguality-monolingual #language-English #region-us \n", "# Dataset Description", "## Structure\n\n- Consists of 5 fields\n- Each row corresponds to a policy - sequence of actions, given an initial '<START>' state, and corresponding rewards at each step.", "## Fields\n\n'steps', 'step_attn_masks', 'rewards', 'actions', 'dones'", "## Field descriptions\n\n- 'steps' (List of lists of 'Int's) - tokenized step tokens of all the steps in the policy sequence (here we use the 'roberta-base' tokenizer, as 'roberta-base' would be used to encode each step of a recipe)\n- 'step_attn_masks' (List of lists of 'Int's) - Attention masks corresponding to 'steps'\n- 'rewards' (List of 'Float's) - Sequence of rewards (normalized b/w 0 and 1) assigned per step.\n- 'actions' (List of lists of 'Int's) - Sequence of actions (one-hot encoded, as the action space is discrete). There are '33' different actions possible (we consider the maximum number of steps per recipe = '16', so the action can vary from '-16' to '+16'; The class label is got by adding 16 to the actual action value)\n- 'dones' (List of 'Bool') - Sequence of flags, conveying if the work is completed when that step is reached, or not.", "## Dataset Size\n\n- Number of rows = '2255673'\n- Maximum number of steps per row = '16'" ]

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

[]

[ "en" ]

[]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for S&P 500 Dataset", "## 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:\n\nThis Dataset was created by combining the daily close prices for each stock in the current (as of 10/29/2022) S&P 500 index dating back to January 1, 1970. This data came from the Kaggle dataset (URL and was aggregated using PANDAS before being converted to a HuggingFace Dataset.", "### Dataset Summary\n\nThis dataset has 407 columns specifying dates and associated close prices of the stocks in the S&P 500 that had data which could be accessed from the above Kaggle dataset. 94 stocks are missing due to issues loading that stock data into the dataset (i.e. stock name changes (like FB to META)). These items will need further review. There are many NA values due to stocks that were not in existence as early as 1970.", "### Supported Tasks and Leaderboards", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits\n\nNo split has currently been created for the dataset.", "## Dataset Creation", "### Curation Rationale", "### Source Data\n\nURL", "#### 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.\n\n---\ndataset_info:\n features:\n - name: MMM\n dtype: float64\n - name: AOS\n dtype: float64\n - name: ABT\n dtype: float64\n - name: ABBV\n dtype: float64\n - name: ABMD\n dtype: float64\n - name: ACN\n dtype: float64\n - name: ATVI\n dtype: float64\n - name: ADM\n dtype: float64\n - name: ADBE\n dtype: float64\n - name: ADP\n dtype: float64\n - name: AAP\n dtype: float64\n - name: A\n dtype: float64\n - name: APD\n dtype: float64\n - name: AKAM\n dtype: float64\n - name: ALK\n dtype: float64\n - name: ALB\n dtype: float64\n - name: ARE\n dtype: float64\n - name: ALGN\n dtype: float64\n - name: ALLE\n dtype: float64\n - name: LNT\n dtype: float64\n - name: GOOG\n dtype: float64\n - name: MO\n dtype: float64\n - name: AMZN\n dtype: float64\n - name: AMD\n dtype: float64\n - name: AEE\n dtype: float64\n - name: AAL\n dtype: float64\n - name: AEP\n dtype: float64\n - name: AXP\n dtype: float64\n - name: AIG\n dtype: float64\n - name: AMT\n dtype: float64\n - name: AWK\n dtype: float64\n - name: AMP\n dtype: float64\n - name: ABC\n dtype: float64\n - name: AME\n dtype: float64\n - name: AMGN\n dtype: float64\n - name: APH\n dtype: float64\n - name: ADI\n dtype: float64\n - name: AON\n dtype: float64\n - name: APA\n dtype: float64\n - name: AAPL\n dtype: float64\n - name: AMAT\n dtype: float64\n - name: ANET\n dtype: float64\n - name: AJG\n dtype: float64\n - name: AIZ\n dtype: float64\n - name: T\n dtype: float64\n - name: ATO\n dtype: float64\n - name: ADSK\n dtype: float64\n - name: AZO\n dtype: float64\n - name: AVB\n dtype: float64\n - name: AVY\n dtype: float64\n - name: BAC\n dtype: float64\n - name: BAX\n dtype: float64\n - name: BDX\n dtype: float64\n - name: WRB\n dtype: float64\n - name: BBY\n dtype: float64\n - name: BIO\n dtype: float64\n - name: BIIB\n dtype: float64\n - name: BLK\n dtype: float64\n - name: BK\n dtype: float64\n - name: BA\n dtype: float64\n - name: BWA\n dtype: float64\n - name: BXP\n dtype: float64\n - name: BSX\n dtype: float64\n - name: BMY\n dtype: float64\n - name: AVGO\n dtype: float64\n - name: BR\n dtype: float64\n - name: BRO\n dtype: float64\n - name: CHRW\n dtype: float64\n - name: CDNS\n dtype: float64\n - name: CZR\n dtype: float64\n - name: CPT\n dtype: float64\n - name: CPB\n dtype: float64\n - name: COF\n dtype: float64\n - name: CAH\n dtype: float64\n - name: KMX\n dtype: float64\n - name: CAT\n dtype: float64\n - name: CBOE\n dtype: float64\n - name: CDW\n dtype: float64\n - name: CNC\n dtype: float64\n - name: CNP\n dtype: float64\n - name: CF\n dtype: float64\n - name: CRL\n dtype: float64\n - name: SCHW\n dtype: float64\n - name: CHTR\n dtype: float64\n - name: CMG\n dtype: float64\n - name: CB\n dtype: float64\n - name: CHD\n dtype: float64\n - name: CINF\n dtype: float64\n - name: CTAS\n dtype: float64\n - name: CSCO\n dtype: float64\n - name: C\n dtype: float64\n - name: CFG\n dtype: float64\n - name: CLX\n dtype: float64\n - name: CME\n dtype: float64\n - name: CMS\n dtype: float64\n - name: KO\n dtype: float64\n - name: CTSH\n dtype: float64\n - name: CL\n dtype: float64\n - name: CMCSA\n dtype: float64\n - name: CAG\n dtype: float64\n - name: COP\n dtype: float64\n - name: ED\n dtype: float64\n - name: COO\n dtype: float64\n - name: CPRT\n dtype: float64\n - name: GLW\n dtype: float64\n - name: CSGP\n dtype: float64\n - name: COST\n dtype: float64\n - name: CCI\n dtype: float64\n - name: CMI\n dtype: float64\n - name: DHI\n dtype: float64\n - name: DRI\n dtype: float64\n - name: DVA\n dtype: float64\n - name: DE\n dtype: float64\n - name: DAL\n dtype: float64\n - name: DVN\n dtype: float64\n - name: DXCM\n dtype: float64\n - name: FANG\n dtype: float64\n - name: DLR\n dtype: float64\n - name: DFS\n dtype: float64\n - name: DISH\n dtype: float64\n - name: DIS\n dtype: float64\n - name: DG\n dtype: float64\n - name: DLTR\n dtype: float64\n - name: D\n dtype: float64\n - name: DPZ\n dtype: float64\n - name: DOV\n dtype: float64\n - name: DOW\n dtype: float64\n - name: DTE\n dtype: float64\n - name: DD\n dtype: float64\n - name: EMN\n dtype: float64\n - name: ETN\n dtype: float64\n - name: EBAY\n dtype: float64\n - name: ECL\n dtype: float64\n - name: EIX\n dtype: float64\n - name: EW\n dtype: float64\n - name: EA\n dtype: float64\n - name: LLY\n dtype: float64\n - name: EMR\n dtype: float64\n - name: ENPH\n dtype: float64\n - name: EOG\n dtype: float64\n - name: EPAM\n dtype: float64\n - name: EFX\n dtype: float64\n - name: EQIX\n dtype: float64\n - name: EQR\n dtype: float64\n - name: ESS\n dtype: float64\n - name: EL\n dtype: float64\n - name: RE\n dtype: float64\n - name: ES\n dtype: float64\n - name: EXC\n dtype: float64\n - name: EXPE\n dtype: float64\n - name: EXPD\n dtype: float64\n - name: EXR\n dtype: float64\n - name: XOM\n dtype: float64\n - name: FFIV\n dtype: float64\n - name: FDS\n dtype: float64\n - name: FAST\n dtype: float64\n - name: FRT\n dtype: float64\n - name: FDX\n dtype: float64\n - name: FITB\n dtype: float64\n - name: FRC\n dtype: float64\n - name: FE\n dtype: float64\n - name: FIS\n dtype: float64\n - name: FISV\n dtype: float64\n - name: FLT\n dtype: float64\n - name: FMC\n dtype: float64\n - name: F\n dtype: float64\n - name: FTNT\n dtype: float64\n - name: FBHS\n dtype: float64\n - name: FOXA\n dtype: float64\n - name: BEN\n dtype: float64\n - name: FCX\n dtype: float64\n - name: GRMN\n dtype: float64\n - name: IT\n dtype: float64\n - name: GNRC\n dtype: float64\n - name: GD\n dtype: float64\n - name: GE\n dtype: float64\n - name: GIS\n dtype: float64\n - name: GM\n dtype: float64\n - name: GPC\n dtype: float64\n - name: GILD\n dtype: float64\n - name: GPN\n dtype: float64\n - name: HAL\n dtype: float64\n - name: HIG\n dtype: float64\n - name: HAS\n dtype: float64\n - name: HCA\n dtype: float64\n - name: HSIC\n dtype: float64\n - name: HSY\n dtype: float64\n - name: HES\n dtype: float64\n - name: HPE\n dtype: float64\n - name: HLT\n dtype: float64\n - name: HOLX\n dtype: float64\n - name: HD\n dtype: float64\n - name: HON\n dtype: float64\n - name: HRL\n dtype: float64\n - name: HST\n dtype: float64\n - name: HPQ\n dtype: float64\n - name: HUM\n dtype: float64\n - name: HBAN\n dtype: float64\n - name: HII\n dtype: float64\n - name: IBM\n dtype: float64\n - name: IEX\n dtype: float64\n - name: IDXX\n dtype: float64\n - name: ITW\n dtype: float64\n - name: ILMN\n dtype: float64\n - name: INCY\n dtype: float64\n - name: IR\n dtype: float64\n - name: INTC\n dtype: float64\n - name: ICE\n dtype: float64\n - name: IP\n dtype: float64\n - name: IPG\n dtype: float64\n - name: IFF\n dtype: float64\n - name: INTU\n dtype: float64\n - name: ISRG\n dtype: float64\n - name: IVZ\n dtype: float64\n - name: IRM\n dtype: float64\n - name: JBHT\n dtype: float64\n - name: JKHY\n dtype: float64\n - name: JNJ\n dtype: float64\n - name: JCI\n dtype: float64\n - name: JPM\n dtype: float64\n - name: JNPR\n dtype: float64\n - name: K\n dtype: float64\n - name: KEY\n dtype: float64\n - name: KEYS\n dtype: float64\n - name: KMB\n dtype: float64\n - name: KIM\n dtype: float64\n - name: KLAC\n dtype: float64\n - name: KHC\n dtype: float64\n - name: KR\n dtype: float64\n - name: LH\n dtype: float64\n - name: LRCX\n dtype: float64\n - name: LVS\n dtype: float64\n - name: LDOS\n dtype: float64\n - name: LNC\n dtype: float64\n - name: LYV\n dtype: float64\n - name: LKQ\n dtype: float64\n - name: LMT\n dtype: float64\n - name: LOW\n dtype: float64\n - name: LYB\n dtype: float64\n - name: MRO\n dtype: float64\n - name: MPC\n dtype: float64\n - name: MKTX\n dtype: float64\n - name: MAR\n dtype: float64\n - name: MMC\n dtype: float64\n - name: MLM\n dtype: float64\n - name: MA\n dtype: float64\n - name: MKC\n dtype: float64\n - name: MCD\n dtype: float64\n - name: MCK\n dtype: float64\n - name: MDT\n dtype: float64\n - name: MRK\n dtype: float64\n - name: MET\n dtype: float64\n - name: MTD\n dtype: float64\n - name: MGM\n dtype: float64\n - name: MCHP\n dtype: float64\n - name: MU\n dtype: float64\n - name: MSFT\n dtype: float64\n - name: MAA\n dtype: float64\n - name: MHK\n dtype: float64\n - name: MOH\n dtype: float64\n - name: TAP\n dtype: float64\n - name: MDLZ\n dtype: float64\n - name: MPWR\n dtype: float64\n - name: MNST\n dtype: float64\n - name: MCO\n dtype: float64\n - name: MOS\n dtype: float64\n - name: MSI\n dtype: float64\n - name: MSCI\n dtype: float64\n - name: NDAQ\n dtype: float64\n - name: NTAP\n dtype: float64\n - name: NFLX\n dtype: float64\n - name: NWL\n dtype: float64\n - name: NEM\n dtype: float64\n - name: NWSA\n dtype: float64\n - name: NEE\n dtype: float64\n - name: NI\n dtype: float64\n - name: NDSN\n dtype: float64\n - name: NSC\n dtype: float64\n - name: NTRS\n dtype: float64\n - name: NOC\n dtype: float64\n - name: NCLH\n dtype: float64\n - name: NRG\n dtype: float64\n - name: NVDA\n dtype: float64\n - name: NVR\n dtype: float64\n - name: NXPI\n dtype: float64\n - name: ORLY\n dtype: float64\n - name: OXY\n dtype: float64\n - name: ODFL\n dtype: float64\n - name: OMC\n dtype: float64\n - name: OKE\n dtype: float64\n - name: PCAR\n dtype: float64\n - name: PKG\n dtype: float64\n - name: PH\n dtype: float64\n - name: PAYX\n dtype: float64\n - name: PAYC\n dtype: float64\n - name: PNR\n dtype: float64\n - name: PEP\n dtype: float64\n - name: PKI\n dtype: float64\n - name: PFE\n dtype: float64\n - name: PM\n dtype: float64\n - name: PSX\n dtype: float64\n - name: PNW\n dtype: float64\n - name: PXD\n dtype: float64\n - name: PNC\n dtype: float64\n - name: POOL\n dtype: float64\n - name: PPG\n dtype: float64\n - name: PFG\n dtype: float64\n - name: PG\n dtype: float64\n - name: PLD\n dtype: float64\n - name: PRU\n dtype: float64\n - name: PEG\n dtype: float64\n - name: PTC\n dtype: float64\n - name: PHM\n dtype: float64\n - name: QRVO\n dtype: float64\n - name: PWR\n dtype: float64\n - name: QCOM\n dtype: float64\n - name: DGX\n dtype: float64\n - name: RL\n dtype: float64\n - name: RJF\n dtype: float64\n - name: O\n dtype: float64\n - name: REG\n dtype: float64\n - name: REGN\n dtype: float64\n - name: RF\n dtype: float64\n - name: RSG\n dtype: float64\n - name: RMD\n dtype: float64\n - name: RHI\n dtype: float64\n - name: ROK\n dtype: float64\n - name: ROL\n dtype: float64\n - name: ROP\n dtype: float64\n - name: ROST\n dtype: float64\n - name: RCL\n dtype: float64\n - name: CRM\n dtype: float64\n - name: SBAC\n dtype: float64\n - name: SLB\n dtype: float64\n - name: STX\n dtype: float64\n - name: SEE\n dtype: float64\n - name: SRE\n dtype: float64\n - name: NOW\n dtype: float64\n - name: SHW\n dtype: float64\n - name: SBNY\n dtype: float64\n - name: SPG\n dtype: float64\n - name: SWKS\n dtype: float64\n - name: SO\n dtype: float64\n - name: LUV\n dtype: float64\n - name: SWK\n dtype: float64\n - name: SBUX\n dtype: float64\n - name: STT\n dtype: float64\n - name: SYK\n dtype: float64\n - name: SIVB\n dtype: float64\n - name: SYF\n dtype: float64\n - name: SNPS\n dtype: float64\n - name: TMUS\n dtype: float64\n - name: TROW\n dtype: float64\n - name: TTWO\n dtype: float64\n - name: TRGP\n dtype: float64\n - name: TEL\n dtype: float64\n - name: TDY\n dtype: float64\n - name: TSLA\n dtype: float64\n - name: TXN\n dtype: float64\n - name: TXT\n dtype: float64\n - name: TMO\n dtype: float64\n - name: TJX\n dtype: float64\n - name: TSCO\n dtype: float64\n - name: TDG\n dtype: float64\n - name: TRV\n dtype: float64\n - name: TYL\n dtype: float64\n - name: TSN\n dtype: float64\n - name: USB\n dtype: float64\n - name: UDR\n dtype: float64\n - name: ULTA\n dtype: float64\n - name: UNP\n dtype: float64\n - name: UAL\n dtype: float64\n - name: UPS\n dtype: float64\n - name: URI\n dtype: float64\n - name: UNH\n dtype: float64\n - name: UHS\n dtype: float64\n - name: VTR\n dtype: float64\n - name: VRSN\n dtype: float64\n - name: VRSK\n dtype: float64\n - name: VZ\n dtype: float64\n - name: VRTX\n dtype: float64\n - name: VFC\n dtype: float64\n - name: V\n dtype: float64\n - name: VMC\n dtype: float64\n - name: WAB\n dtype: float64\n - name: WBA\n dtype: float64\n - name: WMT\n dtype: float64\n - name: WM\n dtype: float64\n - name: WAT\n dtype: float64\n - name: WEC\n dtype: float64\n - name: WFC\n dtype: float64\n - name: WST\n dtype: float64\n - name: WDC\n dtype: float64\n - name: WRK\n dtype: float64\n - name: WY\n dtype: float64\n - name: WHR\n dtype: float64\n - name: WMB\n dtype: float64\n - name: WTW\n dtype: float64\n - name: GWW\n dtype: float64\n - name: WYNN\n dtype: float64\n - name: XEL\n dtype: float64\n - name: XYL\n dtype: float64\n - name: YUM\n dtype: float64\n - name: ZBRA\n dtype: float64\n - name: ZBH\n dtype: float64\n - name: ZION\n dtype: float64\n - name: ZTS\n dtype: float64\n - name: Date\n dtype: timestamp[ns]\n splits:\n - name: train\n num_bytes: 44121086\n num_examples: 13322\n download_size: 0\n dataset_size: 44121086\n---", "# Dataset Card for \"sp500\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for S&P 500 Dataset", "## 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:\n\nThis Dataset was created by combining the daily close prices for each stock in the current (as of 10/29/2022) S&P 500 index dating back to January 1, 1970. This data came from the Kaggle dataset (URL and was aggregated using PANDAS before being converted to a HuggingFace Dataset.", "### Dataset Summary\n\nThis dataset has 407 columns specifying dates and associated close prices of the stocks in the S&P 500 that had data which could be accessed from the above Kaggle dataset. 94 stocks are missing due to issues loading that stock data into the dataset (i.e. stock name changes (like FB to META)). These items will need further review. There are many NA values due to stocks that were not in existence as early as 1970.", "### Supported Tasks and Leaderboards", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits\n\nNo split has currently been created for the dataset.", "## Dataset Creation", "### Curation Rationale", "### Source Data\n\nURL", "#### 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.\n\n---\ndataset_info:\n features:\n - name: MMM\n dtype: float64\n - name: AOS\n dtype: float64\n - name: ABT\n dtype: float64\n - name: ABBV\n dtype: float64\n - name: ABMD\n dtype: float64\n - name: ACN\n dtype: float64\n - name: ATVI\n dtype: float64\n - name: ADM\n dtype: float64\n - name: ADBE\n dtype: float64\n - name: ADP\n dtype: float64\n - name: AAP\n dtype: float64\n - name: A\n dtype: float64\n - name: APD\n dtype: float64\n - name: AKAM\n dtype: float64\n - name: ALK\n dtype: float64\n - name: ALB\n dtype: float64\n - name: ARE\n dtype: float64\n - name: ALGN\n dtype: float64\n - name: ALLE\n dtype: float64\n - name: LNT\n dtype: float64\n - name: GOOG\n dtype: float64\n - name: MO\n dtype: float64\n - name: AMZN\n dtype: float64\n - name: AMD\n dtype: float64\n - name: AEE\n dtype: float64\n - name: AAL\n dtype: float64\n - name: AEP\n dtype: float64\n - name: AXP\n dtype: float64\n - name: AIG\n dtype: float64\n - name: AMT\n dtype: float64\n - name: AWK\n dtype: float64\n - name: AMP\n dtype: float64\n - name: ABC\n dtype: float64\n - name: AME\n dtype: float64\n - name: AMGN\n dtype: float64\n - name: APH\n dtype: float64\n - name: ADI\n dtype: float64\n - name: AON\n dtype: float64\n - name: APA\n dtype: float64\n - name: AAPL\n dtype: float64\n - name: AMAT\n dtype: float64\n - name: ANET\n dtype: float64\n - name: AJG\n dtype: float64\n - name: AIZ\n dtype: float64\n - name: T\n dtype: float64\n - name: ATO\n dtype: float64\n - name: ADSK\n dtype: float64\n - name: AZO\n dtype: float64\n - name: AVB\n dtype: float64\n - name: AVY\n dtype: float64\n - name: BAC\n dtype: float64\n - name: BAX\n dtype: float64\n - name: BDX\n dtype: float64\n - name: WRB\n dtype: float64\n - name: BBY\n dtype: float64\n - name: BIO\n dtype: float64\n - name: BIIB\n dtype: float64\n - name: BLK\n dtype: float64\n - name: BK\n dtype: float64\n - name: BA\n dtype: float64\n - name: BWA\n dtype: float64\n - name: BXP\n dtype: float64\n - name: BSX\n dtype: float64\n - name: BMY\n dtype: float64\n - name: AVGO\n dtype: float64\n - name: BR\n dtype: float64\n - name: BRO\n dtype: float64\n - name: CHRW\n dtype: float64\n - name: CDNS\n dtype: float64\n - name: CZR\n dtype: float64\n - name: CPT\n dtype: float64\n - name: CPB\n dtype: float64\n - name: COF\n dtype: float64\n - name: CAH\n dtype: float64\n - name: KMX\n dtype: float64\n - name: CAT\n dtype: float64\n - name: CBOE\n dtype: float64\n - name: CDW\n dtype: float64\n - name: CNC\n dtype: float64\n - name: CNP\n dtype: float64\n - name: CF\n dtype: float64\n - name: CRL\n dtype: float64\n - name: SCHW\n dtype: float64\n - name: CHTR\n dtype: float64\n - name: CMG\n dtype: float64\n - name: CB\n dtype: float64\n - name: CHD\n dtype: float64\n - name: CINF\n dtype: float64\n - name: CTAS\n dtype: float64\n - name: CSCO\n dtype: float64\n - name: C\n dtype: float64\n - name: CFG\n dtype: float64\n - name: CLX\n dtype: float64\n - name: CME\n dtype: float64\n - name: CMS\n dtype: float64\n - name: KO\n dtype: float64\n - name: CTSH\n dtype: float64\n - name: CL\n dtype: float64\n - name: CMCSA\n dtype: float64\n - name: CAG\n dtype: float64\n - name: COP\n dtype: float64\n - name: ED\n dtype: float64\n - name: COO\n dtype: float64\n - name: CPRT\n dtype: float64\n - name: GLW\n dtype: float64\n - name: CSGP\n dtype: float64\n - name: COST\n dtype: float64\n - name: CCI\n dtype: float64\n - name: CMI\n dtype: float64\n - name: DHI\n dtype: float64\n - name: DRI\n dtype: float64\n - name: DVA\n dtype: float64\n - name: DE\n dtype: float64\n - name: DAL\n dtype: float64\n - name: DVN\n dtype: float64\n - name: DXCM\n dtype: float64\n - name: FANG\n dtype: float64\n - name: DLR\n dtype: float64\n - name: DFS\n dtype: float64\n - name: DISH\n dtype: float64\n - name: DIS\n dtype: float64\n - name: DG\n dtype: float64\n - name: DLTR\n dtype: float64\n - name: D\n dtype: float64\n - name: DPZ\n dtype: float64\n - name: DOV\n dtype: float64\n - name: DOW\n dtype: float64\n - name: DTE\n dtype: float64\n - name: DD\n dtype: float64\n - name: EMN\n dtype: float64\n - name: ETN\n dtype: float64\n - name: EBAY\n dtype: float64\n - name: ECL\n dtype: float64\n - name: EIX\n dtype: float64\n - name: EW\n dtype: float64\n - name: EA\n dtype: float64\n - name: LLY\n dtype: float64\n - name: EMR\n dtype: float64\n - name: ENPH\n dtype: float64\n - name: EOG\n dtype: float64\n - name: EPAM\n dtype: float64\n - name: EFX\n dtype: float64\n - name: EQIX\n dtype: float64\n - name: EQR\n dtype: float64\n - name: ESS\n dtype: float64\n - name: EL\n dtype: float64\n - name: RE\n dtype: float64\n - name: ES\n dtype: float64\n - name: EXC\n dtype: float64\n - name: EXPE\n dtype: float64\n - name: EXPD\n dtype: float64\n - name: EXR\n dtype: float64\n - name: XOM\n dtype: float64\n - name: FFIV\n dtype: float64\n - name: FDS\n dtype: float64\n - name: FAST\n dtype: float64\n - name: FRT\n dtype: float64\n - name: FDX\n dtype: float64\n - name: FITB\n dtype: float64\n - name: FRC\n dtype: float64\n - name: FE\n dtype: float64\n - name: FIS\n dtype: float64\n - name: FISV\n dtype: float64\n - name: FLT\n dtype: float64\n - name: FMC\n dtype: float64\n - name: F\n dtype: float64\n - name: FTNT\n dtype: float64\n - name: FBHS\n dtype: float64\n - name: FOXA\n dtype: float64\n - name: BEN\n dtype: float64\n - name: FCX\n dtype: float64\n - name: GRMN\n dtype: float64\n - name: IT\n dtype: float64\n - name: GNRC\n dtype: float64\n - name: GD\n dtype: float64\n - name: GE\n dtype: float64\n - name: GIS\n dtype: float64\n - name: GM\n dtype: float64\n - name: GPC\n dtype: float64\n - name: GILD\n dtype: float64\n - name: GPN\n dtype: float64\n - name: HAL\n dtype: float64\n - name: HIG\n dtype: float64\n - name: HAS\n dtype: float64\n - name: HCA\n dtype: float64\n - name: HSIC\n dtype: float64\n - name: HSY\n dtype: float64\n - name: HES\n dtype: float64\n - name: HPE\n dtype: float64\n - name: HLT\n dtype: float64\n - name: HOLX\n dtype: float64\n - name: HD\n dtype: float64\n - name: HON\n dtype: float64\n - name: HRL\n dtype: float64\n - name: HST\n dtype: float64\n - name: HPQ\n dtype: float64\n - name: HUM\n dtype: float64\n - name: HBAN\n dtype: float64\n - name: HII\n dtype: float64\n - name: IBM\n dtype: float64\n - name: IEX\n dtype: float64\n - name: IDXX\n dtype: float64\n - name: ITW\n dtype: float64\n - name: ILMN\n dtype: float64\n - name: INCY\n dtype: float64\n - name: IR\n dtype: float64\n - name: INTC\n dtype: float64\n - name: ICE\n dtype: float64\n - name: IP\n dtype: float64\n - name: IPG\n dtype: float64\n - name: IFF\n dtype: float64\n - name: INTU\n dtype: float64\n - name: ISRG\n dtype: float64\n - name: IVZ\n dtype: float64\n - name: IRM\n dtype: float64\n - name: JBHT\n dtype: float64\n - name: JKHY\n dtype: float64\n - name: JNJ\n dtype: float64\n - name: JCI\n dtype: float64\n - name: JPM\n dtype: float64\n - name: JNPR\n dtype: float64\n - name: K\n dtype: float64\n - name: KEY\n dtype: float64\n - name: KEYS\n dtype: float64\n - name: KMB\n dtype: float64\n - name: KIM\n dtype: float64\n - name: KLAC\n dtype: float64\n - name: KHC\n dtype: float64\n - name: KR\n dtype: float64\n - name: LH\n dtype: float64\n - name: LRCX\n dtype: float64\n - name: LVS\n dtype: float64\n - name: LDOS\n dtype: float64\n - name: LNC\n dtype: float64\n - name: LYV\n dtype: float64\n - name: LKQ\n dtype: float64\n - name: LMT\n dtype: float64\n - name: LOW\n dtype: float64\n - name: LYB\n dtype: float64\n - name: MRO\n dtype: float64\n - name: MPC\n dtype: float64\n - name: MKTX\n dtype: float64\n - name: MAR\n dtype: float64\n - name: MMC\n dtype: float64\n - name: MLM\n dtype: float64\n - name: MA\n dtype: float64\n - name: MKC\n dtype: float64\n - name: MCD\n dtype: float64\n - name: MCK\n dtype: float64\n - name: MDT\n dtype: float64\n - name: MRK\n dtype: float64\n - name: MET\n dtype: float64\n - name: MTD\n dtype: float64\n - name: MGM\n dtype: float64\n - name: MCHP\n dtype: float64\n - name: MU\n dtype: float64\n - name: MSFT\n dtype: float64\n - name: MAA\n dtype: float64\n - name: MHK\n dtype: float64\n - name: MOH\n dtype: float64\n - name: TAP\n dtype: float64\n - name: MDLZ\n dtype: float64\n - name: MPWR\n dtype: float64\n - name: MNST\n dtype: float64\n - name: MCO\n dtype: float64\n - name: MOS\n dtype: float64\n - name: MSI\n dtype: float64\n - name: MSCI\n dtype: float64\n - name: NDAQ\n dtype: float64\n - name: NTAP\n dtype: float64\n - name: NFLX\n dtype: float64\n - name: NWL\n dtype: float64\n - name: NEM\n dtype: float64\n - name: NWSA\n dtype: float64\n - name: NEE\n dtype: float64\n - name: NI\n dtype: float64\n - name: NDSN\n dtype: float64\n - name: NSC\n dtype: float64\n - name: NTRS\n dtype: float64\n - name: NOC\n dtype: float64\n - name: NCLH\n dtype: float64\n - name: NRG\n dtype: float64\n - name: NVDA\n dtype: float64\n - name: NVR\n dtype: float64\n - name: NXPI\n dtype: float64\n - name: ORLY\n dtype: float64\n - name: OXY\n dtype: float64\n - name: ODFL\n dtype: float64\n - name: OMC\n dtype: float64\n - name: OKE\n dtype: float64\n - name: PCAR\n dtype: float64\n - name: PKG\n dtype: float64\n - name: PH\n dtype: float64\n - name: PAYX\n dtype: float64\n - name: PAYC\n dtype: float64\n - name: PNR\n dtype: float64\n - name: PEP\n dtype: float64\n - name: PKI\n dtype: float64\n - name: PFE\n dtype: float64\n - name: PM\n dtype: float64\n - name: PSX\n dtype: float64\n - name: PNW\n dtype: float64\n - name: PXD\n dtype: float64\n - name: PNC\n dtype: float64\n - name: POOL\n dtype: float64\n - name: PPG\n dtype: float64\n - name: PFG\n dtype: float64\n - name: PG\n dtype: float64\n - name: PLD\n dtype: float64\n - name: PRU\n dtype: float64\n - name: PEG\n dtype: float64\n - name: PTC\n dtype: float64\n - name: PHM\n dtype: float64\n - name: QRVO\n dtype: float64\n - name: PWR\n dtype: float64\n - name: QCOM\n dtype: float64\n - name: DGX\n dtype: float64\n - name: RL\n dtype: float64\n - name: RJF\n dtype: float64\n - name: O\n dtype: float64\n - name: REG\n dtype: float64\n - name: REGN\n dtype: float64\n - name: RF\n dtype: float64\n - name: RSG\n dtype: float64\n - name: RMD\n dtype: float64\n - name: RHI\n dtype: float64\n - name: ROK\n dtype: float64\n - name: ROL\n dtype: float64\n - name: ROP\n dtype: float64\n - name: ROST\n dtype: float64\n - name: RCL\n dtype: float64\n - name: CRM\n dtype: float64\n - name: SBAC\n dtype: float64\n - name: SLB\n dtype: float64\n - name: STX\n dtype: float64\n - name: SEE\n dtype: float64\n - name: SRE\n dtype: float64\n - name: NOW\n dtype: float64\n - name: SHW\n dtype: float64\n - name: SBNY\n dtype: float64\n - name: SPG\n dtype: float64\n - name: SWKS\n dtype: float64\n - name: SO\n dtype: float64\n - name: LUV\n dtype: float64\n - name: SWK\n dtype: float64\n - name: SBUX\n dtype: float64\n - name: STT\n dtype: float64\n - name: SYK\n dtype: float64\n - name: SIVB\n dtype: float64\n - name: SYF\n dtype: float64\n - name: SNPS\n dtype: float64\n - name: TMUS\n dtype: float64\n - name: TROW\n dtype: float64\n - name: TTWO\n dtype: float64\n - name: TRGP\n dtype: float64\n - name: TEL\n dtype: float64\n - name: TDY\n dtype: float64\n - name: TSLA\n dtype: float64\n - name: TXN\n dtype: float64\n - name: TXT\n dtype: float64\n - name: TMO\n dtype: float64\n - name: TJX\n dtype: float64\n - name: TSCO\n dtype: float64\n - name: TDG\n dtype: float64\n - name: TRV\n dtype: float64\n - name: TYL\n dtype: float64\n - name: TSN\n dtype: float64\n - name: USB\n dtype: float64\n - name: UDR\n dtype: float64\n - name: ULTA\n dtype: float64\n - name: UNP\n dtype: float64\n - name: UAL\n dtype: float64\n - name: UPS\n dtype: float64\n - name: URI\n dtype: float64\n - name: UNH\n dtype: float64\n - name: UHS\n dtype: float64\n - name: VTR\n dtype: float64\n - name: VRSN\n dtype: float64\n - name: VRSK\n dtype: float64\n - name: VZ\n dtype: float64\n - name: VRTX\n dtype: float64\n - name: VFC\n dtype: float64\n - name: V\n dtype: float64\n - name: VMC\n dtype: float64\n - name: WAB\n dtype: float64\n - name: WBA\n dtype: float64\n - name: WMT\n dtype: float64\n - name: WM\n dtype: float64\n - name: WAT\n dtype: float64\n - name: WEC\n dtype: float64\n - name: WFC\n dtype: float64\n - name: WST\n dtype: float64\n - name: WDC\n dtype: float64\n - name: WRK\n dtype: float64\n - name: WY\n dtype: float64\n - name: WHR\n dtype: float64\n - name: WMB\n dtype: float64\n - name: WTW\n dtype: float64\n - name: GWW\n dtype: float64\n - name: WYNN\n dtype: float64\n - name: XEL\n dtype: float64\n - name: XYL\n dtype: float64\n - name: YUM\n dtype: float64\n - name: ZBRA\n dtype: float64\n - name: ZBH\n dtype: float64\n - name: ZION\n dtype: float64\n - name: ZTS\n dtype: float64\n - name: Date\n dtype: timestamp[ns]\n splits:\n - name: train\n num_bytes: 44121086\n num_examples: 13322\n download_size: 0\n dataset_size: 44121086\n---", "# Dataset Card for \"sp500\"\n\nMore Information needed" ]

[ "task_categories:conversational", "task_categories:text-classification", "task_ids:dialogue-generation", "task_ids:multi-class-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "size_categories:100K<n<1M", "source_datasets:original", "source_datasets:extended|social_bias_frames", "language:en", "license:cc-by-4.0", "dialogue", "dialogue safety", "social norm", "rules-of-thumb", "arxiv:2205.12688", "region:us" ]

[ "2205.12688" ]

[ "en" ]

[ "### Data Attributes\n\n\nattribute: 'context', type: str, description: the potentially unsafe utterance\nattribute: 'response', type: str, description: the guiding utterance grounded on rules-of-thumb ('rots')\nattribute: 'rots', type: list of str|null, description: the relevant rules-of-thumb for 'text' *not* labeled as \\_\\_casual\\_\\_\nattribute: 'safety\\_label', type: str, description: the final verdict of the context according to 'safety\\_annotations': {\\_\\_casual\\_\\_, \\_\\_possibly\\_needs\\_caution\\_\\_, \\_\\_probably\\_needs\\_caution\\_\\_, \\_\\_needs\\_caution\\_\\_, \\_\\_needs\\_intervention\\_\\_}\nattribute: 'safety\\_annotations', type: list of str, description: raw annotations from three workers: {casual, needs caution, needs intervention}\nattribute: 'safety\\_annotation\\_reasons', type: list of str, description: the reasons behind the safety annotations in free-form text from each worker\nattribute: 'source', type: str, description: the source of the seed text that was used to craft the first utterance of the dialogue: {socialchemistry, sbic, ethics\\_amt, ethics\\_reddit}\nattribute: 'etc', type: str|null, description: other information\nattribute: 'dialogue\\_id', type: int, description: the dialogue index\nattribute: 'response\\_id', type: int, description: the response index\nattribute: 'episode\\_done', type: bool, description: an indicator of whether it is the end of the dialogue\n\n\nDataset Creation\n----------------\n\n\nTo create ProsocialDialog, we set up a human-AI collaborative data creation framework, where GPT-3 generates the potentially unsafe utterances, and crowdworkers provide prosocial responses to them. This approach allows us to circumvent two substantial challenges: (1) there are no available large-scale corpora of multiturn prosocial conversations between humans, and (2) asking humans to write unethical, toxic, or problematic utterances could result in psychological harms (Roberts, 2017; Steiger et al., 2021).", "### Further Details, Social Impacts, and Limitations\n\n\nPlease refer to our paper.\n\n\nAdditional Information\n----------------------\n\n\nPlease cite our work if you found the resources in this repository useful:" ]

[ "TAGS\n#task_categories-conversational #task_categories-text-classification #task_ids-dialogue-generation #task_ids-multi-class-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #size_categories-100K<n<1M #source_datasets-original #source_datasets-extended|social_bias_frames #language-English #license-cc-by-4.0 #dialogue #dialogue safety #social norm #rules-of-thumb #arxiv-2205.12688 #region-us \n", "### Data Attributes\n\n\nattribute: 'context', type: str, description: the potentially unsafe utterance\nattribute: 'response', type: str, description: the guiding utterance grounded on rules-of-thumb ('rots')\nattribute: 'rots', type: list of str|null, description: the relevant rules-of-thumb for 'text' *not* labeled as \\_\\_casual\\_\\_\nattribute: 'safety\\_label', type: str, description: the final verdict of the context according to 'safety\\_annotations': {\\_\\_casual\\_\\_, \\_\\_possibly\\_needs\\_caution\\_\\_, \\_\\_probably\\_needs\\_caution\\_\\_, \\_\\_needs\\_caution\\_\\_, \\_\\_needs\\_intervention\\_\\_}\nattribute: 'safety\\_annotations', type: list of str, description: raw annotations from three workers: {casual, needs caution, needs intervention}\nattribute: 'safety\\_annotation\\_reasons', type: list of str, description: the reasons behind the safety annotations in free-form text from each worker\nattribute: 'source', type: str, description: the source of the seed text that was used to craft the first utterance of the dialogue: {socialchemistry, sbic, ethics\\_amt, ethics\\_reddit}\nattribute: 'etc', type: str|null, description: other information\nattribute: 'dialogue\\_id', type: int, description: the dialogue index\nattribute: 'response\\_id', type: int, description: the response index\nattribute: 'episode\\_done', type: bool, description: an indicator of whether it is the end of the dialogue\n\n\nDataset Creation\n----------------\n\n\nTo create ProsocialDialog, we set up a human-AI collaborative data creation framework, where GPT-3 generates the potentially unsafe utterances, and crowdworkers provide prosocial responses to them. This approach allows us to circumvent two substantial challenges: (1) there are no available large-scale corpora of multiturn prosocial conversations between humans, and (2) asking humans to write unethical, toxic, or problematic utterances could result in psychological harms (Roberts, 2017; Steiger et al., 2021).", "### Further Details, Social Impacts, and Limitations\n\n\nPlease refer to our paper.\n\n\nAdditional Information\n----------------------\n\n\nPlease cite our work if you found the resources in this repository useful:" ]

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

[]

[]

[ "# Dataset Card for \"sroie_document_understanding\"", "## Table of Contents\n- Table of Contents\n- Dataset Description\n- Dataset Structure\n - Data Fields\n- Dataset Creation\n - Source Data\n - Annotations\n- Additional Information\n - Licensing Information\n - Contributions", "## Dataset Description\n\nThis dataset is an enriched version of SROIE 2019 dataset with additional labels for line descriptions and line totals for OCR and layout understanding.", "## Dataset Structure", "### Data Fields", "## Dataset Creation", "### Source Data\n\nThe dataset was obtained from ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction", "### Annotations", "#### Annotation process\n\nAdditional labels to receipt line items were added using open source labelme tool.", "#### Who are the annotators?\n\nArvind Rajan (adding labels to the original text boxes from source)", "## Additional Information", "### Licensing Information\n\nMIT License", "### Contributions\n\nThanks to @arvindrajan92 for adding this dataset." ]

[ "TAGS\n#license-mit #region-us \n", "# Dataset Card for \"sroie_document_understanding\"", "## Table of Contents\n- Table of Contents\n- Dataset Description\n- Dataset Structure\n - Data Fields\n- Dataset Creation\n - Source Data\n - Annotations\n- Additional Information\n - Licensing Information\n - Contributions", "## Dataset Description\n\nThis dataset is an enriched version of SROIE 2019 dataset with additional labels for line descriptions and line totals for OCR and layout understanding.", "## Dataset Structure", "### Data Fields", "## Dataset Creation", "### Source Data\n\nThe dataset was obtained from ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction", "### Annotations", "#### Annotation process\n\nAdditional labels to receipt line items were added using open source labelme tool.", "#### Who are the annotators?\n\nArvind Rajan (adding labels to the original text boxes from source)", "## Additional Information", "### Licensing Information\n\nMIT License", "### Contributions\n\nThanks to @arvindrajan92 for adding this dataset." ]

[ "license:ofl-1.1", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-ofl-1.1 #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[ "en" ]

[]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #region-us \n" ]

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

[]

[ "en" ]

[]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #region-us \n" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

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[ "# Dataset Card for \"answerable_tydiqa_5fbde19f5f4ac461c405a962adddaeb6\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"answerable_tydiqa_5fbde19f5f4ac461c405a962adddaeb6\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"answerable_tydiqa_6fe3e6eac99651ae0255a686875476a4\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"answerable_tydiqa_6fe3e6eac99651ae0255a686875476a4\"\n\nMore Information needed" ]

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

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[ "#### Usage\n\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder\nTo use it in a prompt add\n*art by assassin\\_style* \n\n\nadd **[ ]** around it to reduce its weight.", "#### Included Files\n\n\n* 6500 steps *Usage: art by assassin\\_style-6500*\n* 10,000 steps *Usage: art by assassin\\_style-10000*\n* 15,000 steps *Usage: art by assassin\\_style*\n\n\ncheers \n\nWipeout", "#### Example Pictures", "#### prompt comparison\n\n\n *click the image to enlarge*\n[<img height=\"50%\" width=\"50%\" src=\"https://i.URL](https://i.URL target=)" ]

[ "TAGS\n#license-creativeml-openrail-m #region-us \n", "#### Usage\n\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder\nTo use it in a prompt add\n*art by assassin\\_style* \n\n\nadd **[ ]** around it to reduce its weight.", "#### Included Files\n\n\n* 6500 steps *Usage: art by assassin\\_style-6500*\n* 10,000 steps *Usage: art by assassin\\_style-10000*\n* 15,000 steps *Usage: art by assassin\\_style*\n\n\ncheers \n\nWipeout", "#### Example Pictures", "#### prompt comparison\n\n\n *click the image to enlarge*\n[<img height=\"50%\" width=\"50%\" src=\"https://i.URL](https://i.URL target=)" ]

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

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[ "## User split\nuser split status in 'user' table, see SQL below:", "## User feature\n'user_feature_train' and 'user_feature_test' are pre-processed user feature\nsee SQL below:", "## User behavior" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "## User split\nuser split status in 'user' table, see SQL below:", "## User feature\n'user_feature_train' and 'user_feature_test' are pre-processed user feature\nsee SQL below:", "## User behavior" ]

[ "multilinguality:monolingual", "size_categories:<1K", "language:en", "license:cc-by-4.0", "arxiv:2210.03992", "region:us" ]

[ "2210.03992" ]

[ "en" ]

[ "# Dataset Card for \"lmqg/qg_annotation\"", "## Dataset Description\n- Repository: URL\n- Paper: URL\n- Point of Contact: Asahi Ushio", "### Dataset Summary\nThis is the annotated questions generated by different models, used to measure the correlation of automatic metrics against \nhuman in \"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference\".", "### Languages\nEnglish (en)", "## Dataset Structure\nAn example of 'train' looks as follows." ]

[ "TAGS\n#multilinguality-monolingual #size_categories-<1K #language-English #license-cc-by-4.0 #arxiv-2210.03992 #region-us \n", "# Dataset Card for \"lmqg/qg_annotation\"", "## Dataset Description\n- Repository: URL\n- Paper: URL\n- Point of Contact: Asahi Ushio", "### Dataset Summary\nThis is the annotated questions generated by different models, used to measure the correlation of automatic metrics against \nhuman in \"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference\".", "### Languages\nEnglish (en)", "## Dataset Structure\nAn example of 'train' looks as follows." ]

[ "region:us" ]

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[]

[]

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

[ "task_categories:image-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:en", "license:other", "biodiversity", "camera trap data", "wildlife monitoring", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for LILA", "## 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 - Tutorial\n - Working with Taxonomies\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: https://lila.science/\n- Repository: N/A\n- Paper: N/A\n- Leaderboard: N/A\n- Point of Contact: [email protected]", "### Dataset Summary\n\nLILA Camera Traps is an aggregate data set of images taken by camera traps, which are devices that automatically (e.g. via motion detection) capture images of wild animals to help ecological research.\n\nThis data set is the first time when disparate camera trap data sets have been aggregated into a single training environment with a single taxonomy.\n\nThis data set consists of only camera trap image data sets, whereas the broader LILA website also has other data sets related to biology and conservation, intended as a resource for both machine learning (ML) researchers and those that want to harness ML for this topic.\n\n\nSee below for information about each specific dataset that LILA contains:\n\n<details>\n<summary> Caltech Camera Traps </summary>\n\nThis data set contains 243,100 images from 140 camera locations in the Southwestern United States, with labels for 21 animal categories (plus empty), primarily at the species level (for example, the most common labels are opossum, raccoon, and coyote), and approximately 66,000 bounding box annotations. Approximately 70% of images are labeled as empty.\nMore information about this data set is available here.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n\nFor questions about this data set, contact caltechcameratraps@URL.\n\nIf you use this data set, please cite the associated manuscript:\n\n</details>\n\n<details>\n<summary> ENA24 </summary>\n\nThis data set contains approximately 10,000 camera trap images representing 23 classes from Eastern North America, with bounding boxes on each image. The most common classes are “American Crow”, “American Black Bear”, and “Dog”.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n\nPlease cite this manuscript if you use this data set:\n\nFor questions about this data set, contact Hayder Yousif.\n\n</details>\n\n<details>\n<summary> Missouri Camera Traps </summary>\n\nThis data set contains approximately 25,000 camera trap images representing 20 species (for example, the most common labels are red deer, mouflon, and white-tailed deer). Images within each sequence share the same species label (even though the animal may not have been recorded in all the images in the sequence). Around 900 bounding boxes are included. These are very challenging sequences with highly cluttered and dynamic scenes. Spatial resolutions of the images vary from 1920 × 1080 to 2048 × 1536. Sequence lengths vary from 3 to more than 300 frames.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n\nIf you use this data set, please cite the associated manuscript:\n\nFor questions about this data set, contact Hayder Yousif and Zhi Zhang.\n</details>\n\n<details>\n<summary> North American Camera Trap Images (NACTI) </summary>\n\nThis data set contains 3.7M camera trap images from five locations across the United States, with labels for 28 animal categories, primarily at the species level (for example, the most common labels are cattle, boar, and red deer). Approximately 12% of images are labeled as empty. We have also added bounding box annotations to 8892 images (mostly vehicles and birds).\nThis data set is released under the Community Data License Agreement (permissive variant).\n\nPlease cite this manuscript if you use this data set:\n\n\nFor questions about this data set, contact northamericancameratrapimages@URL.\n\n</details>\n\n<details>\n<summary> WCS Camera Traps </summary>\n\nThis data set contains approximately 1.4M camera trap images representing around 675 species from 12 countries, making it one of the most diverse camera trap data sets available publicly. Data were provided by the Wildlife Conservation Society. The most common classes are tayassu pecari (peccary), meleagris ocellata (ocellated turkey), and bos taurus (cattle). A complete list of classes and associated image counts is available here. Approximately 50% of images are empty. We have also added approximately 375,000 bounding box annotations to approximately 300,000 of those images, which come from sequences covering almost all locations.\n\nSequences are inferred from timestamps, so may not strictly represent bursts. Images were labeled at a combination of image and sequence level, so – as is the case with most camera trap data sets – empty images may be labeled as non-empty (if an animal was present in one frame of a sequence but not in others). Images containing humans are referred to in metadata, but are not included in the data files. You can find more information about the data set on the LILA website.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>\n\n<details>\n<summary> Wellington Camera Traps </summary>\n\nThis data set contains 270,450 images from 187 camera locations in Wellington, New Zealand. The cameras (Bushnell 119537, 119476, and 119436) recorded sequences of three images when triggered. Each sequence was labelled by citizen scientists and/or professional ecologists from Victoria University of Wellington into 17 classes: 15 animal categories (for example, the most common labels are bird, cat, and hedgehog), empty, and unclassifiable. Approximately 17% of images are labeled as empty. Images within each sequence share the same species label (even though the animal may not have been recorded in all three images).\n\nIf you use this data set, please cite the associated manuscript:\n\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n\nFor questions about this data set, contact Victor Anton.\n</details>\n\n<details>\n<summary> Island Conservation Camera Traps </summary>\n\nThis data set contains approximately 123,000 camera trap images from 123 camera locations from 7 islands in 6 countries. Data were provided by Island Conservation during projects conducted to prevent the extinction of threatened species on islands.\n\nThe most common classes are rabbit, rat, petrel, iguana, cat, goat, and pig, with both rat and cat represented between multiple island sites representing significantly different ecosystems (tropical forest, dry forest, and temperate forests). Additionally, this data set represents data from locations and ecosystems that, to our knowledge, are not well represented in publicly available datasets including >1,000 images each of iguanas, petrels, and shearwaters. A complete list of classes and associated image counts is available here. Approximately 60% of the images are empty. We have also included approximately 65,000 bounding box annotations for about 50,000 images.\n\nIn general cameras were dispersed across each project site to detect the presence of invasive vertebrate species that threaten native island species. Cameras were set to capture bursts of photos for each motion detection event (between three and eight photos) with a set delay between events (10 to 30 seconds) to minimize the number of photos. Images containing humans are referred to in metadata, but are not included in the data files.\n\nFor questions about this data set, contact David Will at Island Conservation.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n\nThe original data set included a “human” class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata. If those images are important to your work, contact us; in some cases it will be possible to release those images under an alternative license.\n</details>\n\n<details>\n<summary> Channel Islands Camera Traps </summary>\n\nThis data set contains 246,529 camera trap images from 73 camera locations in the Channel Islands, California. All animals are annotated with bounding boxes. Data were provided by The Nature Conservancy. Animals are classified as rodent1 (82914), fox (48150), bird (11099), skunk (1071), or other (159). 114,949 images (47%) are empty. All images of rats were taken on islands already known to have rat populations.\n\nIf you use these data in a publication or report, please use the following citation:\n\nThe Nature Conservancy (2021): Channel Islands Camera Traps 1.0. The Nature Conservancy. Dataset.\n\nFor questions about this data set, contact Nathaniel Rindlaub at The Nature Conservancy.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n\nThe original data set included a “human” class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata.\n\n</details>\n\n<details>\n<summary> Idaho Camera Traps </summary>\n\nThis data set contains approximately 1.5 million camera trap images from Idaho. Labels are provided for 62 categories, most of which are animal classes (“deer”, “elk”, and “cattle” are the most common animal classes), but labels also include some state indicators (e.g. “snow on lens”, “foggy lens”). Approximately 70.5% of images are labeled as empty. Annotations were assigned to image sequences, rather than individual images, so annotations are meaningful only at the sequence level.\n\nThe metadata contains references to images containing humans, but these have been removed from the dataset (along with images containing vehicles and domestic dogs).\n\nImages were provided by the Idaho Department of Fish and Game. No representations or warranties are made regarding the data, including but not limited to warranties of non-infringement or fitness for a particular purpose. Some information shared under this agreement may not have undergone quality assurance procedures and should be considered provisional. Images may not be sold in any format, but may be used for scientific publications. Please acknowledge the Idaho Department of Fish and Game when using images for publication or scientific communication.\n</details>\n\n<details>\n<summary> Snapshot Serengeti </summary>\n\nThis data set contains approximately 2.65M sequences of camera trap images, totaling 7.1M images, from seasons one through eleven of the Snapshot Serengeti project -- the flagship project of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Serengeti National Park in Tanzania is best known for the massive annual migrations of wildebeest and zebra that drive the cycling of its dynamic ecosystem.\n\nLabels are provided for 61 categories, primarily at the species level (for example, the most common labels are wildebeest, zebra, and Thomson’s gazelle). Approximately 76% of images are labeled as empty. A full list of species and associated image counts is available here. We have also added approximately 150,000 bounding box annotations to approximately 78,000 of those images.\n\nThe images and species-level labels are described in more detail in the associated manuscript:\n\n\n\nFor questions about this data set, contact Sarah Huebner at the University of Minnesota.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>\n\n<details>\n<summary> Snapshot Karoo </summary>\n\nThis data set contains 14889 sequences of camera trap images, totaling 38074 images, from the Snapshot Karoo project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Karoo National Park, located in the arid Nama Karoo biome of South Africa, is defined by its endemic vegetation and mountain landscapes. Its unique topographical gradient has led to a surprising amount of biodiversity, with 58 mammals and more than 200 bird species recorded, as well as a multitude of reptilian species.\n\nLabels are provided for 38 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, hartebeestred, and kudu). Approximately 83.02% of images are labeled as empty. A full list of species and associated image counts is available here.\n\nFor questions about this data set, contact Sarah Huebner at the University of Minnesota.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>\n\n\n<details>\n<summary> Snapshot Kgalagadi </summary>\n\nThis data set contains 3611 sequences of camera trap images, totaling 10222 images, from the Snapshot Kgalagadi project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. The Kgalagadi Transfrontier Park stretches from the Namibian border across South Africa and into Botswana, covering a landscape commonly referred to as the Kalahari – an arid savanna. This region is of great interest to help us understand how animals cope with extreme temperatures at both ends of the scale.\n\nLabels are provided for 31 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, birdother, and ostrich). Approximately 76.14% of images are labeled as empty. A full list of species and associated image counts is available here.\n\nFor questions about this data set, contact Sarah Huebner at the University of Minnesota.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>\n\n\n<details>\n<summary> Snapshot Enonkishu </summary>\n\nThis data set contains 13301 sequences of camera trap images, totaling 28544 images, from the Snapshot Enonkishu project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Enonkishu Conservancy is located on the northern boundary of the Mara-Serengeti ecosystem in Kenya, and is managed by a consortium of stakeholders and land-owning Maasai families. Their aim is to promote coexistence between wildlife and livestock in order to encourage regenerative grazing and build stability in the Mara conservancies.\n\nLabels are provided for 39 categories, primarily at the species level (for example, the most common labels are impala, warthog, and zebra). Approximately 64.76% of images are labeled as empty. A full list of species and associated image counts is available here.\n\nFor questions about this data set, contact Sarah Huebner at the University of Minnesota.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>\n\n\n<details>\n<summary> Snapshot Camdeboo </summary>\n\nThis data set contains 12132 sequences of camera trap images, totaling 30227 images, from the Snapshot Camdeboo project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Camdeboo National Park, South Africa is crucial habitat for many birds on a global scale, with greater than fifty endemic and near-endemic species and many migratory species.\n\nLabels are provided for 43 categories, primarily at the species level (for example, the most common labels are kudu, springbok, and ostrich). Approximately 43.74% of images are labeled as empty. A full list of species and associated image counts is available here.\n\nFor questions about this data set, contact Sarah Huebner at the University of Minnesota.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>\n\n\n<details>\n<summary> Snapshot Mountain Zebra </summary>\n\nThis data set contains 71688 sequences of camera trap images, totaling 73034 images, from the Snapshot Mountain Zebra project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Mountain Zebra National Park is located in the Eastern Cape of South Africa in a transitional area between several distinct biomes, which means it is home to many endemic species. As the name suggests, this park contains the largest remnant population of Cape Mountain zebras, ~700 as of 2019 and increasing steadily every year.\n\nLabels are provided for 54 categories, primarily at the species level (for example, the most common labels are zebramountain, kudu, and springbok). Approximately 91.23% of images are labeled as empty. A full list of species and associated image counts is available here.\n\nFor questions about this data set, contact Sarah Huebner at the University of Minnesota.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>\n\n\n<details>\n<summary> Snapshot Kruger </summary>\n\nThis data set contains 4747 sequences of camera trap images, totaling 10072 images, from the Snapshot Kruger project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Kruger National Park, South Africa has been a refuge for wildlife since its establishment in 1898, and it houses one of the most diverse wildlife assemblages remaining in Africa. The Snapshot Safari grid was established in 2018 as part of a research project assessing the impacts of large mammals on plant life as boundary fences were removed and wildlife reoccupied areas of previous extirpation.\n\nLabels are provided for 46 categories, primarily at the species level (for example, the most common labels are impala, elephant, and buffalo). Approximately 61.60% of images are labeled as empty. A full list of species and associated image counts is available here.\n\nFor questions about this data set, contact Sarah Huebner at the University of Minnesota.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>\n\n\n<details>\n<summary> SWG Camera Traps </summary>\n\nThis data set contains 436,617 sequences of camera trap images from 982 locations in Vietnam and Lao, totaling 2,039,657 images. Labels are provided for 120 categories, primarily at the species level (for example, the most common labels are “Eurasian Wild Pig”, “Large-antlered Muntjac”, and “Unidentified Murid”). Approximately 12.98% of images are labeled as empty. A full list of species and associated image counts is available here. 101,659 bounding boxes are provided on 88,135 images.\n\nThis data set is provided by the Saola Working Group; providers include:\n\n- IUCN SSC Asian Wild Cattle Specialist Group’s Saola Working Group (SWG)\n- Asian Arks\n- Wildlife Conservation Society (Lao)\n- WWF Lao\n- Integrated Conservation of Biodiversity and Forests project, Lao (ICBF)\n- Center for Environment and Rural Development, Vinh University, Vietnam\n\nIf you use these data in a publication or report, please use the following citation:\n\nSWG (2021): Northern and Central Annamites Camera Traps 2.0. IUCN SSC Asian Wild Cattle Specialist Group’s Saola Working Group. Dataset.\n\nFor questions about this data set, contact saolawg@URL.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n\n</details>\n\n<details>\n<summary> Orinoquia Camera Traps </summary>\n\nThis data set contains 104,782 images collected from a 50-camera-trap array deployed from January to July 2020 within the private natural reserves El Rey Zamuro (31 km2) and Las Unamas (40 km2), located in the Meta department in the Orinoquía region in central Colombia. We deployed cameras using a stratified random sampling design across forest core area strata. Cameras were spaced 1 km apart from one another, located facing wildlife trails, and deployed with no bait. Images were stored and reviewed by experts using the Wildlife Insights platform.\n\nThis data set contains 51 classes, predominantly mammals such as the collared peccary, black agouti, spotted paca, white-lipped peccary, lowland tapir, and giant anteater. Approximately 20% of images are empty.\n\nThe main purpose of the study is to understand how humans, wildlife, and domestic animals interact in multi-functional landscapes (e.g., agricultural livestock areas with native forest remnants). However, this data set was also used to review model performance of AI-powered platforms – Wildlife Insights (WI), MegaDetector (MD), and Machine Learning for Wildlife Image Classification (MLWIC2). We provide a demonstration of the use of WI, MD, and MLWIC2 and R code for evaluating model performance of these platforms in the accompanying GitHub repository.\n\nIf you use these data in a publication or report, please use the following citation:\n\nFor questions about this data set, contact Juliana Velez Gomez.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>", "### Supported Tasks and Leaderboards\n\nNo leaderboards exist for LILA.", "### Languages\n\nThe LILA taxonomy is provided in English.", "## Dataset Structure", "### Data Instances\n\nThe data annotations are provided in COCO Camera Traps format.\n\nAll of the datasets share a common category taxonomy, which is defined on the LILA website.", "### Data Fields\n\nDifferent datasets may have slightly varying fields, which include:\n\n'file_name': the file name \\\n'width' and 'height': the dimensions of the image \\\n'study': which research study the image was collected as part of \\\n'location' : the name of the location at which the image was taken \\\n 'annotations': information about image annotation, which includes the taxonomy information, bounding box/boxes ('bbox'/'bboxes') if any, as well as any other annotation information. \\\n 'image' : the 'path' to download the image and any other information that is available, e.g. its size in 'bytes'.", "### Data Splits\n\nThis dataset does not have a predefined train/test split.", "## Dataset Creation", "### Curation Rationale\n\nThe datasets that constitute LILA have been provided by the organizations, projects and researchers who collected them.", "### Source Data", "#### Initial data collection and normalization\n\nN/A", "#### Who are the source language producers?\n\nN/A", "### Annotations", "#### Annotation process\n\nEach dataset has been annotated by the members of the project/organization that provided it.", "#### Who are the annotators?\n\nThe annotations have been provided by domain experts in fields such as biology and ecology.", "### Personal and Sensitive Information\n\nSome of the original data sets included a “human” class label; for privacy reasons, these images were removed. Those labels are still present in the metadata. If those images are important to your work, contact the LILA maintainers, since in some cases it will be possible to release those images under an alternative license.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nMachine learning depends on labeled data, but accessing such data in biology and conservation is a challenge. Consequently, everyone benefits when labeled data is made available. Biologists and conservation scientists benefit by having data to train on, and free hosting allows teams to multiply the impact of their data (we suggest listing this benefit in grant proposals that fund data collection). ML researchers benefit by having data to experiment with.", "### Discussion of Biases\n\nThese datasets do not represent global diversity, but are examples of local ecosystems and animals.", "### Other Known Limitations\n\nN/A", "## Additional Information", "### Tutorial\n\nThe tutorial in this Google Colab notebook demonstrates how to work with this dataset, including filtering by species, collating configurations, and downloading images.", "### Working with Taxonomies\n\nAll the taxonomy categories are saved as ClassLabels, which can be converted to strings as needed. Strings can likewise be converted to integers as needed, to filter the dataset. In the example below we filter the \"Caltech Camera Traps\" dataset to find all the entries with a \"felis catus\" as the species for the first annotation.\n\n\n\nThe original common names have been saved with their taxonomy mappings in this repository in 'common_names_to_tax.json'. These can be used, for example, to map from a taxonomy combination to a common name to help make queries more legible. Note, however, that there is a small number of duplicate common names with different taxonomy values which you will need to disambiguate.\n\nThe following example loads the first \"sea turtle\" in the \"Island Conservation Camera Traps\" dataset.\n\n\n\nThe example below selects a random item from the dataset, and then maps from the taxonomy to a common name:", "### Dataset Curators\n\nLILA BC is maintained by a working group that includes representatives from Ecologize, Zooniverse, the Evolving AI Lab, Snapshot Safari, and Microsoft AI for Earth. Hosting on Microsoft Azure is provided by Microsoft AI for Earth.", "### Licensing Information\n\nMany, but not all, LILA data sets were released under the Community Data License Agreement (permissive variant). Check the details of the specific dataset you are using in its section above.\n\n\n\nCitations for each dataset (if they exist) are provided in its section above.", "### Contributions\n\nThanks to @NimaBoscarino for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-English #license-other #biodiversity #camera trap data #wildlife monitoring #region-us \n", "# Dataset Card for LILA", "## 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 - Tutorial\n - Working with Taxonomies\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: https://lila.science/\n- Repository: N/A\n- Paper: N/A\n- Leaderboard: N/A\n- Point of Contact: [email protected]", "### Dataset Summary\n\nLILA Camera Traps is an aggregate data set of images taken by camera traps, which are devices that automatically (e.g. via motion detection) capture images of wild animals to help ecological research.\n\nThis data set is the first time when disparate camera trap data sets have been aggregated into a single training environment with a single taxonomy.\n\nThis data set consists of only camera trap image data sets, whereas the broader LILA website also has other data sets related to biology and conservation, intended as a resource for both machine learning (ML) researchers and those that want to harness ML for this topic.\n\n\nSee below for information about each specific dataset that LILA contains:\n\n<details>\n<summary> Caltech Camera Traps </summary>\n\nThis data set contains 243,100 images from 140 camera locations in the Southwestern United States, with labels for 21 animal categories (plus empty), primarily at the species level (for example, the most common labels are opossum, raccoon, and coyote), and approximately 66,000 bounding box annotations. Approximately 70% of images are labeled as empty.\nMore information about this data set is available here.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n\nFor questions about this data set, contact caltechcameratraps@URL.\n\nIf you use this data set, please cite the associated manuscript:\n\n</details>\n\n<details>\n<summary> ENA24 </summary>\n\nThis data set contains approximately 10,000 camera trap images representing 23 classes from Eastern North America, with bounding boxes on each image. The most common classes are “American Crow”, “American Black Bear”, and “Dog”.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n\nPlease cite this manuscript if you use this data set:\n\nFor questions about this data set, contact Hayder Yousif.\n\n</details>\n\n<details>\n<summary> Missouri Camera Traps </summary>\n\nThis data set contains approximately 25,000 camera trap images representing 20 species (for example, the most common labels are red deer, mouflon, and white-tailed deer). Images within each sequence share the same species label (even though the animal may not have been recorded in all the images in the sequence). Around 900 bounding boxes are included. These are very challenging sequences with highly cluttered and dynamic scenes. Spatial resolutions of the images vary from 1920 × 1080 to 2048 × 1536. Sequence lengths vary from 3 to more than 300 frames.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n\nIf you use this data set, please cite the associated manuscript:\n\nFor questions about this data set, contact Hayder Yousif and Zhi Zhang.\n</details>\n\n<details>\n<summary> North American Camera Trap Images (NACTI) </summary>\n\nThis data set contains 3.7M camera trap images from five locations across the United States, with labels for 28 animal categories, primarily at the species level (for example, the most common labels are cattle, boar, and red deer). Approximately 12% of images are labeled as empty. We have also added bounding box annotations to 8892 images (mostly vehicles and birds).\nThis data set is released under the Community Data License Agreement (permissive variant).\n\nPlease cite this manuscript if you use this data set:\n\n\nFor questions about this data set, contact northamericancameratrapimages@URL.\n\n</details>\n\n<details>\n<summary> WCS Camera Traps </summary>\n\nThis data set contains approximately 1.4M camera trap images representing around 675 species from 12 countries, making it one of the most diverse camera trap data sets available publicly. Data were provided by the Wildlife Conservation Society. The most common classes are tayassu pecari (peccary), meleagris ocellata (ocellated turkey), and bos taurus (cattle). A complete list of classes and associated image counts is available here. Approximately 50% of images are empty. We have also added approximately 375,000 bounding box annotations to approximately 300,000 of those images, which come from sequences covering almost all locations.\n\nSequences are inferred from timestamps, so may not strictly represent bursts. Images were labeled at a combination of image and sequence level, so – as is the case with most camera trap data sets – empty images may be labeled as non-empty (if an animal was present in one frame of a sequence but not in others). Images containing humans are referred to in metadata, but are not included in the data files. You can find more information about the data set on the LILA website.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>\n\n<details>\n<summary> Wellington Camera Traps </summary>\n\nThis data set contains 270,450 images from 187 camera locations in Wellington, New Zealand. The cameras (Bushnell 119537, 119476, and 119436) recorded sequences of three images when triggered. Each sequence was labelled by citizen scientists and/or professional ecologists from Victoria University of Wellington into 17 classes: 15 animal categories (for example, the most common labels are bird, cat, and hedgehog), empty, and unclassifiable. Approximately 17% of images are labeled as empty. Images within each sequence share the same species label (even though the animal may not have been recorded in all three images).\n\nIf you use this data set, please cite the associated manuscript:\n\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n\nFor questions about this data set, contact Victor Anton.\n</details>\n\n<details>\n<summary> Island Conservation Camera Traps </summary>\n\nThis data set contains approximately 123,000 camera trap images from 123 camera locations from 7 islands in 6 countries. Data were provided by Island Conservation during projects conducted to prevent the extinction of threatened species on islands.\n\nThe most common classes are rabbit, rat, petrel, iguana, cat, goat, and pig, with both rat and cat represented between multiple island sites representing significantly different ecosystems (tropical forest, dry forest, and temperate forests). Additionally, this data set represents data from locations and ecosystems that, to our knowledge, are not well represented in publicly available datasets including >1,000 images each of iguanas, petrels, and shearwaters. A complete list of classes and associated image counts is available here. Approximately 60% of the images are empty. We have also included approximately 65,000 bounding box annotations for about 50,000 images.\n\nIn general cameras were dispersed across each project site to detect the presence of invasive vertebrate species that threaten native island species. Cameras were set to capture bursts of photos for each motion detection event (between three and eight photos) with a set delay between events (10 to 30 seconds) to minimize the number of photos. Images containing humans are referred to in metadata, but are not included in the data files.\n\nFor questions about this data set, contact David Will at Island Conservation.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n\nThe original data set included a “human” class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata. If those images are important to your work, contact us; in some cases it will be possible to release those images under an alternative license.\n</details>\n\n<details>\n<summary> Channel Islands Camera Traps </summary>\n\nThis data set contains 246,529 camera trap images from 73 camera locations in the Channel Islands, California. All animals are annotated with bounding boxes. Data were provided by The Nature Conservancy. Animals are classified as rodent1 (82914), fox (48150), bird (11099), skunk (1071), or other (159). 114,949 images (47%) are empty. All images of rats were taken on islands already known to have rat populations.\n\nIf you use these data in a publication or report, please use the following citation:\n\nThe Nature Conservancy (2021): Channel Islands Camera Traps 1.0. The Nature Conservancy. Dataset.\n\nFor questions about this data set, contact Nathaniel Rindlaub at The Nature Conservancy.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n\nThe original data set included a “human” class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata.\n\n</details>\n\n<details>\n<summary> Idaho Camera Traps </summary>\n\nThis data set contains approximately 1.5 million camera trap images from Idaho. Labels are provided for 62 categories, most of which are animal classes (“deer”, “elk”, and “cattle” are the most common animal classes), but labels also include some state indicators (e.g. “snow on lens”, “foggy lens”). Approximately 70.5% of images are labeled as empty. Annotations were assigned to image sequences, rather than individual images, so annotations are meaningful only at the sequence level.\n\nThe metadata contains references to images containing humans, but these have been removed from the dataset (along with images containing vehicles and domestic dogs).\n\nImages were provided by the Idaho Department of Fish and Game. No representations or warranties are made regarding the data, including but not limited to warranties of non-infringement or fitness for a particular purpose. Some information shared under this agreement may not have undergone quality assurance procedures and should be considered provisional. Images may not be sold in any format, but may be used for scientific publications. Please acknowledge the Idaho Department of Fish and Game when using images for publication or scientific communication.\n</details>\n\n<details>\n<summary> Snapshot Serengeti </summary>\n\nThis data set contains approximately 2.65M sequences of camera trap images, totaling 7.1M images, from seasons one through eleven of the Snapshot Serengeti project -- the flagship project of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Serengeti National Park in Tanzania is best known for the massive annual migrations of wildebeest and zebra that drive the cycling of its dynamic ecosystem.\n\nLabels are provided for 61 categories, primarily at the species level (for example, the most common labels are wildebeest, zebra, and Thomson’s gazelle). Approximately 76% of images are labeled as empty. A full list of species and associated image counts is available here. We have also added approximately 150,000 bounding box annotations to approximately 78,000 of those images.\n\nThe images and species-level labels are described in more detail in the associated manuscript:\n\n\n\nFor questions about this data set, contact Sarah Huebner at the University of Minnesota.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>\n\n<details>\n<summary> Snapshot Karoo </summary>\n\nThis data set contains 14889 sequences of camera trap images, totaling 38074 images, from the Snapshot Karoo project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Karoo National Park, located in the arid Nama Karoo biome of South Africa, is defined by its endemic vegetation and mountain landscapes. Its unique topographical gradient has led to a surprising amount of biodiversity, with 58 mammals and more than 200 bird species recorded, as well as a multitude of reptilian species.\n\nLabels are provided for 38 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, hartebeestred, and kudu). Approximately 83.02% of images are labeled as empty. A full list of species and associated image counts is available here.\n\nFor questions about this data set, contact Sarah Huebner at the University of Minnesota.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>\n\n\n<details>\n<summary> Snapshot Kgalagadi </summary>\n\nThis data set contains 3611 sequences of camera trap images, totaling 10222 images, from the Snapshot Kgalagadi project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. The Kgalagadi Transfrontier Park stretches from the Namibian border across South Africa and into Botswana, covering a landscape commonly referred to as the Kalahari – an arid savanna. This region is of great interest to help us understand how animals cope with extreme temperatures at both ends of the scale.\n\nLabels are provided for 31 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, birdother, and ostrich). Approximately 76.14% of images are labeled as empty. A full list of species and associated image counts is available here.\n\nFor questions about this data set, contact Sarah Huebner at the University of Minnesota.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>\n\n\n<details>\n<summary> Snapshot Enonkishu </summary>\n\nThis data set contains 13301 sequences of camera trap images, totaling 28544 images, from the Snapshot Enonkishu project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Enonkishu Conservancy is located on the northern boundary of the Mara-Serengeti ecosystem in Kenya, and is managed by a consortium of stakeholders and land-owning Maasai families. Their aim is to promote coexistence between wildlife and livestock in order to encourage regenerative grazing and build stability in the Mara conservancies.\n\nLabels are provided for 39 categories, primarily at the species level (for example, the most common labels are impala, warthog, and zebra). Approximately 64.76% of images are labeled as empty. A full list of species and associated image counts is available here.\n\nFor questions about this data set, contact Sarah Huebner at the University of Minnesota.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>\n\n\n<details>\n<summary> Snapshot Camdeboo </summary>\n\nThis data set contains 12132 sequences of camera trap images, totaling 30227 images, from the Snapshot Camdeboo project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Camdeboo National Park, South Africa is crucial habitat for many birds on a global scale, with greater than fifty endemic and near-endemic species and many migratory species.\n\nLabels are provided for 43 categories, primarily at the species level (for example, the most common labels are kudu, springbok, and ostrich). Approximately 43.74% of images are labeled as empty. A full list of species and associated image counts is available here.\n\nFor questions about this data set, contact Sarah Huebner at the University of Minnesota.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>\n\n\n<details>\n<summary> Snapshot Mountain Zebra </summary>\n\nThis data set contains 71688 sequences of camera trap images, totaling 73034 images, from the Snapshot Mountain Zebra project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Mountain Zebra National Park is located in the Eastern Cape of South Africa in a transitional area between several distinct biomes, which means it is home to many endemic species. As the name suggests, this park contains the largest remnant population of Cape Mountain zebras, ~700 as of 2019 and increasing steadily every year.\n\nLabels are provided for 54 categories, primarily at the species level (for example, the most common labels are zebramountain, kudu, and springbok). Approximately 91.23% of images are labeled as empty. A full list of species and associated image counts is available here.\n\nFor questions about this data set, contact Sarah Huebner at the University of Minnesota.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>\n\n\n<details>\n<summary> Snapshot Kruger </summary>\n\nThis data set contains 4747 sequences of camera trap images, totaling 10072 images, from the Snapshot Kruger project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Kruger National Park, South Africa has been a refuge for wildlife since its establishment in 1898, and it houses one of the most diverse wildlife assemblages remaining in Africa. The Snapshot Safari grid was established in 2018 as part of a research project assessing the impacts of large mammals on plant life as boundary fences were removed and wildlife reoccupied areas of previous extirpation.\n\nLabels are provided for 46 categories, primarily at the species level (for example, the most common labels are impala, elephant, and buffalo). Approximately 61.60% of images are labeled as empty. A full list of species and associated image counts is available here.\n\nFor questions about this data set, contact Sarah Huebner at the University of Minnesota.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>\n\n\n<details>\n<summary> SWG Camera Traps </summary>\n\nThis data set contains 436,617 sequences of camera trap images from 982 locations in Vietnam and Lao, totaling 2,039,657 images. Labels are provided for 120 categories, primarily at the species level (for example, the most common labels are “Eurasian Wild Pig”, “Large-antlered Muntjac”, and “Unidentified Murid”). Approximately 12.98% of images are labeled as empty. A full list of species and associated image counts is available here. 101,659 bounding boxes are provided on 88,135 images.\n\nThis data set is provided by the Saola Working Group; providers include:\n\n- IUCN SSC Asian Wild Cattle Specialist Group’s Saola Working Group (SWG)\n- Asian Arks\n- Wildlife Conservation Society (Lao)\n- WWF Lao\n- Integrated Conservation of Biodiversity and Forests project, Lao (ICBF)\n- Center for Environment and Rural Development, Vinh University, Vietnam\n\nIf you use these data in a publication or report, please use the following citation:\n\nSWG (2021): Northern and Central Annamites Camera Traps 2.0. IUCN SSC Asian Wild Cattle Specialist Group’s Saola Working Group. Dataset.\n\nFor questions about this data set, contact saolawg@URL.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n\n</details>\n\n<details>\n<summary> Orinoquia Camera Traps </summary>\n\nThis data set contains 104,782 images collected from a 50-camera-trap array deployed from January to July 2020 within the private natural reserves El Rey Zamuro (31 km2) and Las Unamas (40 km2), located in the Meta department in the Orinoquía region in central Colombia. We deployed cameras using a stratified random sampling design across forest core area strata. Cameras were spaced 1 km apart from one another, located facing wildlife trails, and deployed with no bait. Images were stored and reviewed by experts using the Wildlife Insights platform.\n\nThis data set contains 51 classes, predominantly mammals such as the collared peccary, black agouti, spotted paca, white-lipped peccary, lowland tapir, and giant anteater. Approximately 20% of images are empty.\n\nThe main purpose of the study is to understand how humans, wildlife, and domestic animals interact in multi-functional landscapes (e.g., agricultural livestock areas with native forest remnants). However, this data set was also used to review model performance of AI-powered platforms – Wildlife Insights (WI), MegaDetector (MD), and Machine Learning for Wildlife Image Classification (MLWIC2). We provide a demonstration of the use of WI, MD, and MLWIC2 and R code for evaluating model performance of these platforms in the accompanying GitHub repository.\n\nIf you use these data in a publication or report, please use the following citation:\n\nFor questions about this data set, contact Juliana Velez Gomez.\n\nThis data set is released under the Community Data License Agreement (permissive variant).\n</details>", "### Supported Tasks and Leaderboards\n\nNo leaderboards exist for LILA.", "### Languages\n\nThe LILA taxonomy is provided in English.", "## Dataset Structure", "### Data Instances\n\nThe data annotations are provided in COCO Camera Traps format.\n\nAll of the datasets share a common category taxonomy, which is defined on the LILA website.", "### Data Fields\n\nDifferent datasets may have slightly varying fields, which include:\n\n'file_name': the file name \\\n'width' and 'height': the dimensions of the image \\\n'study': which research study the image was collected as part of \\\n'location' : the name of the location at which the image was taken \\\n 'annotations': information about image annotation, which includes the taxonomy information, bounding box/boxes ('bbox'/'bboxes') if any, as well as any other annotation information. \\\n 'image' : the 'path' to download the image and any other information that is available, e.g. its size in 'bytes'.", "### Data Splits\n\nThis dataset does not have a predefined train/test split.", "## Dataset Creation", "### Curation Rationale\n\nThe datasets that constitute LILA have been provided by the organizations, projects and researchers who collected them.", "### Source Data", "#### Initial data collection and normalization\n\nN/A", "#### Who are the source language producers?\n\nN/A", "### Annotations", "#### Annotation process\n\nEach dataset has been annotated by the members of the project/organization that provided it.", "#### Who are the annotators?\n\nThe annotations have been provided by domain experts in fields such as biology and ecology.", "### Personal and Sensitive Information\n\nSome of the original data sets included a “human” class label; for privacy reasons, these images were removed. Those labels are still present in the metadata. If those images are important to your work, contact the LILA maintainers, since in some cases it will be possible to release those images under an alternative license.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nMachine learning depends on labeled data, but accessing such data in biology and conservation is a challenge. Consequently, everyone benefits when labeled data is made available. Biologists and conservation scientists benefit by having data to train on, and free hosting allows teams to multiply the impact of their data (we suggest listing this benefit in grant proposals that fund data collection). ML researchers benefit by having data to experiment with.", "### Discussion of Biases\n\nThese datasets do not represent global diversity, but are examples of local ecosystems and animals.", "### Other Known Limitations\n\nN/A", "## Additional Information", "### Tutorial\n\nThe tutorial in this Google Colab notebook demonstrates how to work with this dataset, including filtering by species, collating configurations, and downloading images.", "### Working with Taxonomies\n\nAll the taxonomy categories are saved as ClassLabels, which can be converted to strings as needed. Strings can likewise be converted to integers as needed, to filter the dataset. In the example below we filter the \"Caltech Camera Traps\" dataset to find all the entries with a \"felis catus\" as the species for the first annotation.\n\n\n\nThe original common names have been saved with their taxonomy mappings in this repository in 'common_names_to_tax.json'. These can be used, for example, to map from a taxonomy combination to a common name to help make queries more legible. Note, however, that there is a small number of duplicate common names with different taxonomy values which you will need to disambiguate.\n\nThe following example loads the first \"sea turtle\" in the \"Island Conservation Camera Traps\" dataset.\n\n\n\nThe example below selects a random item from the dataset, and then maps from the taxonomy to a common name:", "### Dataset Curators\n\nLILA BC is maintained by a working group that includes representatives from Ecologize, Zooniverse, the Evolving AI Lab, Snapshot Safari, and Microsoft AI for Earth. Hosting on Microsoft Azure is provided by Microsoft AI for Earth.", "### Licensing Information\n\nMany, but not all, LILA data sets were released under the Community Data License Agreement (permissive variant). Check the details of the specific dataset you are using in its section above.\n\n\n\nCitations for each dataset (if they exist) are provided in its section above.", "### Contributions\n\nThanks to @NimaBoscarino for adding this dataset." ]

[ "task_categories:question-answering", "task_categories:text-classification", "task_ids:entity-linking-classification", "task_ids:natural-language-inference", "annotations_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:en", "license:cc-by-4.0", "region:us" ]

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[ "en" ]

[ "# Dataset Card for openpi_v2", "## 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\n\nOpen PI is the first dataset for tracking state changes in procedural text from arbitrary domains by using an unrestricted (open) vocabulary. Our solution is a new task formulation in which just the text is provided, from which a set of state changes (entity, attribute, before, after) is generated for each step, where the entity, attribute, and values must all be predicted from an open vocabulary.", "### Supported Tasks and Leaderboards\n\n- 'Task 1': Given paragraph (e.g., with 5 steps), identify entities that change (challenge: implicit entities, some explicit entities that don’t change) \n\n- 'Task 3': Given paragraph, identify the attributes of entity that change (challenge: implicit entities, attributes & many combinations) \n\n- 'Task 4': Given paragraph & an entity, identify the sequence of attribute value changes (challenge: implicit attributes) \n\n- 'Task 7': Given image url, identify the visual attributes of entity and non-visual attributes of entity that change", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\nA typical instance in the dataset:", "### Data Fields\n\nThe following is an excerpt from the dataset README:\n\nWithin \"goal\", \"steps\", \"topics\", and \"image_urls\", the fields should be self-explanatory. Listed below is an explanation about those within \"states\":", "#### Fields specific to questions:", "### Data Splits\n\nTrain, Valid, Dev", "## 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_categories-text-classification #task_ids-entity-linking-classification #task_ids-natural-language-inference #annotations_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for openpi_v2", "## 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\n\nOpen PI is the first dataset for tracking state changes in procedural text from arbitrary domains by using an unrestricted (open) vocabulary. Our solution is a new task formulation in which just the text is provided, from which a set of state changes (entity, attribute, before, after) is generated for each step, where the entity, attribute, and values must all be predicted from an open vocabulary.", "### Supported Tasks and Leaderboards\n\n- 'Task 1': Given paragraph (e.g., with 5 steps), identify entities that change (challenge: implicit entities, some explicit entities that don’t change) \n\n- 'Task 3': Given paragraph, identify the attributes of entity that change (challenge: implicit entities, attributes & many combinations) \n\n- 'Task 4': Given paragraph & an entity, identify the sequence of attribute value changes (challenge: implicit attributes) \n\n- 'Task 7': Given image url, identify the visual attributes of entity and non-visual attributes of entity that change", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\nA typical instance in the dataset:", "### Data Fields\n\nThe following is an excerpt from the dataset README:\n\nWithin \"goal\", \"steps\", \"topics\", and \"image_urls\", the fields should be self-explanatory. Listed below is an explanation about those within \"states\":", "#### Fields specific to questions:", "### Data Splits\n\nTrain, Valid, Dev", "## 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." ]

[ "task_categories:image-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "size_categories:10K<n<100K", "source_datasets:original", "license:agpl-3.0", "myology", "biology", "histology", "muscle", "cells", "fibers", "myopathy", "SDH", "myoquant", "region:us" ]

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[ "### Dataset Summary\n\n\n\n\n\n\n\nThis dataset contains images of individual muscle fiber used to train MyoQuant SDH Model. The goal of these data is to train a tool to classify SDH stained muscle fibers depending on the presence of mitochondria repartition anomalies. A pathological feature useful for diagnosis and classification in patient with congenital myopathies.\n\n\nDataset Structure\n-----------------", "### Data Instances and Splits\n\n\nA total of 16 787 single muscle fiber images are in the dataset, split in three sets: train, validation and test set. \n\nSee the table for the exact count of images in each category:\n\n\n\nDataset Creation and Annotations\n--------------------------------", "### Source Data and annotation process\n\n\nTo create this dataset of single muscle images, whole slide image of mice muscle fiber with SDH staining were taken from WT mice (1), BIN1 KO mice (10) and mutated DNM2 mice (7). Cells contained within these slides manually counted, labeled and classified in two categories: control (no anomaly) or sick (mitochondria anomaly) by two experts/annotators. Then all single muscle images were extracted from the image using CellPose to detect each individual cell’s boundaries. Resulting in 16787 images from 18 whole image slides.", "### Who are the annotators?\n\n\nAll data in this dataset were generated and manually annotated by two experts:\n\n\n* Quentin GIRAUD, PhD Student @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)\n* Charlotte GINESTE, Post-Doc @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [charlotte.gineste@URL](mailto:charlotte.gineste@URL)\n\n\nA second pass of verification was done by:\n\n\n* Bertrand VERNAY, Platform Leader @ Light Microscopy Facility, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)", "### Personal and Sensitive Information\n\n\nAll image data comes from mice, there is no personal nor sensitive information in this dataset.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe aim of this dataset is to improve congenital myopathies diagnosis by providing tools to automatically quantify specific pathogenic features in muscle fiber histology images.", "### Discussion of Biases and Limitations\n\n\nThis dataset has several limitations (non-exhaustive list):\n\n\n* The images are from mice and thus might not be ideal to represent actual mechanism in human muscle\n* The image comes only from two mice models with mutations in two genes (BIN1, DNM2) while congenital myopathies can be caused by a mutation in more than 35+ genes.\n* Only mitochondria anomaly was considered to classify cells as \"sick\", other anomalies were not considered, thus control cells might present other anomalies (such as what is called \"cores\" in congenital myopathies for examples)\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThis dataset is under the GNU AFFERO GENERAL PUBLIC LICENSE Version 3, to ensure that what's open-source, stays open-source and available to the community.\n\n\nMyoQuant publication with model and data is yet to come.\n\n\nThe Team Behind this Dataset\n----------------------------\n\n\nThe creator, uploader and main maintainer of this dataset, associated model and MyoQuant is:\n\n\n* Corentin Meyer, 3rd year PhD Student in the CSTB Team, ICube — CNRS — Unistra Email: [URL@URL](mailto:URL@URL) Github: @lambda-science\n\n\nSpecial thanks to the experts that created the data for this dataset and all the time they spend counting cells :\n\n\n* Quentin GIRAUD, PhD Student @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)\n* Charlotte GINESTE, Post-Doc @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [charlotte.gineste@URL](mailto:charlotte.gineste@URL)\n\n\nLast but not least thanks to Bertrand Vernay being at the origin of this project:\n\n\n* Bertrand VERNAY, Platform Leader @ Light Microscopy Facility, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)\n\n\nPartners\n--------\n\n\n\n\n\n\n\nMyoQuant-SDH-Data is born within the collaboration between the CSTB Team @ ICube led by Julie D. Thompson, the Morphological Unit of the Institute of Myology of Paris led by Teresinha Evangelista, the imagery platform MyoImage of Center of Research in Myology led by Bruno Cadot, the photonic microscopy platform of the IGMBC led by Bertrand Vernay and the Pathophysiology of neuromuscular diseases team @ IGBMC led by Jocelyn Laporte" ]

[ "TAGS\n#task_categories-image-classification #annotations_creators-expert-generated #language_creators-expert-generated #size_categories-10K<n<100K #source_datasets-original #license-agpl-3.0 #myology #biology #histology #muscle #cells #fibers #myopathy #SDH #myoquant #region-us \n", "### Dataset Summary\n\n\n\n\n\n\n\nThis dataset contains images of individual muscle fiber used to train MyoQuant SDH Model. The goal of these data is to train a tool to classify SDH stained muscle fibers depending on the presence of mitochondria repartition anomalies. A pathological feature useful for diagnosis and classification in patient with congenital myopathies.\n\n\nDataset Structure\n-----------------", "### Data Instances and Splits\n\n\nA total of 16 787 single muscle fiber images are in the dataset, split in three sets: train, validation and test set. \n\nSee the table for the exact count of images in each category:\n\n\n\nDataset Creation and Annotations\n--------------------------------", "### Source Data and annotation process\n\n\nTo create this dataset of single muscle images, whole slide image of mice muscle fiber with SDH staining were taken from WT mice (1), BIN1 KO mice (10) and mutated DNM2 mice (7). Cells contained within these slides manually counted, labeled and classified in two categories: control (no anomaly) or sick (mitochondria anomaly) by two experts/annotators. Then all single muscle images were extracted from the image using CellPose to detect each individual cell’s boundaries. Resulting in 16787 images from 18 whole image slides.", "### Who are the annotators?\n\n\nAll data in this dataset were generated and manually annotated by two experts:\n\n\n* Quentin GIRAUD, PhD Student @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)\n* Charlotte GINESTE, Post-Doc @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [charlotte.gineste@URL](mailto:charlotte.gineste@URL)\n\n\nA second pass of verification was done by:\n\n\n* Bertrand VERNAY, Platform Leader @ Light Microscopy Facility, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)", "### Personal and Sensitive Information\n\n\nAll image data comes from mice, there is no personal nor sensitive information in this dataset.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe aim of this dataset is to improve congenital myopathies diagnosis by providing tools to automatically quantify specific pathogenic features in muscle fiber histology images.", "### Discussion of Biases and Limitations\n\n\nThis dataset has several limitations (non-exhaustive list):\n\n\n* The images are from mice and thus might not be ideal to represent actual mechanism in human muscle\n* The image comes only from two mice models with mutations in two genes (BIN1, DNM2) while congenital myopathies can be caused by a mutation in more than 35+ genes.\n* Only mitochondria anomaly was considered to classify cells as \"sick\", other anomalies were not considered, thus control cells might present other anomalies (such as what is called \"cores\" in congenital myopathies for examples)\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThis dataset is under the GNU AFFERO GENERAL PUBLIC LICENSE Version 3, to ensure that what's open-source, stays open-source and available to the community.\n\n\nMyoQuant publication with model and data is yet to come.\n\n\nThe Team Behind this Dataset\n----------------------------\n\n\nThe creator, uploader and main maintainer of this dataset, associated model and MyoQuant is:\n\n\n* Corentin Meyer, 3rd year PhD Student in the CSTB Team, ICube — CNRS — Unistra Email: [URL@URL](mailto:URL@URL) Github: @lambda-science\n\n\nSpecial thanks to the experts that created the data for this dataset and all the time they spend counting cells :\n\n\n* Quentin GIRAUD, PhD Student @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)\n* Charlotte GINESTE, Post-Doc @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [charlotte.gineste@URL](mailto:charlotte.gineste@URL)\n\n\nLast but not least thanks to Bertrand Vernay being at the origin of this project:\n\n\n* Bertrand VERNAY, Platform Leader @ Light Microscopy Facility, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)\n\n\nPartners\n--------\n\n\n\n\n\n\n\nMyoQuant-SDH-Data is born within the collaboration between the CSTB Team @ ICube led by Julie D. Thompson, the Morphological Unit of the Institute of Myology of Paris led by Teresinha Evangelista, the imagery platform MyoImage of Center of Research in Myology led by Bruno Cadot, the photonic microscopy platform of the IGMBC led by Bertrand Vernay and the Pathophysiology of neuromuscular diseases team @ IGBMC led by Jocelyn Laporte" ]

[ "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: Multi-class Text Classification\n* Model: autoevaluate/multi-class-classification\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: autoevaluate/multi-class-classification\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" ]

[]

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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: Multi-class Text Classification\n* Model: autoevaluate/multi-class-classification\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: autoevaluate/multi-class-classification\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" ]

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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: Multi-class Text Classification\n* Model: autoevaluate/multi-class-classification\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: autoevaluate/multi-class-classification\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" ]

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

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

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

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

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

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

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[]

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

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "arxiv:2204.02952", "region:us" ]

[ "2204.02952" ]

[]

[ "# positive-reframing-ptbr-dataset\n\nVersion translated into pt-br of the dataset available in the work \"Inducing Positive Perspectives with Text Reframing\". Used in model positive-reframing-ptbr.\n\n\nCitation:\n\n> Ziems, C., Li, M., Zhang, A., & Yang, D. (2022). Inducing Positive Perspectives with Text Reframing. In _Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL)_.\n\nBibTeX:" ]

[ "TAGS\n#arxiv-2204.02952 #region-us \n", "# positive-reframing-ptbr-dataset\n\nVersion translated into pt-br of the dataset available in the work \"Inducing Positive Perspectives with Text Reframing\". Used in model positive-reframing-ptbr.\n\n\nCitation:\n\n> Ziems, C., Li, M., Zhang, A., & Yang, D. (2022). Inducing Positive Perspectives with Text Reframing. In _Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL)_.\n\nBibTeX:" ]

[ "region:us" ]

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[ "# Dataset Card for \"FoodBase\"\n\nDataset for FoodBase corpus introduced in this paper.\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"FoodBase\"\n\nDataset for FoodBase corpus introduced in this paper.\n\nMore Information needed" ]

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

[]

[ "pt" ]

[ "# Portuguese Legal Sentences\nCollection of Legal Sentences from the Portuguese Supreme Court of Justice\nThe goal of this dataset was to be used for MLM and TSDAE\nExtended version of rufimelo/PortugueseLegalSentences-v1\n\n200000/200000/100000", "### Contributions\n@rufimelo99" ]

[ "TAGS\n#annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #source_datasets-original #language-Portuguese #license-apache-2.0 #region-us \n", "# Portuguese Legal Sentences\nCollection of Legal Sentences from the Portuguese Supreme Court of Justice\nThe goal of this dataset was to be used for MLM and TSDAE\nExtended version of rufimelo/PortugueseLegalSentences-v1\n\n200000/200000/100000", "### Contributions\n@rufimelo99" ]

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

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

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

[]

[ "en" ]

[ "# Dataset Card for \"cartoon-blip-captions\"" ]

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

[ "region:us" ]

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

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

[ "license:mit", "image-text-dataset", "synthetic-dataset", "region:us" ]

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[]

[ "TAGS\n#license-mit #image-text-dataset #synthetic-dataset #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

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[ "# BioNLP2021 dataset (Task2)\n___\n\nData fields:\n* text (str): source text; Section and Article (train_mul subset only) are separated by &lt;SAS&gt; ; Single Documents are separated by &lt;DOC&gt; ; Sentences are separated by &lt;SS&gt;\n* summ_abs, summ_ext (str): abstractive and extractive summarization, whose Sentences are separated by &lt;SS&gt;\n* question (str): question, whose Sentences are separated by &lt;SS&gt;\n* key (str): key in the origin dataset (for submitting)" ]

[ "TAGS\n#region-us \n", "# BioNLP2021 dataset (Task2)\n___\n\nData fields:\n* text (str): source text; Section and Article (train_mul subset only) are separated by &lt;SAS&gt; ; Single Documents are separated by &lt;DOC&gt; ; Sentences are separated by &lt;SS&gt;\n* summ_abs, summ_ext (str): abstractive and extractive summarization, whose Sentences are separated by &lt;SS&gt;\n* question (str): question, whose Sentences are separated by &lt;SS&gt;\n* key (str): key in the origin dataset (for submitting)" ]

[ "region:us" ]

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[ "# Dataset Card for \"text_summarization_dataset1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"text_summarization_dataset1\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"text_summarization_dataset2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"text_summarization_dataset2\"\n\nMore Information needed" ]