Datasets:

---

ftopal

/

huggingface-datasets

like 0

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"SmokeFire\"\nWildfires or forest fires are unpredictable catastrophic and destructive events that affect rural areas. The impact of these events affects both vegetation and wildlife.\nThis dataset can be used to train networks able to detect smoke and/or fire in forest environments.", "## Data Sources & Description\n- This dataset consist of sample from two datasets hosted on Kaggle:\n - Forest Fire\n - Forest Fire Images\n- The datasets consist of:\n - 2525 Fire samples\n - 2525 Smoke samples\n - 2525 Normal samples\n- The dataset is splitted into:\n - Train Set -> 6060 samples\n - Validation Set -> 756 samples\n - Test Set -> 759 samples" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"SmokeFire\"\nWildfires or forest fires are unpredictable catastrophic and destructive events that affect rural areas. The impact of these events affects both vegetation and wildlife.\nThis dataset can be used to train networks able to detect smoke and/or fire in forest environments.", "## Data Sources & Description\n- This dataset consist of sample from two datasets hosted on Kaggle:\n - Forest Fire\n - Forest Fire Images\n- The datasets consist of:\n - 2525 Fire samples\n - 2525 Smoke samples\n - 2525 Normal samples\n- The dataset is splitted into:\n - Train Set -> 6060 samples\n - Validation Set -> 756 samples\n - Test Set -> 759 samples" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Introduction\n\nFace Synthetics dataset is a collection of diverse synthetic face images with ground truth labels.\n\nIt was introduced in our paper Fake It Till You Make It: Face analysis in the wild using synthetic data alone.\n\nOur dataset contains:\n\n100,000 images of faces at 512 x 512 pixel resolution\n70 standard facial landmark annotations\nper-pixel semantic class anotations\nIt can be used to train machine learning systems for face-related tasks such as landmark localization and face parsing, showing that synthetic data can both match real data in accuracy as well as open up new approaches where manual labelling would be impossible.\n\nSome images also include hands and off-center distractor faces in addition to primary faces centered in the image.\n\nThe Face Synthetics dataset can be used for non-commercial research, and is licensed under the license found in URL.", "# Dataset Layout\nThe Face Synthetics dataset is a single .zip file containing color images, segmentation images, and 2D landmark coordinates in a text file.", "# Download\nA small subset of the original dataset can be found here; in order to train models in the entire dataset, please refer to Microsoft original repo." ]

[ "TAGS\n#region-us \n", "# Introduction\n\nFace Synthetics dataset is a collection of diverse synthetic face images with ground truth labels.\n\nIt was introduced in our paper Fake It Till You Make It: Face analysis in the wild using synthetic data alone.\n\nOur dataset contains:\n\n100,000 images of faces at 512 x 512 pixel resolution\n70 standard facial landmark annotations\nper-pixel semantic class anotations\nIt can be used to train machine learning systems for face-related tasks such as landmark localization and face parsing, showing that synthetic data can both match real data in accuracy as well as open up new approaches where manual labelling would be impossible.\n\nSome images also include hands and off-center distractor faces in addition to primary faces centered in the image.\n\nThe Face Synthetics dataset can be used for non-commercial research, and is licensed under the license found in URL.", "# Dataset Layout\nThe Face Synthetics dataset is a single .zip file containing color images, segmentation images, and 2D landmark coordinates in a text file.", "# Download\nA small subset of the original dataset can be found here; in order to train models in the entire dataset, please refer to Microsoft original repo." ]

[ "region:us" ]

[]

[]

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

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

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

[]

[ "en" ]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"test-16722377061524\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"test-16722377061524\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"ehr-roberta-tokenized_datasets-12-2022\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"ehr-roberta-tokenized_datasets-12-2022\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "### URL" ]

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

[ "arxiv:2105.05241", "arxiv:2107.06499", "arxiv:2209.00099", "region:us" ]

[ "2105.05241", "2107.06499", "2209.00099" ]

[]

[ "# Dataset Card for \"bookcorpus_deduplicated\"", "## Dataset Summary\nThis is a deduplicated version of the original Book Corpus dataset.\nThe Book Corpus (Zhu et al., 2015), which was used to train popular models such as BERT, has a substantial amount of exact-duplicate documents according to Bandy and Vincent (2021)\nBandy and Vincent (2021) find that thousands of books in BookCorpus are duplicated, with only 7,185 unique books out of 11,038 total.\n\nEffect of deduplication\n- Num of lines: 38832894 VS 74004228 \n- Dataset size: 2.91GB VS 4.63GB\n\nThe duplicate text has been droped and only the first appearance is kept. \nThe order of text appearance is kept.", "## Why deduplicate?\nDeduplication of training data has showed various advantages, including:\n- require fewer training steps to achieve the same or better accuracy\n- train models that emit memorized text ten times less frequently\n- reduce carbon emission and energy consumption\n\ncf Deduplicating Training Data Makes Language Models Better", "## Deduplication script\n\nThe running time is short, less than several minutes.\nMore sophicated deduplication algorithms can be applied to improve the performance, such as URL", "## Reference\n\n\n\n\n\n\nMore Information needed" ]

[ "TAGS\n#arxiv-2105.05241 #arxiv-2107.06499 #arxiv-2209.00099 #region-us \n", "# Dataset Card for \"bookcorpus_deduplicated\"", "## Dataset Summary\nThis is a deduplicated version of the original Book Corpus dataset.\nThe Book Corpus (Zhu et al., 2015), which was used to train popular models such as BERT, has a substantial amount of exact-duplicate documents according to Bandy and Vincent (2021)\nBandy and Vincent (2021) find that thousands of books in BookCorpus are duplicated, with only 7,185 unique books out of 11,038 total.\n\nEffect of deduplication\n- Num of lines: 38832894 VS 74004228 \n- Dataset size: 2.91GB VS 4.63GB\n\nThe duplicate text has been droped and only the first appearance is kept. \nThe order of text appearance is kept.", "## Why deduplicate?\nDeduplication of training data has showed various advantages, including:\n- require fewer training steps to achieve the same or better accuracy\n- train models that emit memorized text ten times less frequently\n- reduce carbon emission and energy consumption\n\ncf Deduplicating Training Data Makes Language Models Better", "## Deduplication script\n\nThe running time is short, less than several minutes.\nMore sophicated deduplication algorithms can be applied to improve the performance, such as URL", "## Reference\n\n\n\n\n\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"EHR-roberta-base-tokenized-dataset-2022-12\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"EHR-roberta-base-tokenized-dataset-2022-12\"\n\nMore Information needed" ]

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

[]

[ "en" ]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"dreambooth-hackathon-rick-and-morty-images-2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dreambooth-hackathon-rick-and-morty-images-2\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"bookcorpus_deduplicated_small\"\n\nFirst 10K(0.25%) examples of bookcorpus_deduplicated\n\nsize: 7.4MB\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_deduplicated_small\"\n\nFirst 10K(0.25%) examples of bookcorpus_deduplicated\n\nsize: 7.4MB\n\nMore Information needed" ]

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

[]

[]

[ "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on November 21, 2022 at 6:50 PM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 1232 images.\nTrack-players-and-football are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n\nNo image augmentation techniques were applied." ]

[ "TAGS\n#task_categories-object-detection #roboflow #region-us \n", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on November 21, 2022 at 6:50 PM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 1232 images.\nTrack-players-and-football are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n\nNo image augmentation techniques were applied." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:expert-generated", "language_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "multilinguality:other-english-based-creole", "size_categories:n<1K", "source_datasets:original", "language:jam", "license:other", "creole", "low-resource-language", "arxiv:2212.03419", "region:us" ]

[ "2212.03419" ]

[ "jam" ]

[ "# 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- URL\n- Repository:\n- URL\n- Paper:\n- URL\n- Point of Contact:\n- Ruth-Ann Armsrong: armstrongruthanna@URL", "### Dataset Summary\n\nJamPatoisNLI provides the first dataset for natural language inference in a creole language, Jamaican Patois. \nMany of the most-spoken low-resource languages are creoles. These languages commonly have a lexicon derived from \na major world language and a distinctive grammar reflecting the languages of the original speakers and the process\nof language birth by creolization. This gives them a distinctive place in exploring the effectiveness of transfer\nfrom large monolingual or multilingual pretrained models.", "### Supported Tasks and Leaderboards\n\nNatural language inference", "### Languages\n\nJamaican Patois", "### Data Fields\n\npremise, hypothesis, label", "### Data Splits\n\nTrain: 250\nVal: 200\nTest: 200", "### Data set creation + Annotations\n\nPremise collection: \n97% of examples from Twitter; remaining pulled from literature and online cultural website\n\nHypothesis construction: \nFor each premise, hypothesis written by native speaker (our first author) so that pair’s classification would be E, N or C\n\nLabel validation: \nRandom sample of 100 sentence pairs double annotated by fluent speakers", "### Social Impact of Dataset\n\nJamPatoisNLI is a low-resource language dataset in an English-based Creole spoken in the Caribbean,\nJamaican Patois. The creation of the dataset contributes to expanding the scope of NLP research\nto under-explored languages across the world.", "### Dataset Curators\n\n@ruth-ann\n\n\n\n\n@misc{URL\n doi = {10.48550/ARXIV.2212.03419},\n \n url = {URL\n \n author = {Armstrong, Ruth-Ann and Hewitt, John and Manning, Christopher},\n \n keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7},\n \n title = {JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset},\n \n publisher = {arXiv},\n \n year = {2022},\n \n copyright = {URL perpetual, non-exclusive license}\n}", "### Contributions\n\nThanks to Prof. Christopher Manning and John Hewitt for their contributions, guidance, facilitation and support related to the creation of this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #annotations_creators-expert-generated #language_creators-expert-generated #language_creators-found #multilinguality-monolingual #multilinguality-other-english-based-creole #size_categories-n<1K #source_datasets-original #language-Jamaican Creole English #license-other #creole #low-resource-language #arxiv-2212.03419 #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- URL\n- Repository:\n- URL\n- Paper:\n- URL\n- Point of Contact:\n- Ruth-Ann Armsrong: armstrongruthanna@URL", "### Dataset Summary\n\nJamPatoisNLI provides the first dataset for natural language inference in a creole language, Jamaican Patois. \nMany of the most-spoken low-resource languages are creoles. These languages commonly have a lexicon derived from \na major world language and a distinctive grammar reflecting the languages of the original speakers and the process\nof language birth by creolization. This gives them a distinctive place in exploring the effectiveness of transfer\nfrom large monolingual or multilingual pretrained models.", "### Supported Tasks and Leaderboards\n\nNatural language inference", "### Languages\n\nJamaican Patois", "### Data Fields\n\npremise, hypothesis, label", "### Data Splits\n\nTrain: 250\nVal: 200\nTest: 200", "### Data set creation + Annotations\n\nPremise collection: \n97% of examples from Twitter; remaining pulled from literature and online cultural website\n\nHypothesis construction: \nFor each premise, hypothesis written by native speaker (our first author) so that pair’s classification would be E, N or C\n\nLabel validation: \nRandom sample of 100 sentence pairs double annotated by fluent speakers", "### Social Impact of Dataset\n\nJamPatoisNLI is a low-resource language dataset in an English-based Creole spoken in the Caribbean,\nJamaican Patois. The creation of the dataset contributes to expanding the scope of NLP research\nto under-explored languages across the world.", "### Dataset Curators\n\n@ruth-ann\n\n\n\n\n@misc{URL\n doi = {10.48550/ARXIV.2212.03419},\n \n url = {URL\n \n author = {Armstrong, Ruth-Ann and Hewitt, John and Manning, Christopher},\n \n keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7},\n \n title = {JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset},\n \n publisher = {arXiv},\n \n year = {2022},\n \n copyright = {URL perpetual, non-exclusive license}\n}", "### Contributions\n\nThanks to Prof. Christopher Manning and John Hewitt for their contributions, guidance, facilitation and support related to the creation of this dataset." ]

[ "task_categories:translation", "annotations_creators:expert-generated", "annotations_creators:found", "language_creators:found", "language_creators:expert-generated", "multilinguality:translation", "parallel-corpus", "region:us" ]

[]

[]

[ "# Dataset Card for [opus]", "## 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\nDisclaimer. Loading of dataset is slow, thus it may not be feasible when loading at scale. I'd suggest to use the other OPUS datasets on Huggingface which loads a specific corpus.\n\nLoads OPUS as HuggingFace dataset. OPUS is an open parallel corpus covering 700+ languages and 1100+ datasets.\n\nGiven a 'src' and 'tgt' language, this repository can load *all* available parallel corpus. To my knowledge, other OPUS datasets on Huggingface loads a specific corpus\n\n\nRequirements. \n\n\n\nExample Usage.\n\n\n\nDisclaimer.\n\nThis repository is still in active development. Do make a PR if there're any issues!", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages\n\nAvailable languages can be viewed on the OPUS API", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits\n\nMerged all data into train split.", "## 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 @larrylawl for adding this dataset." ]

[ "TAGS\n#task_categories-translation #annotations_creators-expert-generated #annotations_creators-found #language_creators-found #language_creators-expert-generated #multilinguality-translation #parallel-corpus #region-us \n", "# Dataset Card for [opus]", "## 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\nDisclaimer. Loading of dataset is slow, thus it may not be feasible when loading at scale. I'd suggest to use the other OPUS datasets on Huggingface which loads a specific corpus.\n\nLoads OPUS as HuggingFace dataset. OPUS is an open parallel corpus covering 700+ languages and 1100+ datasets.\n\nGiven a 'src' and 'tgt' language, this repository can load *all* available parallel corpus. To my knowledge, other OPUS datasets on Huggingface loads a specific corpus\n\n\nRequirements. \n\n\n\nExample Usage.\n\n\n\nDisclaimer.\n\nThis repository is still in active development. Do make a PR if there're any issues!", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages\n\nAvailable languages can be viewed on the OPUS API", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits\n\nMerged all data into train split.", "## 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 @larrylawl for adding this dataset." ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "task_categories:table-to-text", "task_ids:rdf-to-text", "annotations_creators:found", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:as", "language:bn", "language:gu", "language:hi", "language:kn", "language:ml", "language:mr", "language:or", "language:pa", "language:ta", "language:te", "language:en", "license:cc-by-nc-sa-4.0", "license:mit", "xalign", "NLG", "low-resource", "LRL", "arxiv:2202.00291", "arxiv:2209.11252", "region:us" ]

[ "2202.00291", "2209.11252" ]

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

[ "### Dataset Summary\n\n\nIt consists of an extensive collection of a high quality cross-lingual fact-to-text dataset where facts are in English and corresponding sentences are in native language for person biographies. The Train & validation splits are created using distant supervision methods and Test data is generated through human annotations.", "### Supported Tasks and Leaderboards\n\n\n* 'Data-to-text Generation': XAlign dataset can be used to train cross-lingual data-to-text generation models. The model performance can measured through any text generation evaluation metrics by taking average across all the languages. Sagare et al. (2022) reported average BLEU score of 29.27 and average METEOR score of 53.64 over the test set.\n* 'Relation Extraction': XAlign could also be used for cross-lingual relation extraction where relations in English can be extracted from associated native sentence.\n\n\nSee Papers With Code Leaderboard for more models.", "### Languages\n\n\nAssamese (as), Bengali (bn), Gujarati (gu), Hindi (hi), Kannada (kn), Malayalam (ml), Marathi (mr), Oriya (or), Punjabi (pa), Tamil (ta), Telugu (te), and English (en).\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\nEach record consist of the following entries:\n\n\n* sentence (string) : Native language wikipedia sentence. (non-native language strings were removed.)\n* 'facts' (List[Dict]) : List of facts associated with the sentence where each fact is stored as dictionary.\n* language (string) : Language identifier.\n\n\nThe 'facts' key contains list of facts where each facts is stored as dictionary. A single record within fact list contains following entries:\n\n\n* subject (string) : central entity.\n* object (string) : entity or a piece of information about the subject.\n* predicate (string) : relationship that connects the subject and the object.\n* qualifiers (List[Dict]) : It provide additional information about the fact, is stored as list of qualifier where each record is a dictionary. The dictionary contains two keys: qualifier\\_predicate to represent property of qualifer and qualifier\\_object to store value for the qualifier's predicate.", "### Data Instances\n\n\nExample from English\n\n\nExample from one of the low-resource languages (i.e. Hindi)", "### Data Splits\n\n\nThe XAlign dataset has 3 splits: train, validation, and test. Below are the statistics the dataset.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nMost of the existing Data-to-Text datasets are available in English. Also, the structured Wikidata entries for person entities in low resource languages are minuscule in number compared to that in English. Thus, monolingual Data-to-Text for low resource languages suffers from data sparsity. XAlign dataset would be useful in creation of cross-lingual Data-to-Text generation systems that take a set of English facts as input and generates a sentence capturing the fact-semantics in the specified language.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe dataset creation process starts with an intial list of ~95K person entities selected from Wikidata and each of which has a link to a corresponding Wikipedia page in at least one of our 11 low resource languages. This leads to a dataset where every instance is a tuple containing entityID, English Wikidata facts, language identifier, Wikipedia URL for the entityID. The facts (in English) are extracted from the 20201221 WikiData dump for each entity using the WikiData APIs. The facts are gathered only for the speficied Wikidata property (or relation) types that captures most useful factual information for person entities: WikibaseItem, Time, Quantity, and Monolingualtext.This leads to overall ~0.55M data instances across all the 12 languages. Also, for each language, the sentences (along with section information) are extracted from 20210520 Wikipedia XML dump using the pre-processing steps as described here.\n\n\nFor every (entity, language) pair, the pre-processed dataset contains a set of English Wikidata facts and a set of Wikipedia sentences in that language. In order to create train and validation dataset, these are later passed through a two-stage automatic aligner as proposed in abhishek et al. (2022) to associate a sentence with a subset of facts.", "#### Who are the source language producers?\n\n\nThe text are extracted from Wikipedia and facts are retrieved from Wikidata.", "### Annotations", "#### Annotation process\n\n\nThe Manual annotation of Test dataset was done in two phases. For both the phases, the annotators were presented with (low resource language sentence, list of English facts). They were asked to mark facts present in the given sentence. There were also specific guidelines to ignore redundant facts, handle abbreviations, etc. More detailed annotation guidelines and ethical statement are mentioned here\n. In the first phase, we got 60 instances labeled per language by a set of 8 expert annotators (trusted graduate students who understood the task very well). In phase 2, we selected 8 annotators per language from the National Register of Translators. We tested these annotators using phase 1 data as golden control set, and shortlisted up to 4 annotators per language who scored highest (on Kappa score with golden annotations).", "#### Who are the annotators?\n\n\nHuman annotators were selected appropriately (after screening) from National Translation Mission for Test set creation.", "### Personal and Sensitive Information\n\n\nThe dataset does not involve collection or storage of any personally identifiable information or offensive information at any stage.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of the this dataset is to help develop cross-lingual Data-to-Text generation systems that are vital in many downstream Natural Language Processing (NLP) applications like automated dialog systems, domain-specific chatbots, open domain question answering, authoring sports reports, etc. These systems will be useful for powering business applications like Wikipedia text generation given English Infoboxes, automated generation of non-English product descriptions using English product attributes, etc.", "### Known Limitations\n\n\nThe XAlign dataset focus only on person biographies and system developed on this dataset might not be generalized to other domains.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThis dataset is collected by Tushar Abhishek, Shivprasad Sagare, Bhavyajeet Singh, Anubhav Sharma, Manish Gupta and Vasudeva Varma of Information Retrieval and Extraction Lab (IREL), Hyderabad, India. They released scripts to collect and process the data into the Data-to-Text format.", "### Licensing Information\n\n\nThe XAlign dataset is released under the MIT License.", "### Contributions\n\n\nThanks to Tushar Abhishek, Shivprasad Sagare, Bhavyajeet Singh, Anubhav Sharma, Manish Gupta and Vasudeva Varma for adding this dataset.\n\n\nAdditional thanks to the annotators from National Translation Mission for their crucial contributions to creation of the test dataset: Bhaswati Bhattacharya, Aditi Sarkar, Raghunandan B. S., Satish M., Rashmi G.Rao, Vidyarashmi PN, Neelima Bhide, Anand Bapat, Krishna Rao N V, Nagalakshmi DV, Aditya Bhardwaj\nVuppula, Nirupama Patel, Asir. T, Sneha Gupta, Dinesh Kumar, Jasmin Gilani, Vivek R, Sivaprasad S, Pranoy J, Ashutosh Bharadwaj, Balaji Venkateshwar, Vinkesh Bansal, Vaishnavi Udyavara, Ramandeep Singh, Khushi Goyal, Yashasvi LN Pasumarthy and Naren Akash." ]

[ "TAGS\n#task_categories-table-to-text #task_ids-rdf-to-text #annotations_creators-found #language_creators-crowdsourced #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-Assamese #language-Bengali #language-Gujarati #language-Hindi #language-Kannada #language-Malayalam #language-Marathi #language-Oriya (macrolanguage) #language-Panjabi #language-Tamil #language-Telugu #language-English #license-cc-by-nc-sa-4.0 #license-mit #xalign #NLG #low-resource #LRL #arxiv-2202.00291 #arxiv-2209.11252 #region-us \n", "### Dataset Summary\n\n\nIt consists of an extensive collection of a high quality cross-lingual fact-to-text dataset where facts are in English and corresponding sentences are in native language for person biographies. The Train & validation splits are created using distant supervision methods and Test data is generated through human annotations.", "### Supported Tasks and Leaderboards\n\n\n* 'Data-to-text Generation': XAlign dataset can be used to train cross-lingual data-to-text generation models. The model performance can measured through any text generation evaluation metrics by taking average across all the languages. Sagare et al. (2022) reported average BLEU score of 29.27 and average METEOR score of 53.64 over the test set.\n* 'Relation Extraction': XAlign could also be used for cross-lingual relation extraction where relations in English can be extracted from associated native sentence.\n\n\nSee Papers With Code Leaderboard for more models.", "### Languages\n\n\nAssamese (as), Bengali (bn), Gujarati (gu), Hindi (hi), Kannada (kn), Malayalam (ml), Marathi (mr), Oriya (or), Punjabi (pa), Tamil (ta), Telugu (te), and English (en).\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\nEach record consist of the following entries:\n\n\n* sentence (string) : Native language wikipedia sentence. (non-native language strings were removed.)\n* 'facts' (List[Dict]) : List of facts associated with the sentence where each fact is stored as dictionary.\n* language (string) : Language identifier.\n\n\nThe 'facts' key contains list of facts where each facts is stored as dictionary. A single record within fact list contains following entries:\n\n\n* subject (string) : central entity.\n* object (string) : entity or a piece of information about the subject.\n* predicate (string) : relationship that connects the subject and the object.\n* qualifiers (List[Dict]) : It provide additional information about the fact, is stored as list of qualifier where each record is a dictionary. The dictionary contains two keys: qualifier\\_predicate to represent property of qualifer and qualifier\\_object to store value for the qualifier's predicate.", "### Data Instances\n\n\nExample from English\n\n\nExample from one of the low-resource languages (i.e. Hindi)", "### Data Splits\n\n\nThe XAlign dataset has 3 splits: train, validation, and test. Below are the statistics the dataset.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nMost of the existing Data-to-Text datasets are available in English. Also, the structured Wikidata entries for person entities in low resource languages are minuscule in number compared to that in English. Thus, monolingual Data-to-Text for low resource languages suffers from data sparsity. XAlign dataset would be useful in creation of cross-lingual Data-to-Text generation systems that take a set of English facts as input and generates a sentence capturing the fact-semantics in the specified language.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe dataset creation process starts with an intial list of ~95K person entities selected from Wikidata and each of which has a link to a corresponding Wikipedia page in at least one of our 11 low resource languages. This leads to a dataset where every instance is a tuple containing entityID, English Wikidata facts, language identifier, Wikipedia URL for the entityID. The facts (in English) are extracted from the 20201221 WikiData dump for each entity using the WikiData APIs. The facts are gathered only for the speficied Wikidata property (or relation) types that captures most useful factual information for person entities: WikibaseItem, Time, Quantity, and Monolingualtext.This leads to overall ~0.55M data instances across all the 12 languages. Also, for each language, the sentences (along with section information) are extracted from 20210520 Wikipedia XML dump using the pre-processing steps as described here.\n\n\nFor every (entity, language) pair, the pre-processed dataset contains a set of English Wikidata facts and a set of Wikipedia sentences in that language. In order to create train and validation dataset, these are later passed through a two-stage automatic aligner as proposed in abhishek et al. (2022) to associate a sentence with a subset of facts.", "#### Who are the source language producers?\n\n\nThe text are extracted from Wikipedia and facts are retrieved from Wikidata.", "### Annotations", "#### Annotation process\n\n\nThe Manual annotation of Test dataset was done in two phases. For both the phases, the annotators were presented with (low resource language sentence, list of English facts). They were asked to mark facts present in the given sentence. There were also specific guidelines to ignore redundant facts, handle abbreviations, etc. More detailed annotation guidelines and ethical statement are mentioned here\n. In the first phase, we got 60 instances labeled per language by a set of 8 expert annotators (trusted graduate students who understood the task very well). In phase 2, we selected 8 annotators per language from the National Register of Translators. We tested these annotators using phase 1 data as golden control set, and shortlisted up to 4 annotators per language who scored highest (on Kappa score with golden annotations).", "#### Who are the annotators?\n\n\nHuman annotators were selected appropriately (after screening) from National Translation Mission for Test set creation.", "### Personal and Sensitive Information\n\n\nThe dataset does not involve collection or storage of any personally identifiable information or offensive information at any stage.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of the this dataset is to help develop cross-lingual Data-to-Text generation systems that are vital in many downstream Natural Language Processing (NLP) applications like automated dialog systems, domain-specific chatbots, open domain question answering, authoring sports reports, etc. These systems will be useful for powering business applications like Wikipedia text generation given English Infoboxes, automated generation of non-English product descriptions using English product attributes, etc.", "### Known Limitations\n\n\nThe XAlign dataset focus only on person biographies and system developed on this dataset might not be generalized to other domains.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThis dataset is collected by Tushar Abhishek, Shivprasad Sagare, Bhavyajeet Singh, Anubhav Sharma, Manish Gupta and Vasudeva Varma of Information Retrieval and Extraction Lab (IREL), Hyderabad, India. They released scripts to collect and process the data into the Data-to-Text format.", "### Licensing Information\n\n\nThe XAlign dataset is released under the MIT License.", "### Contributions\n\n\nThanks to Tushar Abhishek, Shivprasad Sagare, Bhavyajeet Singh, Anubhav Sharma, Manish Gupta and Vasudeva Varma for adding this dataset.\n\n\nAdditional thanks to the annotators from National Translation Mission for their crucial contributions to creation of the test dataset: Bhaswati Bhattacharya, Aditi Sarkar, Raghunandan B. S., Satish M., Rashmi G.Rao, Vidyarashmi PN, Neelima Bhide, Anand Bapat, Krishna Rao N V, Nagalakshmi DV, Aditya Bhardwaj\nVuppula, Nirupama Patel, Asir. T, Sneha Gupta, Dinesh Kumar, Jasmin Gilani, Vivek R, Sivaprasad S, Pranoy J, Ashutosh Bharadwaj, Balaji Venkateshwar, Vinkesh Bansal, Vaishnavi Udyavara, Ramandeep Singh, Khushi Goyal, Yashasvi LN Pasumarthy and Naren Akash." ]

[ "region:us" ]

[]

[]

[ "# Naver 영화 평점 데이터셋" ]

[ "TAGS\n#region-us \n", "# Naver 영화 평점 데이터셋" ]

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

[]

[]

[ "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on December 28, 2022 at 8:08 PM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 4454 images.\nCt-cthead-t-thead are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 416x416 (Fill (with center crop))\n\nThe following augmentation was applied to create 3 versions of each source image:\n* Random brigthness adjustment of between -15 and +15 percent" ]

[ "TAGS\n#task_categories-object-detection #roboflow #roboflow2huggingface #region-us \n", "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on December 28, 2022 at 8:08 PM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 4454 images.\nCt-cthead-t-thead are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 416x416 (Fill (with center crop))\n\nThe following augmentation was applied to create 3 versions of each source image:\n* Random brigthness adjustment of between -15 and +15 percent" ]

[ "task_categories:text-classification", "language:en", "reddit", "not-for-all-eyes", "not-for-all-audiences", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for reddit_one_ups_2014", "## Dataset Description\n\n- Homepage: URL", "### Dataset Summary\n\nReddit 'one-ups' or 'clapbacks' - replies which scored higher than the original comments. This task makes one-ups easier by focusing on a set of common, often meme-like replies (e.g. 'yes', 'nope', '(͡°͜ʖ͡°)').\n\nFor commentary on predictions with a previous version of the dataset, see URL\n\nFor unique / non-meme seq2seq version of this dataset, see URL\n\nReplies were selected from PushShift's archive of posts from 2014.", "### Supported Tasks\n\nText classification task: finding the common reply (out of ~37) to match the parent comment text.\n\nText prediction task: estimating the vote score, or parent:reply ratio, of a meme response, as a measure of relevancy/cleverness of reply.", "### Languages\n\nPrimarily English - includes some emoticons such as ┬─┬ノ(ಠ_ಠノ)", "## Dataset Structure", "### Data Instances\n\n29,375 rows", "### Data Fields\n\n- id: the Reddit alphanumeric ID for the reply\n- body: the content of the original reply\n- score: the net vote score of the original reply\n- parent_id: the Reddit alphanumeric ID for the parent\n- author: the Reddit username of the reply\n- subreddit: the Reddit community where the discussion occurred\n- parent_score: the net vote score of the parent comment\n- cleantext: the simplified reply (one of 37 classes)\n- tstamp: the timestamp of the reply\n- parent_body: the content of the original parent", "## Dataset Creation", "### Source Data\n\nReddit comments collected through URL archives for 2014.", "#### Initial Data Collection and Normalization\n\n- Removed deleted or empty comments.\n- Selected only replies which scored 1.5x higher than a parent comment, where both have a positive score.\n- Found the top/repeating phrases common to these one-ups/clapback comments.\n- Selected only replies which had one of these top/repeating phrases.\n- Made rows in PostgreSQL and output as CSV.", "## Considerations for Using the Data\n\nComments and responses in the Reddit archives and output datasets all include NSFW and otherwise toxic language and links!\n\n- You can use the subreddit and score columns to filter content.\n- Imbalanced dataset: replies 'yes' and 'no' are more common than others.\n- Overlap of labels: replies such as 'yes', 'yep', and 'yup' serve similar purposes; in other cases 'no' vs. 'nope' may be interesting.\n- Timestamps: the given timestamp may help identify trends in meme replies\n- Usernames: a username was included to identify the 'username checks out' meme, but this was not common enough in 2014, and the included username is from the reply.\n\nReddit comments are properties of Reddit and comment owners using their Terms of Service." ]

[ "TAGS\n#task_categories-text-classification #language-English #reddit #not-for-all-eyes #not-for-all-audiences #region-us \n", "# Dataset Card for reddit_one_ups_2014", "## Dataset Description\n\n- Homepage: URL", "### Dataset Summary\n\nReddit 'one-ups' or 'clapbacks' - replies which scored higher than the original comments. This task makes one-ups easier by focusing on a set of common, often meme-like replies (e.g. 'yes', 'nope', '(͡°͜ʖ͡°)').\n\nFor commentary on predictions with a previous version of the dataset, see URL\n\nFor unique / non-meme seq2seq version of this dataset, see URL\n\nReplies were selected from PushShift's archive of posts from 2014.", "### Supported Tasks\n\nText classification task: finding the common reply (out of ~37) to match the parent comment text.\n\nText prediction task: estimating the vote score, or parent:reply ratio, of a meme response, as a measure of relevancy/cleverness of reply.", "### Languages\n\nPrimarily English - includes some emoticons such as ┬─┬ノ(ಠ_ಠノ)", "## Dataset Structure", "### Data Instances\n\n29,375 rows", "### Data Fields\n\n- id: the Reddit alphanumeric ID for the reply\n- body: the content of the original reply\n- score: the net vote score of the original reply\n- parent_id: the Reddit alphanumeric ID for the parent\n- author: the Reddit username of the reply\n- subreddit: the Reddit community where the discussion occurred\n- parent_score: the net vote score of the parent comment\n- cleantext: the simplified reply (one of 37 classes)\n- tstamp: the timestamp of the reply\n- parent_body: the content of the original parent", "## Dataset Creation", "### Source Data\n\nReddit comments collected through URL archives for 2014.", "#### Initial Data Collection and Normalization\n\n- Removed deleted or empty comments.\n- Selected only replies which scored 1.5x higher than a parent comment, where both have a positive score.\n- Found the top/repeating phrases common to these one-ups/clapback comments.\n- Selected only replies which had one of these top/repeating phrases.\n- Made rows in PostgreSQL and output as CSV.", "## Considerations for Using the Data\n\nComments and responses in the Reddit archives and output datasets all include NSFW and otherwise toxic language and links!\n\n- You can use the subreddit and score columns to filter content.\n- Imbalanced dataset: replies 'yes' and 'no' are more common than others.\n- Overlap of labels: replies such as 'yes', 'yep', and 'yup' serve similar purposes; in other cases 'no' vs. 'nope' may be interesting.\n- Timestamps: the given timestamp may help identify trends in meme replies\n- Usernames: a username was included to identify the 'username checks out' meme, but this was not common enough in 2014, and the included username is from the reply.\n\nReddit comments are properties of Reddit and comment owners using their Terms of Service." ]

[ "language:en", "reddit", "not-for-all-eyes", "not-for-all-audiences", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for reddit_one_ups_seq2seq_2014", "## Dataset Description\n\n- Homepage: URL", "### Dataset Summary\n\nReddit 'one-ups' or 'clapbacks' - replies which scored higher than the original comments.\n\nThis dataset chose freeform replies, which did not follow repetitive meme replies. The IAmA subreddit was excluded to avoid an issue where their answers frequently score higher than questions.\n\nFor commentary on predictions with a previous version of the dataset, see URL\n\nFor meme / text-classification version of this dataset, see URL\n\nReplies were selected from PushShift's archive of posts from 2014.", "### Supported Tasks\n\nseq2seq writing of replies to Reddit comments", "### Languages\n\nPrimarily English - includes some emoticons such as ┬─┬ノ(ಠ_ಠノ)", "## Dataset Structure", "### Data Instances\n\n19,992 rows", "### Data Fields\n\n- id: the Reddit alphanumeric ID for the reply\n- body: the content of the original reply\n- score: the net vote score of the original reply\n- parent_id: the Reddit alphanumeric ID for the parent\n- author: the Reddit username of the reply\n- subreddit: the Reddit community where the discussion occurred\n- parent_score: the net vote score of the parent comment\n- tstamp: the timestamp of the reply\n- parent_body: the content of the original parent", "## Dataset Creation", "### Source Data\n\nReddit comments collected through URL archives for 2014.", "#### Initial Data Collection and Normalization\n\n- Removed deleted or empty comments.\n- Selected only replies which scored 1.5x higher than a parent comment, where both have a positive score.\n- Found top/repeating phrases common to these one-ups/clapback comments; selected only replies which DID NOT have these phrases.\n- Selected the top-scored ~1,667 replies from each month in 2014, avoiding /r/IAmA.\n- Made rows in PostgreSQL and output as CSV.", "## Considerations for Using the Data\n\nComments and responses in the Reddit archives and output datasets all include NSFW and otherwise toxic language and links!\n\nYou can use the subreddit and score columns to filter, and subreddit and timestamps to improve predictions of reply content.\n\nReddit comments are properties of Reddit and comment owners using their Terms of Service." ]

[ "TAGS\n#language-English #reddit #not-for-all-eyes #not-for-all-audiences #region-us \n", "# Dataset Card for reddit_one_ups_seq2seq_2014", "## Dataset Description\n\n- Homepage: URL", "### Dataset Summary\n\nReddit 'one-ups' or 'clapbacks' - replies which scored higher than the original comments.\n\nThis dataset chose freeform replies, which did not follow repetitive meme replies. The IAmA subreddit was excluded to avoid an issue where their answers frequently score higher than questions.\n\nFor commentary on predictions with a previous version of the dataset, see URL\n\nFor meme / text-classification version of this dataset, see URL\n\nReplies were selected from PushShift's archive of posts from 2014.", "### Supported Tasks\n\nseq2seq writing of replies to Reddit comments", "### Languages\n\nPrimarily English - includes some emoticons such as ┬─┬ノ(ಠ_ಠノ)", "## Dataset Structure", "### Data Instances\n\n19,992 rows", "### Data Fields\n\n- id: the Reddit alphanumeric ID for the reply\n- body: the content of the original reply\n- score: the net vote score of the original reply\n- parent_id: the Reddit alphanumeric ID for the parent\n- author: the Reddit username of the reply\n- subreddit: the Reddit community where the discussion occurred\n- parent_score: the net vote score of the parent comment\n- tstamp: the timestamp of the reply\n- parent_body: the content of the original parent", "## Dataset Creation", "### Source Data\n\nReddit comments collected through URL archives for 2014.", "#### Initial Data Collection and Normalization\n\n- Removed deleted or empty comments.\n- Selected only replies which scored 1.5x higher than a parent comment, where both have a positive score.\n- Found top/repeating phrases common to these one-ups/clapback comments; selected only replies which DID NOT have these phrases.\n- Selected the top-scored ~1,667 replies from each month in 2014, avoiding /r/IAmA.\n- Made rows in PostgreSQL and output as CSV.", "## Considerations for Using the Data\n\nComments and responses in the Reddit archives and output datasets all include NSFW and otherwise toxic language and links!\n\nYou can use the subreddit and score columns to filter, and subreddit and timestamps to improve predictions of reply content.\n\nReddit comments are properties of Reddit and comment owners using their Terms of Service." ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:semantic-similarity-scoring", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:sick", "language:pl", "license:cc-by-nc-sa-3.0", "region:us" ]

[]

[ "pl" ]

[ "# SICK_PL - Sentences Involving Compositional Knowledge (Polish)", "### Dataset Summary\n\nThis dataset is a manually translated version of popular English natural language inference (NLI) corpus consisting of 10,000 sentence pairs. NLI is the task of determining whether one statement (premise) semantically entails other statement (hypothesis). Such relation can be classified as entailment (if the first sentence entails second sentence), neutral (the first statement does not determine the truth value of the second statement), or contradiction (if the first sentence is true, the second is false). Additionally, the original SICK dataset contains semantic relatedness scores for the sentence pairs as real numbers ranging from 1 to 5. When translating the corpus to Polish, we tried to be as close as possible to the original meaning. In some cases, however, two different English sentences had an identical translation in Polish. Such instances were slightly modified in order to preserve both the meaning and the syntactic differences in sentence pair.", "### Data Instances\n\nExample instance:", "### Data Fields\n\n- pair_ID: sentence pair ID\n- sentence_A: sentence A\n- sentence_B: sentence B\n- entailment_judgment: textual entailment gold label: entailment (0), neutral (1) or contradiction (2)\n- relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale)" ]

[ "TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #task_ids-semantic-similarity-scoring #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-sick #language-Polish #license-cc-by-nc-sa-3.0 #region-us \n", "# SICK_PL - Sentences Involving Compositional Knowledge (Polish)", "### Dataset Summary\n\nThis dataset is a manually translated version of popular English natural language inference (NLI) corpus consisting of 10,000 sentence pairs. NLI is the task of determining whether one statement (premise) semantically entails other statement (hypothesis). Such relation can be classified as entailment (if the first sentence entails second sentence), neutral (the first statement does not determine the truth value of the second statement), or contradiction (if the first sentence is true, the second is false). Additionally, the original SICK dataset contains semantic relatedness scores for the sentence pairs as real numbers ranging from 1 to 5. When translating the corpus to Polish, we tried to be as close as possible to the original meaning. In some cases, however, two different English sentences had an identical translation in Polish. Such instances were slightly modified in order to preserve both the meaning and the syntactic differences in sentence pair.", "### Data Instances\n\nExample instance:", "### Data Fields\n\n- pair_ID: sentence pair ID\n- sentence_A: sentence A\n- sentence_B: sentence B\n- entailment_judgment: textual entailment gold label: entailment (0), neutral (1) or contradiction (2)\n- relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale)" ]

[ "task_categories:text-classification", "task_ids:semantic-similarity-classification", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:pl", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "pl" ]

[ "# PPC - Polish Paraphrase Corpus", "### Dataset Summary\n\nPolish Paraphrase Corpus contains 7000 manually labeled sentence pairs. The dataset was divided into training, validation and test splits. The training part includes 5000 examples, while the other parts contain 1000 examples each. The main purpose of creating such a dataset was to verify how machine learning models perform in the challenging problem of paraphrase identification, where most records contain semantically overlapping parts. Technically, this is a three-class classification task, where each record can be assigned to one of the following categories:\n- Exact paraphrases - Sentence pairs that convey exactly the same information. We are interested only in the semantic meaning of the sentence, therefore this category also includes sentences that are semantically identical but, for example, have different emotional emphasis.\n- Close paraphrases - Sentence pairs with similar semantic meaning. In this category we include all pairs which contain the same information, but in addition to it there may be other semantically non-overlapping parts. This category also contains context-dependent paraphrases - sentence pairs that may have the same meaning in some contexts but are different in others.\n- Non-paraphrases - All other cases, including contradictory sentences and semantically unrelated sentences.\n\nThe corpus contains 2911, 1297, and 2792 examples for the above three categories, respectively. The process of annotating the dataset was preceded by an automated generation of candidate pairs, which were then manually labeled. We experimented with two popular techniques of generating possible paraphrases: backtranslation with a set of neural machine translation models and paraphrase mining using a pre-trained multilingual sentence encoder. The extracted sentence pairs are drawn from different data sources: Taboeba, Polish news articles, Wikipedia and Polish version of SICK dataset. Since most of the sentence pairs obtained in this way fell into the first two categories, in order to balance the dataset, some of the examples were manually modified to convey different information. In this way, even negative examples often have high semantic overlap, making this problem difficult for machine learning models.", "### Data Instances\n\nExample instance:", "### Data Fields\n\n- sentence_A: first sentence text\n- sentence_B: second sentence text\n- label: label identifier corresponding to one of three categories" ]

[ "TAGS\n#task_categories-text-classification #task_ids-semantic-similarity-classification #multilinguality-monolingual #size_categories-1K<n<10K #language-Polish #license-cc-by-nc-sa-4.0 #region-us \n", "# PPC - Polish Paraphrase Corpus", "### Dataset Summary\n\nPolish Paraphrase Corpus contains 7000 manually labeled sentence pairs. The dataset was divided into training, validation and test splits. The training part includes 5000 examples, while the other parts contain 1000 examples each. The main purpose of creating such a dataset was to verify how machine learning models perform in the challenging problem of paraphrase identification, where most records contain semantically overlapping parts. Technically, this is a three-class classification task, where each record can be assigned to one of the following categories:\n- Exact paraphrases - Sentence pairs that convey exactly the same information. We are interested only in the semantic meaning of the sentence, therefore this category also includes sentences that are semantically identical but, for example, have different emotional emphasis.\n- Close paraphrases - Sentence pairs with similar semantic meaning. In this category we include all pairs which contain the same information, but in addition to it there may be other semantically non-overlapping parts. This category also contains context-dependent paraphrases - sentence pairs that may have the same meaning in some contexts but are different in others.\n- Non-paraphrases - All other cases, including contradictory sentences and semantically unrelated sentences.\n\nThe corpus contains 2911, 1297, and 2792 examples for the above three categories, respectively. The process of annotating the dataset was preceded by an automated generation of candidate pairs, which were then manually labeled. We experimented with two popular techniques of generating possible paraphrases: backtranslation with a set of neural machine translation models and paraphrase mining using a pre-trained multilingual sentence encoder. The extracted sentence pairs are drawn from different data sources: Taboeba, Polish news articles, Wikipedia and Polish version of SICK dataset. Since most of the sentence pairs obtained in this way fell into the first two categories, in order to balance the dataset, some of the examples were manually modified to convey different information. In this way, even negative examples often have high semantic overlap, making this problem difficult for machine learning models.", "### Data Instances\n\nExample instance:", "### Data Fields\n\n- sentence_A: first sentence text\n- sentence_B: second sentence text\n- label: label identifier corresponding to one of three categories" ]

[ "task_categories:text-classification", "task_ids:topic-classification", "task_ids:multi-class-classification", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:pl", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "pl" ]

[ "# 8TAGS", "### Dataset Summary\n\nA Polish topic classification dataset consisting of headlines from social media posts. It contains about 50,000 sentences annotated with 8 topic labels: film, history, food, medicine, motorization, work, sport and technology. This dataset was created automatically by extracting sentences from headlines and short descriptions of articles posted on Polish social networking site URL. The service allows users to annotate articles with one or more tags (categories). Dataset represents a selection of article sentences from 8 popular categories. The resulting corpus contains cleaned and tokenized, unambiguous sentences (tagged with only one of the selected categories), and longer than 30 characters.", "### Data Instances\n\nExample instance:", "### Data Fields\n\n- sentence: sentence text\n- label: label identifier corresponding to one of 8 topics" ]

[ "TAGS\n#task_categories-text-classification #task_ids-topic-classification #task_ids-multi-class-classification #multilinguality-monolingual #size_categories-10K<n<100K #language-Polish #license-cc-by-nc-sa-4.0 #region-us \n", "# 8TAGS", "### Dataset Summary\n\nA Polish topic classification dataset consisting of headlines from social media posts. It contains about 50,000 sentences annotated with 8 topic labels: film, history, food, medicine, motorization, work, sport and technology. This dataset was created automatically by extracting sentences from headlines and short descriptions of articles posted on Polish social networking site URL. The service allows users to annotate articles with one or more tags (categories). Dataset represents a selection of article sentences from 8 popular categories. The resulting corpus contains cleaned and tokenized, unambiguous sentences (tagged with only one of the selected categories), and longer than 30 characters.", "### Data Instances\n\nExample instance:", "### Data Fields\n\n- sentence: sentence text\n- label: label identifier corresponding to one of 8 topics" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"speech2text\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"speech2text\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"results_valid_20rows_2022-12-29\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"results_valid_20rows_2022-12-29\"\n\nMore Information needed" ]

[ "task_categories:summarization", "task_ids:news-articles-summarization", "annotations_creators:no-annotation", "language_creators:found", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Pochita Dataset", "## Dataset created for fine-tuning Stable Diffusion model on HuggingFace Diffusion Hackaton\nConsists of 19 photos of Pochita plushie. (ye, he's cute)" ]

[ "TAGS\n#task_categories-summarization #task_ids-news-articles-summarization #annotations_creators-no-annotation #language_creators-found #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc #region-us \n", "# Dataset Card for Pochita Dataset", "## Dataset created for fine-tuning Stable Diffusion model on HuggingFace Diffusion Hackaton\nConsists of 19 photos of Pochita plushie. (ye, he's cute)" ]

[ "task_categories:text-classification", "size_categories:1K<n<10K", "language:nl", "region:us" ]

[]

[ "nl" ]

[]

[ "TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Dutch #region-us \n" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[ "# trpfrog-icons Dataset\n\nThis is a dataset of TrpFrog's icons. By the way, what do you use this for?", "## How to use", "## License\n\nMIT License" ]

[ "TAGS\n#license-mit #region-us \n", "# trpfrog-icons Dataset\n\nThis is a dataset of TrpFrog's icons. By the way, what do you use this for?", "## How to use", "## License\n\nMIT License" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

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

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

[ "task_categories:object-detection", "roboflow", "roboflow2huggingface", "Construction", "Logistics", "Utilities", "Damage Risk", "Ppe", "Manufacturing", "Assembly Line", "Warehouse", "Factory", "region:us" ]

[]

[]

[ "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on December 29, 2022 at 11:22 AM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 398 images.\nConstruction are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n\nNo image augmentation techniques were applied." ]

[ "TAGS\n#task_categories-object-detection #roboflow #roboflow2huggingface #Construction #Logistics #Utilities #Damage Risk #Ppe #Manufacturing #Assembly Line #Warehouse #Factory #region-us \n", "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on December 29, 2022 at 11:22 AM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 398 images.\nConstruction are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n\nNo image augmentation techniques were applied." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"diffusion_db_10k_processed\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"diffusion_db_10k_processed\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"bookcorpus_compact_512\"\n\nNum samples: 1,219,333\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_compact_512\"\n\nNum samples: 1,219,333\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"bookcorpus_compact_256\"\n\nNum samples: 2,389,359\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_compact_256\"\n\nNum samples: 2,389,359\n\nMore Information needed" ]

[ "task_categories:image-classification", "task_ids:multi-label-image-classification", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "language:en", "license:mit", "golf-courses", "region:us" ]

[]

[ "en" ]

[ "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Point of Contact: Kevin Leffew – kleffew94@URL", "### Dataset Summary: golf-course\n\nThis dataset (bethecloud/golf-courses) includes 21 unique images of golf courses pulled from Unsplash. \n\nThe dataset is a collection of photographs taken at various golf courses around the world. The images depict a variety of scenes, including fairways, greens, bunkers, water hazards, and clubhouse facilities. The images are high resolution and have been carefully selected to provide a diverse range of visual content for fine-tuning a machine learning model.\n\nThe dataset is intended to be used in the context of the Hugging Face Dream Booth hackathon, a competition that challenges participants to build innovative applications using the Hugging Face transformers library. The submission is for the category of landscape. \n\nOverall, this dataset provides a rich source of visual data for machine learning models looking to understand and classify elements of golf courses. Its diverse range of images and high-quality resolution make it well-suited for use in fine-tuning models for tasks such as image classification, object detection, and image segmentation.\n\nBy using the golf course images as part of their training data, participants can fine-tune their models to recognize and classify specific features and elements commonly found on golf courses. The ultimate goal after the hackathon is to pull this dataset from decentralized cloud storage (like Storj DCS), increasing its accessibility, performance, and resilience by distributing across an edge of over 17,000 uncorrelated participants.", "## Example Output\n![URL]URL", "# Usage \nThe golf-courses dataset can be used by modifying the instance_prompt: a photo of golf course", "### Languages\n\nThe language data in golf-courses is in English (BCP-47 en)", "## Dataset Structure\n\nThe complete dataset is GBs and consists of 21 objects.", "### Parallelized download using Decentralized Object Storage (Storj DCS)\n\nA direct download for the dataset is located at URL\n\nIn the future, Storj DCS will be used to download large datasets (exceeding 1TB) in a highly parallel, highly performant, and highly economical manner (by utilizing a network of over 17,000 diverse and economically incentivized datacenter node endpoints.", "### Curation Rationale\n\nThis model was created as a sample by Kevin Leffew as part of the DreamBooth Hackathon.", "### Source Data\n\nThe source data for the dataset is simply pulled from Unsplash", "### Licensing Information\n\nMIT License", "## Thanks to John Whitaker and Lewis Tunstall\n\nThanks to John Whitaker and Lewis Tunstallfor writing out and describing the initial hackathon parameters at URL", "## Example Training Data\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL" ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-label-image-classification #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #language-English #license-mit #golf-courses #region-us \n", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Point of Contact: Kevin Leffew – kleffew94@URL", "### Dataset Summary: golf-course\n\nThis dataset (bethecloud/golf-courses) includes 21 unique images of golf courses pulled from Unsplash. \n\nThe dataset is a collection of photographs taken at various golf courses around the world. The images depict a variety of scenes, including fairways, greens, bunkers, water hazards, and clubhouse facilities. The images are high resolution and have been carefully selected to provide a diverse range of visual content for fine-tuning a machine learning model.\n\nThe dataset is intended to be used in the context of the Hugging Face Dream Booth hackathon, a competition that challenges participants to build innovative applications using the Hugging Face transformers library. The submission is for the category of landscape. \n\nOverall, this dataset provides a rich source of visual data for machine learning models looking to understand and classify elements of golf courses. Its diverse range of images and high-quality resolution make it well-suited for use in fine-tuning models for tasks such as image classification, object detection, and image segmentation.\n\nBy using the golf course images as part of their training data, participants can fine-tune their models to recognize and classify specific features and elements commonly found on golf courses. The ultimate goal after the hackathon is to pull this dataset from decentralized cloud storage (like Storj DCS), increasing its accessibility, performance, and resilience by distributing across an edge of over 17,000 uncorrelated participants.", "## Example Output\n![URL]URL", "# Usage \nThe golf-courses dataset can be used by modifying the instance_prompt: a photo of golf course", "### Languages\n\nThe language data in golf-courses is in English (BCP-47 en)", "## Dataset Structure\n\nThe complete dataset is GBs and consists of 21 objects.", "### Parallelized download using Decentralized Object Storage (Storj DCS)\n\nA direct download for the dataset is located at URL\n\nIn the future, Storj DCS will be used to download large datasets (exceeding 1TB) in a highly parallel, highly performant, and highly economical manner (by utilizing a network of over 17,000 diverse and economically incentivized datacenter node endpoints.", "### Curation Rationale\n\nThis model was created as a sample by Kevin Leffew as part of the DreamBooth Hackathon.", "### Source Data\n\nThe source data for the dataset is simply pulled from Unsplash", "### Licensing Information\n\nMIT License", "## Thanks to John Whitaker and Lewis Tunstall\n\nThanks to John Whitaker and Lewis Tunstallfor writing out and describing the initial hackathon parameters at URL", "## Example Training Data\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL\n!URL" ]

[ "size_categories:100K<n<1M", "region:us" ]

[]

[]

[ "# Dataset Card for \"bookcorpus_compact_1024\"\n\nNum samples: 616,051\n\nThe number of tokens for each sequence is not exactly 1024, but all slightly shorter than 1024.\nThe sequences were built by merging sentences to the maximal length shorter than 1024 tokens.\nTherefore, padding is necessary for batch processing.\n\n\n\n\nMore Information needed" ]

[ "TAGS\n#size_categories-100K<n<1M #region-us \n", "# Dataset Card for \"bookcorpus_compact_1024\"\n\nNum samples: 616,051\n\nThe number of tokens for each sequence is not exactly 1024, but all slightly shorter than 1024.\nThe sequences were built by merging sentences to the maximal length shorter than 1024 tokens.\nTherefore, padding is necessary for batch processing.\n\n\n\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"bookcorpus_small_compact_512\"\n\nNum samples: 3,109\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_small_compact_512\"\n\nNum samples: 3,109\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"bookcorpus_small_compact_256\"\n\nNum samples: 6,104\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_small_compact_256\"\n\nNum samples: 6,104\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"bookcorpus_small_compact_1024\"\n\nNum samples: 1,571\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_small_compact_1024\"\n\nNum samples: 1,571\n\nMore Information needed" ]

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

[]

[]

[ "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on March 30, 2022 at 4:31 PM GMT\n\nIt includes 125 images.\nCoC are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 1920x1920 (Fit (black edges))\n\nNo image augmentation techniques were applied." ]

[ "TAGS\n#task_categories-object-detection #roboflow #roboflow2huggingface #Gaming #region-us \n", "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on March 30, 2022 at 4:31 PM GMT\n\nIt includes 125 images.\nCoC are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 1920x1920 (Fit (black edges))\n\nNo image augmentation techniques were applied." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"portraits-512\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"portraits-512\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"results_valid_100rows_2022-12-30\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"results_valid_100rows_2022-12-30\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

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

[]

[]

[ "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nPublic Domain", "### Dataset Summary\nThis dataset was exported via URL on December 29, 2022 at 8:12 PM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 9947 images.\nHelmets are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 1280x720 (Stretch)\n\nNo image augmentation techniques were applied." ]

[ "TAGS\n#task_categories-object-detection #roboflow #roboflow2huggingface #region-us \n", "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nPublic Domain", "### Dataset Summary\nThis dataset was exported via URL on December 29, 2022 at 8:12 PM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 9947 images.\nHelmets are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 1280x720 (Stretch)\n\nNo image augmentation techniques were applied." ]

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

[]

[]

[]

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

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

[]

[]

[]

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# test" ]

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

[ "task_categories:other", "annotations_creators:no-annotation", "language_creators:other", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|other", "language:en", "license:apache-2.0", "research papers", "edgar", "sec", "finance", "financial", "filings", "10K", "10-K", "nlp", "research", "econlp", "economics", "business", "arxiv:2109.14394", "region:us" ]

[ "2109.14394" ]

[ "en" ]

[ "### Dataset Summary\n\n\nThis dataset card is based on the paper EDGAR-CORPUS: Billions of Tokens Make The World Go Round authored by *Lefteris Loukas et.al*, as published in the *ECONLP 2021* workshop.\n\n\nThis dataset contains the annual reports of public companies from 1993-2020 from SEC EDGAR filings.\n\n\nThere is supported functionality to load a specific year.\n\n\nCare: since this is a corpus dataset, different 'train/val/test' splits do not have any special meaning. It's the default HF card format to have train/val/test splits.\n\n\nIf you wish to load specific year(s) of specific companies, you probably want to use the open-source software which generated this dataset, EDGAR-CRAWLER: URL\n\n\nIf this work helps or inspires you in any way, please consider citing the relevant paper published at the 3rd Economics and Natural Language Processing (ECONLP) workshop at EMNLP 2021 (Punta Cana, Dominican Republic):", "### Supported Tasks\n\n\nThis is a raw dataset/corpus for financial NLP.\nAs such, there are no annotations or labels.", "### Languages\n\n\nThe EDGAR Filings are in English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nRefer to the dataset preview.", "### Data Fields\n\n\nfilename: Name of file on EDGAR from which the report was extracted. \n\ncik: EDGAR identifier for a firm. \n\nyear: Year of report. \n\nsection\\_1: Corressponding section of the Annual Report. \n\nsection\\_1A: Corressponding section of the Annual Report. \n\nsection\\_1B: Corressponding section of the Annual Report. \n\nsection\\_2: Corressponding section of the Annual Report. \n\nsection\\_3: Corressponding section of the Annual Report. \n\nsection\\_4: Corressponding section of the Annual Report. \n\nsection\\_5: Corressponding section of the Annual Report. \n\nsection\\_6: Corressponding section of the Annual Report. \n\nsection\\_7: Corressponding section of the Annual Report. \n\nsection\\_7A: Corressponding section of the Annual Report. \n\nsection\\_8: Corressponding section of the Annual Report. \n\nsection\\_9: Corressponding section of the Annual Report. \n\nsection\\_9A: Corressponding section of the Annual Report. \n\nsection\\_9B: Corressponding section of the Annual Report. \n\nsection\\_10: Corressponding section of the Annual Report. \n\nsection\\_11: Corressponding section of the Annual Report. \n\nsection\\_12: Corressponding section of the Annual Report. \n\nsection\\_13: Corressponding section of the Annual Report. \n\nsection\\_14: Corressponding section of the Annual Report. \n\nsection\\_15: Corressponding section of the Annual Report.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nInitial data was collected and processed by the authors of the research paper EDGAR-CORPUS: Billions of Tokens Make The World Go Round.", "#### Who are the source language producers?\n\n\nPublic firms filing with the SEC.", "### Annotations", "#### Annotation process\n\n\nNA", "#### Who are the annotators?\n\n\nNA", "### Personal and Sensitive Information\n\n\nThe dataset contains public filings data from SEC.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nLow to none.", "### Discussion of Biases\n\n\nThe dataset is about financial information of public companies and as such the tone and style of text is in line with financial literature.", "### Other Known Limitations\n\n\nThe dataset needs further cleaning for improved performance.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nEDGAR data is publicly available.", "### Shoutout\n\n\nHuge shoutout to @JanosAudran for the HF Card setup!", "### References\n\n\n* [Research Paper] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CORPUS: Billions of Tokens Make The World Go Round. Third Workshop on Economics and Natural Language Processing (ECONLP). URL - Punta Cana, Dominican Republic, November 2021.\n* [Software] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CRAWLER. URL (2021)\n* [EDGAR CORPUS, but in zip files] EDGAR CORPUS: A corpus for financial NLP research, built from SEC's EDGAR. URL (2021)\n* [Word Embeddings] EDGAR-W2V: Word2vec Embeddings trained on EDGAR-CORPUS. URL (2021)\n* [Applied Research paper where EDGAR-CORPUS is used] Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos, and, George Paliouras. FiNER: Financial Numeric Entity Recognition for XBRL Tagging. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). URL (2022)" ]

[ "TAGS\n#task_categories-other #annotations_creators-no-annotation #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|other #language-English #license-apache-2.0 #research papers #edgar #sec #finance #financial #filings #10K #10-K #nlp #research #econlp #economics #business #arxiv-2109.14394 #region-us \n", "### Dataset Summary\n\n\nThis dataset card is based on the paper EDGAR-CORPUS: Billions of Tokens Make The World Go Round authored by *Lefteris Loukas et.al*, as published in the *ECONLP 2021* workshop.\n\n\nThis dataset contains the annual reports of public companies from 1993-2020 from SEC EDGAR filings.\n\n\nThere is supported functionality to load a specific year.\n\n\nCare: since this is a corpus dataset, different 'train/val/test' splits do not have any special meaning. It's the default HF card format to have train/val/test splits.\n\n\nIf you wish to load specific year(s) of specific companies, you probably want to use the open-source software which generated this dataset, EDGAR-CRAWLER: URL\n\n\nIf this work helps or inspires you in any way, please consider citing the relevant paper published at the 3rd Economics and Natural Language Processing (ECONLP) workshop at EMNLP 2021 (Punta Cana, Dominican Republic):", "### Supported Tasks\n\n\nThis is a raw dataset/corpus for financial NLP.\nAs such, there are no annotations or labels.", "### Languages\n\n\nThe EDGAR Filings are in English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nRefer to the dataset preview.", "### Data Fields\n\n\nfilename: Name of file on EDGAR from which the report was extracted. \n\ncik: EDGAR identifier for a firm. \n\nyear: Year of report. \n\nsection\\_1: Corressponding section of the Annual Report. \n\nsection\\_1A: Corressponding section of the Annual Report. \n\nsection\\_1B: Corressponding section of the Annual Report. \n\nsection\\_2: Corressponding section of the Annual Report. \n\nsection\\_3: Corressponding section of the Annual Report. \n\nsection\\_4: Corressponding section of the Annual Report. \n\nsection\\_5: Corressponding section of the Annual Report. \n\nsection\\_6: Corressponding section of the Annual Report. \n\nsection\\_7: Corressponding section of the Annual Report. \n\nsection\\_7A: Corressponding section of the Annual Report. \n\nsection\\_8: Corressponding section of the Annual Report. \n\nsection\\_9: Corressponding section of the Annual Report. \n\nsection\\_9A: Corressponding section of the Annual Report. \n\nsection\\_9B: Corressponding section of the Annual Report. \n\nsection\\_10: Corressponding section of the Annual Report. \n\nsection\\_11: Corressponding section of the Annual Report. \n\nsection\\_12: Corressponding section of the Annual Report. \n\nsection\\_13: Corressponding section of the Annual Report. \n\nsection\\_14: Corressponding section of the Annual Report. \n\nsection\\_15: Corressponding section of the Annual Report.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nInitial data was collected and processed by the authors of the research paper EDGAR-CORPUS: Billions of Tokens Make The World Go Round.", "#### Who are the source language producers?\n\n\nPublic firms filing with the SEC.", "### Annotations", "#### Annotation process\n\n\nNA", "#### Who are the annotators?\n\n\nNA", "### Personal and Sensitive Information\n\n\nThe dataset contains public filings data from SEC.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nLow to none.", "### Discussion of Biases\n\n\nThe dataset is about financial information of public companies and as such the tone and style of text is in line with financial literature.", "### Other Known Limitations\n\n\nThe dataset needs further cleaning for improved performance.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nEDGAR data is publicly available.", "### Shoutout\n\n\nHuge shoutout to @JanosAudran for the HF Card setup!", "### References\n\n\n* [Research Paper] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CORPUS: Billions of Tokens Make The World Go Round. Third Workshop on Economics and Natural Language Processing (ECONLP). URL - Punta Cana, Dominican Republic, November 2021.\n* [Software] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CRAWLER. URL (2021)\n* [EDGAR CORPUS, but in zip files] EDGAR CORPUS: A corpus for financial NLP research, built from SEC's EDGAR. URL (2021)\n* [Word Embeddings] EDGAR-W2V: Word2vec Embeddings trained on EDGAR-CORPUS. URL (2021)\n* [Applied Research paper where EDGAR-CORPUS is used] Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos, and, George Paliouras. FiNER: Financial Numeric Entity Recognition for XBRL Tagging. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). URL (2022)" ]

[ "region:us" ]

[]

[]

[ "# emo" ]

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

[ "task_categories:text-to-speech", "task_categories:automatic-speech-recognition", "multilinguality:monolingual", "source_datasets:original", "language:zh", "region:us" ]

[]

[ "zh" ]

[ "# Dataset Card for Genshin Voice", "## Dataset Description", "### Dataset Summary\n\nThe Genshin Voice dataset is a text-to-voice dataset of different Genshin Impact characters unpacked from the game.", "### Languages\n\nThe text in the dataset is in Mandarin.", "## Dataset Creation", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data was obtained by unpacking the Genshin Impact game.", "#### Who are the source language producers?\n\nThe language producers are the employee of Hoyoverse and contractors from EchoSky Studio.", "### Annotations\n\nThe dataset contains official annotations from the game, including ingame speaker name and transcripts.", "## Additional Information", "### Dataset Curators\n\nThe dataset was created by w4123 initially in his GitHub repository.", "### Licensing Information\n\nCopyright © COGNOSPHERE. All Rights Reserved." ]

[ "TAGS\n#task_categories-text-to-speech #task_categories-automatic-speech-recognition #multilinguality-monolingual #source_datasets-original #language-Chinese #region-us \n", "# Dataset Card for Genshin Voice", "## Dataset Description", "### Dataset Summary\n\nThe Genshin Voice dataset is a text-to-voice dataset of different Genshin Impact characters unpacked from the game.", "### Languages\n\nThe text in the dataset is in Mandarin.", "## Dataset Creation", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data was obtained by unpacking the Genshin Impact game.", "#### Who are the source language producers?\n\nThe language producers are the employee of Hoyoverse and contractors from EchoSky Studio.", "### Annotations\n\nThe dataset contains official annotations from the game, including ingame speaker name and transcripts.", "## Additional Information", "### Dataset Curators\n\nThe dataset was created by w4123 initially in his GitHub repository.", "### Licensing Information\n\nCopyright © COGNOSPHERE. All Rights Reserved." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"bookcorpus_small_compact_512_meta\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_small_compact_512_meta\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for LegalCaseDocumentSummarization", "## 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: GitHub\n- Repository: Zenodo\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 @JoelNiklaus for adding this dataset." ]

[ "TAGS\n#region-us \n", "# Dataset Card for LegalCaseDocumentSummarization", "## 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: GitHub\n- Repository: Zenodo\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 @JoelNiklaus for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for PlainEnglishContractsSummarization", "## 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: GitHub\n- Repository:\n- Paper: ACL Anthology\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 @JoelNiklaus for adding this dataset." ]

[ "TAGS\n#region-us \n", "# Dataset Card for PlainEnglishContractsSummarization", "## 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: GitHub\n- Repository:\n- Paper: ACL Anthology\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 @JoelNiklaus for adding this dataset." ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"pochita_v2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"pochita_v2\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"bookcorpus_small_compact_256_meta\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_small_compact_256_meta\"\n\nMore Information needed" ]

[ "arxiv:2004.05150", "region:us" ]

[ "2004.05150" ]

[]

[ "# Hyperpartisan news detection\n\nThis dataset has the hyperpartisan new dataset, processed and split exactly as it was for longformer experiments.\nCode for processing was found at here." ]

[ "TAGS\n#arxiv-2004.05150 #region-us \n", "# Hyperpartisan news detection\n\nThis dataset has the hyperpartisan new dataset, processed and split exactly as it was for longformer experiments.\nCode for processing was found at here." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"bookcorpus_small_compact_1024_meta\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_small_compact_1024_meta\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[]

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

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

[]

[]

[ "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nPublic Domain", "### Dataset Summary\nThis dataset was exported via URL on November 4, 2022 at 7:46 PM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 364 images.\nCells are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 416x416 (Stretch)\n\nNo image augmentation techniques were applied." ]

[ "TAGS\n#task_categories-object-detection #roboflow #roboflow2huggingface #Biology #region-us \n", "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nPublic Domain", "### Dataset Summary\nThis dataset was exported via URL on November 4, 2022 at 7:46 PM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 364 images.\nCells are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 416x416 (Stretch)\n\nNo image augmentation techniques were applied." ]

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

[]

[]

[ "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on January 13, 2022 at 5:20 PM GMT\n\nIt includes 8823 images.\nVRP are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n\nNo image augmentation techniques were applied." ]

[ "TAGS\n#task_categories-object-detection #roboflow #roboflow2huggingface #Self Driving #Anpr #region-us \n", "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on January 13, 2022 at 5:20 PM GMT\n\nIt includes 8823 images.\nVRP are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n\nNo image augmentation techniques were applied." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"whisper-small-hindi\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

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

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

[ "task_categories:unconditional-image-generation", "task_categories:text-to-image", "size_categories:n<10K", "language:en", "license:mit", "emoji", "fluentui", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"microsoft-fluentui-emoji-512-whitebg\"\n\nsvg and their file names were converted to images and text from Microsoft's fluentui-emoji repo" ]

[ "TAGS\n#task_categories-unconditional-image-generation #task_categories-text-to-image #size_categories-n<10K #language-English #license-mit #emoji #fluentui #region-us \n", "# Dataset Card for \"microsoft-fluentui-emoji-512-whitebg\"\n\nsvg and their file names were converted to images and text from Microsoft's fluentui-emoji repo" ]

[ "task_categories:text-to-image", "size_categories:n<10K", "language:en", "license:mit", "emoji", "fluentui", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"microsoft-fluentui-emoji-768\"\n\nsvg and their file names were converted to images and text from Microsoft's fluentui-emoji repo" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<10K #language-English #license-mit #emoji #fluentui #region-us \n", "# Dataset Card for \"microsoft-fluentui-emoji-768\"\n\nsvg and their file names were converted to images and text from Microsoft's fluentui-emoji repo" ]

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

[]

[]

[ "### Roboflow Dataset Page\nURL", "### Dataset Labels", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on July 27, 2022 at 5:44 AM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 10464 images.\nGARBAGE-GARBAGE-CLASSIFICATION are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 416x416 (Stretch)\n\nThe following augmentation was applied to create 1 versions of each source image:\n* 50% probability of horizontal flip\n* 50% probability of vertical flip\n* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down" ]

[ "TAGS\n#task_categories-object-detection #roboflow #region-us \n", "### Roboflow Dataset Page\nURL", "### Dataset Labels", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on July 27, 2022 at 5:44 AM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 10464 images.\nGARBAGE-GARBAGE-CLASSIFICATION are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 416x416 (Stretch)\n\nThe following augmentation was applied to create 1 versions of each source image:\n* 50% probability of horizontal flip\n* 50% probability of vertical flip\n* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down" ]

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

[]

[]

[ "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on April 3, 2022 at 9:01 PM GMT\n\nIt includes 421 images.\nForklift are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n\nNo image augmentation techniques were applied." ]

[ "TAGS\n#task_categories-object-detection #roboflow #roboflow2huggingface #Manufacturing #region-us \n", "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on April 3, 2022 at 9:01 PM GMT\n\nIt includes 421 images.\nForklift are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n\nNo image augmentation techniques were applied." ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"rlhf-reward-datasets\"\n\nMore Information needed" ]

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

[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:bsd", "logical reasoning", "nli", "natural-language-inference", "reasoning", "logic", "region:us" ]

[]

[ "en" ]

[ "# bAbi_nli\n\nbAbI tasks recasted as natural language inference.\nURL\n\ntasksource recasting code:\nURL" ]

[ "TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-bsd #logical reasoning #nli #natural-language-inference #reasoning #logic #region-us \n", "# bAbi_nli\n\nbAbI tasks recasted as natural language inference.\nURL\n\ntasksource recasting code:\nURL" ]

[ "region:us" ]

[]

[]

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

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

[ "license:mit", "stable-diffusion", "paint-journey", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-mit #stable-diffusion #paint-journey #region-us \n" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"w2v2_0\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"w2v2_0\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mediumroast-press-releases\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[]

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