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ee0fefac8bae648f9a85e33f52fc39fd2fd2ddce
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: bigscience/bloom-7b1 * Dataset: phpthinh/examplei * Config: mismatch * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model.
autoevaluate/autoeval-eval-phpthinh__examplei-mismatch-1389aa-1748961037
[ "autotrain", "evaluation", "region:us" ]
2022-10-13T14:49:05+00:00
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["phpthinh/examplei"], "eval_info": {"task": "text_zero_shot_classification", "model": "bigscience/bloom-7b1", "metrics": ["f1"], "dataset_name": "phpthinh/examplei", "dataset_config": "mismatch", "dataset_split": "test", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}}
2022-10-13T15:08:31+00:00
55a7cf0a0b66ce56ba9c35e5a56bf52c88adfd30
# Dataset Card for "BanglaParaphrase" ## Table of Contents - [Dataset Card Creation Guide](#dataset-card-creation-guide) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [https://github.com/csebuetnlp/banglaparaphrase](https://github.com/csebuetnlp/banglaparaphrase) - **Paper:** [BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset](https://arxiv.org/abs/2210.05109) - **Point of Contact:** [Najrin Sultana](mailto:[email protected]) ### Dataset Summary We present BanglaParaphrase, a high quality synthetic Bangla paraphrase dataset containing about 466k paraphrase pairs. The paraphrases ensures high quality by being semantically coherent and syntactically diverse. ### Supported Tasks and Leaderboards [More information needed](https://github.com/csebuetnlp/banglaparaphrase) ### Languages - `bengali` ## Loading the dataset ```python from datasets import load_dataset from datasets import load_dataset ds = load_dataset("csebuetnlp/BanglaParaphrase") ``` ## Dataset Structure ### Data Instances One example from the `train` part of the dataset is given below in JSON format. ``` { "source": "বেশিরভাগ সময় প্রকৃতির দয়ার ওপরেই বেঁচে থাকতেন উপজাতিরা।", "target": "বেশিরভাগ সময়ই উপজাতিরা প্রকৃতির দয়ার উপর নির্ভরশীল ছিল।" } ``` ### Data Fields - 'source': A string representing the source sentence. - 'target': A string representing the target sentence. ### Data Splits Dataset with train-dev-test example counts are given below: Language | ISO 639-1 Code | Train | Validation | Test | -------------- | ---------------- | ------- | ----- | ------ | Bengali | bn | 419, 967 | 233, 31 | 233, 32 | ## Dataset Creation ### Curation Rationale [More information needed](https://github.com/csebuetnlp/banglaparaphrase) ### Source Data [Roar Bangla](https://roar.media/bangla) #### Initial Data Collection and Normalization [Detailed in the paper](https://arxiv.org/abs/2210.05109) #### Who are the source language producers? [Detailed in the paper](https://arxiv.org/abs/2210.05109) ### Annotations [Detailed in the paper](https://arxiv.org/abs/2210.05109) #### Annotation process [Detailed in the paper](https://arxiv.org/abs/2210.05109) #### Who are the annotators? [Detailed in the paper](https://arxiv.org/abs/2210.05109) ### Personal and Sensitive Information [More information needed](https://github.com/csebuetnlp/banglaparaphrase) ## Considerations for Using the Data ### Social Impact of Dataset [More information needed](https://github.com/csebuetnlp/banglaparaphrase) ### Discussion of Biases [More information needed](https://github.com/csebuetnlp/banglaparaphrase) ### Other Known Limitations [More information needed](https://github.com/csebuetnlp/banglaparaphrase) ## Additional Information ### Dataset Curators [More information needed](https://github.com/csebuetnlp/banglaparaphrase) ### Licensing Information Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders. ### Citation Information ``` @article{akil2022banglaparaphrase, title={BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset}, author={Akil, Ajwad and Sultana, Najrin and Bhattacharjee, Abhik and Shahriyar, Rifat}, journal={arXiv preprint arXiv:2210.05109}, year={2022} } ``` ### Contributions
csebuetnlp/BanglaParaphrase
[ "task_categories:text2text-generation", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:100k<n<1M", "source_datasets:original", "language:bn", "license:cc-by-nc-sa-4.0", "conditional-text-generation", "paraphrase-generation", "arxiv:2210.05109", "region:us" ]
2022-10-13T15:06:21+00:00
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["bn"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100k<n<1M"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "pretty_name": "BanglaParaphrase", "tags": ["conditional-text-generation", "paraphrase-generation"]}
2022-11-14T15:39:43+00:00
816f7881391c6ee586eb9fbdb784619871fc04e2
williambr/snowmed_signsymptom
[ "license:mit", "region:us" ]
2022-10-13T16:34:31+00:00
{"license": "mit"}
2022-10-13T16:34:49+00:00
fd35c6358fd302556f3c8d52acdd19ed8e61381e
annotations_creators: - machine-generated language: - en language_creators: - crowdsourced license: [] multilinguality: - monolingual paperswithcode_id: wikitext-2 pretty_name: Whisper-Transcripts size_categories: - 1M<n<10M source_datasets: - original tags: [] task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling
Whispering-GPT/whisper-transcripts-the-verge
[ "region:us" ]
2022-10-13T16:58:45+00:00
{}
2022-10-23T09:54:59+00:00
5a28efd1123b3a08a64878f48dd171a8a859389d
ChiangLz/zapotecojuchitan
[ "license:cc-by-nc-nd-4.0", "region:us" ]
2022-10-13T17:40:33+00:00
{"license": "cc-by-nc-nd-4.0"}
2022-10-23T17:48:42+00:00
ddb7af253443c37bc559afd65936fe21a5177d15
DimDymov/Vilmarina
[ "license:cc-by-nd-4.0", "region:us" ]
2022-10-13T18:03:47+00:00
{"license": "cc-by-nd-4.0"}
2022-10-13T18:12:38+00:00
f41838f3135528d90d7727487737421a01b7866d
# Dataset Card for "sidewalk-imagery" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dpasch01/sidewalk-imagery
[ "region:us" ]
2022-10-13T18:11:58+00:00
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3202716.0, "num_examples": 10}], "download_size": 3192547, "dataset_size": 3202716.0}}
2022-10-13T18:12:05+00:00
9a8e1119eccce3f5559d8d26538230d3a4f90f3f
# Dataset Card for "celeb-identities" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Kavindu99/celeb-identities
[ "region:us" ]
2022-10-13T19:27:31+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Emilia_Clarke", "1": "Henry_Cavil", "2": "Jason_Mamoa", "3": "Sadie_Sink", "4": "Sangakkara", "5": "Zendaya"}}}}], "splits": [{"name": "train", "num_bytes": 160371.0, "num_examples": 18}], "download_size": 160832, "dataset_size": 160371.0}}
2022-10-13T19:27:44+00:00
174b3afde4a8dec38e49d843fc9fc0857c4a8bd9
The YouTube transcriptions dataset contains technical tutorials (currently from [James Briggs](https://www.youtube.com/c/jamesbriggs), [Daniel Bourke](https://www.youtube.com/channel/UCr8O8l5cCX85Oem1d18EezQ), and [AI Coffee Break](https://www.youtube.com/c/aicoffeebreak)) transcribed using [OpenAI's Whisper](https://huggingface.co/openai/whisper-large) (large). Each row represents roughly a sentence-length chunk of text alongside the video URL and timestamp. Note that each item in the dataset contains just a short chunk of text. For most use cases you will likely need to merge multiple rows to create more substantial chunks of text, if you need to do that, this code snippet will help: ```python from datasets import load_dataset # first download the dataset data = load_dataset( 'jamescalam/youtube-transcriptions', split='train' ) new_data = [] # this will store adjusted data window = 6 # number of sentences to combine stride = 3 # number of sentences to 'stride' over, used to create overlap for i in range(0, len(data), stride): i_end = min(len(data)-1, i+window) if data[i]['title'] != data[i_end]['title']: # in this case we skip this entry as we have start/end of two videos continue # create larger text chunk text = ' '.join(data[i:i_end]['text']) # add to adjusted data list new_data.append({ 'start': data[i]['start'], 'end': data[i_end]['end'], 'title': data[i]['title'], 'text': text, 'id': data[i]['id'], 'url': data[i]['url'], 'published': data[i]['published'] }) ```
jamescalam/youtube-transcriptions
[ "task_categories:conversational", "task_categories:question-answering", "task_categories:text-retrieval", "task_categories:visual-question-answering", "task_ids:open-domain-qa", "task_ids:extractive-qa", "task_ids:document-retrieval", "task_ids:visual-question-answering", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:afl-3.0", "youtube", "technical", "speech to text", "speech", "video", "video search", "audio", "audio search", "region:us" ]
2022-10-13T19:31:27+00:00
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["afl-3.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["conversational", "question-answering", "text-retrieval", "visual-question-answering"], "task_ids": ["open-domain-qa", "extractive-qa", "document-retrieval", "visual-question-answering"], "pretty_name": "Youtube Transcriptions", "tags": ["youtube", "technical", "speech to text", "speech", "video", "video search", "audio", "audio search"]}
2022-10-22T00:20:07+00:00
bb4424259da93902b3ec2ece55a744f23d0793d0
# Dataset Card for [Dataset Name] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards Natural Language Inference Text Classification ### Languages en ## Dataset Structure ### Data Instances ### Data Fields premise: hypothesis: label: ### Data Splits Evaluation: 258 samples ## Dataset Creation ### Curation Rationale Extracting samples corresponding to different linguistics constructions of negation. ### Source Data Geoffrey K. Pullum and Rodney Huddleston. 2002. Negation, chapter 9. Cambridge University Press. #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The annotators are the authors of the papers, one of whom holds a graduate degree in linguistics. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@joey234](https://github.com/joey234) for adding this dataset.
joey234/nan-nli
[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "negation", "region:us" ]
2022-10-13T22:16:18+00:00
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"], "pretty_name": "nan-nli", "tags": ["negation"]}
2022-10-13T22:18:18+00:00
cacce71315e1bbff74962098ea588386b63ee60c
annaludicode/ladiesInColoredWaterStyle
[ "license:artistic-2.0", "region:us" ]
2022-10-13T22:43:33+00:00
{"license": "artistic-2.0"}
2022-10-13T22:43:33+00:00
c8468b5b341979f7e59f79c048a2ab61870f6c98
## test
zhenzi/test
[ "region:us" ]
2022-10-14T00:38:17+00:00
{}
2022-10-18T01:03:54+00:00
1eeb1fb9c1d9e3c8c6c9e5becd15a560e2ab29c5
# Dataset Card for Dicionário Português It is a list of 53138 portuguese words with its inflections. How to use it: ``` from datasets import load_dataset remote_dataset = load_dataset("VanessaSchenkel/pt-inflections", field="data") remote_dataset ``` Output: ``` DatasetDict({ train: Dataset({ features: ['word', 'pos', 'forms'], num_rows: 53138 }) }) ``` Exemple: ``` remote_dataset["train"][42] ``` Output: ``` {'word': 'numeral', 'pos': 'noun', 'forms': [{'form': 'numerais', 'tags': ['plural']}]} ```
VanessaSchenkel/pt-inflections
[ "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|wikipedia", "language:pt", "region:us" ]
2022-10-14T00:41:22+00:00
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["pt"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended|wikipedia"], "task_categories": [], "task_ids": [], "pretty_name": "dicion\u00e1rio de portugu\u00eas", "tags": []}
2022-11-07T03:44:23+00:00
2af016d62b5b4de22045d3385ff117b9c2d11ce5
# About Dataset The dataset consists of data from a bunch of youtube videos ranging from videos from fastai lessons, FSDL lesson to random videos teaching something. In total this dataset contains 600 chapter markers in youtube and contains 25, 000 lesson transcript. This dataset can be used for NLP tasks like summarization, topic segmentation etc. You can refer to some of the models we have trained with this dataset in [github repo link](https://github.com/ohmeow/fsdl_2022_course_project) for Full stack deep learning 2022 projects.
recapper/Course_summaries_dataset
[ "task_categories:summarization", "task_categories:text2text-generation", "size_categories:1M<n<10M", "language:en", "license:apache-2.0", "conditional-text-generation", "region:us" ]
2022-10-14T03:10:12+00:00
{"language": ["en"], "license": "apache-2.0", "size_categories": ["1M<n<10M"], "task_categories": ["summarization", "text2text-generation"], "task_ids": [], "tags": ["conditional-text-generation"]}
2022-10-25T15:03:24+00:00
aaaa35d10817ea9ca2550c3970aa413f9fb30bd4
# Dataset Card for "celeb-identities" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bburns/celeb-identities
[ "region:us" ]
2022-10-14T03:21:48+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Geohot", "1": "Grimes", "2": "Kanye", "3": "PG", "4": "Riva", "5": "Trump"}}}}], "splits": [{"name": "train", "num_bytes": 4350264.0, "num_examples": 18}], "download_size": 4342420, "dataset_size": 4350264.0}}
2022-10-14T14:20:20+00:00
bfbba48d89b4213fa5cd9df07b675ba461d51d4f
Dataset containing video metadata from a few tech channels, i.e. * [James Briggs](https://youtube.com/c/JamesBriggs) * [Yannic Kilcher](https://www.youtube.com/c/YannicKilcher) * [sentdex](https://www.youtube.com/c/sentdex) * [Daniel Bourke](https://www.youtube.com/channel/UCr8O8l5cCX85Oem1d18EezQ) * [AI Coffee Break with Letitia](https://www.youtube.com/c/AICoffeeBreak) * [Alex Ziskind](https://youtube.com/channel/UCajiMK_CY9icRhLepS8_3ug)
jamescalam/channel-metadata
[ "task_categories:other", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:afl-3.0", "youtube", "video", "video metadata", "tech", "science and tech", "region:us" ]
2022-10-14T04:29:45+00:00
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["afl-3.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["other"], "task_ids": [], "pretty_name": "Tech Channels Metadata", "tags": ["youtube", "video", "video metadata", "tech", "science and tech"]}
2022-10-26T00:05:55+00:00
8f7568a6bea2403221f304edd9212a7d00a980a2
ratishsp/newshead
[ "license:mit", "region:us" ]
2022-10-14T05:05:56+00:00
{"license": "mit"}
2022-10-14T06:42:08+00:00
2d78d4a8000795b3520df6d58966673ae099e912
# Dataset Card for "leaflet_offers-clone" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dpasch01/leaflet_offers-clone
[ "region:us" ]
2022-10-14T05:11:21+00:00
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 5623867.0, "num_examples": 4}], "download_size": 5356712, "dataset_size": 5623867.0}}
2022-10-14T05:11:34+00:00
f3e50ecc00155232eda7815b4a26796130c91bc6
# Dataset Card for "audio-diffusion-256-isolated-drums" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ndxbxrme/audio-diffusion-256-isolated-drums
[ "region:us" ]
2022-10-14T06:06:24+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "audio_file", "dtype": "string"}, {"name": "slice", "dtype": "int16"}], "splits": [{"name": "train", "num_bytes": 367170599.374, "num_examples": 8589}], "download_size": 366838959, "dataset_size": 367170599.374}}
2022-10-14T06:06:35+00:00
623e04e36c086b61aa56e426471a64b952c32024
pandaman2020/SDTraining
[ "license:cc-by-4.0", "region:us" ]
2022-10-14T08:14:41+00:00
{"license": "cc-by-4.0"}
2023-06-14T05:49:31+00:00
72659de0f473e99331c92038be331d7c864a7439
zhenzi/data_process
[ "region:us" ]
2022-10-14T09:00:16+00:00
{}
2022-10-18T01:13:05+00:00
da31fa7be019faa58aeff0ee22bb93307298a41a
This dataset will be to create my dogs stable-diffusion model
mikelalda/txoko
[ "doi:10.57967/hf/0047", "region:us" ]
2022-10-14T10:13:22+00:00
{}
2022-10-19T12:30:00+00:00
bc167f78800fbaa9da3c7d66e28c3d24f6fd00ee
# AutoTrain Dataset for project: trackerlora_less_data ## Dataset Description This dataset has been automatically processed by AutoTrain for project trackerlora_less_data. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "id": 444, "feat_rssi": -113.0, "feat_snr": -9.25, "feat_spreading_factor": 7, "feat_potencia": 14, "target": 308.0 }, { "id": 144, "feat_rssi": -77.0, "feat_snr": 8.800000190734863, "feat_spreading_factor": 7, "feat_potencia": 14, "target": 126.0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "id": "Value(dtype='int64', id=None)", "feat_rssi": "Value(dtype='float64', id=None)", "feat_snr": "Value(dtype='float64', id=None)", "feat_spreading_factor": "Value(dtype='int64', id=None)", "feat_potencia": "Value(dtype='int64', id=None)", "target": "Value(dtype='float32', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 139 | | valid | 40 |
pcoloc/autotrain-data-trackerlora_less_data
[ "region:us" ]
2022-10-14T10:34:20+00:00
{}
2022-10-14T11:06:37+00:00
678e10f1ea8f5995950f72f9abac070c00759051
gregkowal/crime-time-game-style
[ "license:other", "region:us" ]
2022-10-14T10:48:03+00:00
{"license": "other"}
2022-10-14T11:14:15+00:00
205ca64c78a48e01e0ba211163c89e77c027a4ff
# cloth **CLOTH** is a dataset which is a collection of nearly 100,000 cloze questions from middle school and high school English exams. The detail of CLOTH dataset is shown below. | Number of questions | Train | Valid | Test | | ------------------- | ----- | ----- | ----- | | **Middle school** | 22056 | 3273 | 3198 | | **High school** | 54794 | 7794 | 8318 | | **Total** | 76850 | 11067 | 11516 | Source: https://www.cs.cmu.edu/~glai1/data/cloth/
AndyChiang/cloth
[ "task_categories:fill-mask", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:en", "license:mit", "cloze", "mid-school", "high-school", "exams", "region:us" ]
2022-10-14T11:28:41+00:00
{"language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "task_categories": ["fill-mask"], "pretty_name": "cloth", "tags": ["cloze", "mid-school", "high-school", "exams"]}
2022-10-14T13:10:37+00:00
830447e72563191bcd52dce78495d7153f02c757
# wine-ratings Processing, EDA, and ML on wine ratings
alfredodeza/wine-ratings
[ "region:us" ]
2022-10-14T11:28:47+00:00
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "region", "dtype": "string"}, {"name": "variety", "dtype": "string"}, {"name": "rating", "dtype": "float32"}, {"name": "notes", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 82422, "num_examples": 200}, {"name": "train", "num_bytes": 13538613, "num_examples": 32780}, {"name": "validation", "num_bytes": 83047, "num_examples": 200}], "download_size": 0, "dataset_size": 13704082}}
2022-10-15T12:09:06+00:00
60582e99b1ebd35b4ba41cf11b19a6aaa87db726
# Dataset Card for "dummy_swin_pipe_5k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
FSDL-Fashion/dummy_swin_pipe_5k
[ "region:us" ]
2022-10-14T11:45:57+00:00
{"dataset_info": {"features": [{"name": "path", "dtype": "string"}, {"name": "embedding", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 20800000, "num_examples": 5000}], "download_size": 21312459, "dataset_size": 20800000}}
2022-10-14T11:46:02+00:00
104c7e6a9c489be3b34bfdb905cf124063473ea7
# dgen **DGen** is a cloze questions dataset which covers multiple domains including science, vocabulary, common sense and trivia. It is compiled from a wide variety of datasets including SciQ, MCQL, AI2 Science Questions, etc. The detail of DGen dataset is shown below. | DGen dataset | Train | Valid | Test | Total | | ----------------------- | ----- | ----- | ---- | ----- | | **Number of questions** | 2321 | 300 | 259 | 2880 | Source: https://github.com/DRSY/DGen
AndyChiang/dgen
[ "task_categories:fill-mask", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:mit", "cloze", "sciq", "mcql", "ai2 science questions", "region:us" ]
2022-10-14T11:56:15+00:00
{"language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "task_categories": ["fill-mask"], "pretty_name": "dgen", "tags": ["cloze", "sciq", "mcql", "ai2 science questions"]}
2022-10-14T13:19:16+00:00
72eb2ea815e2924593d458534c6d68d5471e5019
# Dataset Card for "figaro_hair_segmentation_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Allison/figaro_hair_segmentation_1000
[ "region:us" ]
2022-10-14T12:27:05+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 68214218.0, "num_examples": 1000}, {"name": "validation", "num_bytes": 3542245.0, "num_examples": 50}], "download_size": 0, "dataset_size": 71756463.0}}
2022-10-15T15:28:24+00:00
86d7547dd834ab89cc6715b07eb8bef15a8ee9f3
randomwalksky/cup
[ "license:openrail", "region:us" ]
2022-10-14T12:48:10+00:00
{"license": "openrail"}
2022-10-14T12:49:09+00:00
41b0cc22d1bf22ab270d99a902d0e349eb766d8e
# Dataset Card for "dummy_swin_pipe" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
FSDL-Fashion/dummy_swin_pipe
[ "region:us" ]
2022-10-14T13:29:08+00:00
{"dataset_info": {"features": [{"name": "path", "dtype": "string"}, {"name": "embedding", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 416000000, "num_examples": 100000}], "download_size": 420001566, "dataset_size": 416000000}}
2022-10-14T13:33:52+00:00
4b964f60f7265990c1b72454e48305e460135281
A few images of Echo
batchku/echo
[ "region:us" ]
2022-10-14T15:14:13+00:00
{}
2022-10-14T16:27:07+00:00
80e34a787a6c757d2e9cad051ac26c3353b70225
## Message Content Rephrasing Dataset Introduced by Einolghozati et al. in Sound Natural: Content Rephrasing in Dialog Systems https://aclanthology.org/2020.emnlp-main.414/ We introduce a new task of rephrasing for amore natural virtual assistant. Currently, vir-tual assistants work in the paradigm of intent-slot tagging and the slot values are directlypassed as-is to the execution engine. However,this setup fails in some scenarios such as mes-saging when the query given by the user needsto be changed before repeating it or sending itto another user. For example, for queries like‘ask my wife if she can pick up the kids’ or ‘re-mind me to take my pills’, we need to rephrasethe content to ‘can you pick up the kids’ and‘take your pills’. In this paper, we study theproblem of rephrasing with messaging as ause case and release a dataset of 3000 pairs oforiginal query and rephrased query. We showthat BART, a pre-trained transformers-basedmasked language model with auto-regressivedecoding, is a strong baseline for the task, andshow improvements by adding a copy-pointerand copy loss to it. We analyze different trade-offs of BART-based and LSTM-based seq2seqmodels, and propose a distilled LSTM-basedseq2seq as the best practical model.
facebook/content_rephrasing
[ "license:cc-by-sa-4.0", "region:us" ]
2022-10-14T16:25:22+00:00
{"license": "cc-by-sa-4.0"}
2022-10-14T16:41:05+00:00
d114b6fff871e11d1bb5835432f461cd3148e452
# Dataset Card for "Quran_Hadith" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/Quran_Hadith
[ "region:us" ]
2022-10-14T16:45:31+00:00
{"dataset_info": {"features": [{"name": "SS", "dtype": "string"}, {"name": "SV", "dtype": "string"}, {"name": "Verse1", "dtype": "string"}, {"name": "TS", "dtype": "string"}, {"name": "TV", "dtype": "string"}, {"name": "Verse2", "dtype": "string"}, {"name": "Label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7351452, "num_examples": 8144}], "download_size": 2850963, "dataset_size": 7351452}}
2022-10-14T16:45:37+00:00
6d008011ac5b47dcd75029f46901da81382b6d89
Paper: https://arxiv.org/abs/2210.12478 --- license: apache-2.0 ---
prajjwal1/discosense
[ "arxiv:2210.12478", "region:us" ]
2022-10-14T18:09:30+00:00
{}
2023-07-21T10:21:26+00:00
c3f6bd8acd77dc0d3f4e8df3961f2f82aedbb7d2
# Dataset Card for "AlRiyadh_Newspaper_Covid" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/AlRiyadh_Newspaper_Covid
[ "region:us" ]
2022-10-14T18:20:23+00:00
{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "string"}, {"name": "ID", "dtype": "string"}, {"name": "Category", "dtype": "string"}, {"name": "Source", "dtype": "string"}, {"name": "Title", "dtype": "string"}, {"name": "Subtitle", "dtype": "string"}, {"name": "Image", "dtype": "string"}, {"name": "Caption", "dtype": "string"}, {"name": "Text", "dtype": "string"}, {"name": "URL", "dtype": "string"}, {"name": "FullText", "dtype": "string"}, {"name": "FullTextCleaned", "dtype": "string"}, {"name": "FullTextWords", "dtype": "string"}, {"name": "WordsCounts", "dtype": "string"}, {"name": "Date", "dtype": "string"}, {"name": "Time", "dtype": "string"}, {"name": "Images", "dtype": "string"}, {"name": "Captions", "dtype": "string"}, {"name": "Terms", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 376546224, "num_examples": 24084}], "download_size": 164286254, "dataset_size": 376546224}}
2022-10-14T18:20:34+00:00
3ded52588975a96bbce202da4cdf605278e88274
This dataset is created by translating a part of the Stanford QA dataset. It contains 5k QA pairs from the original SQuad dataset translated to Hindi using the googletrans api.
aneesh-b/SQuAD_Hindi
[ "license:unknown", "region:us" ]
2022-10-14T18:20:33+00:00
{"license": "unknown"}
2022-10-16T05:18:33+00:00
c2c253732cadc497dd41ab0029779f7735060e52
# Dataset Card for "celeb-identities" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rick012/celeb-identities
[ "region:us" ]
2022-10-14T18:32:12+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Cristiano_Ronaldo", "1": "Jay_Z", "2": "Nicki_Minaj", "3": "Peter_Obi", "4": "Roger_Federer", "5": "Serena_Williams"}}}}], "splits": [{"name": "train", "num_bytes": 195536.0, "num_examples": 18}], "download_size": 193243, "dataset_size": 195536.0}}
2022-10-14T18:48:57+00:00
e56902acc46a67a5f18623dd73a38d6685672a3f
# Dataset Card for "BRAD" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/BRAD
[ "region:us" ]
2022-10-14T18:38:23+00:00
{"dataset_info": {"features": [{"name": "review_id", "dtype": "string"}, {"name": "book_id", "dtype": "string"}, {"name": "user_id", "dtype": "string"}, {"name": "review", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": 1, "1": 2, "2": 3, "3": 4, "4": 5}}}}], "splits": [{"name": "train", "num_bytes": 407433642, "num_examples": 510598}], "download_size": 211213150, "dataset_size": 407433642}}
2022-10-14T18:38:36+00:00
4b2ea7773f47fa46fef6408a38620fd08d19e055
# Dataset Card for OpenSLR Nepali Large ASR Cleaned ## Table of Contents - [Dataset Card for OpenSLR Nepali Large ASR Cleaned](#dataset-card-for-openslr-nepali-large-asr-cleaned) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [How to use?](#how-to-use) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) ## Dataset Description - **Homepage:** [Original OpenSLR Large Nepali ASR Dataset link](https://www.openslr.org/54/) - **Repository:** [Needs More Information] - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Sagar Sapkota](mailto:[email protected]) ### Dataset Summary This data set contains transcribed audio data for Nepali. The data set consists of flac files, and a TSV file. The file utt_spk_text.tsv contains a FileID, anonymized UserID and the transcription of audio in the file. The data set has been manually quality-checked, but there might still be errors. The audio files are sampled at a rate of 16KHz, and leading and trailing silences are trimmed using torchaudio's voice activity detection. For your reference, following was the function applied on each of the original openslr utterances. ```python import torchaudio SAMPLING_RATE = 16000 def process_audio_file(orig_path, new_path): """Read and process file in `orig_path` and save it to `new_path`""" waveform, sampling_rate = torchaudio.load(orig_path) if sampling_rate != SAMPLING_RATE: waveform = torchaudio.functional.resample(waveform, sampling_rate, SAMPLING_RATE) # trim end silences with Voice Activity Detection waveform = torchaudio.functional.vad(waveform, sample_rate=SAMPLING_RATE) torchaudio.save(new_path, waveform, sample_rate=SAMPLING_RATE) ``` ### How to use? There are two configurations for the data: one to download the original data and the other to download the preprocessed data as described above. 1. First, to download the original dataset with HuggingFace's [Dataset](https://huggingface.co/docs/datasets/) API: ```python from datasets import load_dataset dataset = load_dataset("spktsagar/openslr-nepali-asr-cleaned", name="original", split='train') ``` 2. To download the preprocessed dataset: ```python from datasets import load_dataset dataset = load_dataset("spktsagar/openslr-nepali-asr-cleaned", name="cleaned", split='train') ``` ### Supported Tasks and Leaderboards - `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition. ### Languages Nepali ## Dataset Structure ### Data Instances ```js { 'utterance_id': 'e1c4d414df', 'speaker_id': '09da0', 'utterance': { 'path': '/root/.cache/huggingface/datasets/downloads/extracted/e3cf9a618900289ecfd4a65356633d7438317f71c500cbed122960ab908e1e8a/cleaned/asr_nepali/data/e1/e1c4d414df.flac', 'array': array([-0.00192261, -0.00204468, -0.00158691, ..., 0.00323486, 0.00256348, 0.00262451], dtype=float32), 'sampling_rate': 16000 }, 'transcription': '२००५ मा बिते', 'num_frames': 42300 } ``` ### Data Fields - utterance_id: a string identifying the utterances - speaker_id: obfuscated unique id of the speaker whose utterances is in the current instance - utterance: - path: path to the utterance .flac file - array: numpy array of the utterance - sampling_rate: sample rate of the utterance - transcription: Nepali text which spoken in the utterance - num_frames: length of waveform array ### Data Splits The dataset is not split. The consumer should split it as per their requirements.
spktsagar/openslr-nepali-asr-cleaned
[ "license:cc-by-sa-4.0", "region:us" ]
2022-10-14T18:44:31+00:00
{"license": "cc-by-sa-4.0", "dataset_info": [{"config_name": "original", "features": [{"name": "utterance_id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "utterance", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}, {"name": "num_frames", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 40925646, "num_examples": 157905}], "download_size": 9340083067, "dataset_size": 40925646}, {"config_name": "cleaned", "features": [{"name": "utterance_id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "utterance", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}, {"name": "num_frames", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 40925646, "num_examples": 157905}], "download_size": 5978669282, "dataset_size": 40925646}]}
2022-10-23T17:15:15+00:00
da93d7ca5f81aaae854ade8bcaf8147a6d0a0cb5
from datasets import load_dataset dataset = load_dataset("Ariela/muneca-papel")
Ariela/muneca-papel
[ "license:unknown", "region:us" ]
2022-10-14T18:44:36+00:00
{"license": "unknown"}
2022-10-15T18:56:12+00:00
c4a17a7a5dbacb594c23e8ff0aafca7250121013
# Dataset Card for "OSACT4_hatespeech" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/OSACT4_hatespeech
[ "region:us" ]
2022-10-14T18:48:30+00:00
{"dataset_info": {"features": [{"name": "tweet", "dtype": "string"}, {"name": "offensive", "dtype": "string"}, {"name": "hate", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1417732, "num_examples": 6838}, {"name": "validation", "num_bytes": 204725, "num_examples": 999}], "download_size": 802812, "dataset_size": 1622457}}
2022-10-14T18:48:40+00:00
37c7175b2b6f07d4c749f7390ce9784e999aa1d5
# Dataset Card for "Sentiment_Lexicons" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/Sentiment_Lexicons
[ "region:us" ]
2022-10-14T18:56:58+00:00
{"dataset_info": {"features": [{"name": "Term", "dtype": "string"}, {"name": "bulkwalter", "dtype": "string"}, {"name": "sentiment_score", "dtype": "string"}, {"name": "positive_occurrence_count", "dtype": "string"}, {"name": "negative_occurrence_count", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2039703, "num_examples": 43308}], "download_size": 1068103, "dataset_size": 2039703}}
2022-10-14T18:57:04+00:00
e43dbe88d29779bc0440e214fc4de451d22392bc
## Córpus de Complexidade Textual para Estágios Escolares do Sistema Educacional Brasileiro O córpus inclui trechos de: livros-textos cuja lista completa é apresentada abaixo, notícias da Seção Para Seu Filho Ler (PSFL) do jornal Zero Hora que apresenta algumas notícias sobre o mesmo córpus do jornal do Zero Hora, mas escritas para crianças de 8 a 11 anos de idade , Exames do SAEB , Livros Digitais do Wikilivros em Português, Exames do Enem dos anos 2015, 2016 e 2017. Todo o material em português foi disponibilizado para avaliar a tarefa de complexidade textual (readability). Lista completa dos Livros Didáticos e suas fontes originais Esse corpus faz parte dos recursos de meu doutorado na área de Natural Language Processing, sendo realizado no Núcleo Interinstitucional de Linguística Computacional da USP de São Carlos. Esse trabalho foi orientado pela Profa. Sandra Maria Aluísio. http://nilc.icmc.usp.br @inproceedings{mgazzola19, title={Predição da Complexidade Textual de Recursos Educacionais Abertos em Português}, author={Murilo Gazzola, Sidney Evaldo Leal, Sandra Maria Aluisio}, booktitle={Proceedings of the Brazilian Symposium in Information and Human Language Technology}, year={2019} }
tiagoblima/nilc-school-books
[ "license:mit", "region:us" ]
2022-10-14T20:09:32+00:00
{"license": "mit", "dataset_info": {"features": [{"name": "text_id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "level", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1276559.048483246, "num_examples": 8321}, {"name": "train", "num_bytes": 4595060.28364021, "num_examples": 29952}, {"name": "validation", "num_bytes": 510715.6678765444, "num_examples": 3329}], "download_size": 3645953, "dataset_size": 6382335.0}}
2022-11-13T01:03:20+00:00
c2f48f68766a519e06a81cbc405d36dd4762d785
# Dataset Card for "Commonsense_Validation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/Commonsense_Validation
[ "region:us" ]
2022-10-14T20:52:13+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "first_sentence", "dtype": "string"}, {"name": "second_sentence", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": 0, "1": 1}}}}], "splits": [{"name": "train", "num_bytes": 1420233, "num_examples": 10000}, {"name": "validation", "num_bytes": 133986, "num_examples": 1000}], "download_size": 837486, "dataset_size": 1554219}}
2022-10-14T20:52:21+00:00
fed92167f9ae45fac1207017212a0c5bc6da02cd
# Dataset Card for "arastance" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/arastance
[ "region:us" ]
2022-10-14T21:14:14+00:00
{"dataset_info": {"features": [{"name": "filename", "dtype": "string"}, {"name": "claim", "dtype": "string"}, {"name": "claim_url", "dtype": "string"}, {"name": "article", "dtype": "string"}, {"name": "stance", "dtype": {"class_label": {"names": {"0": "Discuss", "1": "Disagree", "2": "Unrelated", "3": "Agree"}}}}, {"name": "article_title", "dtype": "string"}, {"name": "article_url", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 5611165, "num_examples": 646}, {"name": "train", "num_bytes": 29682402, "num_examples": 2848}, {"name": "validation", "num_bytes": 7080226, "num_examples": 569}], "download_size": 18033579, "dataset_size": 42373793}}
2022-10-14T21:14:25+00:00
f89f0029a9dd992ff5e43eadde0ac821406d9cbe
# Dataset Card for "TUNIZI" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/TUNIZI
[ "region:us" ]
2022-10-14T21:28:41+00:00
{"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "negative", "1": "positive"}}}}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 188084, "num_examples": 2997}], "download_size": 127565, "dataset_size": 188084}}
2022-10-14T21:28:45+00:00
d25e904472d19ac8cb639bff14cd59f31a90991b
# Dataset Card for "AQAD" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/AQAD
[ "region:us" ]
2022-10-14T21:35:33+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 23343014, "num_examples": 17911}], "download_size": 3581662, "dataset_size": 23343014}}
2022-10-14T21:35:38+00:00
e9674e9345c66631d1cd1f89ca1f00d8ae119c4f
# Dataset Card for "MArSum" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/MArSum
[ "region:us" ]
2022-10-14T21:42:30+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 3332778, "num_examples": 1981}], "download_size": 1743254, "dataset_size": 3332778}}
2022-10-14T21:42:35+00:00
d337fbd0337b6eda3282433826f037770ee94f69
# Dataset Card for "arabicReviews-ds-mini" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
omerist/arabicReviews-ds-mini
[ "region:us" ]
2022-10-14T22:25:48+00:00
{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "content_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 11505614.4, "num_examples": 3600}, {"name": "validation", "num_bytes": 1278401.6, "num_examples": 400}], "download_size": 6325726, "dataset_size": 12784016.0}}
2022-10-14T22:53:38+00:00
8068419f931b965fce6f7ee08a2ad07d7397d039
# Dataset Card for Dicionário Português It is a list of portuguese words with its inflections How to use it: ``` from datasets import load_dataset remote_dataset = load_dataset("VanessaSchenkel/pt-all-words") remote_dataset ```
VanessaSchenkel/pt-all-words
[ "task_categories:other", "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:pt", "region:us" ]
2022-10-14T23:52:20+00:00
{"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["pt"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["other", "text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "sbwce", "pretty_name": "Dicion\u00e1rio em Portugu\u00eas", "tags": []}
2022-10-15T00:59:29+00:00
d5c7c07268056a1b294d5815bdf012f92c327c1d
# Dataset Card for "arab-ds-mini" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
omerist/arab-ds-mini
[ "region:us" ]
2022-10-15T00:12:24+00:00
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "review", "dtype": "string"}, {"name": "review_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 87011869.13722204, "num_examples": 27116}, {"name": "validation", "num_bytes": 9668342.001417983, "num_examples": 3013}], "download_size": 49392988, "dataset_size": 96680211.13864002}}
2022-10-15T00:12:49+00:00
042361486f09031154629eff1e6059a609456f5a
randomwalksky/toy
[ "license:openrail", "region:us" ]
2022-10-15T02:30:33+00:00
{"license": "openrail"}
2022-10-15T02:30:33+00:00
5dd31b4c66365c698c3e2e92d86b0d11ec6598cc
zhenzi/imagenette
[ "region:us" ]
2022-10-15T02:39:41+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "config_name": "tests", "splits": [{"name": "train", "num_bytes": 459616258, "num_examples": 10500}], "download_size": 467583804, "dataset_size": 459616258}}
2022-10-19T02:37:03+00:00
da9a982d6ee573ec8c72df9e6e78a0d92fa56eb2
mrajbrahma/bodo-words
[ "license:cc-by-sa-4.0", "region:us" ]
2022-10-15T03:55:31+00:00
{"license": "cc-by-sa-4.0"}
2022-10-15T03:56:25+00:00
1974c2c4a875f5da8848ce9adf4821f825352382
CrispyShark/emoji_hairpin
[ "region:us" ]
2022-10-15T05:22:22+00:00
{}
2022-10-15T13:27:59+00:00
2eefce06256e84521bdff3e3a0df0248bd28cb27
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: husnu/bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3TQUAD2-finetuned_lr-2e-05_epochs-1 * Dataset: squad_v2 * Config: squad_v2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Jets](https://huggingface.co/Jets) for evaluating this model.
autoevaluate/autoeval-eval-squad_v2-squad_v2-ea058a-1765461442
[ "autotrain", "evaluation", "region:us" ]
2022-10-15T05:28:22+00:00
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["squad_v2"], "eval_info": {"task": "extractive_question_answering", "model": "husnu/bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3TQUAD2-finetuned_lr-2e-05_epochs-1", "metrics": [], "dataset_name": "squad_v2", "dataset_config": "squad_v2", "dataset_split": "validation", "col_mapping": {"context": "context", "question": "question", "answers-text": "answers.text", "answers-answer_start": "answers.answer_start"}}}
2022-10-15T05:31:40+00:00
54b7e788d34f58904c6a02941ca9270f5179db65
Shushant/PubmedQuestionAnsweringDataset
[ "license:other", "region:us" ]
2022-10-15T06:21:53+00:00
{"license": "other"}
2022-10-15T06:22:44+00:00
2ccad53104e75b5ec10f8abc1ac16f4c5f7ea384
# Dataset Card for uneune_image1 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary 今まで私が描いたイラスト100枚のデータセットです。 512×512にトリミングしてあります。 さっくりとstableDiffusionでの学習用に使えるデータセットが欲しかったので作りました。 This is a data set of 100 illustrations I have drawn so far. Cropped to 512x512. I wanted a dataset that can be used for learning with stableDiffusion, so I made it.
une/uneune_image1
[ "license:cc-by-4.0", "region:us" ]
2022-10-15T07:41:22+00:00
{"license": "cc-by-4.0"}
2022-10-15T08:07:58+00:00
7c729d53bec09f9400a0b4ea7fe19d286178d273
Harsit/xnli2.0_train_french
[ "language:fr", "region:us" ]
2022-10-15T08:17:22+00:00
{"language": ["fr"]}
2023-10-03T06:37:59+00:00
3c01cebd3e2d75dbf0987f1bc4c2b424923d733d
language: ["Urdu"]
Harsit/xnli2.0_train_urdu
[ "region:us" ]
2022-10-15T08:26:47+00:00
{}
2022-10-15T08:30:11+00:00
d11e6d5bb369ca02a87fd48611f640afa98c7962
CG80499/Inverse-scaling-test
[ "task_categories:multiple-choice", "task_categories:question-answering", "task_categories:zero-shot-classification", "license:bigscience-openrail-m", "region:us" ]
2022-10-15T10:47:40+00:00
{"license": "bigscience-openrail-m", "task_categories": ["multiple-choice", "question-answering", "zero-shot-classification"], "train-eval-index": [{"config": "inverse-scaling-test", "task": "text-generation", "task_id": "text_zero_shot_classification", "splits": {"eval_split": "train"}, "col_mapping": {"prompt": "text", "classes": "classes", "answer_index": "target"}}]}
2022-10-16T10:33:06+00:00
3a1b88eba215ea26ae74e6884e793bda02d2442f
siberspace/elisabeth-borne
[ "region:us" ]
2022-10-15T10:57:43+00:00
{}
2022-10-15T10:58:16+00:00
70cdab03f29a290ff14d21f9f8080286cd72dd86
siberspace/ricardo
[ "region:us" ]
2022-10-15T11:25:49+00:00
{}
2022-10-15T15:19:40+00:00
d563042b2a16501be4c7eeb7b71998db3a24adec
# Dataset Card for "turknews-mini" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
omerist/turknews-mini
[ "region:us" ]
2022-10-15T11:38:03+00:00
{"dataset_info": {"features": [{"name": "review", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "review_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 9064933.18105424, "num_examples": 3534}, {"name": "validation", "num_bytes": 1008069.8189457601, "num_examples": 393}], "download_size": 5732599, "dataset_size": 10073003.0}}
2022-10-15T11:38:10+00:00
c15baed0307c4fcc7b375258a182ea49ef2d4e8b
# Dataset Card for "balloon" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nielsr/balloon
[ "region:us" ]
2022-10-15T11:59:06+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 30808803.0, "num_examples": 61}, {"name": "validation", "num_bytes": 8076058.0, "num_examples": 13}], "download_size": 38814125, "dataset_size": 38884861.0}}
2022-10-15T12:02:05+00:00
f6b502b946c723ef3dd51efcbe15f1753cbad6a1
Fantomas78/Tamburro
[ "region:us" ]
2022-10-15T12:15:05+00:00
{}
2022-10-15T12:15:32+00:00
b2d765c28484069c071934ac7858b682c4e798e8
Michaelber123/mike
[ "license:artistic-2.0", "region:us" ]
2022-10-15T12:25:52+00:00
{"license": "artistic-2.0"}
2022-10-15T12:26:50+00:00
519c6f85f8dc6cbbf4878ebdb71dd39054c5357d
topia Sport topia Documentaire topia Song Of Topia topia
Sethyyann3572/glue-topia
[ "license:openrail", "region:us" ]
2022-10-15T12:31:25+00:00
{"license": "openrail"}
2022-10-15T12:32:42+00:00
a0bd554a17af724da30bd7b22b77022d9cb67991
# Dataset Card for "celebrity_in_movie_demo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
deman539/celebrity_in_movie_demo
[ "region:us" ]
2022-10-15T12:33:39+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "output"}}}}], "splits": [{"name": "train", "num_bytes": 2237547.0, "num_examples": 5}], "download_size": 1373409, "dataset_size": 2237547.0}}
2022-10-15T13:50:25+00:00
fcd42e249fed48dbd1d3b9b969528ef9298d3464
# Allison Parrish's Gutenberg Poetry Corpus This corpus was originally published under the CC0 license by [Allison Parrish](https://www.decontextualize.com/). Please visit Allison's fantastic [accompanying GitHub repository](https://github.com/aparrish/gutenberg-poetry-corpus) for usage inspiration as well as more information on how the data was mined, how to create your own version of the corpus, and examples of projects using it. This dataset contains 3,085,117 lines of poetry from hundreds of Project Gutenberg books. Each line has a corresponding `gutenberg_id` (1191 unique values) from project Gutenberg. ```python Dataset({ features: ['line', 'gutenberg_id'], num_rows: 3085117 }) ``` A row of data looks like this: ```python {'line': 'And retreated, baffled, beaten,', 'gutenberg_id': 19} ```
biglam/gutenberg-poetry-corpus
[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "language:en", "license:cc0-1.0", "poetry", "stylistics", "poems", "gutenberg", "region:us" ]
2022-10-15T12:42:22+00:00
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "Gutenberg Poetry Corpus", "tags": ["poetry", "stylistics", "poems", "gutenberg"]}
2022-10-18T09:53:52+00:00
e078a9a8bb873844031a65f6a0cc198ddcc1c6a5
## Dataset Summary Depth-of-Field(DoF) dataset is comprised of 1200 annotated images, binary annotated with(0) and without(1) bokeh effect, shallow or deep depth of field. It is a forked data set from the [Unsplash 25K](https://github.com/unsplash/datasets) data set. ## Dataset Description - **Repository:** [https://github.com/sniafas/photography-style-analysis](https://github.com/sniafas/photography-style-analysis) - **Paper:** [More Information Needed](https://www.researchgate.net/publication/355917312_Photography_Style_Analysis_using_Machine_Learning) ### Citation Information ``` @article{sniafas2021, title={DoF: An image dataset for depth of field classification}, author={Niafas, Stavros}, doi= {10.13140/RG.2.2.29880.62722}, url= {https://www.researchgate.net/publication/364356051_DoF_depth_of_field_datase} year={2021} } ``` Note that each DoF dataset has its own citation. Please see the source to get the correct citation for each contained dataset.
svnfs/depth-of-field
[ "task_categories:image-classification", "task_categories:image-segmentation", "annotations_creators:Stavros Niafas", "license:apache-2.0", "region:us" ]
2022-10-15T12:57:29+00:00
{"annotations_creators": ["Stavros Niafas"], "license": "apache-2.0", "task_categories": ["image-classification", "image-segmentation"], "sample_number": [1200], "class_number": [2], "image_size": ["(200,300,3)"], "source_dataset": ["unsplash"], "dataset_info": [{"config_name": "depth-of-field", "features": [{"name": "image", "dtype": "string"}, {"name": "class", "dtype": {"class_label": {"names": {"0": "bokeh", "1": "no-bokeh"}}}}]}, {"config_name": "default", "features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}], "splits": [{"name": "train", "num_bytes": 192150, "num_examples": 1200}], "download_size": 38792692, "dataset_size": 192150}]}
2022-11-13T23:33:39+00:00
0eea994c2f3958629e34934373d4b48ccd53c20e
SamHernandez/my-style
[ "license:afl-3.0", "region:us" ]
2022-10-15T13:15:28+00:00
{"license": "afl-3.0"}
2022-10-15T13:17:13+00:00
6c9e42b0a14c5b017947313f7098d871fb498b91
Mbermudez/mike
[ "license:openrail", "region:us" ]
2022-10-15T14:43:32+00:00
{"license": "openrail"}
2022-10-15T14:43:53+00:00
75321e3f022839c10b67ba9c08bb6efac8e17aca
# Dataset Card for "clothes_sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ghoumrassi/clothes_sample
[ "region:us" ]
2022-10-15T14:50:15+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20078406.0, "num_examples": 990}], "download_size": 0, "dataset_size": 20078406.0}}
2022-10-15T17:07:22+00:00
b302b4605dd1a192ee9999e260009eadd110fd7d
jaxmetaverse/wukong
[ "license:openrail", "region:us" ]
2022-10-15T14:51:53+00:00
{"license": "openrail"}
2022-10-16T01:07:16+00:00
540de892a1be8640934c938b4177e1de14ca3559
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: gpt2-xl * Dataset: inverse-scaling/NeQA * Config: inverse-scaling--NeQA * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@rololbot](https://huggingface.co/rololbot) for evaluating this model.
autoevaluate/autoeval-eval-inverse-scaling__NeQA-inverse-scaling__NeQA-4df82b-1769161494
[ "autotrain", "evaluation", "region:us" ]
2022-10-15T15:00:08+00:00
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["inverse-scaling/NeQA"], "eval_info": {"task": "text_zero_shot_classification", "model": "gpt2-xl", "metrics": [], "dataset_name": "inverse-scaling/NeQA", "dataset_config": "inverse-scaling--NeQA", "dataset_split": "train", "col_mapping": {"text": "prompt", "classes": "classes", "target": "answer_index"}}}
2022-10-15T15:03:51+00:00
4516f87f63964a28cae1eda838ebc267388703ea
blancoloureiro/fotos
[ "license:openrail", "region:us" ]
2022-10-15T16:13:41+00:00
{"license": "openrail"}
2022-10-15T16:14:17+00:00
c4650f60157ba9efe405db5e3ee243e1bc7d0713
alexinigoc/AlejandroTraining
[ "license:afl-3.0", "region:us" ]
2022-10-15T17:04:27+00:00
{"license": "afl-3.0"}
2022-10-15T20:02:59+00:00
65bb3029428ccce24e597b76531e6af13b389f19
alexinigoc/DatasetTraining
[ "license:afl-3.0", "region:us" ]
2022-10-15T20:03:44+00:00
{"license": "afl-3.0"}
2022-10-15T20:04:07+00:00
efce2cf816cf1abad0c590e9e737e5289e1f9394
# Dataset Card for "Iraqi_Dialect" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/Iraqi_Dialect
[ "region:us" ]
2022-10-15T20:16:56+00:00
{"dataset_info": {"features": [{"name": "No.", "dtype": "string"}, {"name": " Tex", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "False", "1": "IDK", "2": "N", "3": "True"}}}}], "splits": [{"name": "train", "num_bytes": 365478, "num_examples": 1672}], "download_size": 134999, "dataset_size": 365478}}
2022-10-15T20:17:07+00:00
991d85ba7b296eb212731f44c61e7cc3e1543700
oscarmutante/oscar
[ "license:unlicense", "region:us" ]
2022-10-15T20:27:15+00:00
{"license": "unlicense"}
2022-10-15T20:28:32+00:00
ee7fc57264b8056f8341f8215e5307a680a78f0a
# Dataset Card for "Sudanese_Dialect_Tweet" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/Sudanese_Dialect_Tweet
[ "region:us" ]
2022-10-15T20:39:50+00:00
{"dataset_info": {"features": [{"name": "Tweet", "dtype": "string"}, {"name": "Annotator 1", "dtype": "string"}, {"name": "Annotator 2", "dtype": "string"}, {"name": "Annotator 3", "dtype": "string"}, {"name": "Mode", "dtype": "string"}, {"name": "Date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 345088, "num_examples": 2123}], "download_size": 141675, "dataset_size": 345088}}
2022-10-15T20:40:01+00:00
8e2e32d0832c597e4ba2b1f252e59cec765a8c37
# Dataset Card for "Sudanese_Dialect_Tweet_Tele" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/Sudanese_Dialect_Tweet_Tele
[ "region:us" ]
2022-10-15T20:47:08+00:00
{"dataset_info": {"features": [{"name": "Tweet ID", "dtype": "string"}, {"name": "Tweet Text", "dtype": "string"}, {"name": "Date", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "NEGATIVE", "1": "POSITIVE", "2": "OBJECTIVE"}}}}], "splits": [{"name": "train", "num_bytes": 872272, "num_examples": 5346}], "download_size": 353611, "dataset_size": 872272}}
2022-10-15T20:47:19+00:00
62ccc10bb5eb840553d8a5bfb7635a8e2597172f
Romecr/testImages
[ "license:other", "region:us" ]
2022-10-15T20:48:10+00:00
{"license": "other"}
2022-12-29T21:55:23+00:00
1bf5e6c1c2761f004eb867b20ad5d8a173ace8da
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: Luciano/xlm-roberta-base-finetuned-lener-br * Dataset: lener_br * Config: lener_br * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Luciano](https://huggingface.co/Luciano) for evaluating this model.
autoevaluate/autoeval-eval-lener_br-lener_br-c4cf3f-1771961515
[ "autotrain", "evaluation", "region:us" ]
2022-10-15T20:52:11+00:00
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["lener_br"], "eval_info": {"task": "entity_extraction", "model": "Luciano/xlm-roberta-base-finetuned-lener-br", "metrics": [], "dataset_name": "lener_br", "dataset_config": "lener_br", "dataset_split": "test", "col_mapping": {"tokens": "tokens", "tags": "ner_tags"}}}
2022-10-15T20:53:08+00:00
8b2593845c16fa3deed61cb75900f4d472fc90f5
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: Luciano/xlm-roberta-large-finetuned-lener-br * Dataset: lener_br * Config: lener_br * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Luciano](https://huggingface.co/Luciano) for evaluating this model.
autoevaluate/autoeval-eval-lener_br-lener_br-c4cf3f-1771961516
[ "autotrain", "evaluation", "region:us" ]
2022-10-15T20:52:15+00:00
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["lener_br"], "eval_info": {"task": "entity_extraction", "model": "Luciano/xlm-roberta-large-finetuned-lener-br", "metrics": [], "dataset_name": "lener_br", "dataset_config": "lener_br", "dataset_split": "test", "col_mapping": {"tokens": "tokens", "tags": "ner_tags"}}}
2022-10-15T20:53:37+00:00
cd13b81d7a5f2a2097052eee7be3652d71c7e698
# Dataset Card for "cheques_sample_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shivi/cheques_sample_data
[ "region:us" ]
2022-10-15T21:25:47+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 7518544.0, "num_examples": 400}, {"name": "train", "num_bytes": 56481039.4, "num_examples": 2800}, {"name": "validation", "num_bytes": 15034990.0, "num_examples": 800}], "download_size": 58863727, "dataset_size": 79034573.4}}
2022-11-05T21:31:01+00:00
c14be6279b7e817d010409aaad46df114f0af3f5
# Dataset Card for "Satirical_Fake_News" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/Satirical_Fake_News
[ "region:us" ]
2022-10-15T21:37:45+00:00
{"dataset_info": {"features": [{"name": "Text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6131349, "num_examples": 3221}], "download_size": 3223892, "dataset_size": 6131349}}
2022-10-15T21:37:57+00:00
4be22018d039ee657dbeb7ff2e62fc9ae8eefdb6
# Dataset Card for "NArabizi" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/NArabizi
[ "region:us" ]
2022-10-15T21:47:54+00:00
{"dataset_info": {"features": [{"name": "ID", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "NEU", "1": "NEG", "2": "MIX", "3": "POS"}}}}], "splits": [{"name": "test", "num_bytes": 4034, "num_examples": 144}, {"name": "train", "num_bytes": 27839, "num_examples": 998}, {"name": "validation", "num_bytes": 3823, "num_examples": 137}], "download_size": 12217, "dataset_size": 35696}}
2022-10-15T21:48:18+00:00
619c18ba46019c28099c82a430e773e98471b5db
# Dataset Card for "ArSAS" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/ArSAS
[ "region:us" ]
2022-10-15T21:51:23+00:00
{"dataset_info": {"features": [{"name": "#Tweet_ID", "dtype": "string"}, {"name": "Tweet_text", "dtype": "string"}, {"name": "Topic", "dtype": "string"}, {"name": "Sentiment_label_confidence", "dtype": "string"}, {"name": "Speech_act_label", "dtype": "string"}, {"name": "Speech_act_label_confidence", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive", "3": "Mixed"}}}}], "splits": [{"name": "train", "num_bytes": 6147723, "num_examples": 19897}], "download_size": 2998319, "dataset_size": 6147723}}
2022-10-15T21:51:35+00:00
4da955d842c7487009e4db5c48f02da09a7d2057
Alfitauwu/Ejemplo
[ "region:us" ]
2022-10-15T22:35:39+00:00
{}
2022-10-15T22:39:15+00:00
2d787d3f9d73323bcafa04c7fd3edb791aff5589
Alfitauwu/Pruebitaaaxd
[ "license:openrail", "region:us" ]
2022-10-15T22:48:14+00:00
{"license": "openrail"}
2022-10-15T22:48:35+00:00
30f442e1ec9c22dd717f6eaa4ca9f3c146e7eea8
PonBonPepega/Aia
[ "license:other", "region:us" ]
2022-10-15T23:41:49+00:00
{"license": "other"}
2022-10-15T23:41:49+00:00
0281194d215c73170d30add87e5f16f9dec1d641
# Dataset Card for OLM September/October 2022 Common Crawl Cleaned and deduplicated pretraining dataset, created with the OLM repo [here](https://github.com/huggingface/olm-datasets) from 16% of the September/October 2022 Common Crawl snapshot. Note: `last_modified_timestamp` was parsed from whatever a website returned in it's `Last-Modified` header; there are likely a small number of outliers that are incorrect, so we recommend removing the outliers before doing statistics with `last_modified_timestamp`.
olm/olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295
[ "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "language:en", "pretraining", "language modelling", "common crawl", "web", "region:us" ]
2022-10-16T02:32:35+00:00
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": [], "task_categories": [], "task_ids": [], "pretty_name": "OLM September/October 2022 Common Crawl", "tags": ["pretraining", "language modelling", "common crawl", "web"]}
2022-11-04T17:14:25+00:00
3cba5a6b651b0ec3ad8ecef4efa9906f5b764a7f
seraldu/sergio_prueba
[ "license:bigscience-openrail-m", "region:us" ]
2022-10-16T07:02:00+00:00
{"license": "bigscience-openrail-m"}
2022-10-16T07:02:35+00:00
4f2dc2ad903dd9e297a4169a7fb54c4492af8a22
ohtaras/Kn
[ "license:unknown", "region:us" ]
2022-10-16T09:19:18+00:00
{"license": "unknown"}
2022-10-16T09:19:18+00:00
78f73995b25140373869016fcd809fbd710b4c9c
akashrai/dreambooth_image_training
[ "license:unknown", "region:us" ]
2022-10-16T09:42:32+00:00
{"license": "unknown"}
2022-10-16T09:43:49+00:00
02bbbd4aedd6c9809d7c4527bb5d9f3fb6fefbdc
siberspace/carton
[ "region:us" ]
2022-10-16T09:42:58+00:00
{}
2022-10-16T09:43:33+00:00
6b01b9b18c3b40be4aac81fac9952fd37ca2e4dc
poojaruhal/NLBSE-class-comment-classification
[ "license:cc-by-nc-sa-4.0", "region:us" ]
2022-10-16T10:24:39+00:00
{"license": "cc-by-nc-sa-4.0"}
2022-10-16T10:24:39+00:00