sha
stringlengths 40
40
| text
stringlengths 0
13.4M
| id
stringlengths 2
117
| tags
list | created_at
stringlengths 25
25
| metadata
stringlengths 2
31.7M
| last_modified
stringlengths 25
25
|
|---|---|---|---|---|---|---|
57a3f0faf7f3cb646b9d0418d78905666943b822
|
aframson/eng_to_frn
|
[
"license:mit",
"region:us"
] |
2023-02-13T15:19:05+00:00
|
{"license": "mit"}
|
2023-02-13T15:20:27+00:00
|
|
6046fc3e81cd0225fb9eb9ca4b51701f58910bf2
|
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Axel578/test
|
[
"region:us"
] |
2023-02-13T15:21:29+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1147232.0, "num_examples": 8}], "download_size": 1148603, "dataset_size": 1147232.0}}
|
2023-02-13T15:21:44+00:00
|
aa5e7b9770ea24a619e3956c27fba7f709435e7e
|
# Dataset Card for "sq-babi_nli_simple-negation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
niv-al/sq-babi_nli_simple-negation
|
[
"language:sq",
"region:us"
] |
2023-02-13T15:35:51+00:00
|
{"language": ["sq"], "dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "not-entailed", "1": "entailed"}}}}], "splits": [{"name": "train", "num_bytes": 215572, "num_examples": 1000}, {"name": "validation", "num_bytes": 32872, "num_examples": 144}, {"name": "test", "num_bytes": 32290, "num_examples": 144}], "download_size": 51452, "dataset_size": 280734}}
|
2023-02-18T19:59:51+00:00
|
8e9fc42d666467702a3fd4874d5c580709dce55f
|
# Dataset Card for "wikipedia.reorder.SOV"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/wikipedia.reorder.SOV
|
[
"region:us"
] |
2023-02-13T15:49:48+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4084815753, "num_examples": 2312333}], "download_size": 2013973114, "dataset_size": 4084815753}}
|
2023-02-13T15:52:08+00:00
|
1c3a1e5200f8577d485bdb6ba7f17b17ce5d0d79
| ERROR: type should be string, got "\nhttps://huggingface.co/spaces/huggingface/datasets-tagging\n\n\n# Dataset Card for Swiss Doc2doc Information Retrieval\n\n## Table of Contents\n- [Table of Contents](#table-of-contents)\n- [Dataset Description](#dataset-description)\n - [Dataset Summary](#dataset-summary)\n - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)\n - [Languages](#languages)\n- [Dataset Structure](#dataset-structure)\n - [Data Instances](#data-instances)\n - [Data Fields](#data-fields)\n - [Data Splits](#data-splits)\n- [Dataset Creation](#dataset-creation)\n - [Curation Rationale](#curation-rationale)\n - [Source Data](#source-data)\n - [Annotations](#annotations)\n - [Personal and Sensitive Information](#personal-and-sensitive-information)\n- [Considerations for Using the Data](#considerations-for-using-the-data)\n - [Social Impact of Dataset](#social-impact-of-dataset)\n - [Discussion of Biases](#discussion-of-biases)\n - [Other Known Limitations](#other-known-limitations)\n- [Additional Information](#additional-information)\n - [Dataset Curators](#dataset-curators)\n - [Licensing Information](#licensing-information)\n - [Citation Information](#citation-information)\n - [Contributions](#contributions)\n\n## Dataset Description\n\n- **Homepage:**\n- **Repository:**\n- **Paper:**\n- **Leaderboard:**\n- **Point of Contact:**\n\n### Dataset Summary\n\nSwiss Doc2doc Information Retrieval is a multilingual, diachronic dataset of 131K Swiss Federal Supreme Court (FSCS) cases annotated with law citations and ruling citations, posing a challenging text classification task. As unique label we are using decision_id of cited rulings and uuid of cited law articles, which can be found in the SwissCourtRulingCorpus. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.\n\n### Supported Tasks and Leaderboards\n\nSwiss Doc2Doc IR can be used as information retrieval task using documents in Swiss Legislation (https://huggingface.co/datasets/rcds/swiss_legislation) and Swiss Leading desicions (https://huggingface.co/datasets/rcds/swiss_leading_decisions).\n\n### Languages\n\nSwitzerland has four official languages with three languages (German 86K, French 30k and Italian 10k) being represented. The decisions are written by the judges and clerks in the language of the proceedings.\n\n## Dataset Structure\n\n### Data Instances\n\n```\n{\n \"decision_id\": \"000127ef-17d2-4ded-8621-c0c962c18fd5\",\n \"language\": de,\n \"year\": 2018,\n \"chamber\": \"CH_BGer_008\",\n \"region\": \"Federation\",\n \"origin_chamber\": 47,\n \"origin_court\": 8,\n \"origin_canton\": 151,\n \"law_area\": \"social_law\",\n \"law_sub_area\": ,\n \"laws\": \"['75488867-c001-4eb9-93b9-04264ea91f55', 'e6b06567-1236-4210-adb3-e11c26e497d5', '04bf6369-99cb-41fa-8aff-413679bc8c18', ...],\n \"cited_rulings\": \"['fe8a76b3-8b0f-4f27-a277-2d887140e7ab', '16fef75e-e8d5-4a51-8230-a9ca3676c8a9', '6d21b282-3b23-41dd-9350-6ba5386df9b1', '302fd9f3-e78a-4a9f-9f8d-cde51fcbdfe7']\",\n \"facts\": \"Sachverhalt: A. A._, geboren 1954, war ab November 2002 als Pflegehilfe im Altersheim C._ angestellt. Am 23. Dezember 2002 meldete sie sich erstmals unter Hinweis auf Depressionen ...\",\n \"considerations\": \"Erwägungen: 1. 1.1. Die Beschwerde kann wegen Rechtsverletzung gemäss Art. 95 und Art. 96 BGG erhoben werden. Das Bundesgericht wendet das ...\",\n \"rulings\": \"Demnach erkennt das Bundesgericht: 1. Die Beschwerde wird abgewiesen. 2. Die Gerichtskosten von Fr. 800.- werden der Beschwerdeführerin ...\",\n}\n```\n\n### Data Fields\n\n```\ndecision_id: (str) a unique identifier of the for the document\nlanguage: (str) one of (de, fr, it)\nyear: (int) the publication year\nchamber: (str) the chamber of the case\nregion: (str) the region of the case\norigin_chamber: (str) the chamber of the origin case\norigin_court: (str) the court of the origin case\norigin_canton: (str) the canton of the origin case\nlaw_area: (str) the law area of the case\nlaw_sub_area:(str) the law sub area of the case\nlaws: (str) a list of law ids\ncited rulings: (str) a list of cited rulings ids\nfacts: (str) the facts of the case\nconsiderations: (str) the considerations of the case\nrulings: (str) the rulings of the case\n```\n\n### Data Splits\n\nThe dataset was split date-stratisfied\n- Train: 2002-2015\n- Validation: 2016-2017\n- Test: 2018-2022\n\n| Language | Subset | Number of Documents (Training/Validation/Test) | \n|------------|------------|------------------------------------------------| \n| German | **de** | 86'832 (59'170 / 19'002 / 8'660) |\n| French | **fr** | 46'203 (30'513 / 10'816 / 4'874) |\n| Italian | **it** | 8'306 (5'673 / 1'855 / 778) |\n\n\n## Dataset Creation\n\n### Curation Rationale\n\nThe dataset was created by Stern et al. (2023).\n\n### Source Data\n\n#### Initial Data Collection and Normalization\n\nThe original data are available at the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML. \n\n#### Who are the source language producers?\n\nThe original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML. \n\n### Annotations\n\n#### Annotation process\n\nThe decisions have been annotated with the citation ids using html tags and parsers.\nFor more details on laws (rcds/swiss_legislation) and rulings (rcds/swiss_rulings).\n\n#### Who are the annotators?\n\nStern annotated the citations.\nMetadata is published by the Swiss Federal Supreme Court (https://www.bger.ch).\n\n### Personal and Sensitive Information\n\nThe dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.\n\n## Considerations for Using the Data\n\n### Social Impact of Dataset\n\n[More Information Needed]\n\n### Discussion of Biases\n\n[More Information Needed]\n\n### Other Known Limitations\n\n[More Information Needed]\n\n## Additional Information\n\n### Dataset Curators\n\n[More Information Needed]\n\n### Licensing Information\n\nWe release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)\n© Swiss Federal Supreme Court, 2002-2022\n\nThe copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.\nSource: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf\n\n### Citation Information\n\nPlease cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237)\n```\n@misc{rasiah2023scale,\n title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation}, \n author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus},\n year={2023},\n eprint={2306.09237},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```\n\n### Contributions\n\nThanks to [@Stern5497](https://github.com/stern5497) for adding this dataset." |
rcds/swiss_doc2doc_ir
|
[
"task_categories:text-classification",
"task_ids:entity-linking-classification",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:de",
"language:fr",
"language:it",
"license:cc-by-sa-4.0",
"arxiv:2306.09237",
"doi:10.57967/hf/0773",
"region:us"
] |
2023-02-13T15:51:17+00:00
|
{"annotations_creators": ["machine-generated"], "language_creators": ["expert-generated"], "language": ["de", "fr", "it"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["entity-linking-classification"], "pretty_name": "Swiss Doc2doc Information Retrieval", "tags": []}
|
2023-07-20T06:33:37+00:00
|
169a56f6c6f3e729077495162fd232fd1fe34064
|
# Dataset Card for "sq-babi_nli_basic-coreference"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
niv-al/sq-babi_nli_basic-coreference
|
[
"language:sq",
"region:us"
] |
2023-02-13T16:03:24+00:00
|
{"language": ["sq"], "dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "not-entailed", "1": "entailed"}}}}], "splits": [{"name": "train", "num_bytes": 209225, "num_examples": 1000}, {"name": "validation", "num_bytes": 29532, "num_examples": 144}, {"name": "test", "num_bytes": 30008, "num_examples": 144}], "download_size": 48437, "dataset_size": 268765}}
|
2023-02-18T19:59:59+00:00
|
cb77b7f808e325c98765cd71ac56a5e3b39e62e1
|
# Dataset Card for "SynthDog-RU_EN-prepared"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Nyaaneet/SynthDog-RU_EN-prepared
|
[
"region:us"
] |
2023-02-13T16:13:49+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 678666738.82, "num_examples": 2430}, {"name": "train", "num_bytes": 2677611433.18, "num_examples": 9570}], "download_size": 3352860165, "dataset_size": 3356278172.0}}
|
2023-02-13T16:18:53+00:00
|
3823deb623b66d25ba52bd1c8d7ea9fd5dfe411c
|
# Dataset Card for "wikipedia.reorder.VSO"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/wikipedia.reorder.VSO
|
[
"region:us"
] |
2023-02-13T16:31:17+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4084815753, "num_examples": 2312333}], "download_size": 0, "dataset_size": 4084815753}}
|
2023-02-13T17:25:29+00:00
|
6daa334ecbe500e6102d7867928b501f8e3dfab2
|
The full dataset card is visible in the JSON file named "original_cacapo_for_e2e_models-02_13_2023_19_30_07", which has been made with GEMs second datacard creation GUI.
|
GEM/CACAPO_E2E
|
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:nl",
"language:en",
"license:cc-by-4.0",
"Reverse Engineered",
"Dutch",
"English",
"RDF to sentence",
"For End To End",
"region:us"
] |
2023-02-13T16:53:11+00:00
|
{"language": ["nl", "en"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "Cacapo_E2E", "tags": ["Reverse Engineered", "Dutch", "English", "RDF to sentence", "For End To End"]}
|
2023-02-26T13:54:49+00:00
|
aced69a1c172a4d808be355944e47f76296e314d
|
# Token Impersonation Dataset
This dataset contains 375 erc-like token impersonation contracts used for phishing scams and 85,716 legitimate Etherscan verified contracts.
The dataset includes the following data attributes:
* contract_address: smart contract address on Ethereum
* contract_creation_tx: smart contract deployment tx
* malicious: boolean flag whether a contract is a token impersonation contract or not
* creation_bytecode: smart contract bytecode that includes both contract initialization and execution code
* contract_creator_etherscan_label: contract creator's Etherscan label
* decompiled_opcodes: bytecode decompiled into EVM opcodes
* contract_tag: contract's Etherscan wallet tag
* contract_creator_tag: contract creator's Etherscan wallet tag
|
forta/token-impersonation-dataset
|
[
"license:mit",
"region:us"
] |
2023-02-13T17:10:56+00:00
|
{"license": "mit"}
|
2023-02-14T17:48:17+00:00
|
e795837368d356ec8f604a8e74bc6f735ceddc36
|
lhoestq/digiface1m_720k
|
[
"license:other",
"region:us"
] |
2023-02-13T17:17:35+00:00
|
{"license": "other"}
|
2023-02-13T17:39:05+00:00
|
|
5fd275a7af72831b7479439ed3745a1d6275c599
|
# Dataset Card for "wikipedia.reorder.OVS"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/wikipedia.reorder.OVS
|
[
"region:us"
] |
2023-02-13T17:27:01+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4084815753, "num_examples": 2312333}], "download_size": 2006658115, "dataset_size": 4084815753}}
|
2023-02-13T17:29:48+00:00
|
77dd4ff669b2f51d12a19e5f673b85138efb0d67
|
lhoestq/digiface1m_500k
|
[
"license:other",
"region:us"
] |
2023-02-13T17:56:17+00:00
|
{"license": "other"}
|
2023-02-13T18:06:17+00:00
|
|
ef1302ccda42491a76ec4396d17e275a4bddcce8
|
Kagero/Azura
|
[
"region:us"
] |
2023-02-13T18:03:18+00:00
|
{}
|
2023-02-13T18:14:23+00:00
|
|
5bc64bb14c9e88f4eb8efe7483a131ef5969bd7f
|
# Urdu_DW-BBC-512
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper: https://doi.org/10.48550/arXiv.2310.02790**
- **Point of Contact: [email protected]**
### Dataset Summary
- Urdu Summarization Dataset containining 76,637 records of Article + Summary pairs scrapped from BBC Urdu and DW Urdu News Websites.
- Preprocessed Version: upto 512 tokens (~words); removed URLs, Pic Captions etc
### Supported Tasks and Leaderboards
Summarization: Extractive and Abstractive
- urT5 adapted from mT5 having monolingual vocabulary only; 40k tokens of Urdu.
- Fine-tuned version @ https://huggingface.co/mbshr/urt5-base-finetuned, ref to https://doi.org/10.48550/arXiv.2310.02790 for details.
- ROUGE-1 F Score: 40.03 combined, 46.35 BBC Urdu datapoints only and 36.91 DW Urdu datapoints only)
- BERTScore: 75.1 combined, 77.0 BBC Urdu datapoints only and 74.16 DW Urdu datapoints only
### Languages
Urdu.
### Data Fields
- url: URL of the article from where it was scrapped (BBC Urdu URLs in english topic text with number & DW Urdu with Urdu topic text)
dtype: {string}
- Summary: Short Summary of article written by author of article like highlights.
dtype: {string}
- Text: Complete Text of article which are intelligently trucated to 512 tokens.
dtype: {string}
### Citation Information
https://doi.org/10.48550/arXiv.2310.02790
|
mbshr/XSUMUrdu-DW_BBC
|
[
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:ur",
"license:cc-by-4.0",
"Urdu",
"Summarization",
"doi:10.57967/hf/1218",
"region:us"
] |
2023-02-13T18:13:37+00:00
|
{"language": ["ur"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["summarization"], "pretty_name": "Urdu Summarization (BBC and DW Urdu News)", "tags": ["Urdu", "Summarization"]}
|
2023-10-05T16:48:41+00:00
|
fbf80b5f607350dc2fd6f265120ead0eecb269c0
|
Sree1994/BabyLMdata
|
[
"region:us"
] |
2023-02-13T18:29:03+00:00
|
{}
|
2023-02-20T01:40:14+00:00
|
|
ff7612239fb22117d1262654636423c4d1957f43
|
# Dataset Card for "wikipedia.reorder.sov.fr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/wikipedia.reorder.sov.fr
|
[
"region:us"
] |
2023-02-13T18:34:17+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 886603410, "num_examples": 490371}], "download_size": 402994481, "dataset_size": 886603410}}
|
2023-02-13T18:34:47+00:00
|
6839dba8e4ef8c6e803d241260a3f5f0fc733b28
|
# Dataset Card for "wikipedia.reorder.vso.fr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/wikipedia.reorder.vso.fr
|
[
"region:us"
] |
2023-02-13T18:35:09+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 886603410, "num_examples": 490371}], "download_size": 404136391, "dataset_size": 886603410}}
|
2023-02-13T18:35:42+00:00
|
ad53c52340aa23d871f1c33b1299c81cc4b225f2
|
# Dataset Card for "wikipedia.reorder.ovs.fr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/wikipedia.reorder.ovs.fr
|
[
"region:us"
] |
2023-02-13T18:36:01+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 886603410, "num_examples": 490371}], "download_size": 403430121, "dataset_size": 886603410}}
|
2023-02-13T18:36:30+00:00
|
481af6df1e1ca169de35c0b14cef19e7c64a6616
|
nateraw/fuego-20230213-195827-b56398
|
[
"fuego",
"region:us"
] |
2023-02-13T18:58:27+00:00
|
{"tags": ["fuego"], "fuego": {"id": "20230213-195827-b56398", "status": "preparing", "script": "main.py", "requirements_file": "requirements.txt", "space_id": "nateraw/fuego-20230213-195827-b56398", "space_hardware": "cpu-basic", "github_repo_id": "pytorch/examples", "github_repo_branch": "main", "github_repo_sha": "e4e8da8467d55d28920dbd137261d82255f68c71"}}
|
2023-02-13T18:58:29+00:00
|
|
1ee6bae62acb40706c673c537ff81d3a99a70833
|
nateraw/fuego-20230213-200026-76714f
|
[
"fuego",
"region:us"
] |
2023-02-13T19:00:27+00:00
|
{"tags": ["fuego"], "fuego": {"id": "20230213-200026-76714f", "status": "done", "script": "main.py", "requirements_file": "requirements.txt", "space_id": "nateraw/fuego-20230213-200026-76714f", "space_hardware": "t4-small", "github_repo_id": "pytorch/examples", "github_repo_branch": "main", "github_repo_sha": "e4e8da8467d55d28920dbd137261d82255f68c71"}}
|
2023-02-13T19:04:10+00:00
|
|
c3b14cb19436657696e82230ee53ee0a45d92a38
|
merve/ner-flags
|
[
"license:apache-2.0",
"region:us"
] |
2023-02-13T19:04:18+00:00
|
{"license": "apache-2.0"}
|
2023-02-13T19:04:18+00:00
|
|
168b839c75c4aa89893ccf1140705c88316bbac7
|
radames/diffusers-gallery-data
|
[
"region:us"
] |
2023-02-13T19:06:39+00:00
|
{"duplicated_from": "huggingface-projects/diffusers-gallery-data"}
|
2023-02-13T19:06:40+00:00
|
|
fd50468b482f700d1ef282de29a8d790146f6f8a
|
cvcio/mediawatch-2302
|
[
"size_categories:10M<n<100M",
"language:el",
"license:gpl-3.0",
"doi:10.57967/hf/0369",
"region:us"
] |
2023-02-13T19:39:20+00:00
|
{"language": ["el"], "license": "gpl-3.0", "size_categories": ["10M<n<100M"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "createdAt", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "link", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 57902257227, "num_examples": 12016379}], "download_size": 28013796843, "dataset_size": 57902257227}}
|
2023-02-15T16:40:43+00:00
|
|
6b37ead34d1c8a5e2fc3dbc395e18bba25be1627
|
# Dataset Card for EusCrawl
## 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:** https://ixa.ehu.eus/euscrawl/
- **Repository:**
- **Paper:** https://arxiv.org/abs/2203.08111
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
EusCrawl (http://www.ixa.eus/euscrawl/) is a high-quality corpus for
Basque comprising 12.5 million documents and 423 million tokens,
totalling 2.1 GiB of uncompressed text. EusCrawl was built using
ad-hoc scrapers to extract text from 33 Basque websites with
high-quality content, resulting in cleaner text compared to general
purpose approaches.
### Supported Tasks and Leaderboards
EusCrawl is intended for pretraining models for language modeling or masked language modeling.
### Languages
Basque (eu)
## Dataset Structure
### Data Instances
```json
{
"id": 6,
"title": "Herriko enpresa handien eta txikien arteko topaketak egingo dituzte",
"text": "09:30ean hasiko da bilera eta aurkezpena egingo dute Tubacex, JEZ, Envases, Guardian eta Vidrala enpresek. Eskualdeko lantegi motorrekin beste enpresa txikiak eta ertainak egongo dira. Erakunde publikoaren helburua da euren artean ezagutzea eta elkarlana sustatzea.",
"source": "aiaraldea",
"license": "cc-by-sa 3.0",
"url": "https://aiaraldea.eus/laudio/1494603159768-herriko-enpresa-handien-eta-txikien-arteko-topaketak-egingo-dituzte",
}
```
### Data Fields
- "id": example id
- "title": article title
- "text": article text
- "source": article source
- "license": article license
- "url": article url
### Data Splits
The dataset only has one training split because it is intended for pretraining language models.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### 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?
[More Information Needed]
### 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
We do not claim ownership of any document in the corpus. All documents
we collected were published under a Creative Commons license in their
original website, and the specific variant can be found in the
"license" field of each document. Should you consider
that our data contains material that is owned by you and you would not
like to be reproduced here, please contact Aitor Soroa at
[email protected].
### Citation Information
If you use our corpus or models for academic research, please cite the paper in question:
```bibtex
@misc{artetxe2022euscrawl,
title={Does corpus quality really matter for low-resource languages?},
author={Mikel Artetxe, Itziar Aldabe, Rodrigo Agerri,
Olatz Perez-de-Viñaspre, Aitor Soroa},
year={2022},
eprint={2203.08111},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@juletx](https://github.com/juletx) for adding this dataset.
|
HiTZ/euscrawl
|
[
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:eu",
"license:cc",
"high-quality",
"scraping",
"arxiv:2203.08111",
"region:us"
] |
2023-02-13T20:13:26+00:00
|
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["eu"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "EusCrawl", "tags": ["high-quality", "scraping"], "dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2314407002, "num_examples": 1724544}], "download_size": 728281801, "dataset_size": 2314407002}}
|
2023-02-14T19:00:22+00:00
|
a0f5cdbba5e38624ab9cfe9c37580f35623830e9
|
# Dataset Card for "large_spanish_corpus_ds_tokenized_and_gropuped"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mrm8488/large_spanish_corpus_ds_tokenized_and_gropuped
|
[
"region:us"
] |
2023-02-13T20:19:48+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 16824296700, "num_examples": 4103487}, {"name": "test", "num_bytes": 885489300, "num_examples": 215973}], "download_size": 8311975924, "dataset_size": 17709786000}}
|
2023-02-13T20:28:55+00:00
|
68efc8a04d4a3613c330e84f4d7a267a67f4bb70
|
UltraMarkoBR/fuego-20230213-213009-e3fd82
|
[
"fuego",
"region:us"
] |
2023-02-13T20:30:10+00:00
|
{"tags": ["fuego"], "fuego": {"id": "20230213-213009-e3fd82", "status": "running", "script": "run_glue.py", "requirements_file": "requirements.txt", "space_id": "UltraMarkoBR/fuego-20230213-213009-e3fd82", "space_hardware": "cpu-basic", "github_repo_id": "huggingface/transformers", "github_repo_branch": "main", "github_repo_sha": "cbecf121cdeff6fb7193471cf759f9d734e37ea9"}}
|
2023-02-13T20:33:12+00:00
|
|
f3d098572a200c0c0cde473e6fab82bd6433b41e
|
The full dataset information can be found in the JSON file named "augmented_cacapo_for_e2e-02_13_2023_22_17_09", which was created with the interactive dataset creator provided by Huggingface.
|
GEM/Augmented_CACAPO_for_E2E
|
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:nl",
"language:en",
"license:cc-by-4.0",
"Dutch",
"English",
"Reverse Engineered",
"RDF To Sentence",
"Augmented Training set",
"region:us"
] |
2023-02-13T20:57:22+00:00
|
{"language": ["nl", "en"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "CACAPO_with_augmented_train", "tags": ["Dutch", "English", "Reverse Engineered", "RDF To Sentence", "Augmented Training set"]}
|
2023-02-26T13:58:32+00:00
|
216a271d3873169ad7975b88db9706f9943fc3f3
|
# Dataset Card for "VQAv2_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_train
|
[
"region:us"
] |
2023-02-13T20:57:35+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 71983155971.0, "num_examples": 443757}], "download_size": 13852702465, "dataset_size": 71983155971.0}}
|
2023-04-26T00:37:08+00:00
|
5fc2db512ac193372421081e4ef7a59a944c929d
|
Dataset information can be found in the JSON file named "elongated_training_cacapo_updated-02_22_2023_23_23_20.json", which was created with the interactive dataset creator provided by Huggingface.
|
GEM/Elongated_CACAPO_for_E2E
|
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:nl",
"language:en",
"license:cc-by-4.0",
"E2E",
"Dutch",
"English",
"Reverse Engineered",
"RDF to Sentence",
"region:us"
] |
2023-02-13T21:40:35+00:00
|
{"language": ["nl", "en"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "CACAPO_elongated_training", "tags": ["E2E", "Dutch", "English", "Reverse Engineered", "RDF to Sentence"]}
|
2023-02-26T13:29:43+00:00
|
5fbf0291e59ce23c406e8025f2fbc7876e112ba2
|
# AI2THOR-Hab
AI2THOR scene datasets include iTHOR, RoboTHOR, ProcTHOR-10K, ArchitecTHOR. Many of the assets of the interactable objects are shared across these datasets.
* **iTHOR**: includes 120 single room scenes, 30 scenes for each bedroom, bathroom, kitchen, and living room. In our extracted dataset, there are additional 30 foyers scenes.
* **RoboTHOR**: includes 89 apartments in maze style, where the rooms are subdivided by wall panels. Same of the scenes share the same room layout, but with different objects and object placements.
* **ArchitecTHOR**: includes 10 multiple-room sized houses, used for evaluation of the ProcTHOR.
* **ProcTHOR** : include 12000 procedurally generated multiple-room sized houses. The rooms in the house have 4 room types same as the iTHOR, and the objects assets are all form the iTHOR assets.
<table style="table-layout: fixed;">
<tr>
<td style="text-align: center; vertical-align: middle; width: 25%"> <img src="https://i.imgur.com/Y3MWIRO.png" alt="ithor" width="70%" style="display: block; margin-left: auto; margin-right: auto;"></td>
<td style="text-align: center; vertical-align: middle; width: 25%"> <img src="https://i.imgur.com/e8NKzc2.png" alt="robothor" width="100%" style="display: block; margin-left: auto; margin-right: auto;"></td>
<td style="text-align: center; vertical-align: middle; width: 25%"> <img src="https://i.imgur.com/yiYofx3.png" alt="architecthor" width="100%" style="display: block; margin-left: auto; margin-right: auto;"></td>
<td style="text-align: center; vertical-align: middle; width: 25%"> <img src="https://i.imgur.com/X6loIpw.png" alt="procthor" width="80%" style="display: block; margin-left: auto; margin-right: auto;"></td>
</tr>
<tr>
<td style="text-align: center; vertical-align: middle; width: 25%">iTHOR</td>
<td style="text-align: center; vertical-align: middle; width: 25%">RoboTHOR</td>
<td style="text-align: center; vertical-align: middle; width: 25%">ArchitecTHOR</td>
<td style="text-align: center; vertical-align: middle; width: 25%">ProcTHOR</td>
</td>
</tr>
</table>
## Dataset Structure
Following is the dataset structure for the AI2Thor Habitat Scene Dataset:
```bash
ai2thorhab
├── assets
│ ├── objects
│ └── stages
│ ├── ArchitecTHOR
│ ├── iTHOR
│ ├── ProcTHOR
│ └── RoboTHOR
└── configs
├── objects
├── scenes
│ ├── ArchitecTHOR
│ ├── iTHOR
│ ├── ProcTHOR
│ └── RoboTHOR
└── stages
├── ArchitecTHOR
├── iTHOR
├── ProcTHOR
└── RoboTHOR
```
Data documentation:
1. [ai2thor.scene_dataset_config.json ](https://aihabitat.org/docs/habitat-sim/attributesJSON.html#scenedatasetattributes): This SceneDataset config file enumerates and aggregates the various assets and metadata necessary to fully describe a set of stages, objects, and/or scenes. It hold relative filepaths to all linked assets and additional configs.
2. [\<objectname\>.object_config.json](https://aihabitat.org/docs/habitat-sim/attributesJSON.html#objectattributes): Object config files with descriptive information for instancing rigid objects into Habitat.
3. [\<scenename\>.scene_instance.json](https://aihabitat.org/docs/habitat-sim/attributesJSON.html#sceneinstanceattributes): A scene is a single 3D world composed of a static stage and a variable number of objects. This folder includes the config files for each scene that pulls together other assets registered in the SceneDataset to form a cohesive 3D world for simulation.
4. `object_semantic_id_mapping.json`: Semantic Scene Descriptor (SSD) with a mapping from object names to unique IDs.
5. `objects/*.glb`: Movable object assets.
6. `stages/*.glb`: Static stage scene asset.
## Load AI2Thor Habitat Scene Dataset in Habitat-Sim
Load the AI2Thor Habitat Scene Dataset into [Habitat-Sim](https://github.com/facebookresearch/habitat-sim).
Run the following command to load the AI2Thor scene into Habitat-Sim:
```bash
habitat-viewer --dataset /path/to/ai2thorhab/ai2thor.scene_dataset_config.json -- name-of-the-scene
# for example:
# habitat-viewer --dataset /path/to/ai2thorhab/ai2thor.scene_dataset_config.json -- FloorPlan1_physics
```
|
hssd/ai2thor-hab
|
[
"language:en",
"3D scenes",
"Embodied AI",
"region:us"
] |
2023-02-13T21:44:18+00:00
|
{"language": ["en"], "pretty_name": "AI2THOR-Hab", "tags": ["3D scenes", "Embodied AI"]}
|
2024-01-31T07:54:37+00:00
|
d400ee8cd114eaa09b1dbf3e44c2f248b2b1b5ec
|
# DreamBank - Dreams
The dataset is a collection of ~20 k textual reports of dreams, originally scraped from the [DreamBank](https://www.dreambank.net/) databased by
[`mattbierner`](https://github.com/mattbierner/DreamScrape). The DreamBank reports are divided into `series`,
which are collections of individuals or research projects/groups that have gathered the dreams.
## Content
The dataset revolves around three main features:
- `dreams`: the content of each dream report.
- `series`: the series to which a report belongs
- `description`: a brief description of the `series`
- `gender`: the gender of the individual(s) in the `series`
- `year`: the time window of the recordings
## Series distribution
The following is a summary of (alphabetically ordered) DreamBank's series together with their total amount of dream reports.
- alta: 422
- angie: 48
- arlie: 212
- b: 3114
- b-baseline: 250
- b2: 1138
- bay_area_girls_456: 234
- bay_area_girls_789: 154
- bea1: 223
- bea2: 63
- blind-f: 238
- blind-m: 143
- bosnak: 53
- chris: 100
- chuck: 75
- dahlia: 24
- david: 166
- dorothea: 899
- ed: 143
- edna: 19
- elizabeth: 1707
- emma: 1221
- emmas_husband: 72
- esther: 110
- hall_female: 681
- jasmine1: 39
- jasmine2: 269
- jasmine3: 259
- jasmine4: 94
- jeff: 87
- joan: 42
- kenneth: 2021
- lawrence: 206
- mack: 38
- madeline1-hs: 98
- madeline2-dorms: 186
- madeline3-offcampus: 348
- madeline4-postgrad: 294
- mark: 23
- melissa: 89
- melora: 211
- melvin: 128
- merri: 315
- miami-home: 171
- miami-lab: 274
- midwest_teens-f: 111
- midwest_teens-m: 83
- nancy: 44
- natural_scientist: 234
- norman: 1235
- norms-f: 490
- norms-m: 491
- pegasus: 1093
- peru-f: 381
- peru-m: 384
- phil1: 106
- phil2: 220
- phil3: 180
- physiologist: 86
- ringo: 16
- samantha: 63
- seventh_graders: 69
- toby: 33
- tom: 27
- ucsc_women: 81
- vickie: 35
- vietnam_vet: 98
- wedding: 65
- west_coast_teens: 89
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
DReAMy-lib/DreamBank-dreams-en
|
[
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-02-13T22:20:25+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "dataset_info": {"features": [{"name": "series", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "dreams", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "year", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21526822, "num_examples": 22415}], "download_size": 11984242, "dataset_size": 21526822}}
|
2023-02-13T22:51:35+00:00
|
677b8bc487983cec6310a26ca1a26e271f184b8a
|
# `coco_superpixels_edge_wt_only_coord_10`
### Dataset Summary
| Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric |
|---|---|---|---|---|---|
| COCO-SP | Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 |
| Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter
|---|---:|---:|---:|:---:|---:|---:|---:|---:|
| COCO-SP | 123,286 | 58,793,216 | 476.88 | 5.65 | 332,091,902 | 2,693.67 | 10.66±0.55 | 27.39±2.14 |
## Additional Information
### Dataset Curators
* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
### Citation Information
```
@article{dwivedi2022LRGB,
title={Long Range Graph Benchmark},
author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
journal={arXiv:2206.08164},
year={2022}
}
```
|
LRGB/coco_superpixels_edge_wt_only_coord_10
|
[
"task_categories:graph-ml",
"size_categories:1M<n<10M",
"license:cc-by-4.0",
"lrgb",
"region:us"
] |
2023-02-13T22:25:08+00:00
|
{"license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"], "dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "edge_index", "dtype": "int64"}, {"name": "edge_attr", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3625184, "num_examples": 113287}, {"name": "val", "num_bytes": 160032, "num_examples": 5001}, {"name": "test", "num_bytes": 160032, "num_examples": 5001}], "download_size": 3250471, "dataset_size": 3945248}}
|
2023-04-14T12:27:36+00:00
|
efaf67c09d87488a0483747396d3486ce5a51620
|
# Dataset Card for Flan V2
## Dataset Description
- **Homepage:** https://ai.googleblog.com/2023/02/the-flan-collection-advancing-open.html
- **Repository:** https://github.com/google-research/FLAN/tree/main/flan/v2
- **Paper:** https://arxiv.org/abs/2301.13688
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a processed version of the Flan V2 dataset.
I'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.
The authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.
## Setup Instructions
Here are the steps I followed to get everything working:
### Build AESLC and WinoGrande datasets manually
The repos for these datasets were updated recently and checksums need to be recomputed in TFDS
- `tfds build --dataset aeslc --register_checksums`
- `tfds build --dataset winogrande --register_checksums`
### Fix dataset versions
I've opened a PR [here](https://github.com/google-research/FLAN/pull/20) to get these updated in the upstream FLAN repo, until that gets merged in run these locally to fix any dataset version errors.
- `sed -i 's/glue\/cola:1.0.0/glue\/cola:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/gem\/common_gen:1.0.0/gem\/common_gen:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/gem\/dart:1.0.0/gem\/dart:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/gem\/e2e_nlg:1.0.0/gem\/e2e_nlg:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/gem\/web_nlg_en:1.0.0/gem\/web_nlg_en:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/gem\/common_gen:1.0.0/gem\/common_gen:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/paws_wiki:1.0.0/paws_wiki:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/mrpc:1.0.0/glue\/mrpc:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/qqp:1.0.0/glue\/qqp:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/sst2:1.0.0/glue\/sst2:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/mnli:1.0.0/glue\/mnli:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/qnli:1.0.0/glue\/qnli:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/wnli:1.0.0/glue\/wnli:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/stsb:1.0.0/glue\/stsb:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/hellaswag:0.0.1/hellaswag:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/xsum:1.0.0/huggingface:xsum/g' flan/v2/task_configs_v1.py`
### Download and install manual steps
Save these to `~/tensorflow_datasets/downloads/manual`.
- [CzEng (deduped ignoring sections)](https://ufal.mff.cuni.cz/czeng/czeng16pre)
- [Newsroom (extract)](https://lil.nlp.cornell.edu/newsroom/download/index.html)
- [Yandex 1M Corpus](https://translate.yandex.ru/corpus?lang=en)
- [Story Cloze (extract and rename to cloze_test_test__spring2016.csv and cloze_test_val__spring2016.csv)](https://cs.rochester.edu/nlp/)
### Finally, export tasks
```python
import tensorflow as tf
tf.config.set_visible_devices([], 'GPU')
from flan.v2 import constants
from flan.v2 import constants_t0
from flan.v2 import mixtures_utils
from flan.v2 import mixtures
from flan.v2 import tasks
import json
import t5
import seqio
import itertools
from multiprocessing import Pool
seqio.add_global_cache_dirs(constants.CACHE_DIRS)
seqio.set_global_cache_dirs(constants.CACHE_DIRS)
vocab = t5.data.get_default_vocabulary()
def prepare_task(split, shots, opt, task):
dataset = seqio.get_mixture_or_task(f'palmflan_{task}_{shots}_{opt}').get_dataset(
split=split,
num_epochs=1,
sequence_length={'inputs':4096,'targets':4096}
)
print("starting", task, shots, opt, split)
with open(f'./data/{task}_{shots}_{opt}_{split}.jsonl', 'w') as f:
for ex in dataset.as_numpy_iterator():
f.write(
json.dumps({
"inputs": vocab.decode(ex["inputs"]),
"targets": vocab.decode(ex["targets"]),
"task": task,
}))
f.write("\n")
print("done with", task, shots, opt, split)
# prepare_task("train", "zs", "noopt", "dialog") # use this to export a single task
tasks = itertools.product(["train"], ["zs", "fs"], ["opt", "noopt"], ["dialog", "t0", "niv2", "flan", "cot"])
with Pool(5) as p:
p.starmap(prepare_task, [(task[0], task[1], task[2], task[3]) for task in tasks])
```
## Dataset Structure
### Data Instances
Flan 2021 (flan), P3 (t0), Super-Natural Instructions (niv2), Chain-of-thought (cot), and Dialog (dialog)
### Data Fields
Instruction data comes in a few formats:
- Few Shot (fs)
- Zero Shot (zs)
- Options Provided in context (i.e. multiple choice pick one) (opt)
- No Options Provided (noopt)
Each combination of the above tasks + formats are saved as a JSONL with following schema `{"input": ..., "target": ..., "task": ...}`
### Data Splits
Everything is saved as a train split
Note: FLAN-fs-opt-train is too big to be uploaded even when gzipped, so its split into 45gb chunks. To combine and recover, run `cat flan_fs_opt_train_*.gz | gunzip -c > flan_fs_opt_train.jsonl`
|
SirNeural/flan_v2
|
[
"license:apache-2.0",
"flan",
"flan 2022",
"flan v2",
"arxiv:2301.13688",
"region:us"
] |
2023-02-13T23:02:33+00:00
|
{"license": "apache-2.0", "pretty_name": "Flan v2", "tags": ["flan", "flan 2022", "flan v2"]}
|
2023-02-24T19:05:00+00:00
|
864c12bdadf998b062fad4c4eae7e1bc0e85549a
|
# `coco_superpixels_edge_wt_only_coord_30`
### Dataset Summary
| Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric |
|---|---|---|---|---|---|
| COCO-SP | Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 |
| Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter
|---|---:|---:|---:|:---:|---:|---:|---:|---:|
| COCO-SP | 123,286 | 58,793,216 | 476.88 | 5.65 | 332,091,902 | 2,693.67 | 10.66±0.55 | 27.39±2.14 |
## Additional Information
### Dataset Curators
* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
### Citation Information
```
@article{dwivedi2022LRGB,
title={Long Range Graph Benchmark},
author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
journal={arXiv:2206.08164},
year={2022}
}
```
|
LRGB/coco_superpixels_edge_wt_only_coord_30
|
[
"task_categories:graph-ml",
"size_categories:1M<n<10M",
"license:cc-by-4.0",
"lrgb",
"region:us"
] |
2023-02-13T23:02:49+00:00
|
{"license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"], "dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "edge_index", "dtype": "int64"}, {"name": "edge_attr", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3625184, "num_examples": 113287}, {"name": "val", "num_bytes": 160032, "num_examples": 5001}, {"name": "test", "num_bytes": 160032, "num_examples": 5001}], "download_size": 3256545, "dataset_size": 3945248}}
|
2023-04-14T15:42:12+00:00
|
0fb616d31727257e792216514ee50aae0558ad87
|
aru2016/nva-Sylveon
|
[
"license:unknown",
"region:us"
] |
2023-02-14T01:50:11+00:00
|
{"license": "unknown"}
|
2023-02-15T03:06:24+00:00
|
|
d11c6084dd2bb5575f9ce224cbcc435a687e67bf
|
More info: https://github.com/songlab-cal/gpn
|
songlab/genomes-brassicales-balanced-v1
|
[
"license:mit",
"dna",
"biology",
"genomics",
"region:us"
] |
2023-02-14T02:48:37+00:00
|
{"license": "mit", "tags": ["dna", "biology", "genomics"]}
|
2024-01-27T18:18:26+00:00
|
c98a19b31a3cd0dd47b3fe851dbfcd5a19f276bc
|
israelfama/semeval2007_task_14
|
[
"license:unknown",
"region:us"
] |
2023-02-14T03:21:54+00:00
|
{"license": "unknown"}
|
2023-02-14T03:30:32+00:00
|
|
a5e5f362b9a3cf50b5f08bf49f2c39cf05ad050b
|
# Dataset Card for "poetry-detailed-analysis"
This dataset contains scraped per-stanza analyses. Poems in this dataset also appear in [isaacrehg/poetry-summary](https://huggingface.co/datasets/isaacrehg/poetry-summary).
Each row contains the following data:
- _id: ID of the poem (for reference in [isaacrehg/poetry-summary](https://huggingface.co/datasets/isaacrehg/poetry-summary))
- title: The title of the poem
- author: The poem's author
- url: URL scraped from analysis content where the full poem can be found (may be missing or incorrect)
- stanza_index: index for the section of the poem that this record pertains to
- stanza_header: natural language description of the pertinant stanza (ie. "Stanza One" or "Lines 10-16")
- content: poem content for this stanza (may be missing or partially ommited, ie. "Curling its coral feet, (…) Men long dead.")
- analysis: analysis of this stanza
|
isaacrehg/poetry-detailed-analysis
|
[
"region:us"
] |
2023-02-14T04:14:12+00:00
|
{"dataset_info": {"features": [{"name": "_id", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "stanza_index", "dtype": "int64"}, {"name": "stanza_header", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "analysis", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18347594, "num_examples": 14507}], "download_size": 9751592, "dataset_size": 18347594}}
|
2023-02-22T05:17:40+00:00
|
385dde338599c65c773edbe19740abe3ba266750
|
# Dataset Card for "poetry-summary"
This dataset contains scraped poem summarizations. Poems in this dataset also appear in [isaacrehg/poetry-detailed-analysis](https://huggingface.co/datasets/isaacrehg/poetry-detailed-analysis).
Each row contains the following data:
- _id: ID of this poem (for reference in [isaacrehg/poetry-detailed-analysis](https://huggingface.co/datasets/isaacrehg/poetry-detailed-analysis))
- title: The title of the poem
- author: The poem's author
- summary: The crawled summarization for this poem
|
isaacrehg/poetry-summary
|
[
"region:us"
] |
2023-02-14T04:14:31+00:00
|
{"dataset_info": {"features": [{"name": "_id", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1881481, "num_examples": 3420}], "download_size": 1080975, "dataset_size": 1881481}}
|
2023-02-22T05:07:40+00:00
|
eb42f25301a20f0dda68d9b4f647731296ee2bee
|
nc33/multispan_singlespan_qa
|
[
"license:mit",
"region:us"
] |
2023-02-14T05:12:20+00:00
|
{"license": "mit"}
|
2023-02-14T05:13:55+00:00
|
|
14270bcfcf2b7854cddce4395c0b38a199edef05
|
# Dataset Card for "simplewiki2023-all-minilm-l6-v2-embedding"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lsb/simplewiki2023-all-minilm-l6-v2-embedding
|
[
"region:us"
] |
2023-02-14T06:06:33+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "embedding", "sequence": "float32"}, {"name": "id_int", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 624653676, "num_examples": 225332}], "download_size": 636699593, "dataset_size": 624653676}}
|
2023-02-14T06:25:28+00:00
|
4238d44b57034e8bf5e46b310ea099d3a1eb451a
|
# Dataset Card for "openwebtext-all-minilm-l6-v2-embedding"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lsb/openwebtext-all-minilm-l6-v2-embedding
|
[
"region:us"
] |
2023-02-14T06:32:39+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "embedding", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 52110695948, "num_examples": 8013769}], "download_size": 41532022145, "dataset_size": 52110695948}}
|
2023-02-14T07:28:40+00:00
|
ec12424c73d1b924a1fbba1713abdaeceb6934d6
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
KG dataset created by using spaCy PoS and Dependency parser.
### Supported Tasks and Leaderboards
Can be leveraged for token classification for detection of knowledge graph entities and relations.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
Important fields for the token classification task are
* tokens - tokenized text
* tags - Tags for each token
{'SRC' - Source, 'REL' - Relation, 'TGT' - Target, 'O' - Others}
### Data Splits
One data file for around 15k records
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### 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?
[More Information Needed]
### 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
[More Information Needed]
|
vishnun/NLP-KnowledgeGraph
|
[
"task_categories:token-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc0-1.0",
"ML",
"NLP",
"region:us"
] |
2023-02-14T06:45:57+00:00
|
{"language": ["en"], "license": "cc0-1.0", "size_categories": ["10K<n<100K"], "task_categories": ["token-classification"], "tags": ["ML", "NLP"]}
|
2023-02-15T04:24:58+00:00
|
d7ed694a318a5500c0a64abca77f7e0ef8d62e03
|
Xieyiyiyi/cceee
|
[
"license:bsl-1.0",
"region:us"
] |
2023-02-14T07:48:03+00:00
|
{"license": "bsl-1.0"}
|
2023-05-29T05:56:37+00:00
|
|
810b10f9ed25a0c828807f74ede579c772decd0e
|
# Dataset Card for "adv-ele"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joheras/adv-ele
|
[
"region:us"
] |
2023-02-14T08:08:34+00:00
|
{"dataset_info": {"features": [{"name": "ADV", "dtype": "string"}, {"name": "ELE", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 430918.56140350876, "num_examples": 1732}, {"name": "test", "num_bytes": 107978.43859649122, "num_examples": 434}], "download_size": 299002, "dataset_size": 538897.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}
|
2024-02-15T15:51:29+00:00
|
6c48d4432c7ee44a27de05e68ab317f63c8e3c98
|
Joe02/men_teru_refs
|
[
"license:other",
"region:us"
] |
2023-02-14T08:08:45+00:00
|
{"license": "other"}
|
2023-02-14T08:08:58+00:00
|
|
a28c0e4bea268c4d07391140d81c271b1f4e2511
|
# Dataset Card for "patched_test_p_40_m1_predictions_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
roa7n/patched_test_p_40_m1_predictions_v3
|
[
"region:us"
] |
2023-02-14T08:15:13+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 1471779018, "num_examples": 2637494}], "download_size": 129165768, "dataset_size": 1471779018}}
|
2023-02-14T08:15:33+00:00
|
1ce9d90a764aec1219db960cbae1d76948864d0b
|
This dataset is curated by [GIZ Data Service Center](https://www.giz.de/expertise/html/63018.html) in the form of Sqaud dataset with features 'question', 'answers', 'answers_start' and 'context'. The source dataset for this
comes from [Climatewatchdata](https://www.climatewatchdata.org/data-explorer/historical-emissions?historical-emissions-data-sources=climate-watch&historical-emissions-gases=all-ghg&historical-emissions-regions=All%20Selected&historical-emissions-sectors=total-including-lucf%2Ctotal-including-lucf&page=1),
where Climatewatch has analysed Intended nationally determined contribution (INDC), NDC and Revised/Updated NDC of the countries to answer some important questions related to Climate change.
Specifications
- Dataset size: 31382
- Average Context length : 50 words
- Language: English
The list of Sectors covered include: Agriculture', 'Coastal Zone', 'Cross-Cutting Area', 'Education', 'Energy', 'Environment', 'Water', 'Buildings', 'Economy-wide', 'Industries', 'Transport', 'Waste', 'Health', 'LULUCF/Forestry', 'Social Development', 'Disaster Risk Management (DRM)', 'Urban','Tourism'.
Some of the important question categories pertaining to climate change(adapted from climatewatchdata) include
- Sectoral Policies
- Sectoral Unconditional Actions
- Building on existing downstream actions
- Sectoral plans
- Sectoral targets
- Action and priority
- Adapt Now sector
- Emission reduction potential
- Capacity Building Needs for Sectoral Implementation
- Sectoral Conditional Actions
- Technology Transfer Needs for Sectoral Implementation
- Conditional part of mitigation target
- Capacity building needs
- Technology needs
- Unconditional part of mitigation target
- Time frame
- Emission reduction potential
No answer category like 'Squad2' is not part of dataset but can be easily curated from existing examples.
|
GIZ/policy_qa_v0
|
[
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"climate",
"region:us"
] |
2023-02-14T08:39:09+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "tags": ["climate"]}
|
2023-05-31T07:59:44+00:00
|
137b9d18f70832f6aa4342c4003de49caf56670f
|
dog/fuego-20230214-040247-80ebbb
|
[
"fuego",
"region:us"
] |
2023-02-14T09:02:48+00:00
|
{"tags": ["fuego"], "fuego": {"id": "20230214-040247-80ebbb", "status": "done", "script": "run.py", "requirements_file": "requirements.txt", "space_id": "dog/fuego-20230214-040247-80ebbb", "space_hardware": "cpu-basic"}}
|
2023-02-14T09:06:29+00:00
|
|
34b55216f2d2f463f23988aa5042268ef13417ef
|
dog/fuego-20230214-041117-63ec52
|
[
"fuego",
"region:us"
] |
2023-02-14T09:11:18+00:00
|
{"tags": ["fuego"], "fuego": {"id": "20230214-041117-63ec52", "status": "preparing", "script": "run.py", "requirements_file": "requirements.txt", "space_id": "dog/fuego-20230214-041117-63ec52", "space_hardware": "cpu-basic"}}
|
2023-02-14T09:11:21+00:00
|
|
f6b9e77eb4f5ea4a4d4cd3e07165b8bff15ddf49
|
ThinZinc/fuego-20230214-101615-0a9f24
|
[
"fuego",
"region:us"
] |
2023-02-14T09:16:16+00:00
|
{"tags": ["fuego"], "fuego": {"id": "20230214-101615-0a9f24", "status": "done", "script": "main.py", "requirements_file": "requirements.txt", "space_id": "ThinZinc/fuego-20230214-101615-0a9f24", "space_hardware": "cpu-basic", "github_repo_id": "pytorch/examples", "github_repo_branch": "main", "github_repo_sha": "e4e8da8467d55d28920dbd137261d82255f68c71"}}
|
2023-02-14T09:23:51+00:00
|
|
3b16dd9a353d7f063261847079641ea7136368f1
|
dog/fuego-20230214-094643-d01849
|
[
"fuego",
"region:us"
] |
2023-02-14T09:46:44+00:00
|
{"tags": ["fuego"], "fuego": {"id": "20230214-094643-d01849", "status": "done", "script": "run.py", "requirements_file": "requirements.txt", "space_id": "dog/fuego-20230214-094643-d01849", "space_hardware": "cpu-basic"}}
|
2023-02-14T09:50:12+00:00
|
|
886206aa22d19c9ee4c1c50e3722ffd22ebe80ac
|
diversoailab/humaneval-rust
|
[
"task_categories:text-generation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"language:code",
"license:mit",
"region:us"
] |
2023-02-14T10:19:21+00:00
|
{"language_creators": ["expert-generated"], "language": ["code"], "license": "mit", "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "task_categories": ["text-generation"], "pretty_name": "Humaneval-rust"}
|
2023-02-14T11:24:24+00:00
|
|
acd2cd916116daf4f6356774098d82601e536494
|
luanng/maillogtest
|
[
"license:apache-2.0",
"region:us"
] |
2023-02-14T10:58:12+00:00
|
{"license": "apache-2.0"}
|
2023-02-14T11:15:48+00:00
|
|
2028d6c4d0a98bf65650c6945edad674d8297cdc
|
Tungchi/dataset-learn-gpt
|
[
"license:gpl-3.0",
"region:us"
] |
2023-02-14T11:10:24+00:00
|
{"license": "gpl-3.0"}
|
2023-02-14T11:10:24+00:00
|
|
612d0cb8a15ec1930a1f49179acd526431c13560
|
# Dataset Card for "python_comment_code_ratio_08"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lz88/python_comment_code_ratio_08
|
[
"region:us"
] |
2023-02-14T11:13:42+00:00
|
{"dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "avg_line_length", "dtype": "float64"}, {"name": "max_line_length", "dtype": "int64"}, {"name": "alphanum_fraction", "dtype": "float64"}, {"name": "licenses", "sequence": "string"}, {"name": "repository_name", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "size", "dtype": "int64"}, {"name": "lang", "dtype": "string"}, {"name": "nl_text", "dtype": "string"}, {"name": "nl_size", "dtype": "int64"}, {"name": "nl_ratio", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 1271905.6847, "num_examples": 131}], "download_size": 324517, "dataset_size": 1271905.6847}}
|
2023-02-14T11:13:53+00:00
|
9dc2c297e548893c02fbb8164676d67152c793f2
|
ybendou/metadataset-hf
|
[
"license:apache-2.0",
"region:us"
] |
2023-02-14T12:43:25+00:00
|
{"license": "apache-2.0"}
|
2023-02-14T12:43:25+00:00
|
|
0f63fd5fb0ae6d60a5334c5a796e2a7aeef395c3
|
# Dataset Card for "Million-AID"
## Dataset Description
- **Paper** [On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid](https://ieeexplore.ieee.org/iel7/4609443/9314330/09393553.pdf)
- **Split** Train
## Split Information
This HuggingFace dataset repository contains just the Train split.
### Licensing Information
[CC BY-NC-ND 4.0](https://competitions.codalab.org/competitions/35974#learn_the_details-terms-and-conditions)
## Citation Information
[On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid](https://ieeexplore.ieee.org/iel7/4609443/9314330/09393553.pdf)
```
@article{long2021creating,
title = {On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid},
author = {Long, Yang and Xia, Gui-Song and Li, Shengyang and Yang, Wen and Yang, Michael Ying and Zhu, Xiao Xiang and Zhang, Liangpei and Li, Deren},
year = 2021,
journal = {IEEE Journal of selected topics in applied earth observations and remote sensing},
publisher = {IEEE},
volume = 14,
pages = {4205--4230}
}
```
|
jonathan-roberts1/Million-AID
|
[
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
] |
2023-02-14T12:49:18+00:00
|
{"license": "other", "task_categories": ["image-classification", "zero-shot-image-classification"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label_1", "dtype": {"class_label": {"names": {"0": "unutilized land", "1": "commercial land", "2": "public service land", "3": "transportation land", "4": "industrial land", "5": "water area", "6": "residential land", "7": "agriculture land"}}}}, {"name": "label_2", "dtype": {"class_label": {"names": {"0": "dam", "1": "religious land", "2": "rock land", "3": "sparse shrub land", "4": "arable land", "5": "factory area", "6": "detached house", "7": "desert", "8": "lake", "9": "power station", "10": "beach", "11": "ice land", "12": "bare land", "13": "island", "14": "woodland", "15": "mobile home park", "16": "railway area", "17": "river", "18": "grassland", "19": "apartment", "20": "special land", "21": "port area", "22": "commercial area", "23": "highway area", "24": "mining area", "25": "sports land", "26": "airport area", "27": "leisure land"}}}}, {"name": "label_3", "dtype": {"class_label": {"names": {"0": "dam", "1": "parking lot", "2": "greenhouse", "3": "pier", "4": "bridge", "5": "mine", "6": "rock land", "7": "baseball field", "8": "apron", "9": "tennis court", "10": "sparse shrub land", "11": "works", "12": "oil field", "13": "meadow", "14": "ground track field", "15": "detached house", "16": "golf course", "17": "forest", "18": "desert", "19": "lake", "20": "beach", "21": "paddy field", "22": "ice land", "23": "bare land", "24": "storage tank", "25": "basketball court", "26": "island", "27": "substation", "28": "mobile home park", "29": "cemetery", "30": "quarry", "31": "solar power plant", "32": "helipad", "33": "roundabout", "34": "runway", "35": "wastewater plant", "36": "river", "37": "apartment", "38": "dry field", "39": "intersection", "40": "swimming pool", "41": "commercial area", "42": "church", "43": "road", "44": "orchard", "45": "terraced field", "46": "stadium", "47": "train station", "48": "railway", "49": "viaduct", "50": "wind turbine"}}}}], "splits": [{"name": "train", "num_bytes": 871962498, "num_examples": 10000}], "download_size": 871644115, "dataset_size": 871962498}}
|
2023-03-31T14:46:07+00:00
|
f5e50880bfc2c650b52430e9f6f2542dcb545a00
|
# `coco_superpixels_edge_wt_only_feat_10`
### Dataset Summary
| Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric |
|---|---|---|---|---|---|
| COCO-SP | Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 |
| Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter
|---|---:|---:|---:|:---:|---:|---:|---:|---:|
| COCO-SP | 123,286 | 58,793,216 | 476.88 | 5.65 | 332,091,902 | 2,693.67 | 10.66±0.55 | 27.39±2.14 |
## Additional Information
### Dataset Curators
* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
### Citation Information
```
@article{dwivedi2022LRGB,
title={Long Range Graph Benchmark},
author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
journal={arXiv:2206.08164},
year={2022}
}
```
|
LRGB/coco_superpixels_edge_wt_coord_feat_10
|
[
"task_categories:graph-ml",
"size_categories:1M<n<10M",
"license:cc-by-4.0",
"lrgb",
"region:us"
] |
2023-02-14T13:23:21+00:00
|
{"license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"], "dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "edge_index", "dtype": "int64"}, {"name": "edge_attr", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3625184, "num_examples": 113287}, {"name": "val", "num_bytes": 160032, "num_examples": 5001}, {"name": "test", "num_bytes": 160032, "num_examples": 5001}], "download_size": 3250471, "dataset_size": 3945248}}
|
2023-04-14T14:58:21+00:00
|
45cf7ab22e74653f1fab46bd236e546f350bf906
|
Denviny/LORA
|
[
"license:other",
"region:us"
] |
2023-02-14T14:33:53+00:00
|
{"license": "other"}
|
2023-02-17T12:36:19+00:00
|
|
b99789ed3c749b70aba4f886a557e7163cd1b15d
|
# Dataset card for personSegSmall
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset description](#dataset-description)
- [Dataset categories](#dataset-categories)
## Dataset description
- **Homepage:** https://segments.ai/shahardekel/personSegSmall
This dataset was created using [Segments.ai](https://segments.ai). It can be found [here](https://segments.ai/shahardekel/personSegSmall).
## Dataset categories
| Id | Name | Description |
| --- | ---- | ----------- |
| 1 | person | - |
|
shahardekel/personSegSmall
|
[
"task_categories:image-segmentation",
"region:us"
] |
2023-02-14T14:39:02+00:00
|
{"task_categories": ["image-segmentation"]}
|
2023-02-14T14:39:05+00:00
|
7393ac3c3b949077c5a289a7b7e8635010d6c8aa
|
# `coco_superpixels_edge_wt_only_feat_30`
### Dataset Summary
| Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric |
|---|---|---|---|---|---|
| COCO-SP | Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 |
| Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter
|---|---:|---:|---:|:---:|---:|---:|---:|---:|
| COCO-SP | 123,286 | 58,793,216 | 476.88 | 5.65 | 332,091,902 | 2,693.67 | 10.66±0.55 | 27.39±2.14 |
## Additional Information
### Dataset Curators
* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
### Citation Information
```
@article{dwivedi2022LRGB,
title={Long Range Graph Benchmark},
author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
journal={arXiv:2206.08164},
year={2022}
}
```
|
LRGB/coco_superpixels_edge_wt_only_feat_30
|
[
"task_categories:graph-ml",
"size_categories:1M<n<10M",
"license:cc-by-4.0",
"lrgb",
"region:us"
] |
2023-02-14T14:43:03+00:00
|
{"license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"], "dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "edge_index", "dtype": "int64"}, {"name": "edge_attr", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3625184, "num_examples": 113287}, {"name": "val", "num_bytes": 160032, "num_examples": 5001}, {"name": "test", "num_bytes": 160032, "num_examples": 5001}], "download_size": 3256545, "dataset_size": 3945248}}
|
2023-04-14T14:33:49+00:00
|
66c6d5190d373c4df6f52fdcb9c58e5883b7aa48
|
# Dataset card for personSegSmall
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset description](#dataset-description)
- [Dataset categories](#dataset-categories)
## Dataset description
- **Homepage:** https://segments.ai/shahardekel/personSegSmall
This dataset was created using [Segments.ai](https://segments.ai). It can be found [here](https://segments.ai/shahardekel/personSegSmall).
## Dataset categories
| Id | Name | Description |
| --- | ---- | ----------- |
| 1 | person | - |
|
shahardekel/personSegSmall1
|
[
"task_categories:image-segmentation",
"region:us"
] |
2023-02-14T14:45:23+00:00
|
{"task_categories": ["image-segmentation"]}
|
2023-02-15T12:18:29+00:00
|
900350daf1710799cafe22c159c5d4a8acd24c9e
|
# Dataset Card for "instruct-foo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lewtun/instruct-foo
|
[
"region:us"
] |
2023-02-14T14:48:42+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "source", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "outputs", "list": [{"name": "model", "dtype": "string"}, {"name": "outputs", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 3591, "num_examples": 1}], "download_size": 6053, "dataset_size": 3591}}
|
2023-02-14T16:15:00+00:00
|
a034c4991a4875cd67fe1611dff685967092cec2
|
# Dataset Card for SF Nexus Extracted Features: Chapters and Chunks
## Dataset Description
- **Homepage: https://sfnexus.io/**
- **Repository: https://github.com/SF-Nexus/extracted-features-notebooks**
- **Point of Contact: Alex Wermer-Colan**
### Dataset Summary
The SF Nexus Extracted Features Chapters and Chunks dataset contains text and metadata from 403 mid-twentieth century science fiction books, originally digitized from Temple University Libraries' Paskow Science Fiction Collection.
After digitization, the books were cleaned using Abbyy FineReader.
Because this is a collection of copyrighted fiction, the books have been disaggregated.
To improve performance of topic modeling and other nlp tasks, each book has also been split into chapters and then into chunks of approx. 1000 words.
Each row of this dataset contains one "chunk" of text as well as metadata about that text's title, author and publication.
### About the SF Nexus Corpus
The Paskow Science Fiction collection contains primarily materials from post-WWII, especially mass-market works of the New Wave era (often dated to 1964-1980).
The digitized texts have also been ingested into HathiTrust's repository for preservation and data curation; they are now viewable on HathiTrust's [Temple page](https://babel.hathitrust.org/cgi/ls?field1=ocr;q1=%2A;a=srchls;facet=htsource%3A%22Temple%20University%22;pn=4) for non-consumptive research.
For more information on the project to digitize and curate a corpus of "New Wave" science fiction, see Alex Wermer-Colan's post on the Temple University Scholars Studio blog, ["Building a New Wave Science Fiction Corpus."](https://sites.temple.edu/tudsc/2017/12/20/building-new-wave-science-fiction-corpus/).
### Languages
English
## Dataset Structure
This dataset contains disaggregated "chunks" of text from mid-twentieth century science fiction books and associated metadata. For example:
```
{'Unnamed': 7299,
'Title': 'MILLENNIUM,
'Author': 'VARLEY',
'Pub Year': '1983',
'Chapter': 'None',
'Chunk': '105',
'Text': '. . . . . . / 1958 1976 249 A Ambler, And As Baker Ben Berkley Bogart Bova Bova, By Casablanca DEMON Eric Eugene, Eugene, Goes HOTLINE, Herman Humphrey Hupfeld It John John MILLENNIUM MacQuitty, Millennium, Mister Night OF OPHIUCHI One Oregon Oregon...” Organisation, PERSISTENCE Rank Remember Roy Sam THE THE TITAN, The The Time Titanic, VISION, Varley Varley WIZARD, William a about acknowledgement: also an and and and asked author be began bestselling by by by by by by completed continued course, directed do excellent film final had in in in is is is is, name nothing novel novel novel, octopus of of of of of of pet play produced published rain-shrouded s screenplay sinking song soon that the the the the the the the this time title title to to to to travel trilogy was was with with with with written written ’ “ ”'
'Clean Text': 'a ambler and as baker ben berkley bogart bova bova by casablanca demon eric eugene eugene goes hotline herman humphrey hupfeld it john john millennium macquitty millennium mister night of ophiuchi one oregon oregon organisation persistence rank remember roy sam the the titan the the time titanic vision varley varley wizard william a about acknowledgement also an and and and asked author be began bestselling by by by by by by completed continued course directed do excellent film final had in in in is is is is name nothing novel novel novel octopus of of of of of of pet play produced published rain shrouded s screenplay sinking song soon that the the the the the the the this time title title to to to to travel trilogy was was with with with with written written ''
'Chunk Word Count': '948',
}
```
### Data Fields
- **Unnamed: int** A unique id for the text
- **Title: str** The title of the book from which the text has been extracted
- **Author: str** The author of the book from which the text has been extracted
- **Pub Year: str** The date on which the book was published (first printing)
- **Chapter: int** The chapter in the book from which the text has been extracted
- **Chunk: int** Number indicating which "chunk" of text has been extracted (chunks are numbered per book; each book was split by chapter and then into n chunks of approx. 1000 words)
- **Text: str** The chunk of text extracted from the book
- **Clean Text: str** The chunk of text extracted from the book with lowercasing performed and punctuation, numbers and extra spaces removed
- **Chunk Word Count: int** The number of words the chunk of text contains
To Be Added:
- **summary: str** A brief summary of the book, if extracted from library records
- **pub_date: int** The date on which the book was published (first printing)
- **pub_city: int** The city in which the book was published (first printing)
- **lcgft_category: str** Information from the Library of Congress Genre/Form Terms for Library and Archival Materials, if known
### Loading the Dataset
Use the following code to load the dataset in a Python environment (note: does not work with repo set to private)
```
from datasets import load_dataset
# If the dataset is gated/private, make sure you have run huggingface-cli login
dataset = load_dataset("SF-Corpus/EF_Chapters_and_Chunks")
```
Or just clone the dataset repo
```
git lfs install
git clone https://huggingface.co/datasets/SF-Corpus/EF_Chapters_and_Chunks
# if you want to clone without large files – just their pointers
# prepend your git clone with the following env var:
GIT_LFS_SKIP_SMUDGE=1
```
## Dataset Creation
### Curation Rationale
For an overview of our approach to data curation of literary texts, see Alex Wermer-Colan’s and James Kopaczewski’s article, “The New Wave of Digital Collections: Speculating on the Future of Library Curation”(2022)
### Source Data
The Loretta C. Duckworth Scholars Studio has partnered with Temple University Libraries’ Special Collections Research Center (SCRC) and Digital Library Initiatives (DLI) to build a digitized corpus of copyrighted science fiction literature. Besides its voluminous Urban Archives, the SCRC also houses a significant collection of science-fiction literature. The Paskow Science Fiction Collection was originally established in 1972, when Temple acquired 5,000 science fiction paperbacks from a Temple alumnus, the late David C. Paskow. Subsequent donations, including troves of fanzines and the papers of such sci-fi writers as John Varley and Stanley G. Weinbaum, expanded the collection over the last few decades, both in size and in the range of genres. SCRC staff and undergraduate student workers recently performed the usual comparison of gift titles against cataloged books, removing science fiction items that were exact duplicates of existing holdings. A refocusing of the SCRC’s collection development policy for science fiction de-emphasized fantasy and horror titles, so some titles in those genres were removed as well.
## Considerations for Using the Data
This data card only exhibits extracted features for copyrighted fiction; no copyrighted work is being made available for consumption. These digitized files are made accessible for purposes of education and research. Temple University Libraries have given attribution to rights holders when possible. If you hold the rights to materials in our digitized collections that are unattributed, please let us know so that we may maintain accurate information about these materials.
If you are a rights holder and are concerned that you have found material on this website for which you have not granted permission (or is not covered by a copyright exception under US copyright laws), you may request the removal of the material from our site by writing to [email protected].
For more information on non-consumptive research, check out HathiTrust Research Center’s Non-Consumptive Use Research Policy.
## Additional Information
### Dataset Curators
For a full list of conributors to the SF Nexus project, visit [https://sfnexus.io/people/](https://sfnexus.io/people/).
|
SF-Corpus/EF_Chapters_and_Chunks
|
[
"language:en",
"region:us"
] |
2023-02-14T14:50:01+00:00
|
{"language": ["en"], "pretty_name": "sf-nexus-ef-chapters-and-chunks"}
|
2023-05-24T13:39:05+00:00
|
8458170de2060f0ddce0cfa1127cfe3a136e798a
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### 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?
[More Information Needed]
### 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
[More Information Needed]
|
yogeshdai/quora_dataset
|
[
"region:us"
] |
2023-02-14T15:39:37+00:00
|
{}
|
2023-02-14T15:51:51+00:00
|
1280870a0d229b53f2f61456f675b8d5edf36199
|
Upload my data
|
Matilde/Homo_ita
|
[
"region:us"
] |
2023-02-14T15:57:44+00:00
|
{}
|
2023-06-08T13:59:35+00:00
|
a3a439f64b98e97eab2e4c1de6699a0814856a10
|
# Dataset Card for "mscoco_50k_10k_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
JotDe/mscoco_50k_10k_test
|
[
"region:us"
] |
2023-02-14T16:03:43+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 889160555.425, "num_examples": 9999}], "download_size": 173395947, "dataset_size": 889160555.425}}
|
2023-02-14T16:05:19+00:00
|
c4b9d19ca71eced688ba6943a1b3f521ea9aeaa9
|
# Dataset Card for "mscoco_50k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
JotDe/mscoco_50k
|
[
"region:us"
] |
2023-02-14T16:05:20+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 889160555.425, "num_examples": 9999}], "download_size": 173395947, "dataset_size": 889160555.425}}
|
2023-02-14T16:06:45+00:00
|
c0565beb3ba83a3d2c199337aed44c35ac5b9310
|
# Dataset Card for "skillspan_job_ner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Maiia/skillspan_job_ner
|
[
"region:us"
] |
2023-02-14T16:06:10+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int64"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "B-Skill I-Knowledge", "1": "I-Skill B-Knowledge", "2": "B-Knowledge", "3": "I-Skill I-Knowledge", "4": "I-Skill", "5": "B-Skill", "6": "I-Knowledge", "7": "O", "8": -100}}}}], "splits": [{"name": "train", "num_bytes": 3144024, "num_examples": 8005}, {"name": "test", "num_bytes": 1004456, "num_examples": 3565}], "download_size": 548417, "dataset_size": 4148480}}
|
2023-02-14T17:33:48+00:00
|
a179e91c8c02cb9f9cf869ae2e4cfb4c52d48386
|
# `coco_superpixels_edge_wt_region_boundary_10`
### Dataset Summary
| Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric |
|---|---|---|---|---|---|
| COCO-SP | Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 |
| Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter
|---|---:|---:|---:|:---:|---:|---:|---:|---:|
| COCO-SP | 123,286 | 58,793,216 | 476.88 | 5.65 | 332,091,902 | 2,693.67 | 10.66±0.55 | 27.39±2.14 |
## Additional Information
### Dataset Curators
* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
### Citation Information
```
@article{dwivedi2022LRGB,
title={Long Range Graph Benchmark},
author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
journal={arXiv:2206.08164},
year={2022}
}
```
|
LRGB/coco_superpixels_edge_wt_region_boundary_10
|
[
"task_categories:graph-ml",
"size_categories:1M<n<10M",
"license:cc-by-4.0",
"lrgb",
"region:us"
] |
2023-02-14T16:16:14+00:00
|
{"license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"], "dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "edge_index", "dtype": "int64"}, {"name": "edge_attr", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3625184, "num_examples": 113287}, {"name": "val", "num_bytes": 160032, "num_examples": 5001}, {"name": "test", "num_bytes": 160032, "num_examples": 5001}], "download_size": 3252505, "dataset_size": 3945248}}
|
2023-04-14T14:33:03+00:00
|
369b968f59c01b9310c56139c36df54be1755e01
|
# Dataset Card for "skillspan_job_ner_without_capitalization"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Maiia/skillspan_job_ner_without_capitalization
|
[
"region:us"
] |
2023-02-14T16:19:17+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int64"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "B-Skill I-Knowledge", "1": "I-Skill B-Knowledge", "2": "B-Knowledge", "3": "I-Skill I-Knowledge", "4": "I-Skill", "5": "B-Skill", "6": "I-Knowledge", "7": "O", "8": -100}}}}], "splits": [{"name": "train", "num_bytes": 7211448, "num_examples": 8005}, {"name": "test", "num_bytes": 2374184, "num_examples": 3565}], "download_size": 0, "dataset_size": 9585632}}
|
2023-02-14T16:42:07+00:00
|
2bd3445338f577d03b47e17ee0564069c3127634
|
# Dataset Card for "VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_8
|
[
"region:us"
] |
2023-02-14T16:31:58+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 202371, "num_examples": 8}], "download_size": 0, "dataset_size": 202371}}
|
2023-02-14T20:17:24+00:00
|
0b1971da6b1598b2f52094c87bd91bff0f311b83
|
# Dataset Card for "VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_10
|
[
"region:us"
] |
2023-02-14T16:37:14+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 252916, "num_examples": 10}, {"name": "fewshot_0_bs_128", "num_bytes": 255042, "num_examples": 10}, {"name": "fewshot_0_bs_16", "num_bytes": 255039, "num_examples": 10}], "download_size": 164794, "dataset_size": 762997}}
|
2023-02-17T22:51:19+00:00
|
c40a551683979656bcebb6f14578a3b6ec0fd985
|
Rockf3ller/NSL-1
|
[
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-02-14T16:42:51+00:00
|
{"license": "cc-by-nc-sa-4.0"}
|
2023-02-14T16:42:51+00:00
|
|
8dfb78595b10c16ffd337366a7cb28f61cc915d5
|
# Dataset Card for "skillspan_job_ner_without_cap"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Maiia/skillspan_job_ner_without_cap
|
[
"region:us"
] |
2023-02-14T16:43:21+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int64"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "B-Skill I-Knowledge", "1": "I-Skill B-Knowledge", "2": "B-Knowledge", "3": "I-Skill I-Knowledge", "4": "I-Skill", "5": "B-Skill", "6": "I-Knowledge", "7": "O", "8": -100}}}}], "splits": [{"name": "train", "num_bytes": 2952664, "num_examples": 8005}, {"name": "test", "num_bytes": 1001832, "num_examples": 3565}], "download_size": 523562, "dataset_size": 3954496}}
|
2023-02-14T17:27:36+00:00
|
fef6947c79868134ae4d2203dd6a593a6427687b
|
# Dataset Card for "jupyter-code-text-pairs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bigcode/jupyter-code-text-pairs
|
[
"region:us"
] |
2023-02-14T16:46:34+00:00
|
{"dataset_info": {"features": [{"name": "markdown", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "repo_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13985979285, "num_examples": 9305991}], "download_size": 6176464336, "dataset_size": 13985979285}}
|
2023-02-21T20:05:33+00:00
|
2ebc84f6111371ce411100023db2a507d94248b4
|
# Dataset Card for "VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_8
|
[
"region:us"
] |
2023-02-14T16:51:27+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 202362, "num_examples": 8}], "download_size": 45171, "dataset_size": 202362}}
|
2023-02-18T00:58:20+00:00
|
7c7e2c679a9bfc6ecb90e5593e7619939fe5daca
|
# `coco_superpixels_edge_wt_region_boundary_30`
### Dataset Summary
| Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric |
|---|---|---|---|---|---|
| COCO-SP | Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 |
| Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter
|---|---:|---:|---:|:---:|---:|---:|---:|---:|
| COCO-SP | 123,286 | 58,793,216 | 476.88 | 5.65 | 332,091,902 | 2,693.67 | 10.66±0.55 | 27.39±2.14 |
## Additional Information
### Dataset Curators
* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
### Citation Information
```
@article{dwivedi2022LRGB,
title={Long Range Graph Benchmark},
author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
journal={arXiv:2206.08164},
year={2022}
}
```
|
LRGB/coco_superpixels_edge_wt_region_boundary_30
|
[
"task_categories:graph-ml",
"size_categories:1M<n<10M",
"license:cc-by-4.0",
"lrgb",
"region:us"
] |
2023-02-14T17:10:42+00:00
|
{"license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"], "dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "edge_index", "dtype": "int64"}, {"name": "edge_attr", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3625184, "num_examples": 113287}, {"name": "val", "num_bytes": 160032, "num_examples": 5001}, {"name": "test", "num_bytes": 160032, "num_examples": 5001}], "download_size": 3257505, "dataset_size": 3945248}}
|
2023-04-14T14:33:24+00:00
|
174b53f504d0d240be3e9601705af008e631bc74
|
transformersbook/emotion-train-split
|
[
"license:apache-2.0",
"region:us"
] |
2023-02-14T17:11:27+00:00
|
{"license": "apache-2.0"}
|
2023-02-14T18:21:24+00:00
|
|
85118a51212a42a13e9bf0ad751e7c7b81a4c177
|
# Dataset Card for "VQAv2_sample_validation_facebook_opt_1.3b_VQAv2_visclues_ns_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_1.3b_VQAv2_visclues_ns_8
|
[
"region:us"
] |
2023-02-14T17:25:32+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 202345, "num_examples": 8}], "download_size": 45104, "dataset_size": 202345}}
|
2023-02-14T17:25:34+00:00
|
633d90f68759ea6f5fccd9c2ed872aab82cecc58
|
# Dataset Card for "mini-algae-rgb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
samitizerxu/mini-algae-rgb
|
[
"region:us"
] |
2023-02-14T17:36:22+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 15787445.414, "num_examples": 4039}, {"name": "test", "num_bytes": 6040387.721, "num_examples": 1521}], "download_size": 21439845, "dataset_size": 21827833.135}}
|
2023-02-14T17:36:33+00:00
|
f91dc8ad1331908abb2a6c69d0831ddcee841134
|
# Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_8
|
[
"region:us"
] |
2023-02-14T17:39:15+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 202345, "num_examples": 8}], "download_size": 0, "dataset_size": 202345}}
|
2023-02-14T20:26:42+00:00
|
eb9680f23234a97f261e2b1cd84d1d965c4d6c7c
|
# Dataset Card for "NaSC-TG2"
## Dataset Description
- **Paper:** [NaSC-TG2: Natural scene classification with Tiangong-2 remotely sensed imagery](https://ieeexplore.ieee.org/iel7/4609443/9314330/09366968.pdf)
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### 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?
[More Information Needed]
### 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
Public Domain
## Citation Information
[https://ieeexplore.ieee.org/iel7/4609443/9314330/09366968.pdf](https://ieeexplore.ieee.org/iel7/4609443/9314330/09366968.pdf)
```
@article{Zhou2021NaSCTG2,
title = {NaSC-TG2: Natural Scene Classification With Tiangong-2 Remotely Sensed Imagery},
author = {Zhuang Zhou and Shengyang Li and Wei Wu and Weilong Guo and Xuan Li and Guisong Xiaand Zifei Zhao},
year = 2021,
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
volume = 14,
pages = {3228--3242}
}
```
### Contributions
[More Information Needed]
|
jonathan-roberts1/NaSC-TG2
|
[
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-02-14T17:46:25+00:00
|
{"license": "cc-by-nc-sa-4.0", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "beach", "1": "circular farmland", "2": "cloud", "3": "desert", "4": "forest", "5": "mountain", "6": "rectangular farmland", "7": "residential", "8": "river", "9": "snowberg"}}}}], "splits": [{"name": "train", "num_bytes": 53937080, "num_examples": 20000}], "download_size": 75374352, "dataset_size": 53937080}}
|
2023-03-26T09:59:31+00:00
|
0b22f730140a3494f7c714ca6035aa54e9d3d1b1
|
# Dataset Card for MC4_Legal: A Corpus Covering the Legal Part of MC4 for European Languages
## 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:** [GitHub](https://github.com/JoelNiklaus/LegalDatasets/tree/main/pretrain/mc4_legal)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:[email protected])
### Dataset Summary
This dataset contains large text resources (~106GB in total) from mc4 filtered for legal data that can be used for pretraining language models.
This dataset uses a different filtering method compared to [mc4_legal](https://huggingface.co/datasets/joelito/mc4_legal) and uses the smaller filtered [c4](https://huggingface.co/datasets/c4) dataset for the English split to speed up the filtering.
Use the dataset like this:
```python
from datasets import load_dataset
dataset = load_dataset("joelito/mc4_legal", "de", split='train', streaming=True)
```
### Supported Tasks and Leaderboards
The dataset supports the task of masked language modeling.
### Languages
The following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv
## Dataset Structure
### Data Instances
The file format is jsonl.xz and there is a validation and train split available.
| Source | Size (MB) | Words | Documents | Words/Document |
|:---------|------------:|------------:|------------:|-----------------:|
| all | 448980 | 28599300521 | 9873288 | 2896 |
| bg | 57 | 2390349 | 379 | 6306 |
| cs | 31005 | 1840827375 | 677796 | 2715 |
| da | 162 | 10466716 | 3231 | 3239 |
| de | 105739 | 6184578784 | 3164461 | 1954 |
| el | 30 | 1155977 | 307 | 3765 |
| en | 13734 | 966539309 | 359283 | 2690 |
| es | 132053 | 9058939804 | 2281888 | 3969 |
| et | 2059 | 110198368 | 49987 | 2204 |
| fi | 1270 | 62799074 | 44875 | 1399 |
| fr | 30878 | 2117306229 | 598983 | 3534 |
| ga | 1 | 32772 | 8 | 4096 |
| hu | 4677 | 244911748 | 58857 | 4161 |
| it | 46957 | 3053920779 | 990823 | 3082 |
| lt | 156 | 9142223 | 1529 | 5979 |
| lv | 1 | 58702 | 16 | 3668 |
| mt | 65 | 3479869 | 731 | 4760 |
| nl | 326 | 21962633 | 6875 | 3194 |
| pl | 37950 | 2235839721 | 827641 | 2701 |
| pt | 20120 | 1338147828 | 382173 | 3501 |
| ro | 8816 | 551372510 | 136513 | 4038 |
| sk | 5850 | 349265172 | 130701 | 2672 |
| sl | 1742 | 107493024 | 32574 | 3299 |
| sv | 5332 | 328471555 | 123657 | 2656 |
### Data Fields
[More Information Needed]
### Data Splits
#### Data Size
```bash
$ xz --list data/*.xz
Strms Blocks Compressed Uncompressed Ratio Check Filename
1 1 2,080.7 KiB 33.4 MiB 0.061 CRC64 data/bg.train.0.jsonl.xz
1 1 22.8 KiB 315.9 KiB 0.072 CRC64 data/bg.validation.0.jsonl.xz
1 1 608.0 MiB 3,881.0 MiB 0.157 CRC64 data/cs.train.0.jsonl.xz
1 1 608.0 MiB 3,902.6 MiB 0.156 CRC64 data/cs.train.1.jsonl.xz
1 1 256.1 MiB 1,644.5 MiB 0.156 CRC64 data/cs.train.2.jsonl.xz
1 1 1,450.6 KiB 8,690.7 KiB 0.167 CRC64 data/cs.validation.0.jsonl.xz
1 1 7,578.6 KiB 38.3 MiB 0.193 CRC64 data/da.train.0.jsonl.xz
1 1 19.7 KiB 82.3 KiB 0.240 CRC64 data/da.validation.0.jsonl.xz
1 1 608.0 MiB 3,026.9 MiB 0.201 CRC64 data/de.train.0.jsonl.xz
1 1 608.0 MiB 3,038.7 MiB 0.200 CRC64 data/de.train.1.jsonl.xz
1 1 608.0 MiB 3,036.1 MiB 0.200 CRC64 data/de.train.2.jsonl.xz
1 1 608.0 MiB 3,040.3 MiB 0.200 CRC64 data/de.train.3.jsonl.xz
1 1 608.0 MiB 3,038.6 MiB 0.200 CRC64 data/de.train.4.jsonl.xz
1 1 608.0 MiB 3,044.2 MiB 0.200 CRC64 data/de.train.5.jsonl.xz
1 1 608.0 MiB 3,043.8 MiB 0.200 CRC64 data/de.train.6.jsonl.xz
1 1 608.0 MiB 3,038.2 MiB 0.200 CRC64 data/de.train.7.jsonl.xz
1 1 55.1 MiB 274.7 MiB 0.201 CRC64 data/de.train.8.jsonl.xz
1 1 5,033.5 KiB 24.5 MiB 0.201 CRC64 data/de.validation.0.jsonl.xz
1 1 1,280.9 KiB 17.0 MiB 0.073 CRC64 data/el.train.0.jsonl.xz
1 1 5,552 B 15.7 KiB 0.346 CRC64 data/el.validation.0.jsonl.xz
1 1 608.0 MiB 2,602.1 MiB 0.234 CRC64 data/en.train.0.jsonl.xz
1 1 90.0 MiB 386.5 MiB 0.233 CRC64 data/en.train.1.jsonl.xz
1 1 826.6 KiB 3,298.8 KiB 0.251 CRC64 data/en.validation.0.jsonl.xz
1 1 608.0 MiB 3,106.5 MiB 0.196 CRC64 data/es.train.0.jsonl.xz
1 1 608.0 MiB 3,118.1 MiB 0.195 CRC64 data/es.train.1.jsonl.xz
1 1 608.0 MiB 3,113.6 MiB 0.195 CRC64 data/es.train.2.jsonl.xz
1 1 608.0 MiB 3,122.5 MiB 0.195 CRC64 data/es.train.3.jsonl.xz
1 1 608.0 MiB 3,121.5 MiB 0.195 CRC64 data/es.train.4.jsonl.xz
1 1 608.0 MiB 3,122.9 MiB 0.195 CRC64 data/es.train.5.jsonl.xz
1 1 608.0 MiB 3,128.4 MiB 0.194 CRC64 data/es.train.6.jsonl.xz
1 1 608.0 MiB 3,129.5 MiB 0.194 CRC64 data/es.train.7.jsonl.xz
1 1 608.0 MiB 3,132.2 MiB 0.194 CRC64 data/es.train.8.jsonl.xz
1 1 528.5 MiB 2,722.5 MiB 0.194 CRC64 data/es.train.9.jsonl.xz
1 1 6,159.9 KiB 30.7 MiB 0.196 CRC64 data/es.validation.0.jsonl.xz
1 1 93.5 MiB 506.2 MiB 0.185 CRC64 data/et.train.0.jsonl.xz
1 1 136.2 KiB 571.3 KiB 0.238 CRC64 data/et.validation.0.jsonl.xz
1 1 60.6 MiB 312.6 MiB 0.194 CRC64 data/fi.train.0.jsonl.xz
1 1 63.2 KiB 262.4 KiB 0.241 CRC64 data/fi.validation.0.jsonl.xz
1 1 608.0 MiB 3,400.7 MiB 0.179 CRC64 data/fr.train.0.jsonl.xz
1 1 608.0 MiB 3,405.5 MiB 0.179 CRC64 data/fr.train.1.jsonl.xz
1 1 135.9 MiB 763.7 MiB 0.178 CRC64 data/fr.train.2.jsonl.xz
1 1 1,414.3 KiB 7,626.1 KiB 0.185 CRC64 data/fr.validation.0.jsonl.xz
1 1 31.2 KiB 146.4 KiB 0.213 CRC64 data/ga.train.0.jsonl.xz
1 0 32 B 0 B --- CRC64 data/ga.validation.0.jsonl.xz
1 1 211.5 MiB 1,407.3 MiB 0.150 CRC64 data/hu.train.0.jsonl.xz
1 1 212.9 KiB 1,287.6 KiB 0.165 CRC64 data/hu.validation.0.jsonl.xz
1 1 608.0 MiB 2,963.4 MiB 0.205 CRC64 data/it.train.0.jsonl.xz
1 1 608.0 MiB 2,970.0 MiB 0.205 CRC64 data/it.train.1.jsonl.xz
1 1 608.0 MiB 2,973.7 MiB 0.204 CRC64 data/it.train.2.jsonl.xz
1 1 315.2 MiB 1,541.6 MiB 0.204 CRC64 data/it.train.3.jsonl.xz
1 1 2,419.3 KiB 11.2 MiB 0.211 CRC64 data/it.validation.0.jsonl.xz
1 1 9,966.7 KiB 38.2 MiB 0.255 CRC64 data/lt.train.0.jsonl.xz
1 1 17.2 KiB 84.7 KiB 0.203 CRC64 data/lt.validation.0.jsonl.xz
1 1 66.4 KiB 326.7 KiB 0.203 CRC64 data/lv.train.0.jsonl.xz
1 0 32 B 0 B --- CRC64 data/lv.validation.0.jsonl.xz
1 1 2,851.6 KiB 16.7 MiB 0.167 CRC64 data/mt.train.0.jsonl.xz
1 1 2,092 B 5,079 B 0.412 CRC64 data/mt.validation.0.jsonl.xz
1 1 14.6 MiB 71.6 MiB 0.203 CRC64 data/nl.train.0.jsonl.xz
1 1 23.5 KiB 79.2 KiB 0.296 CRC64 data/nl.validation.0.jsonl.xz
1 1 608.0 MiB 3,635.5 MiB 0.167 CRC64 data/pl.train.0.jsonl.xz
1 1 608.0 MiB 3,646.0 MiB 0.167 CRC64 data/pl.train.1.jsonl.xz
1 1 401.9 MiB 2,409.0 MiB 0.167 CRC64 data/pl.train.2.jsonl.xz
1 1 1,870.5 KiB 10.5 MiB 0.173 CRC64 data/pl.validation.0.jsonl.xz
1 1 608.0 MiB 3,173.1 MiB 0.192 CRC64 data/pt.train.0.jsonl.xz
1 1 329.1 MiB 1,721.6 MiB 0.191 CRC64 data/pt.train.1.jsonl.xz
1 1 989.0 KiB 4,841.2 KiB 0.204 CRC64 data/pt.validation.0.jsonl.xz
1 1 365.2 MiB 2,237.9 MiB 0.163 CRC64 data/ro.train.0.jsonl.xz
1 1 419.2 KiB 2,320.4 KiB 0.181 CRC64 data/ro.validation.0.jsonl.xz
1 1 266.1 MiB 1,668.1 MiB 0.160 CRC64 data/sk.train.0.jsonl.xz
1 1 304.1 KiB 1,618.2 KiB 0.188 CRC64 data/sk.validation.0.jsonl.xz
1 1 81.6 MiB 416.1 MiB 0.196 CRC64 data/sl.train.0.jsonl.xz
1 1 101.0 KiB 416.6 KiB 0.242 CRC64 data/sl.validation.0.jsonl.xz
1 1 252.0 MiB 1,423.2 MiB 0.177 CRC64 data/sv.train.0.jsonl.xz
1 1 210.8 KiB 1,091.2 KiB 0.193 CRC64 data/sv.validation.0.jsonl.xz
-------------------------------------------------------------------------------
74 72 20.0 GiB 106.2 GiB 0.189 CRC64 74 files
```
## Dataset Creation
The dataset was created by filtering mc4 for legal data.
We used terms indicating legal citations to get the texts.
Note that this dataset can be quite noisy, and the quality is not known.
### Curation Rationale
[More Information Needed]
### Source Data
#### 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?
[More Information Needed]
### 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 [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.
|
joelniklaus/legal-mc4
|
[
"task_categories:fill-mask",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:ga",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"license:cc-by-4.0",
"region:us"
] |
2023-02-14T17:48:58+00:00
|
{"annotations_creators": ["other"], "language_creators": ["found"], "language": ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["fill-mask"], "pretty_name": "MC4_Legal: A Corpus Covering the Legal Part of MC4 for European Languages"}
|
2023-08-06T21:54:20+00:00
|
9534565516f1d94713635b720f2d8a3c9b9aba57
|
# Dataset Card for "mini-algae-wirs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
samitizerxu/mini-algae-wirs
|
[
"region:us"
] |
2023-02-14T17:56:57+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "1", "1": "2", "2": "3", "3": "4", "4": "5", "5": "test"}}}}], "splits": [{"name": "train", "num_bytes": 12520132.715, "num_examples": 4039}, {"name": "test", "num_bytes": 2971288.064, "num_examples": 1521}], "download_size": 15414584, "dataset_size": 15491420.779}}
|
2023-02-14T17:58:04+00:00
|
5511e606d24ee9d655813e31263e2ea31ce6aeb0
|
# Dataset Card for "RS_C11"
## Dataset Description
- **Paper** [Feature significance-based multibag-of-visual-words model for remote sensing image scene classification](https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-10/issue-3/035004/Feature-significance-based-multibag-of-visual-words-model-for-remote/10.1117/1.JRS.10.035004.pdf)
### Licensing Information
Free usage without license.
## Citation Information
[Feature significance-based multibag-of-visual-words model for remote sensing image scene classification](https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-10/issue-3/035004/Feature-significance-based-multibag-of-visual-words-model-for-remote/10.1117/1.JRS.10.035004.pdf)
```
@article{zhao2016feature,
title = {Feature significance-based multibag-of-visual-words model for remote sensing image scene classification},
author = {Zhao, Lijun and Tang, Ping and Huo, Lianzhi},
year = 2016,
journal = {Journal of Applied Remote Sensing},
publisher = {Society of Photo-Optical Instrumentation Engineers},
volume = 10,
number = 3,
pages = {035004--035004}
}
```
|
jonathan-roberts1/RS_C11
|
[
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
] |
2023-02-14T18:12:02+00:00
|
{"license": "other", "task_categories": ["image-classification", "zero-shot-image-classification"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "dense forest", "1": "grassland", "2": "harbor", "3": "high buildings", "4": "low buildings", "5": "overpass", "6": "railway", "7": "residential area", "8": "roads", "9": "sparse forest", "10": "storage tanks"}}}}], "splits": [{"name": "train", "num_bytes": 969136595.28, "num_examples": 1232}], "download_size": 916398984, "dataset_size": 969136595.28}}
|
2023-03-31T16:07:50+00:00
|
3976c543c8b99c310eef4b863503e2041558ceb5
|
# Dataset Card for "Brazilian_Coffee_Scenes"
## Dataset Description
- **Paper** [Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?](https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W13/papers/Penatti_Do_Deep_Features_2015_CVPR_paper.pdf)
### Licensing Information
[CC BY-NC]
## Citation Information
[Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?](https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W13/papers/Penatti_Do_Deep_Features_2015_CVPR_paper.pdf)
```
@inproceedings{penatti2015deep,
title = {Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?},
author = {Penatti, Ot{\'a}vio AB and Nogueira, Keiller and Dos Santos, Jefersson A},
year = 2015,
booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition workshops},
pages = {44--51}
}
```
|
jonathan-roberts1/Brazilian_Coffee_Scenes
|
[
"task_categories:image-classification",
"license:other",
"region:us"
] |
2023-02-14T18:27:36+00:00
|
{"license": "other", "task_categories": ["image-classification"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "coffee", "1": "no coffee"}}}}], "splits": [{"name": "train", "num_bytes": 4256968.464, "num_examples": 2876}], "download_size": 2830232, "dataset_size": 4256968.464}}
|
2023-03-31T14:27:06+00:00
|
7b9d36c4cf0cf8d2f8a26ed401986eebee0baf4b
|
# Dataset Card for "Brazilian_Cerrado-Savanna_Scenes"
## Dataset Description
- **Paper** [Towards vegetation species discrimination by using data-driven descriptors](https://vision.unipv.it/CV/materiale2016-17/3rd%20Choice/0022.pdf)
-
### Licensing Information
[CC BY-NC]
## Citation Information
[Towards vegetation species discrimination by using data-driven descriptors](https://vision.unipv.it/CV/materiale2016-17/3rd%20Choice/0022.pdf)
```
@inproceedings{nogueira2016towards,
title = {Towards vegetation species discrimination by using data-driven descriptors},
author = {Nogueira, Keiller and Dos Santos, Jefersson A and Fornazari, Tamires and Silva, Thiago Sanna Freire and Morellato, Leonor Patricia and Torres, Ricardo da S},
year = 2016,
booktitle = {2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS)},
pages = {1--6},
organization = {Ieee}
}
```
|
jonathan-roberts1/Brazilian_Cerrado-Savanna_Scenes
|
[
"task_categories:zero-shot-image-classification",
"task_categories:image-classification",
"license:other",
"region:us"
] |
2023-02-14T18:28:02+00:00
|
{"license": "other", "task_categories": ["zero-shot-image-classification", "image-classification"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "agriculture", "1": "arboreal vegetation", "2": "herbaceous vegetation", "3": "shrubby vegetation"}}}}], "splits": [{"name": "train", "num_bytes": 16933385.557, "num_examples": 1311}], "download_size": 14574976, "dataset_size": 16933385.557}}
|
2023-03-31T14:28:58+00:00
|
b1aed3941349646e032b1b1abd6452b923307cd6
|
# Dataset Card for "algae-wirs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
samitizerxu/algae-wirs
|
[
"region:us"
] |
2023-02-14T18:56:40+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "1", "1": "2", "2": "3", "3": "4", "4": "5", "5": "test"}}}}], "splits": [{"name": "train", "num_bytes": 33936156.629999995, "num_examples": 17035}, {"name": "test", "num_bytes": 12474396.284, "num_examples": 6494}], "download_size": 45458394, "dataset_size": 46410552.914}}
|
2023-02-14T19:24:28+00:00
|
bb0d4012c364f989325cb205b07c18626604015b
|
# Dataset Card for "age_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bstds/age_dataset
|
[
"region:us"
] |
2023-02-14T19:03:47+00:00
|
{"dataset_info": {"features": [{"name": "entity_id", "dtype": "int32"}, {"name": "name", "dtype": "string"}, {"name": "short_description", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "occupation", "dtype": "string"}, {"name": "birth_year", "dtype": "string"}, {"name": "manner_of_death", "dtype": "string"}, {"name": "age_of_death", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 135675467, "num_examples": 1223009}], "download_size": 54907598, "dataset_size": 135675467}}
|
2023-02-14T19:11:38+00:00
|
2a102f1877fc51f5af21891a3c5210b25fd56a43
|
# Dataset Card for "algae-rgb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
samitizerxu/algae-rgb
|
[
"region:us"
] |
2023-02-14T19:04:53+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "1", "1": "2", "2": "3", "3": "4", "4": "5", "5": "test"}}}}], "splits": [{"name": "train", "num_bytes": 44920154.28, "num_examples": 17035}, {"name": "test", "num_bytes": 17356455.604, "num_examples": 6494}], "download_size": 61006757, "dataset_size": 62276609.884}}
|
2023-02-14T19:23:12+00:00
|
1092e5b41e75f9063026c6196617f86f6306cd12
|
# Dataset Card for "RSI-CB256"
## Dataset Description
- **Paper** [Exploring Models and Data for Remote Sensing Image Caption Generation](https://ieeexplore.ieee.org/iel7/36/4358825/08240966.pdf)
-
### Licensing Information
For academic purposes.
## Citation Information
[Exploring Models and Data for Remote Sensing Image Caption Generation](https://ieeexplore.ieee.org/iel7/36/4358825/08240966.pdf)
```
@article{lu2017exploring,
title = {Exploring Models and Data for Remote Sensing Image Caption Generation},
author = {Lu, Xiaoqiang and Wang, Binqiang and Zheng, Xiangtao and Li, Xuelong},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = 56,
number = 4,
pages = {2183--2195},
doi = {10.1109/TGRS.2017.2776321},
year={2018}
}
```
|
jonathan-roberts1/RSI-CB256
|
[
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
] |
2023-02-14T19:09:45+00:00
|
{"license": "other", "task_categories": ["image-classification", "zero-shot-image-classification"], "dataset_info": {"features": [{"name": "label_1", "dtype": {"class_label": {"names": {"0": "transportation", "1": "other objects", "2": "woodland", "3": "water area", "4": "other land", "5": "cultivated land", "6": "construction land"}}}}, {"name": "label_2", "dtype": {"class_label": {"names": {"0": "parking lot", "1": "avenue", "2": "highway", "3": "bridge", "4": "marina", "5": "crossroads", "6": "airport runway", "7": "pipeline", "8": "town", "9": "airplane", "10": "forest", "11": "mangrove", "12": "artificial grassland", "13": "river protection forest", "14": "shrubwood", "15": "sapling", "16": "sparse forest", "17": "lakeshore", "18": "river", "19": "stream", "20": "coastline", "21": "hirst", "22": "dam", "23": "sea", "24": "snow mountain", "25": "sandbeach", "26": "mountain", "27": "desert", "28": "dry farm", "29": "green farmland", "30": "bare land", "31": "city building", "32": "residents", "33": "container", "34": "storage room"}}}}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 4901667781.625, "num_examples": 24747}], "download_size": 4198991130, "dataset_size": 4901667781.625}}
|
2023-03-31T16:11:50+00:00
|
6d2f0f751d549f99b669ee3cdbb9b7f746f7078c
|
# Dataset Card for "geonames"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[Source](https://download.geonames.org/export/dump/)
```
"geonameid", # integer id of record in geonames database
"name", # name of geographical point (utf8) varchar(200)
"asciiname", # name of geographical point in plain ascii characters, varchar(200)
"alternatenames",
# alternatenames, comma separated, ascii names automatically transliterated, convenience attribute from alternatename table, varchar(10000)
"latitude", # latitude in decimal degrees (wgs84)
"longitude", # longitude in decimal degrees (wgs84)
"feature_class", # see http://www.geonames.org/export/codes.html, char(1)
"feature_code", # see http://www.geonames.org/export/codes.html, varchar(10)
"country_code", # ISO-3166 2-letter country code, 2 characters
"cc2",
# alternate country codes, comma separated, ISO-3166 2-letter country code, 200 characters
"admin1_code",
# fipscode (subject to change to iso code), see exceptions below, see file admin1Codes.txt for display names of this code; varchar(20)
"admin2_code",
# code for the second administrative division, a county in the US, see file admin2Codes.txt; varchar(80)
"admin3_code", # code for third level administrative division, varchar(20)
"admin4_code", # code for fourth level administrative division, varchar(20)
"population", # bigint (8 byte int)
"elevation", # in meters, integer
"dem",
# digital elevation model, srtm3 or gtopo30, average elevation of 3''x3'' (ca 90mx90m) or 30''x30'' (ca 900mx900m) area in meters, integer. srtm processed by cgiar/ciat.
"timezone", # the iana timezone id (see file timeZone.txt) varchar(40)
"modification_date", # date of last modification in yyyy-MM-dd format"
```
|
bstds/geonames
|
[
"region:us"
] |
2023-02-14T19:12:27+00:00
|
{"dataset_info": {"features": [{"name": "geonameid", "dtype": "int64"}, {"name": "name", "dtype": "string"}, {"name": "asciiname", "dtype": "string"}, {"name": "alternatenames", "dtype": "string"}, {"name": "latitude", "dtype": "float64"}, {"name": "longitude", "dtype": "float64"}, {"name": "feature_class", "dtype": "string"}, {"name": "feature_code", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "cc2", "dtype": "string"}, {"name": "admin1_code", "dtype": "string"}, {"name": "admin2_code", "dtype": "string"}, {"name": "admin3_code", "dtype": "string"}, {"name": "admin4_code", "dtype": "string"}, {"name": "population", "dtype": "int64"}, {"name": "elevation", "dtype": "float64"}, {"name": "dem", "dtype": "int64"}, {"name": "timezone", "dtype": "string"}, {"name": "modification_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2378719253, "num_examples": 12368001}], "download_size": 824343833, "dataset_size": 2378719253}}
|
2023-02-14T19:32:06+00:00
|
06a42b8eb06bae0c40b7ab2546d45884aa0cae1d
|
# Dataset Card for "SmallNoisyCommonSpeechEN"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rohitp1/SmallNoisyCommonSpeechEN
|
[
"region:us"
] |
2023-02-14T19:25:23+00:00
|
{"dataset_info": {"features": [{"name": "audio", "struct": [{"name": "array", "sequence": "float64"}, {"name": "path", "dtype": "null"}, {"name": "sampling_rate", "dtype": "int64"}]}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 78272771286, "num_examples": 100000}, {"name": "val", "num_bytes": 3796055210, "num_examples": 5000}, {"name": "test", "num_bytes": 3840811928, "num_examples": 5000}], "download_size": 7663445403, "dataset_size": 85909638424}}
|
2023-02-15T09:18:59+00:00
|
d98e4caa1b3e890210eb68fe4e039153eaa5cda0
|
# Dataset Card for "job_titles"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Normalized dataset of 70k job titles
|
bstds/job_titles
|
[
"region:us"
] |
2023-02-14T19:31:04+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2451067, "num_examples": 73380}], "download_size": 1258591, "dataset_size": 2451067}}
|
2023-02-14T19:34:23+00:00
|
0acc2ccc8982e509b1bc95feedbc6016027891e2
|
# Dataset Card for "us_patent"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Dataset of the U.S. Patent Phrase to Phrase Matching - https://www.kaggle.com/competitions/us-patent-phrase-to-phrase-matching
|
bstds/us_patent
|
[
"region:us"
] |
2023-02-14T19:34:50+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "anchor", "dtype": "string"}, {"name": "target", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "score", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 2580483, "num_examples": 36473}, {"name": "test", "num_bytes": 2521, "num_examples": 36}], "download_size": 1161327, "dataset_size": 2583004}}
|
2023-02-14T19:39:21+00:00
|
bc93bcae2161570bdbded1c752ad04610b015cf9
|
# Dataset Card for "VQAv2_sample_validation_facebook_opt_13b_VQAv2_visclues_ns_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_13b_VQAv2_visclues_ns_8
|
[
"region:us"
] |
2023-02-14T20:02:48+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 202359, "num_examples": 8}], "download_size": 0, "dataset_size": 202359}}
|
2023-02-14T20:22:34+00:00
|
a791f1298435d3c103078043d983f68a5860385e
|
# Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_16"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_16
|
[
"region:us"
] |
2023-02-14T20:37:45+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 404649, "num_examples": 16}], "download_size": 81032, "dataset_size": 404649}}
|
2023-02-14T20:37:47+00:00
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.