sha stringlengths 40 40 | text stringlengths 1 13.4M | id stringlengths 2 117 | tags listlengths 1 7.91k | created_at stringlengths 25 25 | metadata stringlengths 2 875k | last_modified stringlengths 25 25 | arxiv listlengths 0 25 | languages listlengths 0 7.91k | tags_str stringlengths 17 159k | text_str stringlengths 1 447k | text_lists listlengths 0 352 | processed_texts listlengths 1 353 | tokens_length listlengths 1 353 | input_texts listlengths 1 40 |
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a3e22f7e2b4de0329ebe8d89d0fba7727808c123 | annotations_creators:
- crowdsourced
language_creators:
- expert-generated
languages: []
licenses:
- unknown
multilinguality: []
pretty_name: mango quality grading
size_categories:
- n<1K
source_datasets: []
task_categories:
- image-classification
task_ids:
- multi-class-image-classification | jjjonathan14/mango | [
"region:us"
] | 2022-05-19T16:59:24+00:00 | {} | 2022-05-19T18:47:32+00:00 | [] | [] | TAGS
#region-us
| annotations_creators:
- crowdsourced
language_creators:
- expert-generated
languages: []
licenses:
- unknown
multilinguality: []
pretty_name: mango quality grading
size_categories:
- n<1K
source_datasets: []
task_categories:
- image-classification
task_ids:
- multi-class-image-classification | [] | [
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7871d03723e417145e9f8eb2f64cb1ed657522ff | This is the preprocessed training data from msmarco passage(v1) ranking corpus.
*[MS MARCO: A human generated MAchine Reading COmprehension dataset](https://arxiv.org/pdf/1611.09268.pdf)* SPayal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, T... | jacklin/msmarco_passage_ranking_official_train | [
"arxiv:1611.09268",
"region:us"
] | 2022-05-19T17:11:01+00:00 | {} | 2022-06-13T20:46:30+00:00 | [
"1611.09268"
] | [] | TAGS
#arxiv-1611.09268 #region-us
| This is the preprocessed training data from msmarco passage(v1) ranking corpus.
*MS MARCO: A human generated MAchine Reading COmprehension dataset* SPayal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen,. | [] | [
"TAGS\n#arxiv-1611.09268 #region-us \n"
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f63294e4d057cee09247f01be37b40b77ec9424c | annotations_creators:
- crowdsourced
language_creators:
- expert-generated
languages: []
licenses:
- unknown
multilinguality: []
pretty_name: mango quality grading
size_categories:
- n<1K
source_datasets: []
task_categories:
- image-classification
task_ids:
- multi-class-image-classification | jjjonathan14/mango2 | [
"region:us"
] | 2022-05-19T18:22:41+00:00 | {} | 2022-05-19T18:42:42+00:00 | [] | [] | TAGS
#region-us
| annotations_creators:
- crowdsourced
language_creators:
- expert-generated
languages: []
licenses:
- unknown
multilinguality: []
pretty_name: mango quality grading
size_categories:
- n<1K
source_datasets: []
task_categories:
- image-classification
task_ids:
- multi-class-image-classification | [] | [
"TAGS\n#region-us \n"
] | [
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d51519689f32196a32af33b075a01d0e7c51e252 |
# Dataset Card for MTEB Benchmark
## Dataset Description
- **Homepage:** https://github.com/embeddings-benchmark/mteb-draft
- **Repository:** https://github.com/embeddings-benchmark/mteb-draft
- **Paper:** soon
- **Leaderboard:** https://docs.google.com/spreadsheets/d/14P8bdEzsIgTGGlp9oOlMw-THrQbn2fYfZEkZV4NUBos
- *... | mteb/bucc-bitext-mining | [
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"regio... | 2022-05-19T18:44:24+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["de", "en", "fr", "ru", "zh"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual", "multilingual"], "pretty_name": "MTEB Benchmark"} | 2022-09-22T13:17:13+00:00 | [
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|
# Dataset Card for MTEB Benchmark
## Dataset Description
- Homepage: URL
- Repository: URL
- Paper: soon
- Leaderboard: URL
- Point of Contact: nouamane@URL
### Dataset Summary
MTEB is a heterogeneous benchmark that has been built from diverse tasks:
* BitextMining: BUCC, Tatoeba
* Classification: AmazonCounterfac... | [
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fcbc4546b716a7dc23787d45f9ffcc517c17e944 |
# Dataset Card for "coqa"
## 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)
-... | Ruohao/pcmr | [
"language:en",
"region:us"
] | 2022-05-20T03:02:37+00:00 | {"language": ["en"], "paperswithcode_id": "coqa", "pretty_name": "Conversational Question Answering Challenge"} | 2022-10-25T09:25:57+00:00 | [] | [
"en"
] | TAGS
#language-English #region-us
| Dataset Card for "coqa"
=======================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ ... | [
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e1916c2472d388a9194aac1cb871ef2a1aabcdaa |
# Multi-microworld conversational agent dataset (RASA)
Included microworlds (domains of knowledge):
- generic
- memory assistance
- university guidance | readerbench/ConversationalAgent-Ro | [
"language:ro",
"region:us"
] | 2022-05-20T05:44:08+00:00 | {"language": ["ro"]} | 2022-05-20T06:04:52+00:00 | [] | [
"ro"
] | TAGS
#language-Romanian #region-us
|
# Multi-microworld conversational agent dataset (RASA)
Included microworlds (domains of knowledge):
- generic
- memory assistance
- university guidance | [
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f03065371ce62ba8c260c5889ba122100de147a1 |
# Sinhala-English-Code-Mixed-Code-Switched-Dataset
This dataset contains 10,000 comments that have been annotated at the sentence level for sentiment analysis, humor detection, hate speech detection, aspect identification, and language identification.
The following is the tag scheme.
* Sentiment - Positive, Negativ... | NLPC-UOM/Sinhala-English-Code-Mixed-Code-Switched-Dataset | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:hate-speech-detection",
"task_ids:language-identification",
"multilinguality:multilingual",
"language:si",
"language:en",
"license:mit",
"region:us"
] | 2022-05-20T05:44:20+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["si", "en"], "license": ["mit"], "multilinguality": ["multilingual"], "size_categories": [], "source_datasets": [], "task_categories": ["text-classification"], "task_ids": ["sentiment-analysis", "hate-speech-detection", "humor-detection", "language-iden... | 2022-09-22T13:15:53+00:00 | [] | [
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#task_categories-text-classification #task_ids-sentiment-analysis #task_ids-hate-speech-detection #task_ids-language-identification #multilinguality-multilingual #language-Sinhala #language-English #license-mit #region-us
|
# Sinhala-English-Code-Mixed-Code-Switched-Dataset
This dataset contains 10,000 comments that have been annotated at the sentence level for sentiment analysis, humor detection, hate speech detection, aspect identification, and language identification.
The following is the tag scheme.
* Sentiment - Positive, Negativ... | [
"# Sinhala-English-Code-Mixed-Code-Switched-Dataset\n\nThis dataset contains 10,000 comments that have been annotated at the sentence level for sentiment analysis, humor detection, hate speech detection, aspect identification, and language identification.\n\nThe following is the tag scheme.\n* Sentiment - Positive... | [
"TAGS\n#task_categories-text-classification #task_ids-sentiment-analysis #task_ids-hate-speech-detection #task_ids-language-identification #multilinguality-multilingual #language-Sinhala #language-English #license-mit #region-us \n",
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41699cddcb0ce9849d476767b647f6d56aac52b1 |
# Dataset Card for AraStance
## 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-structur... | strombergnlp/ans-stance | [
"task_categories:text-classification",
"task_ids:fact-checking",
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"source_datasets:original",
"language:ar",
"license:apache-2.0",
"stance-detection",
"arxiv:2005.10410",
"... | 2022-05-20T11:30:15+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["ar"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["fact-checking"], "pretty_name": "ans-s... | 2022-10-25T20:45:09+00:00 | [
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| Dataset Card for AraStance
==========================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Sour... | [
"### Dataset Summary\n\n\nThe dataset is a collection of news titles in arabic along with paraphrased and corrupted titles. The stance prediction version is a 3-class classification task. Data contains three columns: s1, s2, stance.",
"### Languages\n\n\nArabic\n\n\nDataset Structure\n-----------------",
"### D... | [
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ae127f0d7aeb202279bcc18c547083ec32554879 | A chunk 3 of the Pile (2.2m documents) scored using the Perspective API (on May 18-20 2022) | tomekkorbak/pile-chunk-toxicity-scored-3 | [
"region:us"
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#region-us
| A chunk 3 of the Pile (2.2m documents) scored using the Perspective API (on May 18-20 2022) | [] | [
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bf7403628151c9b2968c88386e601fcd833fba23 |
# Dataset Card for ImageNet-Sketch
## 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-s... | imagenet_sketch | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
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"source_datasets:extended|imagenet-1k",
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"arxiv:... | 2022-05-20T13:13:58+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|imagenet-1k"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-c... | 2024-01-18T11:19:11+00:00 | [
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| Dataset Card for ImageNet-Sketch
================================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Ratio... | [
"### Dataset Summary\n\n\nImageNet-Sketch data set consists of 50000 images, 50 images for each of the 1000 ImageNet classes. We construct the data set with Google Image queries \"sketch of \\_\\_\", where \\_\\_ is the standard class name. We only search within the \"black and white\" color scheme. We initially qu... | [
"TAGS\n#task_categories-image-classification #task_ids-multi-class-image-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|imagenet-1k #language-English #license-unknown #arxiv-1905.13549 #region-us \n... | [
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34dd73d7e190f0b7f36895a97ac25b9b6f8702a3 | ## Generation procedure
The dataset was constructed using documents from [the Pile](https://pile.eleuther.ai/) scored using using [Perspective API](http://perspectiveapi.com) toxicity scores.
The procedure was the following:
1. A chunk of the Pile (2.2m documents) was scored using the Perspective API (on May 18-20 20... | tomekkorbak/pile-toxicity-balanced3 | [
"region:us"
] | 2022-05-20T13:22:55+00:00 | {} | 2022-05-20T17:36:32+00:00 | [] | [] | TAGS
#region-us
| ## Generation procedure
The dataset was constructed using documents from the Pile scored using using Perspective API toxicity scores.
The procedure was the following:
1. A chunk of the Pile (2.2m documents) was scored using the Perspective API (on May 18-20 2022) giving 'tomekkorbak/pile-chunk-toxicity-scored-3'.
1. ... | [
"## Generation procedure\n\nThe dataset was constructed using documents from the Pile scored using using Perspective API toxicity scores.\n\nThe procedure was the following:\n1. A chunk of the Pile (2.2m documents) was scored using the Perspective API (on May 18-20 2022) giving 'tomekkorbak/pile-chunk-toxicity-scor... | [
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990409f76b7c73da42f216ee4de99d8e02042cd8 | # Kinyarwanda dataset for text to speech model
Kinyarwanda dataset for text to speech model holds data for ai modelling of Kinyarwanda chatbots or other use cases. | DigitalUmuganda/kinyarwanda-tts-dataset | [
"region:us"
] | 2022-05-20T14:20:06+00:00 | {} | 2022-05-20T14:24:55+00:00 | [] | [] | TAGS
#region-us
| # Kinyarwanda dataset for text to speech model
Kinyarwanda dataset for text to speech model holds data for ai modelling of Kinyarwanda chatbots or other use cases. | [
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55c7948f856c532791a4e88a7a73562786e51184 |
# Dataset Card for DigitalUmuganda/common-voice-kinyarwanda-text-dataset
| DigitalUmuganda/common-voice-kinyarwanda-text-dataset | [
"annotations_creators:crowd-sourced",
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] | 2022-05-20T14:26:55+00:00 | {"annotations_creators": ["crowd-sourced"], "language_creators": ["Digital Umuganda"], "language": ["rw"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<3M"], "source_datasets": ["original"], "task_categories": ["Language-model", "Automatic-Speech-Recognition"], "task_ids": ["L... | 2022-10-25T04:36:26+00:00 | [] | [
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# Dataset Card for DigitalUmuganda/common-voice-kinyarwanda-text-dataset
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e964fc1f781ffc86641bc798e3f8d3a8237920c7 | # Dataset Card for ru-med-ner
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields]... | Rexhaif/ru-med-ner | [
"arxiv:2201.06499",
"region:us"
] | 2022-05-20T14:55:37+00:00 | {} | 2022-05-25T19:58:27+00:00 | [
"2201.06499"
] | [] | TAGS
#arxiv-2201.06499 #region-us
| # Dataset Card for ru-med-ner
## Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Additional Information
- Citation Information
## Dataset Description
- Homepage: URL
- Repository: URL
- Paper: URL
- Leaderboard: ... | [
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744088b586423735de4d4a6fcb79443fea0aeeeb | annotations_creators:
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- sentiment... | scoup123/testing | [
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d484d8212528d3cbce359c2f632f464a2d881efe | annotations_creators:
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licenses:
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paperswithcode_id: null
pretty_name: turkish_movie_reviews
size_categories:
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task_categories:
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- s... | scoup123/tr_movie_reviews_training | [
"license:other",
"region:us"
] | 2022-05-20T16:34:16+00:00 | {"license": "other"} | 2022-05-21T17:03:05+00:00 | [] | [] | TAGS
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98cc82c8d6f58fed2fb3280b3f4b73d103c5cf20 | Results of a sentiment analysis of ~70k Reddit posts/comments and 9.5 million Tweets that were classified with a fine-tuned DistilRoBERTa model. These data focus on discussion of COVID-19 vaccine are were collected from Jan 1, 2020 to March 1, 2022.
| NoCaptain/Twitter_Reddit_Comparison | [
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| Results of a sentiment analysis of ~70k Reddit posts/comments and 9.5 million Tweets that were classified with a fine-tuned DistilRoBERTa model. These data focus on discussion of COVID-19 vaccine are were collected from Jan 1, 2020 to March 1, 2022.
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09a707f91f0f0f3650148d7855e01cadc99f99c0 |
# Dataset Card for `reviews_with_drift`
## 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](#data... | arize-ai/movie_reviews_with_context_drift | [
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| Dataset Card for 'reviews\_with\_drift'
=======================================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
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+ Data Splits
* Dataset Creation
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cf7da89fb537074eb702eac535e1ebf7f8b455f2 | Conversational Question Generation (CoQG) | Hongwei/CoQG | [
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ee34247ae1e5c82e72e855a9d4f001112ccab46c |
# MediaSum dataset for summarization
Summarization dataset copied from [MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization](https://github.com/zcgzcgzcg1/MediaSum)
This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pyt... | ccdv/mediasum | [
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"region:us"
] | 2022-05-21T11:29:19+00:00 | {"language": ["en"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "task_categories": ["summarization", "text2text-generation"], "task_ids": [], "tags": ["conditional-text-generation"]} | 2022-10-25T09:56:04+00:00 | [] | [
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| MediaSum dataset for summarization
==================================
Summarization dataset copied from MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization
This dataset is compatible with the 'run\_summarization.py' script from Transformers if you add this line to the 'summarization\_name\_ma... | [
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36bbc805ae11c32ad32e9e8a359bdd770c76a40f | # Dataset Card for Million Headlines
## 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-inst... | rajistics/million-headlines | [
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| # Dataset Card for Million Headlines
## Table of Contents
- Dataset Description
- Dataset Summary
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- Dataset Creation
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89b78d0147c61de45d161c69f9a14beeab69f76f |
# Dataset Card for BBNLI
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#da... | feyzaakyurek/BBNLI | [
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|
# Dataset Card for BBNLI
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6d122e1220b5f19f9037ef86258c38064809adf1 | This dataset contains fake words and real words. The fake words are classified as "1" and the real words are classified as "0" | hidude562/Fake-and-real-words | [
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571644fedece092323049151970c5f7a0fb0c426 | 中国古典诗歌 | zhangqiaobit/chinese_poetrys | [
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32feeaede49fed993aef070bc4da09263fd0429a |
# Dataset Card for GovReport
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Versions](#versions)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset ... | launch/gov_report | [
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|
# Dataset Card for GovReport
## Table of Contents
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8c230d2333761d71def7a96a6b8ee13d64583552 |
# Dataset Card for GovReport-QS
## 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)
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# Dataset Card for GovReport-QS
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520b9744772dc84a3fc20f9468a1f59d0f4a2a24 | 唐诗三百首 | zhangqiaobit/tangshi | [
"region:us"
] | 2022-05-22T23:40:23+00:00 | {} | 2022-05-22T23:43:07+00:00 | [] | [] | TAGS
#region-us
| 唐诗三百首 | [] | [
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6
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93344f79551fab71578755a1b631658c7e85c15a |
# rumi-jawi
Notebooks to gather the dataset at https://github.com/huseinzol05/malay-dataset/tree/master/normalization/rumi-jawi | mesolitica/rumi-jawi | [
"task_categories:text2text-generation",
"language:ms",
"conditional-text-generation",
"region:us"
] | 2022-05-23T01:23:11+00:00 | {"language": "ms", "task_categories": ["text2text-generation"], "task_ids": [], "tags": ["conditional-text-generation"]} | 2023-06-14T14:50:17+00:00 | [] | [
"ms"
] | TAGS
#task_categories-text2text-generation #language-Malay (macrolanguage) #conditional-text-generation #region-us
|
# rumi-jawi
Notebooks to gather the dataset at URL | [
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"TAGS\n#task_categories-text2text-generation #language-Malay (macrolanguage) #conditional-text-generation #region-us \n",
"# rumi-jawi\n\nNotebooks to gather the dataset at URL"
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] |
3a82dabbba21756fef6e74d10968a828e2ca2fde |
### **Dataset summary**
This is a gold-standard benchmark dataset for document alignment, between Sinhala-English-Tamil languages.
Data had been crawled from the following news websites.
| News Source | url |
| ------------- |-----------------------------|
| Army | https://www.ar... | NLPC-UOM/document_alignment_dataset-Sinhala-Tamil-English | [
"task_categories:sentence-similarity",
"language:si",
"language:ta",
"language:en",
"region:us"
] | 2022-05-23T02:08:04+00:00 | {"language": ["si", "ta", "en"], "task_categories": ["sentence-similarity"]} | 2024-02-16T02:14:26+00:00 | [] | [
"si",
"ta",
"en"
] | TAGS
#task_categories-sentence-similarity #language-Sinhala #language-Tamil #language-English #region-us
| ### Dataset summary
This is a gold-standard benchmark dataset for document alignment, between Sinhala-English-Tamil languages.
Data had been crawled from the following news websites.
The aligned documents have been manually annotated.
### Dataset
The folder structure for each news source is as follows.
Sinha... | [
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943c6de2df24981c2717abf73e50adb87eb1a890 |
欢迎扫码加入微信交流群：
| breezedeus/cnocr-wx-qr-code | [
"license:apache-2.0",
"region:us"
] | 2022-05-23T02:18:44+00:00 | {"license": "apache-2.0"} | 2022-09-09T04:53:54+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
欢迎扫码加入微信交流群：
!微信群二维码
| [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] | [
14
] | [
"passage: TAGS\n#license-apache-2.0 #region-us \n"
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a9373ebed10c361d46fc56f38a8ee448d862ed6c | ### **Dataset summary**
This is a gold-standard benchmark dataset for sentence alignment, between Sinhala-English-Tamil languages. Data had been crawled from the following news websites. The aligned documents annotated in the dataset NLPC-UOM/document_alignment_dataset-Sinhala-Tamil-English had been considered to annot... | NLPC-UOM/sentence_alignment_dataset-Sinhala-Tamil-English | [
"task_categories:sentence-similarity",
"task_categories:translation",
"language:si",
"language:ta",
"language:en",
"region:us"
] | 2022-05-23T02:28:07+00:00 | {"language": ["si", "ta", "en"], "task_categories": ["sentence-similarity", "translation"]} | 2024-02-16T02:12:13+00:00 | [] | [
"si",
"ta",
"en"
] | TAGS
#task_categories-sentence-similarity #task_categories-translation #language-Sinhala #language-Tamil #language-English #region-us
| ### Dataset summary
This is a gold-standard benchmark dataset for sentence alignment, between Sinhala-English-Tamil languages. Data had been crawled from the following news websites. The aligned documents annotated in the dataset NLPC-UOM/document\_alignment\_dataset-Sinhala-Tamil-English had been considered to annot... | [
"### Dataset summary\n\n\nThis is a gold-standard benchmark dataset for sentence alignment, between Sinhala-English-Tamil languages. Data had been crawled from the following news websites. The aligned documents annotated in the dataset NLPC-UOM/document\\_alignment\\_dataset-Sinhala-Tamil-English had been considere... | [
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699143e74ee8fc20d035bcb95be5dc17b2147fba | # Dataset Card for "FTRACE"
## 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)
-... | ekinakyurek/ftrace | [
"task_ids:masked-language-modeling",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:TRex",
"source_datasets:Lama",
"language:en",
"license:cc-by-sa-4.0",
"license:cc-by-nc-4.0",
"arxiv:2205.11482",
"region:us"
] | 2022-05-23T03:33:24+00:00 | {"language": ["en"], "license": ["cc-by-sa-4.0", "cc-by-nc-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["TRex", "Lama"], "task_categories": ["influence-attribution", "information-retrieval", "question-answering-retrieval"], "task_ids": ["influence-attribution", "masked... | 2022-10-23T04:56:05+00:00 | [
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| Dataset Card for "FTRACE"
=========================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations... | [
"### Dataset Summary\n\n\n[PAPER]\nFTRACE is a zero-shot infromation retrieval benchmark deviced for tracing a language model’s predictions back to training examples. In the accompanying paper, we evaluate commonly studied influence methods, including gradient-based (TracIn) and embedding-based approaches. The data... | [
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066dd50d0f33c263821aaf7f29923d8b30b14afb | # GEM Submission
Submission name: This is a test name
| GEM-submissions/lewtun__this-is-a-test-name__1653295318 | [
"benchmark:gem",
"evaluation",
"benchmark",
"region:us"
] | 2022-05-23T07:41:59+00:00 | {"benchmark": "gem", "type": "prediction", "submission_name": "This is a test name", "tags": ["evaluation", "benchmark"]} | 2022-05-23T07:42:01+00:00 | [] | [] | TAGS
#benchmark-gem #evaluation #benchmark #region-us
| # GEM Submission
Submission name: This is a test name
| [
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0828fff3308a5b3ecd2672427ee23607caecf499 | # GEM Submission
Submission name: This is a test name
| GEM-submissions/lewtun__this-is-a-test-name__1653295430 | [
"benchmark:gem",
"evaluation",
"benchmark",
"region:us"
] | 2022-05-23T07:43:50+00:00 | {"benchmark": "gem", "type": "prediction", "submission_name": "This is a test name", "tags": ["evaluation", "benchmark"]} | 2022-05-23T07:43:53+00:00 | [] | [] | TAGS
#benchmark-gem #evaluation #benchmark #region-us
| # GEM Submission
Submission name: This is a test name
| [
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3846a9213fa4bd9b99e6e25f3796e410b24a2576 | # AutoTrain Dataset for project: Engage
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project Engage.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"f... | aflah/autotrain-data-Engage | [
"region:us"
] | 2022-05-23T08:15:01+00:00 | {} | 2022-05-23T08:26:40+00:00 | [] | [] | TAGS
#region-us
| AutoTrain Dataset for project: Engage
=====================================
Dataset Descritpion
-------------------
This dataset has been automatically processed by AutoTrain for project Engage.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Ins... | [
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"### Dataset Splits\n\n\nThis d... | [
"TAGS\n#region-us \n",
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829147f8f75a25f005913200eb5ed41fae320aa1 |
** Attention: There appears an overlap in train / test. I trained a model on the train set and achieved 100% acc on test set. With the original emotion dataset this is not the case (92.4% acc)** | mteb/emotion | [
"language:en",
"region:us"
] | 2022-05-23T08:55:39+00:00 | {"language": ["en"]} | 2022-09-27T18:14:18+00:00 | [] | [
"en"
] | TAGS
#language-English #region-us
|
Attention: There appears an overlap in train / test. I trained a model on the train set and achieved 100% acc on test set. With the original emotion dataset this is not the case (92.4% acc) | [] | [
"TAGS\n#language-English #region-us \n"
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10
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08c220ebcca353ac76fb681fa2224aa8ce2641ef |
# Dataset Card for AraStance
## 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-structur... | strombergnlp/ara-stance | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ar",
"license:cc-by-4.0",
"stance-detection",
"arxiv:2104.13559",
"r... | 2022-05-23T11:10:01+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["ar"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["fact-checking"], "pretty_name": "ara-st... | 2022-10-25T20:47:05+00:00 | [
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| Dataset Card for AraStance
==========================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Sour... | [
"### Dataset Summary\n\n\nThe AraStance dataset contains true and false claims, where each claim is paired with one or more documents. Each claim–article pair has a stance label: agree, disagree, discuss, or unrelated.",
"### Languages\n\n\nArabic\n\n\nDataset Structure\n-----------------",
"### Data Instances\... | [
"TAGS\n#task_categories-text-classification #task_ids-fact-checking #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Arabic #license-cc-by-4.0 #stance-detection #arxiv-2104.13559 #region-us \n",
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e10ced5885eee436783059b4b6b4c8cc50d3576b | <div id="top"></div>
<!-- PROJECT SHIELDS -->
<!-- PROJECT LOGO -->
<br />
<div align="center">
<h3 align="center">SimRelUz: Similarity and Relatedness scores as a Semantic Evaluation dataset for Uzbek language</h3>
<p align="center">
We present a semantic model evaluation dataset: SimRelUz - a collection of... | elmurod1202/SimRelUz_semantic_evaluation_dataset | [
"region:us"
] | 2022-05-23T13:50:52+00:00 | {} | 2022-05-23T13:58:05+00:00 | [] | [] | TAGS
#region-us
|
### SimRelUz: Similarity and Relatedness scores as a Semantic Evaluation dataset for Uzbek language
We present a semantic model evaluation dataset: SimRelUz - a collection of similarity and relatedness scores of word pairs for Uzbek language. The dataset consists of more than a thousand pairs of words care... | [
"### SimRelUz: Similarity and Relatedness scores as a Semantic Evaluation dataset for Uzbek language\n\n\n\n We present a semantic model evaluation dataset: SimRelUz - a collection of similarity and relatedness scores of word pairs for Uzbek language. The dataset consists of more than a thousand pairs of words care... | [
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"passage: TAGS\n#region-us \n### SimRelUz: Similarity and Relatedness scores as a Semantic Evaluation dataset for Uzbek language\n\n\n\n We present a semantic model evaluation dataset: SimRelUz - a collection of similarity and relatedness scores of word pairs for Uzbek language. The dataset consists of more than a ... |
d9d7dfd0fc2b54f0dc16165ed2ace396ad90bf22 | Spatialized Libri-Trans and Spatialized SLURP (LT-S and SLURP-S), Enhancement for Translation and Understanding dataset | espnet/Libri-Trans-Spatialized_SLURP-Spatialized_dataset | [
"license:cc-by-4.0",
"region:us"
] | 2022-05-23T13:56:12+00:00 | {"license": "cc-by-4.0"} | 2022-06-12T07:34:02+00:00 | [] | [] | TAGS
#license-cc-by-4.0 #region-us
| Spatialized Libri-Trans and Spatialized SLURP (LT-S and SLURP-S), Enhancement for Translation and Understanding dataset | [] | [
"TAGS\n#license-cc-by-4.0 #region-us \n"
] | [
15
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69be6cc1811cdae2c649ea6d95feaed35c3928c3 | # AutoTrain Dataset for project: osdg-sdg-classifier
## Dataset Descritpion
This dataset has been pre-processed using standard python cleaning functions and further automatically processed by AutoTrain for project osdg-sdg-classifier.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Struc... | jonas/osdg_sdg_data_processed | [
"task_categories:text-classification",
"language:en",
"region:us"
] | 2022-05-23T14:53:20+00:00 | {"language": ["en"], "task_categories": ["text-classification"]} | 2022-10-25T09:26:04+00:00 | [] | [
"en"
] | TAGS
#task_categories-text-classification #language-English #region-us
| AutoTrain Dataset for project: osdg-sdg-classifier
==================================================
Dataset Descritpion
-------------------
This dataset has been pre-processed using standard python cleaning functions and further automatically processed by AutoTrain for project osdg-sdg-classifier.
### Languages... | [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------",
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"### Dataset Splits\n\n\nThis da... | [
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b4bd16976bb5b530be1b6b8dd82a7b4a4c26dc23 | # Dataset Card for "amazon-shoe-reviews"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | juliensimon/amazon-shoe-reviews | [
"language:en",
"region:us"
] | 2022-05-23T15:20:41+00:00 | {"language": "en", "dataset_info": {"features": [{"name": "labels", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16847665.2, "num_examples": 90000}, {"name": "test", "num_bytes": 1871962.8, "num_examples": 10000}], "download_size": 0, "dataset_size": 18719628.0}} | 2023-10-09T12:22:34+00:00 | [] | [
"en"
] | TAGS
#language-English #region-us
| # Dataset Card for "amazon-shoe-reviews"
More Information needed | [
"# Dataset Card for \"amazon-shoe-reviews\"\n\nMore Information needed"
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6a1046e7064c195bdd67487017c684cb1684a2a0 | # Title
EntSUM: A Data Set for Entity-Centric Extractive Summarization
# Author list
Mounica Maddela*, Mayank Kulkarni*, Daniel Preotiuc-Pietro
# Description
Controllable summarization aims to provide summaries that take into account user-specified aspects and preferences to better assist them with their information... | bloomberg/entsum | [
"region:us"
] | 2022-05-23T18:53:38+00:00 | {} | 2022-05-23T20:03:41+00:00 | [] | [] | TAGS
#region-us
| # Title
EntSUM: A Data Set for Entity-Centric Extractive Summarization
# Author list
Mounica Maddela*, Mayank Kulkarni*, Daniel Preotiuc-Pietro
# Description
Controllable summarization aims to provide summaries that take into account user-specified aspects and preferences to better assist them with their information... | [
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"# Author list\nMounica Maddela*, Mayank Kulkarni*, Daniel Preotiuc-Pietro",
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9abd1d1cea118ad7a9946e7f1f5a1a29c2a01762 |
# Dataset Card for DivEMT
*For more details on DivEMT, see our [EMNLP 2022 Paper](https://arxiv.org/abs/2205.12215) and our [Github repository](https://github.com/gsarti/divemt)*
## Dataset Description
- **Source:** [Github](https://github.com/gsarti/divemt)
- **Paper:** [Arxiv](https://arxiv.org/abs/2205.12215)
- *... | GroNLP/divemt | [
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"language:en",
"language:it",
"language:vi",
"language:nl",
"langu... | 2022-05-23T18:56:55+00:00 | {"annotations_creators": ["machine-generated", "expert-generated"], "language_creators": ["found"], "language": ["en", "it", "vi", "nl", "uk", "tr", "ar"], "license": ["gpl-3.0"], "multilinguality": ["translation"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["translation"], "p... | 2023-02-10T11:04:33+00:00 | [
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=======================
*For more details on DivEMT, see our EMNLP 2022 Paper and our Github repository*
Dataset Description
-------------------
* Source: Github
* Paper: Arxiv
* Point of Contact: Gabriele Sarti
Gabriele Sarti • Arianna Bisazza • Ana Guerberof Arenas • Antonio Toral
<i... | [
"### Dataset Summary\n\n\nThis dataset contains the processed 'warmup' and 'main' splits of the DivEMT dataset. A sample of documents extracted from the Flores-101 corpus were either translated from scratch or post-edited from an existing automatic translation by a total of 18 professional translators across six ty... | [
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75ed61a64c911e1b3d28fcb0ea8735a33521382f |
# Dataset Card for resd
## 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)
... | Aniemore/resd | [
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"lice... | 2022-05-23T21:57:03+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated", "crowdsourced"], "language": ["ru"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["audio-classification"], "task_ids": ["audio-emotion... | 2023-06-10T21:15:40+00:00 | [] | [
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# Dataset Card for resd
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal an... | [
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8d0f945f1ffb7c14fe7aab860a06cd267a8a96c3 |
# DCASE 2022 Task 3 Data sets: STARSS22 Dataset + Synthetic SELD mixtures
[Audio Research Group / Tampere University](https://webpages.tuni.fi/arg/)
[Creative AI Lab/ SONY R&D Center](https://www.sony.com/en/SonyInfo/research/research-areas/audio-acoustics/)
## Important
**This is a copy from the Zenodo Original on... | Fhrozen/dcase22_task3 | [
"task_categories:audio-classification",
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"annotations_creators:unknown",
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] | 2022-05-23T22:55:57+00:00 | {"annotations_creators": ["unknown"], "language_creators": ["unknown"], "license": "mit", "size_categories": ["100K<n<100M"], "source_datasets": ["unknown"], "task_categories": ["audio-classification"], "task_ids": ["slot-filling"]} | 2022-10-19T20:37:29+00:00 | [] | [] | TAGS
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|
# DCASE 2022 Task 3 Data sets: STARSS22 Dataset + Synthetic SELD mixtures
Audio Research Group / Tampere University
Creative AI Lab/ SONY R&D Center
## Important
This is a copy from the Zenodo Original one
AUTHORS
Tampere University
- Archontis Politis (contact, profile)
- Parthasaarathy Sudarsanam(contact, profi... | [
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1653b549a5ffd92c52bc5336c0200dead526f5c1 |
## Synthesized voices from Project Echo on the Skyrim voice datasets. | Etephyr/Project-Echo | [
"license:mit",
"region:us"
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#license-mit #region-us
|
## Synthesized voices from Project Echo on the Skyrim voice datasets. | [
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061911863bb36ea787931d7f31588f8773218173 | # Schutz 2008 PubMed dataset for keyphrase extraction
## About
This dataset is made of 1320 articles with full text and author assigned keyphrases.
Details about the dataset can be found in the original paper:
Keyphrase extraction from single documents in the open domain exploiting linguistic and statistical method... | taln-ls2n/pubmed | [
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| Schutz 2008 PubMed dataset for keyphrase extraction
===================================================
About
-----
This dataset is made of 1320 articles with full text and author assigned keyphrases.
Details about the dataset can be found in the original paper:
Keyphrase extraction from single documents in the o... | [] | [
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] |
ccd09a46a73d31fdf3821ff736a52d07e7f21a76 |
# Dataset Card for machine_translated_cnn_dailymail_da_small
### Dataset Summary
This dataset is a machine translated subset of the [CNN Dailymail Dataset](https://huggingface.co/datasets/ccdv/cnn_dailymail) into Danish. The dataset is translated using the [Helsinki-NLP/opus-mt-en-da](https://huggingface.co/Helsinki... | ajders/machine_translated_cnn_dailymail_da_small | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:translation",
"size_categories:1K<n<10K",
"language:da",
"license:apache-2.0",
"region:us"
] | 2022-05-24T10:51:34+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["da"], "license": ["apache-2.0"], "multilinguality": ["translation"], "size_categories": ["1K<n<10K"], "source_datasets": [], "task_categories": ["summarization"], "task_ids": ["news-articles-summarization"], "prett... | 2022-08-26T12:01:36+00:00 | [] | [
"da"
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|
# Dataset Card for machine_translated_cnn_dailymail_da_small
### Dataset Summary
This dataset is a machine translated subset of the CNN Dailymail Dataset into Danish. The dataset is translated using the Helsinki-NLP/opus-mt-en-da-model. The dataset consists of 2872 articles with summaries with intended usage for Dan... | [
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c5aefa5486316e6a69ae5be90e174c41d8824b38 | # AutoTrain Dataset for project: test-auto
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project test-auto.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
... | supermario/autotrain-data-test-auto | [
"region:us"
] | 2022-05-24T13:48:44+00:00 | {"task_categories": ["conditional-text-generation"]} | 2022-05-24T21:49:55+00:00 | [] | [] | TAGS
#region-us
| AutoTrain Dataset for project: test-auto
========================================
Dataset Descritpion
-------------------
This dataset has been automatically processed by AutoTrain for project test-auto.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
###... | [
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967c5bce6c6989b18db41794965a9291d2abecb4 | ss | gzbang/datasetest | [
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9327c1f16fe9fb20d0dadcdd3394edb2fddc3ab2 |
# Dataset Card for CEDR-M7
## 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... | Aniemore/cedr-m7 | [
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] | 2022-05-24T17:01:54+00:00 | {"annotations_creators": ["found"], "language_creators": ["found"], "language": ["ru"], "license": "mit", "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|cedr"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "cedr-... | 2022-07-01T15:39:56+00:00 | [] | [
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|
# Dataset Card for CEDR-M7
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal... | [
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1d2adddeea3cdd10fc9f2c90d96c1967ddf8b066 | ### Dataset Summary
The dataset contains user reviews about medical facilities.
In total it contains 70,597 reviews. The detailed distribution on sentiment scale is:
- 41,419 positive reviews;
- 29,178 negative reviews.
### Data Fields
Each sample contains the following fields:
- **review_id**;
- **category** category... | blinoff/healthcare_facilities_reviews | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:ru",
"region:us"
] | 2022-05-25T09:48:13+00:00 | {"language": ["ru"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"]} | 2022-10-23T15:50:31+00:00 | [] | [
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] | TAGS
#task_categories-text-classification #task_ids-sentiment-classification #multilinguality-monolingual #size_categories-10K<n<100K #language-Russian #region-us
| ### Dataset Summary
The dataset contains user reviews about medical facilities.
In total it contains 70,597 reviews. The detailed distribution on sentiment scale is:
- 41,419 positive reviews;
- 29,178 negative reviews.
### Data Fields
Each sample contains the following fields:
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30bbf57f4947411efadba96fc5a3dde190c2c73b |
Regarding image classification automation, Maderapp's botanical team worked many hours to collect, validate, and correctly label 25000 tree macroscopic images of 25 species from the Peruvian Amazonia.
The team captured these images with a mobile device's camera and a digital microscope. Each image has a resolution o... | anvelezec/maderapp | [
"license:mit",
"region:us"
] | 2022-05-25T16:32:10+00:00 | {"license": "mit"} | 2022-05-25T16:37:17+00:00 | [] | [] | TAGS
#license-mit #region-us
|
Regarding image classification automation, Maderapp's botanical team worked many hours to collect, validate, and correctly label 25000 tree macroscopic images of 25 species from the Peruvian Amazonia.
The team captured these images with a mobile device's camera and a digital microscope. Each image has a resolution o... | [] | [
"TAGS\n#license-mit #region-us \n"
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11
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98af40a17da2d6904a8ef1e0eb2a7e9fa394e6b8 |
# Dataset Card for GTZAN Collection
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data ... | Lehrig/GTZAN-Collection | [
"license:apache-2.0",
"region:us"
] | 2022-05-25T19:16:44+00:00 | {"license": "apache-2.0"} | 2022-06-13T12:54:08+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
# Dataset Card for GTZAN Collection
## Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
-... | [
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e1d8bda201b4f7e71daa3d64d757e0cbebb40e76 |
# Dataset Card for News_Articles_Categorization
## Table of Contents
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Source Data](#source-data)
## Dataset Description
3722 News Articles classified into different categories namely: World, Politics,... | valurank/News_Articles_Categorization | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | 2022-05-25T20:46:45+00:00 | {"language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"]} | 2023-08-27T04:49:31+00:00 | [] | [
"en"
] | TAGS
#task_categories-text-classification #task_ids-multi-class-classification #multilinguality-monolingual #language-English #license-other #region-us
|
# Dataset Card for News_Articles_Categorization
## Table of Contents
- Dataset Description
- Languages
- Dataset Structure
- Source Data
## Dataset Description
3722 News Articles classified into different categories namely: World, Politics, Tech, Entertainment, Sport, Business, Health, and Science
## Languages
Th... | [
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d973e22dbfac5433efd91ba4c5cd4376984fe9e9 |
# Dataset Card for UlyssesNER-Br
## 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-str... | ulysses-camara/ulysses-ner-br | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:pt",
"region:us"
] | 2022-05-26T02:04:36+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["pt"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": [], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "UlyssesNER-br"} | 2022-10-25T09:26:07+00:00 | [] | [
"pt"
] | TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #size_categories-10K<n<100K #language-Portuguese #region-us
|
# Dataset Card for UlyssesNER-Br
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Pe... | [
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98604ca1c1567c474a1301b22527acdc682a9ba6 | ---
annotations_creators:
- no-annotation
languages:
- en
# Dataset Card for GamePhysics_Grand_Theft_Auto_V
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structu... | taesiri/GamePhysics_Grand_Theft_Auto_V | [
"region:us"
] | 2022-05-26T04:43:59+00:00 | {} | 2024-01-10T04:55:24+00:00 | [] | [] | TAGS
#region-us
| ---
annotations_creators:
- no-annotation
languages:
- en
# Dataset Card for GamePhysics_Grand_Theft_Auto_V
## Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
... | [
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2d8a100785abf0ae21420d2a55b0c56e3e1ea996 |
# Amazon Multilingual Counterfactual Dataset
The dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary classification. Counterfactual statements describe events that did not or cannot take place. Counterfactual stateme... | mteb/amazon_counterfactual | [
"language:de",
"language:en",
"language:ja",
"arxiv:2104.06893",
"region:us"
] | 2022-05-26T09:48:56+00:00 | {"language": ["de", "en", "ja"]} | 2022-09-27T18:10:37+00:00 | [
"2104.06893"
] | [
"de",
"en",
"ja"
] | TAGS
#language-German #language-English #language-Japanese #arxiv-2104.06893 #region-us
|
# Amazon Multilingual Counterfactual Dataset
The dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary classification. Counterfactual statements describe events that did not or cannot take place. Counterfactual stateme... | [
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ddb4732a1c1977ae2015ff516e8258a216cba413 | Libriadapt | LTress/lrl_transfer_hubert | [
"region:us"
] | 2022-05-26T10:44:55+00:00 | {} | 2022-07-29T15:51:18+00:00 | [] | [] | TAGS
#region-us
| Libriadapt | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] |
791ae7245f0616c61617304e534d5c4728336523 | annotations_creators:
- expert-generated
language_creators:
- found
languages:
- en
licenses:
- mit
multilinguality:
- monolingual
paperswithcode_id: acronym-identification
pretty_name: disaster
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids: [] | ErenHali/disaster_edited | [
"license:afl-3.0",
"region:us"
] | 2022-05-26T12:28:41+00:00 | {"license": "afl-3.0"} | 2022-05-26T12:41:41+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
| annotations_creators:
- expert-generated
language_creators:
- found
languages:
- en
licenses:
- mit
multilinguality:
- monolingual
paperswithcode_id: acronym-identification
pretty_name: disaster
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids: [] | [] | [
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14
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2f5888c3c452b33e431865a6d4461d7ca823375f | We used the APCD dataset cited hereafter for pretraining the model. The dataset has been cleaned and only the main text and the meter columns were kept:
```
@Article{Yousef2019LearningMetersArabicEnglish-arxiv,
author = {Yousef, Waleed A. and Ibrahime, Omar M. and Madbouly, Taha M. and Mahmoud,
... | Yah216/APCD_only_meter_data | [
"region:us"
] | 2022-05-26T13:19:32+00:00 | {} | 2022-05-28T07:00:57+00:00 | [] | [] | TAGS
#region-us
| We used the APCD dataset cited hereafter for pretraining the model. The dataset has been cleaned and only the main text and the meter columns were kept:
| [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
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43265056790b8f7c59e0139acb4be0a8dad2c8f4 |
# Dataset Card for ParaPhraser
### Dataset Summary
ParaPhraser is a news headlines corpus annotated according to the following schema:
```
1: precise paraphrases
0: near paraphrases
-1: non-paraphrases
```
The _Plus_ part is also available.
It contains clusters of news headline paraphrases labeled automatically by ... | merionum/ru_paraphraser | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:sentence-similarity",
"task_ids:semantic-similarity-scoring",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"annotations_creators:machine-g... | 2022-05-26T13:53:46+00:00 | {"annotations_creators": ["crowdsourced", "expert-generated", "machine-generated"], "language_creators": ["crowdsourced"], "language": ["ru"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-classification", "text-genera... | 2022-07-28T14:01:08+00:00 | [] | [
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# Dataset Card for ParaPhraser
### Dataset Summary
ParaPhraser is a news headlines corpus annotated according to the following schema:
The _Plus_ part is also available.
It contains clusters of news headline paraphrases labeled automatically by a fine-tuned paraphrase detection BERT model.
In order to load it:
... | [
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edfaf9da55d3dd50d43143d90c1ac476895ae6de |
# Toxic Conversation
This is a version of the [Jigsaw Unintended Bias in Toxicity Classification dataset](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview). It contains comments from the Civil Comments platform together with annotations if the comment is toxic or not.
This dataset ... | mteb/toxic_conversations_50k | [
"language:en",
"region:us"
] | 2022-05-26T16:47:49+00:00 | {"language": ["en"]} | 2022-09-27T18:14:35+00:00 | [] | [
"en"
] | TAGS
#language-English #region-us
|
# Toxic Conversation
This is a version of the Jigsaw Unintended Bias in Toxicity Classification dataset. It contains comments from the Civil Comments platform together with annotations if the comment is toxic or not.
This dataset just contains the first 50k training examples.
10 annotators annotated each example an... | [
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303f2ed3d8b3f3915b254a60e9c146b8c4f8402a |
# Citations
```
@misc{Aniemore,
author = {Артем Аментес, Илья Лубенец, Никита Давидчук},
title = {Открытая библиотека искусственного интеллекта для анализа и выявления эмоциональных оттенков речи человека},
year = {2022},
publisher = {Hugging Face},
journal = {Hugging Face Hub},
howpublished = {\url{https:... | Aniemore/REPV | [
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"source_datasets:original",
"language:ru",
"license:... | 2022-05-26T21:15:17+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["expert-generated", "crowdsourced"], "language": ["ru"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["audio-classification"], "task_ids": ["audio-emotion-rec... | 2022-07-01T15:41:13+00:00 | [] | [
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|
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0f565390b7dc577a92da3bc97b47a02bfb4066b5 |
# Citations
```
@misc{Aniemore,
author = {Артем Аментес, Илья Лубенец, Никита Давидчук},
title = {Открытая библиотека искусственного интеллекта для анализа и выявления эмоциональных оттенков речи человека},
year = {2022},
publisher = {Hugging Face},
journal = {Hugging Face Hub},
howpublished = {\url{https:... | Aniemore/REPV-S | [
"task_categories:audio-classification",
"task_ids:audio-emotion-recognition",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ru",
"license:... | 2022-05-26T21:15:35+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["expert-generated", "crowdsourced"], "language": ["ru"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["audio-classification"], "task_ids": ["audio-emotion-rec... | 2022-10-25T09:28:15+00:00 | [] | [
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0f8b622361419262cb23bd36e31c8e182fbff375 | # Kinyarwanda TTS dataset
The dataset consists of 3992 clips of Kinyarwanda TTS corpus recorded in a studio using a voice actress, it was collected in the mbaza project
## Data structure
```
Audio: 3992 Single voice studio recordings by a voice actress
Text: CSV with audio name and corresponding written text
```
## ... | mbazaNLP/kinyarwanda-tts-dataset | [
"language_creators:Digital Umuganda",
"size_categories:3K<n<4K",
"size_categories:~6hours",
"language:rw",
"license:cc-by-4.0",
"region:us"
] | 2022-05-27T07:20:36+00:00 | {"language_creators": ["Digital Umuganda"], "language": ["rw"], "license": ["cc-by-4.0"], "size_categories": ["3K<n<4K", "~6hours"]} | 2023-06-27T07:09:28+00:00 | [] | [
"rw"
] | TAGS
#language_creators-Digital Umuganda #size_categories-3K<n<4K #size_categories-~6hours #language-Kinyarwanda #license-cc-by-4.0 #region-us
| # Kinyarwanda TTS dataset
The dataset consists of 3992 clips of Kinyarwanda TTS corpus recorded in a studio using a voice actress, it was collected in the mbaza project
## Data structure
## Language
The corresponding dataset is in the Kinyarwanda Language
## Dataset Creation
- Text collected had to include Kinyarw... | [
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f0ad03e8c70dd3a3bff36b2ff1b29d2c1a8ce330 |
# Dataset for evaluation of (zero-shot) recommendation with language models
We showed that pretrained large language models can act as a recommender system, and compare few-shot learning results to matrix factorization baselines.
This is the BIG-Bench version of our language-based movie recommendation dataset.
<htt... | sileod/movie_recommendation | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language... | 2022-05-27T07:25:19+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["multiple-choice", "question-answering"], "task_ids": ["multiple-choi... | 2023-05-25T13:53:49+00:00 | [] | [
"en"
] | TAGS
#task_categories-multiple-choice #task_categories-question-answering #task_ids-multiple-choice-qa #task_ids-open-domain-qa #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-apache-2.0 #movie... |
# Dataset for evaluation of (zero-shot) recommendation with language models
We showed that pretrained large language models can act as a recommender system, and compare few-shot learning results to matrix factorization baselines.
This is the BIG-Bench version of our language-based movie recommendation dataset.
<URL... | [
"# Dataset for evaluation of (zero-shot) recommendation with language models\n\nWe showed that pretrained large language models can act as a recommender system, and compare few-shot learning results to matrix factorization baselines.\nThis is the BIG-Bench version of our language-based movie recommendation dataset.... | [
"TAGS\n#task_categories-multiple-choice #task_categories-question-answering #task_ids-multiple-choice-qa #task_ids-open-domain-qa #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-apache-2.0 ... | [
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e34664558b2fe83c83d7886f7623a2d2607cc4db |
# Dataset for evaluation of (zero-shot) discourse marker prediction with language models
This is the Big-Bench version of our discourse marker prediction dataset, [Discovery](https://huggingface.co/datasets/discovery)
Design considerations:
<https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/disc... | sileod/discourse_marker_qa | [
"task_categories:question-answering",
"task_categories:multiple-choice",
"task_ids:open-domain-qa",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language... | 2022-05-27T08:37:00+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["question-answering", "multiple-choice"], "task_ids": ["open-domain-q... | 2022-07-19T12:00:05+00:00 | [] | [
"en"
] | TAGS
#task_categories-question-answering #task_categories-multiple-choice #task_ids-open-domain-qa #task_ids-multiple-choice-qa #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-apache-2.0 #regio... |
# Dataset for evaluation of (zero-shot) discourse marker prediction with language models
This is the Big-Bench version of our discourse marker prediction dataset, Discovery
Design considerations:
<URL
GPT2 has to zero-shot 15% accuracy with on this multiple-choice task based on language modeling perplexity. As a co... | [
"# Dataset for evaluation of (zero-shot) discourse marker prediction with language models\n\nThis is the Big-Bench version of our discourse marker prediction dataset, Discovery\n\nDesign considerations:\n<URL\n\nGPT2 has to zero-shot 15% accuracy with on this multiple-choice task based on language modeling perplexi... | [
"TAGS\n#task_categories-question-answering #task_categories-multiple-choice #task_ids-open-domain-qa #task_ids-multiple-choice-qa #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-apache-2.0 ... | [
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57ed1c1d3e754be42a810973987f8f646cc9d103 | ### Dataset Summary
The dataset contains user reviews about medical institutions.
In total it contains 12,036 reviews. A review tagged with the <em>general</em> sentiment and sentiments on 5 aspects: <em>quality, service, equipment, food, location</em>.
### Data Fields
Each sample contains the following fields:
- **re... | blinoff/medical_institutions_reviews | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:ru",
"region:us"
] | 2022-05-27T09:09:02+00:00 | {"language": ["ru"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"]} | 2022-10-23T15:51:28+00:00 | [] | [
"ru"
] | TAGS
#task_categories-text-classification #task_ids-sentiment-classification #multilinguality-monolingual #size_categories-10K<n<100K #language-Russian #region-us
| ### Dataset Summary
The dataset contains user reviews about medical institutions.
In total it contains 12,036 reviews. A review tagged with the <em>general</em> sentiment and sentiments on 5 aspects: <em>quality, service, equipment, food, location</em>.
### Data Fields
Each sample contains the following fields:
- revi... | [
"### Dataset Summary\nThe dataset contains user reviews about medical institutions.\n\nIn total it contains 12,036 reviews. A review tagged with the <em>general</em> sentiment and sentiments on 5 aspects: <em>quality, service, equipment, food, location</em>.",
"### Data Fields\nEach sample contains the following ... | [
"TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #multilinguality-monolingual #size_categories-10K<n<100K #language-Russian #region-us \n",
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"passage: TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #multilinguality-monolingual #size_categories-10K<n<100K #language-Russian #region-us \n### Dataset Summary\nThe dataset contains user reviews about medical institutions.\n\nIn total it contains 12,036 reviews. A review tagged w... |
cee72850f435666f691a16513734961e9ca0845e | # AutoTrain Dataset for project: Poem_Rawiy_detection
## Dataset Descritpion
We used the APCD dataset cited hereafter for pretraining the model. The dataset has been cleaned and only the main text and the Qafiyah columns were kept:
```
@Article{Yousef2019LearningMetersArabicEnglish-arxiv,
author = {Yousef, Wa... | Yah216/APCD-Poem_Rawiy_detection | [
"task_categories:text-classification",
"language:ar",
"region:us"
] | 2022-05-27T15:11:29+00:00 | {"language": ["ar"], "task_categories": ["text-classification"]} | 2022-10-25T09:28:52+00:00 | [] | [
"ar"
] | TAGS
#task_categories-text-classification #language-Arabic #region-us
| AutoTrain Dataset for project: Poem\_Rawiy\_detection
=====================================================
Dataset Descritpion
-------------------
We used the APCD dataset cited hereafter for pretraining the model. The dataset has been cleaned and only the main text and the Qafiyah columns were kept:
### Languag... | [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is ar.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis da... | [
"TAGS\n#task_categories-text-classification #language-Arabic #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is ar.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the fo... | [
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"passage: TAGS\n#task_categories-text-classification #language-Arabic #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is ar.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following f... |
6dbc19c850ac0c6ff6050b61eedb586a5fd36ad4 | We used the APCD dataset cited hereafter for pretraining the model. The dataset has been cleaned and only the main text column was kept:
```
@Article{Yousef2019LearningMetersArabicEnglish-arxiv,
author = {Yousef, Waleed A. and Ibrahime, Omar M. and Madbouly, Taha M. and Mahmoud,
Moustafa A.},
... | Yah216/Poem_APCD_text_only | [
"region:us"
] | 2022-05-27T16:06:24+00:00 | {} | 2022-05-28T07:00:27+00:00 | [] | [] | TAGS
#region-us
| We used the APCD dataset cited hereafter for pretraining the model. The dataset has been cleaned and only the main text column was kept:
| [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] |
efa172b567cb7b51d9bdf36f97ac0244e1dfb1b1 |
# Inclusive words in German 🏳️🌈 🇩🇪
Pairs of words and phrases in exclusive language and alternative words and phrases in inclusive language.
Inclusivity aims to comprehend all [dimensions of diversity](https://www.charta-der-vielfalt.de/en/understanding-diversity/diversity-dimensions/) (age, ethnic background a... | diversifix/inclusive_words | [
"language:de",
"license:other",
"region:us"
] | 2022-05-28T14:04:51+00:00 | {"language": "de", "license": "other"} | 2022-09-04T12:29:26+00:00 | [] | [
"de"
] | TAGS
#language-German #license-other #region-us
|
# Inclusive words in German ️ 🇩🇪
Pairs of words and phrases in exclusive language and alternative words and phrases in inclusive language.
Inclusivity aims to comprehend all dimensions of diversity (age, ethnic background and nationality, gender and gender identity, physical and mental abilities, religion and wor... | [
"# Inclusive words in German ️ 🇩🇪\n\nPairs of words and phrases in exclusive language and alternative words and phrases in inclusive language.\n\nInclusivity aims to comprehend all dimensions of diversity (age, ethnic background and nationality, gender and gender identity, physical and mental abilities, religion... | [
"TAGS\n#language-German #license-other #region-us \n",
"# Inclusive words in German ️ 🇩🇪\n\nPairs of words and phrases in exclusive language and alternative words and phrases in inclusive language.\n\nInclusivity aims to comprehend all dimensions of diversity (age, ethnic background and nationality, gender and... | [
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"passage: TAGS\n#language-German #license-other #region-us \n# Inclusive words in German ️ 🇩🇪\n\nPairs of words and phrases in exclusive language and alternative words and phrases in inclusive language.\n\nInclusivity aims to comprehend all dimensions of diversity (age, ethnic background and nationality, gender ... |
3e6986ef2c0261ecae40bcb3c3e4bdf63200c917 |
This is a resume sentence classification dataset constructed based on resume text.（https://www.kaggle.com/datasets/oo7kartik/resume-text-batch）
The dataset have seven category.(experience education knowledge project others ) And three element label(header content meta).
Because the dataset is a published paper, i... | ganchengguang/resume_seven_class | [
"license:apache-2.0",
"arxiv:2208.03219",
"arxiv:2209.09450",
"region:us"
] | 2022-05-29T05:31:44+00:00 | {"license": "apache-2.0"} | 2023-05-30T07:11:48+00:00 | [
"2208.03219",
"2209.09450"
] | [] | TAGS
#license-apache-2.0 #arxiv-2208.03219 #arxiv-2209.09450 #region-us
|
This is a resume sentence classification dataset constructed based on resume text.（URL）
The dataset have seven category.(experience education knowledge project others ) And three element label(header content meta).
Because the dataset is a published paper, if you want to use this dataset in a paper or work, pleas... | [] | [
"TAGS\n#license-apache-2.0 #arxiv-2208.03219 #arxiv-2209.09450 #region-us \n"
] | [
31
] | [
"passage: TAGS\n#license-apache-2.0 #arxiv-2208.03219 #arxiv-2209.09450 #region-us \n"
] |
5bd582fa28cd7143f2f9c852e08e23089d677c44 |
# Dataset Card for lccc_large
## Table of Contents
- [Dataset Card for lccc_large](#dataset-card-for-lccc_large)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboa... | silver/lccc | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:zh",
"license:mit",
"dialogue-response-retrieval",
"arxiv:2008.03946",
"... | 2022-05-29T08:19:28+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["zh"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["conversational"], "task_ids": ["dialogue-generation"], "pretty_name": "lccc", "tags": [... | 2022-11-06T04:51:16+00:00 | [
"2008.03946"
] | [
"zh"
] | TAGS
#task_categories-conversational #task_ids-dialogue-generation #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-Chinese #license-mit #dialogue-response-retrieval #arxiv-2008.03946 #region-us
| Dataset Card for lccc\_large
============================
Table of Contents
-----------------
* Dataset Card for lccc\_large
+ Table of Contents
+ Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
+ Dataset Structure
- Data Instances
- Data Fields
- Data Splits
+ D... | [
"### Dataset Summary\n\n\nlccc: Large-scale Cleaned Chinese Conversation corpus (LCCC) is a large Chinese dialogue corpus originate from Chinese social medias. A rigorous data cleaning pipeline is designed to ensure the quality of the corpus. This pipeline involves a set of rules and several classifier-based filter... | [
"TAGS\n#task_categories-conversational #task_ids-dialogue-generation #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-Chinese #license-mit #dialogue-response-retrieval #arxiv-2008.03946 #region-us \n",
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ecff4b19adb1f4161ea79ad947aaf9089217c34b |
# Dataset Card for MMChat
## Table of Contents
- [Dataset Card for MMChat](#dataset-card-for-mmchat)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [... | silver/mmchat | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:no-annotation",
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"source_datasets:original",
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"license:other",
"arxiv:2108.07154",
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"r... | 2022-05-29T10:15:03+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["zh"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["conversational"], "task_ids": ["dialogue-generation"], "paperswithcode_id": "... | 2022-07-10T12:04:36+00:00 | [
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| Dataset Card for MMChat
=======================
Table of Contents
-----------------
* Dataset Card for MMChat
+ Table of Contents
+ Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
+ Dataset Structure
- Data Instances
- Data Fields
- Data Splits
+ Dataset Creation... | [
"### Dataset Summary\n\n\nMMChat is a large-scale dialogue dataset that contains image-grounded dialogues in Chinese. Each dialogue in MMChat is associated with one or more images (maximum 9 images per dialogue). We design various strategies to ensure the quality of the dialogues in MMChat.\n\n\nMMChat comes with 4... | [
"TAGS\n#task_categories-conversational #task_ids-dialogue-generation #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-Chinese #license-other #arxiv-2108.07154 #arxiv-2008.03946 #region-us \n",
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b4c2d1336775fd85839e4e81921dd95d23019ac1 |
# Dataset Card for PersonalDialog
## Table of Contents
- [Dataset Card for PersonalDialog](#dataset-card-for-personaldialog)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-a... | silver/personal_dialog | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:zh",
"license:other",
"arxiv:1901.09672",
"region:us"
] | 2022-05-29T13:23:58+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["zh"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["conversational"], "task_ids": ["dialogue-generation"], "paperswithcode_id": "... | 2022-07-10T12:05:21+00:00 | [
"1901.09672"
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"zh"
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#task_categories-conversational #task_ids-dialogue-generation #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-Chinese #license-other #arxiv-1901.09672 #region-us
| Dataset Card for PersonalDialog
===============================
Table of Contents
-----------------
* Dataset Card for PersonalDialog
+ Table of Contents
+ Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
+ Dataset Structure
- Data Instances
- Data Fields
- Data Sp... | [
"### Dataset Summary\n\n\nThe PersonalDialog dataset is a large-scale multi-turn Chinese dialogue dataset containing various traits from a large number of speakers.\nWe are releasing about 5M sessions of carefully filtered dialogues.\nEach utterance in PersonalDialog is associated with a speaker marked with traits ... | [
"TAGS\n#task_categories-conversational #task_ids-dialogue-generation #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-Chinese #license-other #arxiv-1901.09672 #region-us \n",
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fe55e9af6a900e30cc95a2fb679ab92ea79dfc82 |
# Dataset Card for GEM/squality
## Dataset Description
- **Homepage:** https://github.com/nyu-mll/SQuALITY
- **Repository:** https://github.com/nyu-mll/SQuALITY/data
- **Paper:** https://arxiv.org/abs/2205.11465
- **Leaderboard:** N/A
- **Point of Contact:** Alex Wang
### Link to Main Data Card
You can find the ma... | GEM/squality | [
"task_categories:summarization",
"annotations_creators:crowd-sourced",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2205.11465",
"arxiv:2112.07637",
"arxiv:2104.05938",
"region:us"
] | 2022-05-29T15:40:50+00:00 | {"annotations_creators": ["crowd-sourced"], "language_creators": ["unknown"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["unknown"], "size_categories": ["unknown"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": [], "pretty_name": "squality"} | 2022-10-25T11:58:23+00:00 | [
"2205.11465",
"2112.07637",
"2104.05938"
] | [
"en"
] | TAGS
#task_categories-summarization #annotations_creators-crowd-sourced #language_creators-unknown #multilinguality-unknown #size_categories-unknown #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-2205.11465 #arxiv-2112.07637 #arxiv-2104.05938 #region-us
|
# Dataset Card for GEM/squality
## Dataset Description
- Homepage: URL
- Repository: URL
- Paper: URL
- Leaderboard: N/A
- Point of Contact: Alex Wang
### Link to Main Data Card
You can find the main data card on the GEM Website.
### Dataset Summary
SQuALITY (Summarization-format QUestion Answering with Long In... | [
"# Dataset Card for GEM/squality",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: N/A\n- Point of Contact: Alex Wang",
"### Link to Main Data Card\n\nYou can find the main data card on the GEM Website.",
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"passage: TAGS\n#task_categories-summarization #annotations_creators-crowd-sourced #language_creators-unknown #multilinguality-unknown #size_categories-unknown #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-2205.11465 #arxiv-2112.07637 #arxiv-2104.05938 #region-us \n# Dataset Card for GEM/squ... |
b06eae243263621bd3424e246247a460d81a42ee |
To reproduce, run `pip install -r requirements.txt` and `download.sh`.
| cat-state/mscoco-1st-caption | [
"license:cc-by-4.0",
"region:us"
] | 2022-05-29T18:58:35+00:00 | {"license": "cc-by-4.0"} | 2022-05-29T19:30:35+00:00 | [] | [] | TAGS
#license-cc-by-4.0 #region-us
|
To reproduce, run 'pip install -r URL' and 'URL'.
| [] | [
"TAGS\n#license-cc-by-4.0 #region-us \n"
] | [
15
] | [
"passage: TAGS\n#license-cc-by-4.0 #region-us \n"
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2392b20943d927b51ca9d9f7a0a6bc1824437be3 | This dataset contains two files: a zipped file with segmented audio files from Emirati TV shows, podcasts, or YouTube channels, and a tsv file containing the transcription of the zipped audio files.
The purpose of the dataset is to act as a benchmark for Automatic Speech Recognition models that work with the Emirati di... | eabayed/EmiratiDialictShowsAudioTranscription | [
"license:afl-3.0",
"region:us"
] | 2022-05-30T09:05:41+00:00 | {"license": "afl-3.0"} | 2022-05-30T09:41:58+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
| This dataset contains two files: a zipped file with segmented audio files from Emirati TV shows, podcasts, or YouTube channels, and a tsv file containing the transcription of the zipped audio files.
The purpose of the dataset is to act as a benchmark for Automatic Speech Recognition models that work with the Emirati di... | [] | [
"TAGS\n#license-afl-3.0 #region-us \n"
] | [
14
] | [
"passage: TAGS\n#license-afl-3.0 #region-us \n"
] |
3b885e726812668096a44492a7dc506c4eb57aa9 |
# Dataset Card for XQuAD-XTREME
## 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-instance... | juletxara/xquad_xtreme | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:extended|squad",
"language:en",
"language:es",
"language:de",
"language:el",
... | 2022-05-30T09:49:17+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en", "es", "de", "el", "hi", "th", "ru", "tr", "ar", "vi", "zh", "ro"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["unknown"], "source_datasets": ["extended|squad"], "task_c... | 2022-10-12T07:43:41+00:00 | [
"1910.11856"
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"el",
"hi",
"th",
"ru",
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=============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Ann... | [
"### Dataset Summary\n\n\nXQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering\nperformance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set\nof SQuAD v1.1 (Rajpurkar et al., 2016) together ... | [
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dd3f3c25a869b077e5eac0ef0917ce7c33e45435 | annotations_creators:
- expert-generated
language_creators:
- expert-generated
languages: []
licenses:
- cc0-1.0
multilinguality: []
pretty_name: Monkey-Species-Collection
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
# Data... | Lehrig/Monkey-Species-Collection | [
"region:us"
] | 2022-05-30T10:14:20+00:00 | {} | 2022-05-30T11:33:12+00:00 | [] | [] | TAGS
#region-us
| annotations\_creators:
* expert-generated
language\_creators:
* expert-generated
languages: []
licenses:
* cc0-1.0
multilinguality: []
pretty\_name: Monkey-Species-Collection
size\_categories:
* 1K<n<10K
source\_datasets:
* original
task\_categories:
* image-classification
task\_ids:
* multi-class-image-classificatio... | [
"### Dataset Summary\n\n\nThis dataset is intended as a test case for fine-grain classification tasks (10 different kinds of monkey species). The dataset consists of almost 1400 JPEG images grouped into two splits - training and validation. Each split contains 10 categories labeled as n0~n9, each corresponding a sp... | [
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"### Dataset Summary\n\n\nThis dataset is intended as a test case for fine-grain classification tasks (10 different kinds of monkey species). The dataset consists of almost 1400 JPEG images grouped into two splits - training and validation. Each split contains 10 categories labeled as n0~n9... | [
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84c911c0541875191a4e87f16141cbd6cc99221d |
# Dataset Card for wikitext_linked
## 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-s... | DFKI-SLT/wikitext_linked | [
"task_categories:fill-mask",
"task_categories:token-classification",
"task_categories:text-classification",
"task_ids:masked-language-modeling",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"task_ids:lemmatization",
"task_ids:parsing",
"task_ids:entity-linking-classification",
"... | 2022-05-30T13:26:06+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["extended|wikitext"], "task_categories": ["fill-mask", "token-classification", "text-class... | 2022-07-04T05:09:56+00:00 | [
"1609.07843"
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"en"
] | TAGS
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=================================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rat... | [
"### Dataset Summary\n\n\nThe WikiText language modeling dataset is a collection of over 100 million tokens extracted from\nthe set of verified Good and Featured articles on Wikipedia. Dependency Relations, POS, NER tags\nare marked with trankit, entities are linked with\nentity-fishing, which also tags another fie... | [
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3bdac13927fdc888b903db93b2ffdbd90b295a69 | The `test` split is the `validation` split of [MIND](https://msnews.github.io/). Labels for the original `test` split are unavailable.
Thus, we renamed it to test for consistency in the MTEB benchmark. | mteb/mind_small | [
"region:us"
] | 2022-05-30T17:34:30+00:00 | {} | 2022-08-04T22:00:59+00:00 | [] | [] | TAGS
#region-us
| The 'test' split is the 'validation' split of MIND. Labels for the original 'test' split are unavailable.
Thus, we renamed it to test for consistency in the MTEB benchmark. | [] | [
"TAGS\n#region-us \n"
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5a56a2ba35f82f56859c694b99d245c5aec711e3 | few_nerd few-shot NER dataset in seq2seq format | yananchen/few_nerd_seq2seq | [
"region:us"
] | 2022-05-30T18:24:09+00:00 | {} | 2022-05-30T18:24:56+00:00 | [] | [] | TAGS
#region-us
| few_nerd few-shot NER dataset in seq2seq format | [] | [
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6
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eea2b4fe26a775864c896887d910b76a8098ad3f |
Scores in this dataset have been inverted to be from least to most similar!
The scores in the original STS22 task were from most to least similar. | mteb/sts22-crosslingual-sts | [
"language:ar",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:pl",
"language:ru",
"language:tr",
"language:zh",
"region:us"
] | 2022-05-30T19:19:00+00:00 | {"language": ["ar", "de", "en", "es", "fr", "it", "pl", "ru", "tr", "zh"]} | 2024-01-09T22:08:34+00:00 | [] | [
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#language-Arabic #language-German #language-English #language-Spanish #language-French #language-Italian #language-Polish #language-Russian #language-Turkish #language-Chinese #region-us
|
Scores in this dataset have been inverted to be from least to most similar!
The scores in the original STS22 task were from most to least similar. | [] | [
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] |
66c76eaf5e33b39a41c3d4c757eee3cf23b52ce5 |
MorisienMT is a dataset for Mauritian Creole Machine Translation.
This dataset consists of training, development and test set splits for English--Creole as well as French--Creole translation.
The data comes from a variety of sources and hence can be considered as belonging to the general domain.
The development and ... | prajdabre/KreolMorisienMT | [
"license:cc",
"region:us"
] | 2022-05-31T01:30:11+00:00 | {"license": "cc"} | 2022-06-02T00:25:14+00:00 | [] | [] | TAGS
#license-cc #region-us
|
MorisienMT is a dataset for Mauritian Creole Machine Translation.
This dataset consists of training, development and test set splits for English--Creole as well as French--Creole translation.
The data comes from a variety of sources and hence can be considered as belonging to the general domain.
The development and ... | [] | [
"TAGS\n#license-cc #region-us \n"
] | [
11
] | [
"passage: TAGS\n#license-cc #region-us \n"
] |
257802eb1f65c3eeeaec0a8b4dab2dd9c3f88d44 |
# Dataset Card for CICERO
## Description
- **Homepage:** https://declare-lab.net/CICERO/
- **Repository:** https://github.com/declare-lab/CICERO
- **Paper:** https://aclanthology.org/2022.acl-long.344/
- **arXiv:** https://arxiv.org/abs/2203.13926
### Summary
CICERO is a new dataset for dialogue reasoning with con... | declare-lab/cicero | [
"license:mit",
"arxiv:2203.13926",
"arxiv:1710.03957",
"arxiv:1902.00164",
"arxiv:2004.04494",
"region:us"
] | 2022-05-31T02:48:01+00:00 | {"license": "mit"} | 2022-05-31T03:30:37+00:00 | [
"2203.13926",
"1710.03957",
"1902.00164",
"2004.04494"
] | [] | TAGS
#license-mit #arxiv-2203.13926 #arxiv-1710.03957 #arxiv-1902.00164 #arxiv-2004.04494 #region-us
|
# Dataset Card for CICERO
## Description
- Homepage: URL
- Repository: URL
- Paper: URL
- arXiv: URL
### Summary
CICERO is a new dataset for dialogue reasoning with contextualized commonsense inference. It contains 53K inferences for five commonsense dimensions – cause, subsequent event, prerequisite, motivation, ... | [
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1849a51f3c614bb47c428419968dbf63f6a9e949 | The COVID-19 Vaccine Intent Expressions dataset contains 7,990 varying expressions for common questions about COVID-19 vaccines.
We collaborated with a team at Johns Hopkins University to curate a list 181 such common questions.
We then showed annotators a question from the list and asked them to express it in their ... | ibm/vira-intents | [
"region:us"
] | 2022-05-31T07:49:22+00:00 | {} | 2022-06-01T06:39:11+00:00 | [] | [] | TAGS
#region-us
| The COVID-19 Vaccine Intent Expressions dataset contains 7,990 varying expressions for common questions about COVID-19 vaccines.
We collaborated with a team at Johns Hopkins University to curate a list 181 such common questions.
We then showed annotators a question from the list and asked them to express it in their ... | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
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0f1bd4fc2db86411a9d2187a04b204784c895f2e |
# Dataset Card for Biwi Kinect Head Pose Database
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instanc... | biwi_kinect_head_pose | [
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"language:en",
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|
# Dataset Card for Biwi Kinect Head Pose Database
## Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive... | [
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d5410514058b853de0c27f8b0c23839a27a8251e | ### Dataset Summary
The dataset contains user reviews about restaurants.
In total it contains 47,139 reviews. A review tagged with the <em>general</em> sentiment and sentiments on 3 aspects: <em>food, interior, service</em>.
### Data Fields
Each sample contains the following fields:
- **review_id**;
- **general**;
- **... | blinoff/restaurants_reviews | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:ru",
"region:us"
] | 2022-05-31T11:37:50+00:00 | {"language": ["ru"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"]} | 2022-10-23T15:51:03+00:00 | [] | [
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| ### Dataset Summary
The dataset contains user reviews about restaurants.
In total it contains 47,139 reviews. A review tagged with the <em>general</em> sentiment and sentiments on 3 aspects: <em>food, interior, service</em>.
### Data Fields
Each sample contains the following fields:
- review_id;
- general;
- food;
- in... | [
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e7e6b3b482627fe3da95a3e1dc1b69069a0b74b1 |
# Discursos Perón
Discursos completos pronunciados por el ex Presidente Juan Domingo Perón entre 1ro de diciembre de 1943 y el 19 de septiembre de 1955.
Los documentos, con excepción de los correspondientes al año 1949, fueron suministrados por el historiador Enrique de Alzáa, quien lideró un equipo que transcribió ... | martinolmos/discursos_peron | [
"license:cc-by-sa-4.0",
"region:us"
] | 2022-05-31T14:24:37+00:00 | {"license": "cc-by-sa-4.0"} | 2022-05-31T14:33:31+00:00 | [] | [] | TAGS
#license-cc-by-sa-4.0 #region-us
|
# Discursos Perón
Discursos completos pronunciados por el ex Presidente Juan Domingo Perón entre 1ro de diciembre de 1943 y el 19 de septiembre de 1955.
Los documentos, con excepción de los correspondientes al año 1949, fueron suministrados por el historiador Enrique de Alzáa, quien lideró un equipo que transcribió ... | [
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"TAGS\n#license-cc-by-sa-4.0 #region-us \n",
"# Discursos Perón\nDiscursos completos pronunciados por el ex Presidente Juan Domingo Perón entre 1ro de diciembre de 1943 y el 19 de septiembre de 1955. \n\nLos documentos, con excepción de los correspondientes al año 1949, fueron suministrados por el historiador Enr... | [
17,
205,
28,
53
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"passage: TAGS\n#license-cc-by-sa-4.0 #region-us \n# Discursos Perón\nDiscursos completos pronunciados por el ex Presidente Juan Domingo Perón entre 1ro de diciembre de 1943 y el 19 de septiembre de 1955. \n\nLos documentos, con excepción de los correspondientes al año 1949, fueron suministrados por el historiador ... |
cd57ea580a828f68fe32542d2b9bec7bfcb318b9 |
After I realised problems with Automatic language identification (LangID), and bad quality of web-crawled text corpora for my Language. I curated my own dataset.
Essentially I downloaded multiple versions of the Tajik subset of Leipzig Corpora Collection, which is comprised of texts from diverse sources like news, li... | muhtasham/tajik-corpus | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"language:tg",
"license:cc-by-4.0",
"doi:10.57967/hf/0061",
"region:us"
] | 2022-05-31T20:21:35+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["tg"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"]} | 2022-08-14T15:20:41+00:00 | [] | [
"tg"
] | TAGS
#annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #language-Tajik #license-cc-by-4.0 #doi-10.57967/hf/0061 #region-us
|
After I realised problems with Automatic language identification (LangID), and bad quality of web-crawled text corpora for my Language. I curated my own dataset.
Essentially I downloaded multiple versions of the Tajik subset of Leipzig Corpora Collection, which is comprised of texts from diverse sources like news, li... | [] | [
"TAGS\n#annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #language-Tajik #license-cc-by-4.0 #doi-10.57967/hf/0061 #region-us \n"
] | [
65
] | [
"passage: TAGS\n#annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #language-Tajik #license-cc-by-4.0 #doi-10.57967/hf/0061 #region-us \n"
] |
fead71299eabef45c1fd2bf914c9c0ea724b6775 |
# Dataset Card for `reviews_with_drift`
## 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](#data... | arize-ai/ecommerce_reviews_with_language_drift | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|imdb",
"language:en",
"license:mit",
"region:us"
] | 2022-05-31T22:24:11+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|imdb"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classificatio... | 2022-07-01T16:26:03+00:00 | [] | [
"en"
] | TAGS
#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|imdb #language-English #license-mit #region-us
|
# Dataset Card for 'reviews_with_drift'
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations... | [
"# Dataset Card for 'reviews_with_drift'",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source ... | [
"TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|imdb #language-English #license-mit #region-us \n",
"# Dataset Card for 'revi... | [
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"passage: TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|imdb #language-English #license-mit #region-us \n# Dataset Card for 'r... |
c0cfd00167ef1b1e8df4359ef16a818883df90aa | annotations_creators:
- found
language_creators:
- found
languages:
- zh
licenses:
- other-my-license
multilinguality:
- monolingual
pretty_name: peopledaily_NER
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition | OneFly/NER | [
"region:us"
] | 2022-06-01T08:24:14+00:00 | {} | 2022-06-01T08:42:49+00:00 | [] | [] | TAGS
#region-us
| annotations_creators:
- found
language_creators:
- found
languages:
- zh
licenses:
- other-my-license
multilinguality:
- monolingual
pretty_name: peopledaily_NER
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] |
8d9ca88afe67dc9713ae7aa970f3fd946cc41b10 |
# Dataset Card for enwik8
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#d... | enwik8 | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en"... | 2022-06-01T13:04:46+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["fill-mask", "text-generation"], "task_ids": ["language-modeling", "masked-langu... | 2024-01-18T11:19:13+00:00 | [] | [
"en"
] | TAGS
#task_categories-fill-mask #task_categories-text-generation #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #region-us
| Dataset Card for enwik8
=======================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sens... | [
"### Dataset Summary\n\n\nThe enwik8 dataset is the first 100,000,000 (100M) bytes of the English Wikipedia XML dump on Mar. 3, 2006 and is typically used to measure a model's ability to compress data.",
"### Supported Tasks and Leaderboards\n\n\nA leaderboard for byte-level causal language modelling can be found... | [
"TAGS\n#task_categories-fill-mask #task_categories-text-generation #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #region-us ... | [
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"passage: TAGS\n#task_categories-fill-mask #task_categories-text-generation #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #r... |
0819ebca68519005bc806c753a0c783fc2c65874 | # Dataset Card for tweet_eval
## 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)
... | dianalogan/Marketing-Budget-and-Actual-Sales-Dataset | [
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_creators:diana_logan",
"multilinguality:monolingual",
"source_datasets:other-generated-datasets",
"language:en",
"license:apache-2.0",
"arxiv:2010.12421",
"region:us"
] | 2022-06-01T14:09:02+00:00 | {"annotations_creators": ["diana_logan"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "source_datasets": ["other-generated-datasets"], "task_categories": ["text", "linear-regression"], "task_ids": ["intent-classification", "multi-class-classification", "sentiment-classification"],... | 2022-10-21T09:12:40+00:00 | [
"2010.12421"
] | [
"en"
] | TAGS
#task_ids-intent-classification #task_ids-multi-class-classification #task_ids-sentiment-classification #annotations_creators-diana_logan #multilinguality-monolingual #source_datasets-other-generated-datasets #language-English #license-apache-2.0 #arxiv-2010.12421 #region-us
| Dataset Card for tweet\_eval
============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annot... | [
"### Dataset Summary\n\n\nTweetEval consists of seven heterogenous tasks in Twitter, all framed as multi-class tweet classification. The tasks include - irony, hate, offensive, stance, emoji, emotion, and sentiment. All tasks have been unified into the same benchmark, with each dataset presented in the same format ... | [
"TAGS\n#task_ids-intent-classification #task_ids-multi-class-classification #task_ids-sentiment-classification #annotations_creators-diana_logan #multilinguality-monolingual #source_datasets-other-generated-datasets #language-English #license-apache-2.0 #arxiv-2010.12421 #region-us \n",
"### Dataset Summary\n\n\n... | [
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"passage: TAGS\n#task_ids-intent-classification #task_ids-multi-class-classification #task_ids-sentiment-classification #annotations_creators-diana_logan #multilinguality-monolingual #source_datasets-other-generated-datasets #language-English #license-apache-2.0 #arxiv-2010.12421 #region-us \n### Dataset Summary\n\... |
440df9079e95ce50f75fa69b3f6aed94900eca66 |
# Dataset Card for "lmqg/qg_squadshifts"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asah... | lmqg/qg_squadshifts | [
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:subjqa",
"language:en",
"license:cc-by-4.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] | 2022-06-02T17:56:40+00:00 | {"language": "en", "license": "cc-by-4.0", "multilinguality": "monolingual", "size_categories": "10K<n<100K", "source_datasets": "subjqa", "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "SubjQA for question generation", "tags": ["question-generation"]} | 2022-12-02T18:56:15+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-subjqa #language-English #license-cc-by-4.0 #question-generation #arxiv-2210.03992 #region-us
| Dataset Card for "lmqg/qg\_squadshifts"
=======================================
Dataset Description
-------------------
* Repository: URL
* Paper: URL
* Point of Contact: Asahi Ushio
### Dataset Summary
This is a subset of QG-Bench, a unified question generation benchmark proposed in
"Generative Language Models... | [
"### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified question generation benchmark proposed in\n\"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference\".\nModified version of SQuADShifts for question generation (QG) task.",
... | [
"TAGS\n#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-subjqa #language-English #license-cc-by-4.0 #question-generation #arxiv-2210.03992 #region-us \n",
"### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified questio... | [
86,
79,
77,
290,
5
] | [
"passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-subjqa #language-English #license-cc-by-4.0 #question-generation #arxiv-2210.03992 #region-us \n### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified ques... |
43370528cab140745cd31f19cbcebe0be7733799 | import sagemaker
from sagemaker.huggingface import HuggingFace
# gets role for executing training job
role = sagemaker.get_execution_role()
hyperparameters = {
'model_name_or_path':'etmckinley/BERFALTER',
'output_dir':'/opt/ml/model'
# add your remaining hyperparameters
# more info here https://github.com/huggingf... | benwri/GaryOut | [
"region:us"
] | 2022-06-02T20:24:14+00:00 | {} | 2022-06-02T20:24:22+00:00 | [] | [] | TAGS
#region-us
| import sagemaker
from sagemaker.huggingface import HuggingFace
# gets role for executing training job
role = sagemaker.get_execution_role()
hyperparameters = {
'model_name_or_path':'etmckinley/BERFALTER',
'output_dir':'/opt/ml/model'
# add your remaining hyperparameters
# more info here URL
}
# git configuration ... | [
"# gets role for executing training job\nrole = sagemaker.get_execution_role()\nhyperparameters = {\n\t'model_name_or_path':'etmckinley/BERFALTER',\n\t'output_dir':'/opt/ml/model'\n\t# add your remaining hyperparameters\n\t# more info here URL\n}",
"# git configuration to download our fine-tuning script\ngit_conf... | [
"TAGS\n#region-us \n",
"# gets role for executing training job\nrole = sagemaker.get_execution_role()\nhyperparameters = {\n\t'model_name_or_path':'etmckinley/BERFALTER',\n\t'output_dir':'/opt/ml/model'\n\t# add your remaining hyperparameters\n\t# more info here URL\n}",
"# git configuration to download our fin... | [
6,
84,
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126,
15
] | [
"passage: TAGS\n#region-us \n# gets role for executing training job\nrole = sagemaker.get_execution_role()\nhyperparameters = {\n\t'model_name_or_path':'etmckinley/BERFALTER',\n\t'output_dir':'/opt/ml/model'\n\t# add your remaining hyperparameters\n\t# more info here URL\n}# git configuration to download our fine-t... |
7675f4bb4bd1510f97429f4038723d03ea9b64f7 |
# Dataset Card for "lmqg/qg_esquad"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushi... | lmqg/qg_esquad | [
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:squad_es",
"language:es",
"license:cc-by-4.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] | 2022-06-02T22:41:06+00:00 | {"language": "es", "license": "cc-by-4.0", "multilinguality": "monolingual", "size_categories": "10K<n<100K", "source_datasets": "squad_es", "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "SQuAD-es for question generation", "tags": ["question-generation"]} | 2022-12-02T18:52:05+00:00 | [
"2210.03992"
] | [
"es"
] | TAGS
#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-squad_es #language-Spanish #license-cc-by-4.0 #question-generation #arxiv-2210.03992 #region-us
| Dataset Card for "lmqg/qg\_esquad"
==================================
Dataset Description
-------------------
* Repository: URL
* Paper: URL
* Point of Contact: Asahi Ushio
### Dataset Summary
This is a subset of QG-Bench, a unified question generation benchmark proposed in
"Generative Language Models for Parag... | [
"### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified question generation benchmark proposed in\n\"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference\".\nThis is a modified version of SQuAD-es for question generation (QG) ta... | [
"TAGS\n#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-squad_es #language-Spanish #license-cc-by-4.0 #question-generation #arxiv-2210.03992 #region-us \n",
"### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified quest... | [
88,
121,
80,
295
] | [
"passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-squad_es #language-Spanish #license-cc-by-4.0 #question-generation #arxiv-2210.03992 #region-us \n### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified qu... |
49ad3eba360e4f6c40c0720e19be9d358dd893d0 |
# Dataset Card for "lmqg/qg_korquad"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiush... | lmqg/qg_koquad | [
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:squad_es",
"language:ko",
"license:cc-by-4.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] | 2022-06-02T22:42:21+00:00 | {"language": "ko", "license": "cc-by-4.0", "multilinguality": "monolingual", "size_categories": "10K<n<100K", "source_datasets": "squad_es", "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "KorQuAD for question generation", "tags": ["question-generation"]} | 2022-12-02T18:53:42+00:00 | [
"2210.03992"
] | [
"ko"
] | TAGS
#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-squad_es #language-Korean #license-cc-by-4.0 #question-generation #arxiv-2210.03992 #region-us
| Dataset Card for "lmqg/qg\_korquad"
===================================
Dataset Description
-------------------
* Repository: URL
* Paper: URL
* Point of Contact: Asahi Ushio
### Dataset Summary
This is a subset of QG-Bench, a unified question generation benchmark proposed in
"Generative Language Models for Par... | [
"### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified question generation benchmark proposed in\n\"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference\".\nThis is a modified version of KorQuAD for question generation (QG) tas... | [
"TAGS\n#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-squad_es #language-Korean #license-cc-by-4.0 #question-generation #arxiv-2210.03992 #region-us \n",
"### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified questi... | [
88,
119,
80,
295
] | [
"passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-squad_es #language-Korean #license-cc-by-4.0 #question-generation #arxiv-2210.03992 #region-us \n### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified que... |
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