Upload batch 370 (20 files, last=huggingface_dataset/Dataset_Card/ai4bharat_Aksharantar.md)
Browse files- huggingface_dataset/Dataset_Card/Akshata_autotrain-data-compliance.md +55 -0
- huggingface_dataset/Dataset_Card/BeIR_msmarco.md +285 -0
- huggingface_dataset/Dataset_Card/DJSoft_maccha_artist_style.md +33 -0
- huggingface_dataset/Dataset_Card/Drewd_lex_fridman_podcast_transcripts.md +149 -0
- huggingface_dataset/Dataset_Card/Gustavosta_Stable-Diffusion-Prompts.md +20 -0
- huggingface_dataset/Dataset_Card/NLPC-UOM_Sentiment-tagger.md +22 -0
- huggingface_dataset/Dataset_Card/SALT-NLP_ImplicitHate.md +63 -0
- huggingface_dataset/Dataset_Card/ai4bharat_Aksharantar.md +254 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_test_cot_v2-math-db74ac-2016866706.md +34 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267104.md +34 -0
- huggingface_dataset/Dataset_Card/huggingartists_yung-plague.md +204 -0
- huggingface_dataset/Dataset_Card/irds_pmc_v2.md +35 -0
- huggingface_dataset/Dataset_Card/jed351_rthk_news.md +14 -0
- huggingface_dataset/Dataset_Card/jonatli_the_pile_mystic.md +291 -0
- huggingface_dataset/Dataset_Card/larrylawl_multilexnorm.md +98 -0
- huggingface_dataset/Dataset_Card/nateraw_kitti.md +25 -0
- huggingface_dataset/Dataset_Card/noahshinn024_ts-code2td.md +20 -0
- huggingface_dataset/Dataset_Card/pcoloc_autotrain-data-trackerlora_less_data.md +64 -0
- huggingface_dataset/Dataset_Card/qgallouedec_prj_gia_dataset_metaworld_assembly_v2_1111.md +36 -0
- huggingface_dataset/Dataset_Card/ulysses-camara_ulysses-ner-br.md +150 -0
huggingface_dataset/Dataset_Card/Akshata_autotrain-data-compliance.md
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---
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language:
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- en
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task_categories:
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- text-classification
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---
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# AutoTrain Dataset for project: compliance
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## Dataset Description
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This dataset has been automatically processed by AutoTrain for project compliance.
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### Languages
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The BCP-47 code for the dataset's language is en.
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## Dataset Structure
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### Data Instances
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A sample from this dataset looks as follows:
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```json
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[
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{
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"text": "Welcome back Abhishek! What can I do to help? ",
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"target": 0
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},
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{
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"text": "Hi , I am calling from ABC finance. I would like to inform you that you are eligible for a Personal Loan",
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"target": 0
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}
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]
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```
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### Dataset Fields
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The dataset has the following fields (also called "features"):
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```json
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{
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"text": "Value(dtype='string', id=None)",
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"target": "ClassLabel(num_classes=2, names=['Negative', 'Positive'], id=None)"
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}
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```
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### Dataset Splits
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This dataset is split into a train and validation split. The split sizes are as follow:
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| Split name | Num samples |
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| ------------ | ------------------- |
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| train | 31 |
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| valid | 9 |
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huggingface_dataset/Dataset_Card/BeIR_msmarco.md
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---
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| 2 |
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annotations_creators: []
|
| 3 |
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language_creators: []
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| 4 |
+
language:
|
| 5 |
+
- en
|
| 6 |
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license:
|
| 7 |
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- cc-by-sa-4.0
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| 8 |
+
multilinguality:
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| 9 |
+
- monolingual
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| 10 |
+
paperswithcode_id: beir
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| 11 |
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pretty_name: BEIR Benchmark
|
| 12 |
+
size_categories:
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| 13 |
+
msmarco:
|
| 14 |
+
- 1M<n<10M
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| 15 |
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trec-covid:
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| 16 |
+
- 100k<n<1M
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| 17 |
+
nfcorpus:
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| 18 |
+
- 1K<n<10K
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| 19 |
+
nq:
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| 20 |
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- 1M<n<10M
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| 21 |
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hotpotqa:
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| 22 |
+
- 1M<n<10M
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| 23 |
+
fiqa:
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| 24 |
+
- 10K<n<100K
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| 25 |
+
arguana:
|
| 26 |
+
- 1K<n<10K
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| 27 |
+
touche-2020:
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| 28 |
+
- 100K<n<1M
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| 29 |
+
cqadupstack:
|
| 30 |
+
- 100K<n<1M
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| 31 |
+
quora:
|
| 32 |
+
- 100K<n<1M
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| 33 |
+
dbpedia:
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| 34 |
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- 1M<n<10M
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| 35 |
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scidocs:
|
| 36 |
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- 10K<n<100K
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| 37 |
+
fever:
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| 38 |
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- 1M<n<10M
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| 39 |
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climate-fever:
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| 40 |
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- 1M<n<10M
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| 41 |
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scifact:
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| 42 |
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- 1K<n<10K
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| 43 |
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source_datasets: []
|
| 44 |
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task_categories:
|
| 45 |
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- text-retrieval
|
| 46 |
+
- zero-shot-retrieval
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| 47 |
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- information-retrieval
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| 48 |
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- zero-shot-information-retrieval
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| 49 |
+
task_ids:
|
| 50 |
+
- passage-retrieval
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| 51 |
+
- entity-linking-retrieval
|
| 52 |
+
- fact-checking-retrieval
|
| 53 |
+
- tweet-retrieval
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| 54 |
+
- citation-prediction-retrieval
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| 55 |
+
- duplication-question-retrieval
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| 56 |
+
- argument-retrieval
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| 57 |
+
- news-retrieval
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| 58 |
+
- biomedical-information-retrieval
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| 59 |
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- question-answering-retrieval
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| 60 |
+
---
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| 61 |
+
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| 62 |
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# Dataset Card for BEIR Benchmark
|
| 63 |
+
|
| 64 |
+
## Table of Contents
|
| 65 |
+
- [Dataset Description](#dataset-description)
|
| 66 |
+
- [Dataset Summary](#dataset-summary)
|
| 67 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 68 |
+
- [Languages](#languages)
|
| 69 |
+
- [Dataset Structure](#dataset-structure)
|
| 70 |
+
- [Data Instances](#data-instances)
|
| 71 |
+
- [Data Fields](#data-fields)
|
| 72 |
+
- [Data Splits](#data-splits)
|
| 73 |
+
- [Dataset Creation](#dataset-creation)
|
| 74 |
+
- [Curation Rationale](#curation-rationale)
|
| 75 |
+
- [Source Data](#source-data)
|
| 76 |
+
- [Annotations](#annotations)
|
| 77 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 78 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 79 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 80 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 81 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 82 |
+
- [Additional Information](#additional-information)
|
| 83 |
+
- [Dataset Curators](#dataset-curators)
|
| 84 |
+
- [Licensing Information](#licensing-information)
|
| 85 |
+
- [Citation Information](#citation-information)
|
| 86 |
+
- [Contributions](#contributions)
|
| 87 |
+
|
| 88 |
+
## Dataset Description
|
| 89 |
+
|
| 90 |
+
- **Homepage:** https://github.com/UKPLab/beir
|
| 91 |
+
- **Repository:** https://github.com/UKPLab/beir
|
| 92 |
+
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
|
| 93 |
+
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
|
| 94 |
+
- **Point of Contact:** nandan.thakur@uwaterloo.ca
|
| 95 |
+
|
| 96 |
+
### Dataset Summary
|
| 97 |
+
|
| 98 |
+
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
|
| 99 |
+
|
| 100 |
+
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
|
| 101 |
+
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
|
| 102 |
+
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
|
| 103 |
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- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
|
| 104 |
+
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
|
| 105 |
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- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
|
| 106 |
+
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
|
| 107 |
+
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
|
| 108 |
+
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
|
| 109 |
+
|
| 110 |
+
All these datasets have been preprocessed and can be used for your experiments.
|
| 111 |
+
|
| 112 |
+
|
| 113 |
+
```python
|
| 114 |
+
|
| 115 |
+
```
|
| 116 |
+
|
| 117 |
+
### Supported Tasks and Leaderboards
|
| 118 |
+
|
| 119 |
+
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
|
| 120 |
+
|
| 121 |
+
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
|
| 122 |
+
|
| 123 |
+
### Languages
|
| 124 |
+
|
| 125 |
+
All tasks are in English (`en`).
|
| 126 |
+
|
| 127 |
+
## Dataset Structure
|
| 128 |
+
|
| 129 |
+
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
|
| 130 |
+
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
|
| 131 |
+
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
|
| 132 |
+
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
|
| 133 |
+
|
| 134 |
+
### Data Instances
|
| 135 |
+
|
| 136 |
+
A high level example of any beir dataset:
|
| 137 |
+
|
| 138 |
+
```python
|
| 139 |
+
corpus = {
|
| 140 |
+
"doc1" : {
|
| 141 |
+
"title": "Albert Einstein",
|
| 142 |
+
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
|
| 143 |
+
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
|
| 144 |
+
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
|
| 145 |
+
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
|
| 146 |
+
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
|
| 147 |
+
of the photoelectric effect', a pivotal step in the development of quantum theory."
|
| 148 |
+
},
|
| 149 |
+
"doc2" : {
|
| 150 |
+
"title": "", # Keep title an empty string if not present
|
| 151 |
+
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
|
| 152 |
+
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
|
| 153 |
+
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
|
| 154 |
+
},
|
| 155 |
+
}
|
| 156 |
+
|
| 157 |
+
queries = {
|
| 158 |
+
"q1" : "Who developed the mass-energy equivalence formula?",
|
| 159 |
+
"q2" : "Which beer is brewed with a large proportion of wheat?"
|
| 160 |
+
}
|
| 161 |
+
|
| 162 |
+
qrels = {
|
| 163 |
+
"q1" : {"doc1": 1},
|
| 164 |
+
"q2" : {"doc2": 1},
|
| 165 |
+
}
|
| 166 |
+
```
|
| 167 |
+
|
| 168 |
+
### Data Fields
|
| 169 |
+
|
| 170 |
+
Examples from all configurations have the following features:
|
| 171 |
+
|
| 172 |
+
### Corpus
|
| 173 |
+
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
|
| 174 |
+
- `_id`: a `string` feature representing the unique document id
|
| 175 |
+
- `title`: a `string` feature, denoting the title of the document.
|
| 176 |
+
- `text`: a `string` feature, denoting the text of the document.
|
| 177 |
+
|
| 178 |
+
### Queries
|
| 179 |
+
- `queries`: a `dict` feature representing the query, made up of:
|
| 180 |
+
- `_id`: a `string` feature representing the unique query id
|
| 181 |
+
- `text`: a `string` feature, denoting the text of the query.
|
| 182 |
+
|
| 183 |
+
### Qrels
|
| 184 |
+
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
|
| 185 |
+
- `_id`: a `string` feature representing the query id
|
| 186 |
+
- `_id`: a `string` feature, denoting the document id.
|
| 187 |
+
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
|
| 188 |
+
|
| 189 |
+
|
| 190 |
+
### Data Splits
|
| 191 |
+
|
| 192 |
+
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
|
| 193 |
+
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
|
| 194 |
+
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
|
| 195 |
+
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
|
| 196 |
+
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
|
| 197 |
+
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
|
| 198 |
+
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
|
| 199 |
+
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
|
| 200 |
+
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
|
| 201 |
+
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
|
| 202 |
+
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
|
| 203 |
+
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
|
| 204 |
+
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
|
| 205 |
+
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
|
| 206 |
+
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
|
| 207 |
+
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
|
| 208 |
+
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
|
| 209 |
+
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
|
| 210 |
+
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
|
| 211 |
+
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
|
| 212 |
+
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
|
| 213 |
+
|
| 214 |
+
|
| 215 |
+
## Dataset Creation
|
| 216 |
+
|
| 217 |
+
### Curation Rationale
|
| 218 |
+
|
| 219 |
+
[Needs More Information]
|
| 220 |
+
|
| 221 |
+
### Source Data
|
| 222 |
+
|
| 223 |
+
#### Initial Data Collection and Normalization
|
| 224 |
+
|
| 225 |
+
[Needs More Information]
|
| 226 |
+
|
| 227 |
+
#### Who are the source language producers?
|
| 228 |
+
|
| 229 |
+
[Needs More Information]
|
| 230 |
+
|
| 231 |
+
### Annotations
|
| 232 |
+
|
| 233 |
+
#### Annotation process
|
| 234 |
+
|
| 235 |
+
[Needs More Information]
|
| 236 |
+
|
| 237 |
+
#### Who are the annotators?
|
| 238 |
+
|
| 239 |
+
[Needs More Information]
|
| 240 |
+
|
| 241 |
+
### Personal and Sensitive Information
|
| 242 |
+
|
| 243 |
+
[Needs More Information]
|
| 244 |
+
|
| 245 |
+
## Considerations for Using the Data
|
| 246 |
+
|
| 247 |
+
### Social Impact of Dataset
|
| 248 |
+
|
| 249 |
+
[Needs More Information]
|
| 250 |
+
|
| 251 |
+
### Discussion of Biases
|
| 252 |
+
|
| 253 |
+
[Needs More Information]
|
| 254 |
+
|
| 255 |
+
### Other Known Limitations
|
| 256 |
+
|
| 257 |
+
[Needs More Information]
|
| 258 |
+
|
| 259 |
+
## Additional Information
|
| 260 |
+
|
| 261 |
+
### Dataset Curators
|
| 262 |
+
|
| 263 |
+
[Needs More Information]
|
| 264 |
+
|
| 265 |
+
### Licensing Information
|
| 266 |
+
|
| 267 |
+
[Needs More Information]
|
| 268 |
+
|
| 269 |
+
### Citation Information
|
| 270 |
+
|
| 271 |
+
Cite as:
|
| 272 |
+
```
|
| 273 |
+
@inproceedings{
|
| 274 |
+
thakur2021beir,
|
| 275 |
+
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
|
| 276 |
+
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
|
| 277 |
+
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
|
| 278 |
+
year={2021},
|
| 279 |
+
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
|
| 280 |
+
}
|
| 281 |
+
```
|
| 282 |
+
|
| 283 |
+
### Contributions
|
| 284 |
+
|
| 285 |
+
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
|
huggingface_dataset/Dataset_Card/DJSoft_maccha_artist_style.md
ADDED
|
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: creativeml-openrail-m
|
| 3 |
+
---
|
| 4 |
+
|
| 5 |
+
# Maccha style embedding
|
| 6 |
+
|
| 7 |
+
## Samples
|
| 8 |
+
|
| 9 |
+
<img alt="Samples" src="https://huggingface.co/datasets/DJSoft/maccha_artist_style/resolve/main/samples.jpg" style="max-height: 80vh"/>
|
| 10 |
+
<img alt="Comparsion" src="https://huggingface.co/datasets/DJSoft/maccha_artist_style/resolve/main/steps.png" style="max-height: 80vh"/>
|
| 11 |
+
|
| 12 |
+
## About
|
| 13 |
+
|
| 14 |
+
Use this Stable Diffusion embedding to achieve style of Matcha_ / maccha_(mochancc) [Pixiv](https://www.pixiv.net/en/users/2583663)
|
| 15 |
+
|
| 16 |
+
## Usage
|
| 17 |
+
|
| 18 |
+
To use this embedding you have to download the file and put it into the "\stable-diffusion-webui\embeddings" folder
|
| 19 |
+
To use it in a prompt add __art by maccha-*__
|
| 20 |
+
|
| 21 |
+
Add **( :1.0)** around it to modify its weight
|
| 22 |
+
|
| 23 |
+
## Included Files
|
| 24 |
+
- 8000 steps Usage: **art by maccha-8000**
|
| 25 |
+
- 15000 steps Usage: **art by maccha-15000**
|
| 26 |
+
|
| 27 |
+
## License
|
| 28 |
+
|
| 29 |
+
This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:
|
| 30 |
+
|
| 31 |
+
1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content
|
| 32 |
+
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
|
| 33 |
+
3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
huggingface_dataset/Dataset_Card/Drewd_lex_fridman_podcast_transcripts.md
ADDED
|
@@ -0,0 +1,149 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- found
|
| 4 |
+
language:
|
| 5 |
+
- en
|
| 6 |
+
language_creators:
|
| 7 |
+
- machine-generated
|
| 8 |
+
license: []
|
| 9 |
+
multilinguality:
|
| 10 |
+
- monolingual
|
| 11 |
+
pretty_name: The transcripts from Lex Fridman podcast episodes on Youtube.
|
| 12 |
+
size_categories:
|
| 13 |
+
- n<1K
|
| 14 |
+
source_datasets: []
|
| 15 |
+
tags:
|
| 16 |
+
- podcast
|
| 17 |
+
- ai
|
| 18 |
+
- interviews
|
| 19 |
+
task_categories: []
|
| 20 |
+
task_ids: []
|
| 21 |
+
---
|
| 22 |
+
|
| 23 |
+
# Dataset Card for Lex Fridman Podcast Transcripts
|
| 24 |
+
|
| 25 |
+
## Table of Contents
|
| 26 |
+
- [Dataset Card for Lex Fridman Podcast Transcripts](#dataset-card-for-lex-fridman-podcast-transcripts)
|
| 27 |
+
- [Table of Contents](#table-of-contents)
|
| 28 |
+
- [Dataset Description](#dataset-description)
|
| 29 |
+
- [Dataset Summary](#dataset-summary)
|
| 30 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 31 |
+
- [Languages](#languages)
|
| 32 |
+
- [Dataset Structure](#dataset-structure)
|
| 33 |
+
- [Data Instances](#data-instances)
|
| 34 |
+
- [Data Fields](#data-fields)
|
| 35 |
+
- [Data Splits](#data-splits)
|
| 36 |
+
- [Dataset Creation](#dataset-creation)
|
| 37 |
+
- [Curation Rationale](#curation-rationale)
|
| 38 |
+
- [Source Data](#source-data)
|
| 39 |
+
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
|
| 40 |
+
- [Who are the source language producers?](#who-are-the-source-language-producers)
|
| 41 |
+
- [Annotations](#annotations)
|
| 42 |
+
- [Annotation process](#annotation-process)
|
| 43 |
+
- [Who are the annotators?](#who-are-the-annotators)
|
| 44 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 45 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 46 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 47 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 48 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 49 |
+
- [Additional Information](#additional-information)
|
| 50 |
+
- [Dataset Curators](#dataset-curators)
|
| 51 |
+
- [Licensing Information](#licensing-information)
|
| 52 |
+
- [Citation Information](#citation-information)
|
| 53 |
+
- [Contributions](#contributions)
|
| 54 |
+
|
| 55 |
+
## Dataset Description
|
| 56 |
+
|
| 57 |
+
- **Homepage:** https://karpathy.ai/lexicap/
|
| 58 |
+
- **Repository:**
|
| 59 |
+
- **Paper:**
|
| 60 |
+
- **Leaderboard:**
|
| 61 |
+
- **Point of Contact:** [@drewdresser](https://twitter.com/drewdresser)
|
| 62 |
+
|
| 63 |
+
### Dataset Summary
|
| 64 |
+
|
| 65 |
+
These are transcripts from the Lex Fridman podcast. The podcast is hosted by Lex Fridman, a computer scientist at MIT. The podcast is a mix of interviews with researchers in AI and other fields, and discussions of current events in AI. The transcripts are generated using [OpenAI Whisper](https://github.com/openai/whisper), then made available on [Karpathy AI](https://karpathy.ai/lexicap/).
|
| 66 |
+
|
| 67 |
+
### Supported Tasks and Leaderboards
|
| 68 |
+
|
| 69 |
+
[More Information Needed]
|
| 70 |
+
|
| 71 |
+
### Languages
|
| 72 |
+
|
| 73 |
+
English
|
| 74 |
+
|
| 75 |
+
## Dataset Structure
|
| 76 |
+
|
| 77 |
+
### Data Instances
|
| 78 |
+
|
| 79 |
+
~325
|
| 80 |
+
|
| 81 |
+
### Data Fields
|
| 82 |
+
|
| 83 |
+
[More Information Needed]
|
| 84 |
+
|
| 85 |
+
### Data Splits
|
| 86 |
+
|
| 87 |
+
[More Information Needed]
|
| 88 |
+
|
| 89 |
+
## Dataset Creation
|
| 90 |
+
|
| 91 |
+
### Curation Rationale
|
| 92 |
+
|
| 93 |
+
[More Information Needed]
|
| 94 |
+
|
| 95 |
+
### Source Data
|
| 96 |
+
|
| 97 |
+
#### Initial Data Collection and Normalization
|
| 98 |
+
|
| 99 |
+
[More Information Needed]
|
| 100 |
+
|
| 101 |
+
#### Who are the source language producers?
|
| 102 |
+
|
| 103 |
+
[More Information Needed]
|
| 104 |
+
|
| 105 |
+
### Annotations
|
| 106 |
+
|
| 107 |
+
#### Annotation process
|
| 108 |
+
|
| 109 |
+
[More Information Needed]
|
| 110 |
+
|
| 111 |
+
#### Who are the annotators?
|
| 112 |
+
|
| 113 |
+
[More Information Needed]
|
| 114 |
+
|
| 115 |
+
### Personal and Sensitive Information
|
| 116 |
+
|
| 117 |
+
[More Information Needed]
|
| 118 |
+
|
| 119 |
+
## Considerations for Using the Data
|
| 120 |
+
|
| 121 |
+
### Social Impact of Dataset
|
| 122 |
+
|
| 123 |
+
[More Information Needed]
|
| 124 |
+
|
| 125 |
+
### Discussion of Biases
|
| 126 |
+
|
| 127 |
+
[More Information Needed]
|
| 128 |
+
|
| 129 |
+
### Other Known Limitations
|
| 130 |
+
|
| 131 |
+
[More Information Needed]
|
| 132 |
+
|
| 133 |
+
## Additional Information
|
| 134 |
+
|
| 135 |
+
### Dataset Curators
|
| 136 |
+
|
| 137 |
+
[More Information Needed]
|
| 138 |
+
|
| 139 |
+
### Licensing Information
|
| 140 |
+
|
| 141 |
+
[More Information Needed]
|
| 142 |
+
|
| 143 |
+
### Citation Information
|
| 144 |
+
|
| 145 |
+
[More Information Needed]
|
| 146 |
+
|
| 147 |
+
### Contributions
|
| 148 |
+
|
| 149 |
+
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
huggingface_dataset/Dataset_Card/Gustavosta_Stable-Diffusion-Prompts.md
ADDED
|
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license:
|
| 3 |
+
- unknown
|
| 4 |
+
annotations_creators:
|
| 5 |
+
- no-annotation
|
| 6 |
+
language_creators:
|
| 7 |
+
- found
|
| 8 |
+
language:
|
| 9 |
+
- en
|
| 10 |
+
source_datasets:
|
| 11 |
+
- original
|
| 12 |
+
---
|
| 13 |
+
|
| 14 |
+
# Stable Diffusion Dataset
|
| 15 |
+
|
| 16 |
+
This is a set of about 80,000 prompts filtered and extracted from the image finder for Stable Diffusion: "[Lexica.art](https://lexica.art/)". It was a little difficult to extract the data, since the search engine still doesn't have a public API without being protected by cloudflare.
|
| 17 |
+
|
| 18 |
+
If you want to test the model with a demo, you can go to: "[spaces/Gustavosta/MagicPrompt-Stable-Diffusion](https://huggingface.co/spaces/Gustavosta/MagicPrompt-Stable-Diffusion)".
|
| 19 |
+
|
| 20 |
+
If you want to see the model, go to: "[Gustavosta/MagicPrompt-Stable-Diffusion](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion)".
|
huggingface_dataset/Dataset_Card/NLPC-UOM_Sentiment-tagger.md
ADDED
|
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- si
|
| 4 |
+
license:
|
| 5 |
+
- mit
|
| 6 |
+
---
|
| 7 |
+
|
| 8 |
+
*Sentiment Analysis of Sinhala News Comments*
|
| 9 |
+
|
| 10 |
+
Dataset used in this project is collected by crawling Sinhala online news sites, mainly www.lankadeepa.lk.
|
| 11 |
+
|
| 12 |
+
contact
|
| 13 |
+
Please contact us if you need more information.
|
| 14 |
+
|
| 15 |
+
Surangika Ranathunga-surangika@cse.mrt.ac.lk
|
| 16 |
+
Isuru Liyanage-theisuru@gmail.com
|
| 17 |
+
|
| 18 |
+
https://github.com/theisuru/sentiment-tagger
|
| 19 |
+
|
| 20 |
+
cite
|
| 21 |
+
If you use this data please cite this work
|
| 22 |
+
Ranathunga, S., & Liyanage, I. U. (2021). Sentiment Analysis of Sinhala News Comments. Transactions on Asian and Low-Resource Language Information Processing, 20(4), 1-23.
|
huggingface_dataset/Dataset_Card/SALT-NLP_ImplicitHate.md
ADDED
|
@@ -0,0 +1,63 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Implicit Hate Speech
|
| 2 |
+
|
| 3 |
+
_Latent Hatred: A Benchmark for Understanding Implicit Hate Speech_
|
| 4 |
+
|
| 5 |
+
[[Read the Paper]](https://aclanthology.org/2021.emnlp-main.29/) | [[Take a Survey to Access the Data]](https://forms.gle/QxCpEbVp91Z35hWFA) | [[Download the Data]](https://www.dropbox.com/s/24meryhqi1oo0xk/implicit-hate-corpus.zip?dl=0)
|
| 6 |
+
|
| 7 |
+
<img src="frontpage.png" alt="frontpage" width="650"/>
|
| 8 |
+
|
| 9 |
+
## *Why Implicit Hate?*
|
| 10 |
+
|
| 11 |
+
It is important to consider the subtle tricks that many extremists use to mask their threats and abuse. These more implicit forms of hate speech may easily go undetected by keyword detection systems, and even the most advanced architectures can fail if they have not been trained on implicit hate speech ([Caselli et al. 2020](https://aclanthology.org/2020.lrec-1.760/)).
|
| 12 |
+
|
| 13 |
+
## *Where can I download the data?*
|
| 14 |
+
|
| 15 |
+
If you have not already, please first complete a short [survey](https://forms.gle/QxCpEbVp91Z35hWFA). Then follow [this link to download](https://www.dropbox.com/s/p1ctnsg3xlnupwr/implicit-hate-corpus.zip?dl=0) (2 MB, expands to 6 MB).
|
| 16 |
+
|
| 17 |
+
## *What's 'in the box?'*
|
| 18 |
+
|
| 19 |
+
This dataset contains **22,056** tweets from the most prominent extremist groups in the United States; **6,346** of these tweets contain *implicit hate speech.* We decompose the implicit hate class using the following taxonomy (distribution shown on the left).
|
| 20 |
+
|
| 21 |
+
* (24.2%) **Grievance:** frustration over a minority group's perceived privilege.
|
| 22 |
+
* (20.0%) **Incitement:** implicitly promoting known hate groups and ideologies (e.g. by flaunting in-group power).
|
| 23 |
+
* (13.6%) **Inferiority:** implying some group or person is of lesser value than another.
|
| 24 |
+
* (12.6%) **Irony:** using sarcasm, humor, and satire to demean someone.
|
| 25 |
+
* (17.9%) **Stereotypes:** associating a group with negative attribute using euphemisms, circumlocution, or metaphorical language.
|
| 26 |
+
* (10.5%) **Threats:** making an indirect commitment to attack someone's body, well-being, reputation, liberty, etc.
|
| 27 |
+
* (1.2%) **Other**
|
| 28 |
+
|
| 29 |
+
Each of the 6,346 implicit hate tweets also has free-text annotations for *target demographic group* and an *implied statement* to describe the underlying message (see banner image above).
|
| 30 |
+
|
| 31 |
+
## *What can I do with this data?*
|
| 32 |
+
|
| 33 |
+
State-of-the-art neural models may be able to learn from our data how to (1) classify this more difficult class of hate speech and (3) explain implicit hate by generating descriptions of both the *target* and the *implied message.* As our [paper baselines](#) show, neural models still have a ways to go, especially with classifying *implicit hate categories*, but overall, the results are promising, especially with *implied statement generation,* an admittedly challenging task.
|
| 34 |
+
|
| 35 |
+
We hope you can extend our baselines and further our efforts to understand and address some of these most pernicious forms of language that plague the web, especially among extremist groups.
|
| 36 |
+
|
| 37 |
+
## *How do I cite this work?*
|
| 38 |
+
|
| 39 |
+
**Citation:**
|
| 40 |
+
|
| 41 |
+
> ElSherief, M., Ziems, C., Muchlinski, D., Anupindi, V., Seybolt, J., De Choudhury, M., & Yang, D. (2021). Latent Hatred: A Benchmark for Understanding Implicit Hate Speech. In _Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)_.
|
| 42 |
+
|
| 43 |
+
**BibTeX:**
|
| 44 |
+
|
| 45 |
+
```tex
|
| 46 |
+
@inproceedings{elsherief-etal-2021-latent,
|
| 47 |
+
title = "Latent Hatred: A Benchmark for Understanding Implicit Hate Speech",
|
| 48 |
+
author = "ElSherief, Mai and
|
| 49 |
+
Ziems, Caleb and
|
| 50 |
+
Muchlinski, David and
|
| 51 |
+
Anupindi, Vaishnavi and
|
| 52 |
+
Seybolt, Jordyn and
|
| 53 |
+
De Choudhury, Munmun and
|
| 54 |
+
Yang, Diyi",
|
| 55 |
+
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
|
| 56 |
+
month = nov,
|
| 57 |
+
year = "2021",
|
| 58 |
+
address = "Online and Punta Cana, Dominican Republic",
|
| 59 |
+
publisher = "Association for Computational Linguistics",
|
| 60 |
+
url = "https://aclanthology.org/2021.emnlp-main.29",
|
| 61 |
+
pages = "345--363"
|
| 62 |
+
}
|
| 63 |
+
```
|
huggingface_dataset/Dataset_Card/ai4bharat_Aksharantar.md
ADDED
|
@@ -0,0 +1,254 @@
|
|
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|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators: []
|
| 3 |
+
language_creators:
|
| 4 |
+
- crowdsourced
|
| 5 |
+
- expert-generated
|
| 6 |
+
- machine-generated
|
| 7 |
+
- found
|
| 8 |
+
- other
|
| 9 |
+
language:
|
| 10 |
+
- asm-IN
|
| 11 |
+
- ben-IN
|
| 12 |
+
- brx-IN
|
| 13 |
+
- guj-IN
|
| 14 |
+
- hin-IN
|
| 15 |
+
- kan-IN
|
| 16 |
+
- kas-IN
|
| 17 |
+
- kok-IN
|
| 18 |
+
- mai-IN
|
| 19 |
+
- mal-IN
|
| 20 |
+
- mar-IN
|
| 21 |
+
- mni-IN
|
| 22 |
+
- nep-IN
|
| 23 |
+
- ori-IN
|
| 24 |
+
- pan-IN
|
| 25 |
+
- san-IN
|
| 26 |
+
- sid-IN
|
| 27 |
+
- tam-IN
|
| 28 |
+
- tel-IN
|
| 29 |
+
- urd-IN
|
| 30 |
+
license:
|
| 31 |
+
- cc-by-nc-4.0
|
| 32 |
+
multilinguality:
|
| 33 |
+
- multilingual
|
| 34 |
+
pretty_name: Aksharantar
|
| 35 |
+
size_categories: []
|
| 36 |
+
source_datasets:
|
| 37 |
+
- original
|
| 38 |
+
task_categories:
|
| 39 |
+
- text-generation
|
| 40 |
+
task_ids: []
|
| 41 |
+
---
|
| 42 |
+
|
| 43 |
+
# Dataset Card for Aksharantar
|
| 44 |
+
|
| 45 |
+
## Table of Contents
|
| 46 |
+
- [Table of Contents](#table-of-contents)
|
| 47 |
+
- [Dataset Description](#dataset-description)
|
| 48 |
+
- [Dataset Summary](#dataset-summary)
|
| 49 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 50 |
+
- [Languages](#languages)
|
| 51 |
+
- [Dataset Structure](#dataset-structure)
|
| 52 |
+
- [Data Instances](#data-instances)
|
| 53 |
+
- [Data Fields](#data-fields)
|
| 54 |
+
- [Data Splits](#data-splits)
|
| 55 |
+
- [Dataset Creation](#dataset-creation)
|
| 56 |
+
- [Curation Rationale](#curation-rationale)
|
| 57 |
+
- [Source Data](#source-data)
|
| 58 |
+
- [Annotations](#annotations)
|
| 59 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 60 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 61 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 62 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 63 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 64 |
+
- [Additional Information](#additional-information)
|
| 65 |
+
- [Dataset Curators](#dataset-curators)
|
| 66 |
+
- [Licensing Information](#licensing-information)
|
| 67 |
+
- [Citation Information](#citation-information)
|
| 68 |
+
- [Contributions](#contributions)
|
| 69 |
+
|
| 70 |
+
## Dataset Description
|
| 71 |
+
|
| 72 |
+
- **Homepage:** https://indicnlp.ai4bharat.org/indic-xlit/
|
| 73 |
+
- **Repository:** https://github.com/AI4Bharat/IndicXlit/
|
| 74 |
+
- **Paper:** [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
|
| 75 |
+
- **Leaderboard:**
|
| 76 |
+
- **Point of Contact:**
|
| 77 |
+
|
| 78 |
+
### Dataset Summary
|
| 79 |
+
|
| 80 |
+
Aksharantar is the largest publicly available transliteration dataset for 20 Indic languages. The corpus has 26M Indic language-English transliteration pairs.
|
| 81 |
+
|
| 82 |
+
### Supported Tasks and Leaderboards
|
| 83 |
+
|
| 84 |
+
[More Information Needed]
|
| 85 |
+
|
| 86 |
+
### Languages
|
| 87 |
+
|
| 88 |
+
| <!-- --> | <!-- --> | <!-- --> | <!-- --> | <!-- --> | <!-- --> |
|
| 89 |
+
| -------------- | -------------- | -------------- | --------------- | -------------- | ------------- |
|
| 90 |
+
| Assamese (asm) | Hindi (hin) | Maithili (mai) | Marathi (mar) | Punjabi (pan) | Tamil (tam) |
|
| 91 |
+
| Bengali (ben) | Kannada (kan) | Malayalam (mal)| Nepali (nep) | Sanskrit (san) | Telugu (tel) |
|
| 92 |
+
| Bodo(brx) | Kashmiri (kas) | Manipuri (mni) | Oriya (ori) | Sindhi (snd) | Urdu (urd) |
|
| 93 |
+
| Gujarati (guj) | Konkani (kok) |
|
| 94 |
+
|
| 95 |
+
|
| 96 |
+
## Dataset Structure
|
| 97 |
+
|
| 98 |
+
|
| 99 |
+
### Data Instances
|
| 100 |
+
|
| 101 |
+
```
|
| 102 |
+
A random sample from Hindi (hin) Train dataset.
|
| 103 |
+
|
| 104 |
+
{
|
| 105 |
+
'unique_identifier': 'hin1241393',
|
| 106 |
+
'native word': 'स्वाभिमानिक',
|
| 107 |
+
'english word': 'swabhimanik',
|
| 108 |
+
'source': 'IndicCorp',
|
| 109 |
+
'score': -0.1028788579
|
| 110 |
+
}
|
| 111 |
+
|
| 112 |
+
```
|
| 113 |
+
|
| 114 |
+
### Data Fields
|
| 115 |
+
|
| 116 |
+
- `unique_identifier` (string): 3-letter language code followed by a unique number in each set (Train, Test, Val).
|
| 117 |
+
- `native word` (string): A word in Indic language.
|
| 118 |
+
- `english word` (string): Transliteration of native word in English (Romanised word).
|
| 119 |
+
- `source` (string): Source of the data.
|
| 120 |
+
- `score` (num): Character level log probability of indic word given roman word by IndicXlit (model). Pairs with average threshold of the 0.35 are considered.
|
| 121 |
+
|
| 122 |
+
For created data sources, depending on the destination/sampling method of a pair in a language, it will be one of:
|
| 123 |
+
- Dakshina Dataset
|
| 124 |
+
- IndicCorp
|
| 125 |
+
- Samanantar
|
| 126 |
+
- Wikidata
|
| 127 |
+
- Existing sources
|
| 128 |
+
- Named Entities Indian (AK-NEI)
|
| 129 |
+
- Named Entities Foreign (AK-NEF)
|
| 130 |
+
- Data from Uniform Sampling method. (Ak-Uni)
|
| 131 |
+
- Data from Most Frequent words sampling method. (Ak-Freq)
|
| 132 |
+
|
| 133 |
+
|
| 134 |
+
|
| 135 |
+
|
| 136 |
+
### Data Splits
|
| 137 |
+
|
| 138 |
+
| Subset | asm-en | ben-en | brx-en | guj-en | hin-en | kan-en | kas-en | kok-en | mai-en | mal-en | mni-en | mar-en | nep-en | ori-en | pan-en | san-en | sid-en | tam-en | tel-en | urd-en |
|
| 139 |
+
|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|
|
| 140 |
+
| Training | 179K | 1231K | 36K | 1143K | 1299K | 2907K | 47K | 613K | 283K | 4101K | 10K | 1453K | 2397K | 346K | 515K | 1813K | 60K | 3231K | 2430K | 699K |
|
| 141 |
+
| Validation | 4K | 11K | 3K | 12K | 6K | 7K | 4K | 4K | 4K | 8K | 3K | 8K | 3K | 3K | 9K | 3K | 8K | 9K | 8K | 12K |
|
| 142 |
+
| Test | 5531 | 5009 | 4136 | 7768 | 5693 | 6396 | 7707 | 5093 | 5512 | 6911 | 4925 | 6573 | 4133 | 4256 | 4316 | 5334 | - | 4682 | 4567 | 4463 |
|
| 143 |
+
|
| 144 |
+
|
| 145 |
+
## Dataset Creation
|
| 146 |
+
|
| 147 |
+
Information in the paper. [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
|
| 148 |
+
|
| 149 |
+
### Curation Rationale
|
| 150 |
+
|
| 151 |
+
[More Information Needed]
|
| 152 |
+
|
| 153 |
+
### Source Data
|
| 154 |
+
|
| 155 |
+
#### Initial Data Collection and Normalization
|
| 156 |
+
|
| 157 |
+
Information in the paper. [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
|
| 158 |
+
|
| 159 |
+
#### Who are the source language producers?
|
| 160 |
+
|
| 161 |
+
[More Information Needed]
|
| 162 |
+
|
| 163 |
+
### Annotations
|
| 164 |
+
|
| 165 |
+
Information in the paper. [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
|
| 166 |
+
|
| 167 |
+
#### Annotation process
|
| 168 |
+
|
| 169 |
+
Information in the paper. [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
|
| 170 |
+
|
| 171 |
+
#### Who are the annotators?
|
| 172 |
+
|
| 173 |
+
Information in the paper. [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
|
| 174 |
+
|
| 175 |
+
### Personal and Sensitive Information
|
| 176 |
+
|
| 177 |
+
[More Information Needed]
|
| 178 |
+
|
| 179 |
+
## Considerations for Using the Data
|
| 180 |
+
|
| 181 |
+
### Social Impact of Dataset
|
| 182 |
+
|
| 183 |
+
[More Information Needed]
|
| 184 |
+
|
| 185 |
+
### Discussion of Biases
|
| 186 |
+
|
| 187 |
+
[More Information Needed]
|
| 188 |
+
|
| 189 |
+
### Other Known Limitations
|
| 190 |
+
|
| 191 |
+
[More Information Needed]
|
| 192 |
+
|
| 193 |
+
## Additional Information
|
| 194 |
+
|
| 195 |
+
### Dataset Curators
|
| 196 |
+
|
| 197 |
+
[More Information Needed]
|
| 198 |
+
|
| 199 |
+
### Licensing Information
|
| 200 |
+
|
| 201 |
+
<!-- <a rel="license" float="left" href="http://creativecommons.org/publicdomain/zero/1.0/">
|
| 202 |
+
<img src="https://licensebuttons.net/p/zero/1.0/88x31.png" style="border-style: none;" alt="CC0" width="100" />
|
| 203 |
+
<img src="https://mirrors.creativecommons.org/presskit/buttons/88x31/png/by.png" style="border-style: none;" alt="CC-BY" width="100" href="http://creativecommons.org/publicdomain/zero/1.0/"/>
|
| 204 |
+
</a>
|
| 205 |
+
<br/> -->
|
| 206 |
+
|
| 207 |
+
|
| 208 |
+
This data is released under the following licensing scheme:
|
| 209 |
+
|
| 210 |
+
- Manually collected data: Released under CC-BY license.
|
| 211 |
+
- Mined dataset (from Samanantar and IndicCorp): Released under CC0 license.
|
| 212 |
+
- Existing sources: Released under CC0 license.
|
| 213 |
+
|
| 214 |
+
**CC-BY License**
|
| 215 |
+
|
| 216 |
+
<a rel="license" float="left" href="https://creativecommons.org/about/cclicenses/">
|
| 217 |
+
<img src="https://mirrors.creativecommons.org/presskit/buttons/88x31/png/by.png" style="border-style: none;" alt="CC-BY" width="100"/>
|
| 218 |
+
</a>
|
| 219 |
+
|
| 220 |
+
<br>
|
| 221 |
+
<br>
|
| 222 |
+
<!--
|
| 223 |
+
and the Aksharantar benchmark and all manually transliterated data under the [Creative Commons CC-BY license (“no rights reserved”)](https://creativecommons.org/licenses/by/4.0/). -->
|
| 224 |
+
|
| 225 |
+
|
| 226 |
+
**CC0 License Statement**
|
| 227 |
+
|
| 228 |
+
<a rel="license" float="left" href="https://creativecommons.org/about/cclicenses/">
|
| 229 |
+
<img src="https://licensebuttons.net/p/zero/1.0/88x31.png" style="border-style: none;" alt="CC0" width="100"/>
|
| 230 |
+
</a>
|
| 231 |
+
|
| 232 |
+
<br>
|
| 233 |
+
<br>
|
| 234 |
+
|
| 235 |
+
- We do not own any of the text from which this data has been extracted.
|
| 236 |
+
- We license the actual packaging of the mined data under the [Creative Commons CC0 license (“no rights reserved”)](http://creativecommons.org/publicdomain/zero/1.0).
|
| 237 |
+
- To the extent possible under law, <a rel="dct:publisher" href="https://indicnlp.ai4bharat.org/aksharantar/"> <span property="dct:title">AI4Bharat</span></a> has waived all copyright and related or neighboring rights to <span property="dct:title">Aksharantar</span> manually collected data and existing sources.
|
| 238 |
+
- This work is published from: India.
|
| 239 |
+
|
| 240 |
+
### Citation Information
|
| 241 |
+
|
| 242 |
+
```
|
| 243 |
+
@misc{madhani2022aksharantar,
|
| 244 |
+
title={Aksharantar: Towards Building Open Transliteration Tools for the Next Billion Users},
|
| 245 |
+
author={Yash Madhani and Sushane Parthan and Priyanka Bedekar and Ruchi Khapra and Anoop Kunchukuttan and Pratyush Kumar and Mitesh Shantadevi Khapra},
|
| 246 |
+
year={2022},
|
| 247 |
+
eprint={},
|
| 248 |
+
archivePrefix={arXiv},
|
| 249 |
+
primaryClass={cs.CL}
|
| 250 |
+
}
|
| 251 |
+
```
|
| 252 |
+
|
| 253 |
+
### Contributions
|
| 254 |
+
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_test_cot_v2-math-db74ac-2016866706.md
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- mathemakitten/winobias_antistereotype_test_cot_v2
|
| 8 |
+
eval_info:
|
| 9 |
+
task: text_zero_shot_classification
|
| 10 |
+
model: inverse-scaling/opt-125m_eval
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: mathemakitten/winobias_antistereotype_test_cot_v2
|
| 13 |
+
dataset_config: mathemakitten--winobias_antistereotype_test_cot_v2
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: text
|
| 17 |
+
classes: classes
|
| 18 |
+
target: target
|
| 19 |
+
---
|
| 20 |
+
# Dataset Card for AutoTrain Evaluator
|
| 21 |
+
|
| 22 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 23 |
+
|
| 24 |
+
* Task: Zero-Shot Text Classification
|
| 25 |
+
* Model: inverse-scaling/opt-125m_eval
|
| 26 |
+
* Dataset: mathemakitten/winobias_antistereotype_test_cot_v2
|
| 27 |
+
* Config: mathemakitten--winobias_antistereotype_test_cot_v2
|
| 28 |
+
* Split: test
|
| 29 |
+
|
| 30 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 31 |
+
|
| 32 |
+
## Contributions
|
| 33 |
+
|
| 34 |
+
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267104.md
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- mathemakitten/winobias_antistereotype_test_v5
|
| 8 |
+
eval_info:
|
| 9 |
+
task: text_zero_shot_classification
|
| 10 |
+
model: inverse-scaling/opt-350m_eval
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: mathemakitten/winobias_antistereotype_test_v5
|
| 13 |
+
dataset_config: mathemakitten--winobias_antistereotype_test_v5
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: text
|
| 17 |
+
classes: classes
|
| 18 |
+
target: target
|
| 19 |
+
---
|
| 20 |
+
# Dataset Card for AutoTrain Evaluator
|
| 21 |
+
|
| 22 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 23 |
+
|
| 24 |
+
* Task: Zero-Shot Text Classification
|
| 25 |
+
* Model: inverse-scaling/opt-350m_eval
|
| 26 |
+
* Dataset: mathemakitten/winobias_antistereotype_test_v5
|
| 27 |
+
* Config: mathemakitten--winobias_antistereotype_test_v5
|
| 28 |
+
* Split: test
|
| 29 |
+
|
| 30 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 31 |
+
|
| 32 |
+
## Contributions
|
| 33 |
+
|
| 34 |
+
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
|
huggingface_dataset/Dataset_Card/huggingartists_yung-plague.md
ADDED
|
@@ -0,0 +1,204 @@
|
|
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|
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|
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|
|
|
|
|
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|
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|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
tags:
|
| 5 |
+
- huggingartists
|
| 6 |
+
- lyrics
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Dataset Card for "huggingartists/yung-plague"
|
| 10 |
+
|
| 11 |
+
## Table of Contents
|
| 12 |
+
- [Dataset Description](#dataset-description)
|
| 13 |
+
- [Dataset Summary](#dataset-summary)
|
| 14 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 15 |
+
- [Languages](#languages)
|
| 16 |
+
- [How to use](#how-to-use)
|
| 17 |
+
- [Dataset Structure](#dataset-structure)
|
| 18 |
+
- [Data Fields](#data-fields)
|
| 19 |
+
- [Data Splits](#data-splits)
|
| 20 |
+
- [Dataset Creation](#dataset-creation)
|
| 21 |
+
- [Curation Rationale](#curation-rationale)
|
| 22 |
+
- [Source Data](#source-data)
|
| 23 |
+
- [Annotations](#annotations)
|
| 24 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 25 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 26 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 27 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 28 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 29 |
+
- [Additional Information](#additional-information)
|
| 30 |
+
- [Dataset Curators](#dataset-curators)
|
| 31 |
+
- [Licensing Information](#licensing-information)
|
| 32 |
+
- [Citation Information](#citation-information)
|
| 33 |
+
- [About](#about)
|
| 34 |
+
|
| 35 |
+
## Dataset Description
|
| 36 |
+
|
| 37 |
+
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
|
| 38 |
+
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
|
| 39 |
+
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 40 |
+
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 41 |
+
- **Size of the generated dataset:** 0.109415 MB
|
| 42 |
+
|
| 43 |
+
|
| 44 |
+
<div class="inline-flex flex-col" style="line-height: 1.5;">
|
| 45 |
+
<div class="flex">
|
| 46 |
+
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/6c0f8e02f467c694379f242ea2897efd.1000x1000x1.jpg')">
|
| 47 |
+
</div>
|
| 48 |
+
</div>
|
| 49 |
+
<a href="https://huggingface.co/huggingartists/yung-plague">
|
| 50 |
+
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
|
| 51 |
+
</a>
|
| 52 |
+
<div style="text-align: center; font-size: 16px; font-weight: 800">Yung Plague</div>
|
| 53 |
+
<a href="https://genius.com/artists/yung-plague">
|
| 54 |
+
<div style="text-align: center; font-size: 14px;">@yung-plague</div>
|
| 55 |
+
</a>
|
| 56 |
+
</div>
|
| 57 |
+
|
| 58 |
+
### Dataset Summary
|
| 59 |
+
|
| 60 |
+
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
|
| 61 |
+
Model is available [here](https://huggingface.co/huggingartists/yung-plague).
|
| 62 |
+
|
| 63 |
+
### Supported Tasks and Leaderboards
|
| 64 |
+
|
| 65 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 66 |
+
|
| 67 |
+
### Languages
|
| 68 |
+
|
| 69 |
+
en
|
| 70 |
+
|
| 71 |
+
## How to use
|
| 72 |
+
|
| 73 |
+
How to load this dataset directly with the datasets library:
|
| 74 |
+
|
| 75 |
+
```python
|
| 76 |
+
from datasets import load_dataset
|
| 77 |
+
|
| 78 |
+
dataset = load_dataset("huggingartists/yung-plague")
|
| 79 |
+
```
|
| 80 |
+
|
| 81 |
+
## Dataset Structure
|
| 82 |
+
|
| 83 |
+
An example of 'train' looks as follows.
|
| 84 |
+
```
|
| 85 |
+
This example was too long and was cropped:
|
| 86 |
+
|
| 87 |
+
{
|
| 88 |
+
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
|
| 89 |
+
}
|
| 90 |
+
```
|
| 91 |
+
|
| 92 |
+
### Data Fields
|
| 93 |
+
|
| 94 |
+
The data fields are the same among all splits.
|
| 95 |
+
|
| 96 |
+
- `text`: a `string` feature.
|
| 97 |
+
|
| 98 |
+
|
| 99 |
+
### Data Splits
|
| 100 |
+
|
| 101 |
+
| train |validation|test|
|
| 102 |
+
|------:|---------:|---:|
|
| 103 |
+
|38| -| -|
|
| 104 |
+
|
| 105 |
+
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
|
| 106 |
+
|
| 107 |
+
```python
|
| 108 |
+
from datasets import load_dataset, Dataset, DatasetDict
|
| 109 |
+
import numpy as np
|
| 110 |
+
|
| 111 |
+
datasets = load_dataset("huggingartists/yung-plague")
|
| 112 |
+
|
| 113 |
+
train_percentage = 0.9
|
| 114 |
+
validation_percentage = 0.07
|
| 115 |
+
test_percentage = 0.03
|
| 116 |
+
|
| 117 |
+
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
|
| 118 |
+
|
| 119 |
+
datasets = DatasetDict(
|
| 120 |
+
{
|
| 121 |
+
'train': Dataset.from_dict({'text': list(train)}),
|
| 122 |
+
'validation': Dataset.from_dict({'text': list(validation)}),
|
| 123 |
+
'test': Dataset.from_dict({'text': list(test)})
|
| 124 |
+
}
|
| 125 |
+
)
|
| 126 |
+
```
|
| 127 |
+
|
| 128 |
+
## Dataset Creation
|
| 129 |
+
|
| 130 |
+
### Curation Rationale
|
| 131 |
+
|
| 132 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 133 |
+
|
| 134 |
+
### Source Data
|
| 135 |
+
|
| 136 |
+
#### Initial Data Collection and Normalization
|
| 137 |
+
|
| 138 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 139 |
+
|
| 140 |
+
#### Who are the source language producers?
|
| 141 |
+
|
| 142 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 143 |
+
|
| 144 |
+
### Annotations
|
| 145 |
+
|
| 146 |
+
#### Annotation process
|
| 147 |
+
|
| 148 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 149 |
+
|
| 150 |
+
#### Who are the annotators?
|
| 151 |
+
|
| 152 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 153 |
+
|
| 154 |
+
### Personal and Sensitive Information
|
| 155 |
+
|
| 156 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 157 |
+
|
| 158 |
+
## Considerations for Using the Data
|
| 159 |
+
|
| 160 |
+
### Social Impact of Dataset
|
| 161 |
+
|
| 162 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 163 |
+
|
| 164 |
+
### Discussion of Biases
|
| 165 |
+
|
| 166 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 167 |
+
|
| 168 |
+
### Other Known Limitations
|
| 169 |
+
|
| 170 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 171 |
+
|
| 172 |
+
## Additional Information
|
| 173 |
+
|
| 174 |
+
### Dataset Curators
|
| 175 |
+
|
| 176 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 177 |
+
|
| 178 |
+
### Licensing Information
|
| 179 |
+
|
| 180 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 181 |
+
|
| 182 |
+
### Citation Information
|
| 183 |
+
|
| 184 |
+
```
|
| 185 |
+
@InProceedings{huggingartists,
|
| 186 |
+
author={Aleksey Korshuk}
|
| 187 |
+
year=2021
|
| 188 |
+
}
|
| 189 |
+
```
|
| 190 |
+
|
| 191 |
+
|
| 192 |
+
## About
|
| 193 |
+
|
| 194 |
+
*Built by Aleksey Korshuk*
|
| 195 |
+
|
| 196 |
+
[](https://github.com/AlekseyKorshuk)
|
| 197 |
+
|
| 198 |
+
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
|
| 199 |
+
|
| 200 |
+
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
|
| 201 |
+
|
| 202 |
+
For more details, visit the project repository.
|
| 203 |
+
|
| 204 |
+
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingface_dataset/Dataset_Card/irds_pmc_v2.md
ADDED
|
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
pretty_name: '`pmc/v2`'
|
| 3 |
+
viewer: false
|
| 4 |
+
source_datasets: []
|
| 5 |
+
task_categories:
|
| 6 |
+
- text-retrieval
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Dataset Card for `pmc/v2`
|
| 10 |
+
|
| 11 |
+
The `pmc/v2` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
|
| 12 |
+
For more information about the dataset, see the [documentation](https://ir-datasets.com/pmc#pmc/v2).
|
| 13 |
+
|
| 14 |
+
# Data
|
| 15 |
+
|
| 16 |
+
This dataset provides:
|
| 17 |
+
- `docs` (documents, i.e., the corpus); count=1,255,260
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
This dataset is used by: [`pmc_v2_trec-cds-2016`](https://huggingface.co/datasets/irds/pmc_v2_trec-cds-2016)
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
## Usage
|
| 24 |
+
|
| 25 |
+
```python
|
| 26 |
+
from datasets import load_dataset
|
| 27 |
+
|
| 28 |
+
docs = load_dataset('irds/pmc_v2', 'docs')
|
| 29 |
+
for record in docs:
|
| 30 |
+
record # {'doc_id': ..., 'journal': ..., 'title': ..., 'abstract': ..., 'body': ...}
|
| 31 |
+
|
| 32 |
+
```
|
| 33 |
+
|
| 34 |
+
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
|
| 35 |
+
data in 🤗 Dataset format.
|
huggingface_dataset/Dataset_Card/jed351_rthk_news.md
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- zh
|
| 4 |
+
---
|
| 5 |
+
|
| 6 |
+
### RTHK News Dataset
|
| 7 |
+
(RTHK)[https://www.rthk.hk/] is a public broadcasting service under the Hong Kong Government according to (Wikipedia)[https://en.wikipedia.org/wiki/RTHK]
|
| 8 |
+
|
| 9 |
+
|
| 10 |
+
This dataset at the moment is obtained from exporting messages from their (telegram channel)[https://t.me/rthk_new_c],
|
| 11 |
+
which contains news since April 2018.
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
I will update this dataset with more data in the future.
|
huggingface_dataset/Dataset_Card/jonatli_the_pile_mystic.md
ADDED
|
@@ -0,0 +1,291 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- no-annotation
|
| 4 |
+
language_creators:
|
| 5 |
+
- found
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- other
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
pretty_name: The Pile
|
| 13 |
+
size_categories:
|
| 14 |
+
- unknown
|
| 15 |
+
source_datasets:
|
| 16 |
+
- original
|
| 17 |
+
task_categories:
|
| 18 |
+
- text-generation
|
| 19 |
+
- fill-mask
|
| 20 |
+
task_ids:
|
| 21 |
+
- language-modeling
|
| 22 |
+
- masked-language-modeling
|
| 23 |
+
paperswithcode_id: the-pile
|
| 24 |
+
---
|
| 25 |
+
|
| 26 |
+
# Dataset Card for The Pile
|
| 27 |
+
|
| 28 |
+
## Table of Contents
|
| 29 |
+
- [Table of Contents](#table-of-contents)
|
| 30 |
+
- [Dataset Description](#dataset-description)
|
| 31 |
+
- [Dataset Summary](#dataset-summary)
|
| 32 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 33 |
+
- [Languages](#languages)
|
| 34 |
+
- [Dataset Structure](#dataset-structure)
|
| 35 |
+
- [Data Instances](#data-instances)
|
| 36 |
+
- [Data Fields](#data-fields)
|
| 37 |
+
- [Data Splits](#data-splits)
|
| 38 |
+
- [Dataset Creation](#dataset-creation)
|
| 39 |
+
- [Curation Rationale](#curation-rationale)
|
| 40 |
+
- [Source Data](#source-data)
|
| 41 |
+
- [Annotations](#annotations)
|
| 42 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 43 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 44 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 45 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 46 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 47 |
+
- [Additional Information](#additional-information)
|
| 48 |
+
- [Dataset Curators](#dataset-curators)
|
| 49 |
+
- [Licensing Information](#licensing-information)
|
| 50 |
+
- [Citation Information](#citation-information)
|
| 51 |
+
- [Contributions](#contributions)
|
| 52 |
+
|
| 53 |
+
## Dataset Description
|
| 54 |
+
|
| 55 |
+
- **Homepage:** https://pile.eleuther.ai/
|
| 56 |
+
- **Repository:** https://github.com/EleutherAI/the-pile
|
| 57 |
+
- **Paper:** [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://arxiv.org/abs/2101.00027)
|
| 58 |
+
- **Leaderboard:**
|
| 59 |
+
- **Point of Contact:** [EleutherAI](mailto:contact@eleuther.ai)
|
| 60 |
+
|
| 61 |
+
**This version of the pile relies on `mystic.the-eye.eu`, a mirror of `the-eye.eu` which is currently down for me.**
|
| 62 |
+
|
| 63 |
+
### Dataset Summary
|
| 64 |
+
|
| 65 |
+
The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality
|
| 66 |
+
datasets combined together.
|
| 67 |
+
|
| 68 |
+
|
| 69 |
+
### Supported Tasks and Leaderboards
|
| 70 |
+
|
| 71 |
+
[More Information Needed]
|
| 72 |
+
|
| 73 |
+
### Languages
|
| 74 |
+
|
| 75 |
+
This dataset is in English (`EN`).
|
| 76 |
+
|
| 77 |
+
## Dataset Structure
|
| 78 |
+
|
| 79 |
+
### Data Instances
|
| 80 |
+
|
| 81 |
+
#### all
|
| 82 |
+
```
|
| 83 |
+
{
|
| 84 |
+
'meta': {'pile_set_name': 'Pile-CC'},
|
| 85 |
+
'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web. Playing on...'
|
| 86 |
+
}
|
| 87 |
+
```
|
| 88 |
+
|
| 89 |
+
#### enron_emails
|
| 90 |
+
```
|
| 91 |
+
{
|
| 92 |
+
'text': 'Name\t\t\tNew Title\t\t\t\tEffective Date\t\t\tMid Year promotion Yes/No\n\nFloyd, Jodie\t\tSr Cust Svc Rep (no change)\t\t7/16/01\t\t\t\tNo\n\nBuehler, Craig\t\tSr Mkt/Sup Analyst (no change)\t\t7/16/01\t\t\t\tNo\n\nWagoner, Mike\t\tTeam Advisor - Gas Control\t\t7/1/01\t\t\t\tNo\n\nClapper, Karen\t\tSr Cust Svc Rep\t\t\t8/1/01\t\t\t\tYes\n\nGreaney, Chris\t\tSr Cust Svc Rep\t\t\t8/1/01\t\t\t\tYes\n\nWilkens, Jerry\t\tSr Cust Svc Rep\t\t\t8/1/01\t\t\t\tYes\n\nMinton, Kevin\t\tPipeline Controller\t\t\t8/1/01\t\t\t\tYes\n\nCox, Don\t\tPipeline Controller\t\t\t8/1/01\t\t\t\tYes\n\nHanagriff, Richard\tSr Accounting Control Spec\t\t8/1/01\t\t\t\tYes\n\n\nThanks,\nMS'
|
| 93 |
+
'meta': "{}",
|
| 94 |
+
|
| 95 |
+
}
|
| 96 |
+
```
|
| 97 |
+
|
| 98 |
+
#### europarl
|
| 99 |
+
```
|
| 100 |
+
{
|
| 101 |
+
'text': 'Uvádění biocidních přípravků na trh - Nový návrh revize týkající se biocidních přípravků (rozprava) \nPředsedající\nDalším bodem je společná rozprava o následujících tématech:\nzpráva paní Sârbuové za Výbor pro životní prostředí, veřejné zdraví a bezpečnost potravin o návrhu...'
|
| 102 |
+
'meta': "{'language': 'cs'}",
|
| 103 |
+
|
| 104 |
+
}
|
| 105 |
+
```
|
| 106 |
+
|
| 107 |
+
#### free_law
|
| 108 |
+
```
|
| 109 |
+
{
|
| 110 |
+
'meta': "{'case_jurisdiction': 'scotus.tar.gz', 'case_ID': '110921.json','date_created': '2010-04-28T17:12:49Z'}",
|
| 111 |
+
'text': '\n461 U.S. 238 (1983)\nOLIM ET AL.\nv.\nWAKINEKONA\nNo. 81-1581.\nSupreme Court of United States.\nArgued...'
|
| 112 |
+
}
|
| 113 |
+
```
|
| 114 |
+
|
| 115 |
+
#### hacker_news
|
| 116 |
+
```
|
| 117 |
+
{
|
| 118 |
+
'text': "\nChina Deserves Donald Trump - rm2889\nhttps://www.nytimes.com/2019/05/21/opinion/china-trump-trade.html\n======\nNotPaidToPost\n> so he’d be wise to curb his nationalistic “no-one-tells-China-what-to-do”\n> bluster\n\nThis comment highlights both ignorance of Chinese history and continuing\nAmerican arrogance.\n\nChina has been painfully dictated what to do during the last 200 years. This\nhas had a profound effect on the country and has led to the collapse of\nimperial rule and the drive to 'rejuvenate'...",
|
| 119 |
+
'meta': "{'id': '19979654'}",
|
| 120 |
+
}
|
| 121 |
+
```
|
| 122 |
+
|
| 123 |
+
#### nih_exporter
|
| 124 |
+
```
|
| 125 |
+
{
|
| 126 |
+
'text': "The National Domestic Violence Hotline (NDVH) and the National Dating Abuse Helpline (NDAH), which are supported by the Division of Family Violence Prevention and Services within the Family and Youth Services Bureau, serve as critical partners in the intervention, prevention, and resource assistance efforts of the network of family violence, domestic violence, and dating violence service providers. They provide crisis intervention and support services; information about resources on domestic...",
|
| 127 |
+
'meta': " {'APPLICATION_ID': 100065}",
|
| 128 |
+
}
|
| 129 |
+
```
|
| 130 |
+
|
| 131 |
+
#### pubmed
|
| 132 |
+
```
|
| 133 |
+
{
|
| 134 |
+
'meta': {'pmid': 11409574, 'language': 'eng'},
|
| 135 |
+
'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection.\nTo determine the prevalence of hypoxaemia in children aged under 5 years suffering acute lower respiratory infections (ALRI), the risk factors for hypoxaemia in children under 5 years of age with ALRI, and the association of hypoxaemia with an increased risk of dying in children of the same age. Systematic review of the published literature. Out-patient clinics, emergency departments and hospitalisation wards in 23 health centres from 10 countries. Cohort studies reporting the frequency of hypoxaemia in children under 5 years of age with ALRI, and the association between hypoxaemia and the risk of dying. Prevalence of hypoxaemia measured in children with ARI and relative risks for the association between the severity of illness and the frequency of hypoxaemia, and between hypoxaemia and the risk of dying. Seventeen published studies were found that included 4,021 children under 5 with acute respiratory infections (ARI) and reported the prevalence of hypoxaemia. Out-patient children and those with a clinical diagnosis of upper ARI had a low risk of hypoxaemia (pooled estimate of 6% to 9%). The prevalence increased to 31% and to 43% in patients in emergency departments and in cases with clinical pneumonia, respectively, and it was even higher among hospitalised children (47%) and in those with radiographically confirmed pneumonia (72%). The cumulated data also suggest that hypoxaemia is more frequent in children living at high altitude. Three papers reported an association between hypoxaemia and death, with relative risks varying between 1.4 and 4.6. Papers describing predictors of hypoxaemia have focused on clinical signs for detecting hypoxaemia rather than on identifying risk factors for developing this complication. Hypoxaemia is a common and potentially lethal complication of ALRI in children under 5, particularly among those with severe disease and those living at high altitude. Given the observed high prevalence of hypoxaemia and its likely association with increased mortality, efforts should be made to improve the detection of hypoxaemia and to provide oxygen earlier to more children with severe ALRI.'
|
| 136 |
+
}
|
| 137 |
+
```
|
| 138 |
+
|
| 139 |
+
#### pubmed_central
|
| 140 |
+
```
|
| 141 |
+
{
|
| 142 |
+
'meta': "{id': 'PMC5595690'}",
|
| 143 |
+
'text': 'Introduction {#acel12642-sec-0001}\n============\n\nAlzheimer\\\'s disease (AD), the most common cause of...'
|
| 144 |
+
}
|
| 145 |
+
```
|
| 146 |
+
|
| 147 |
+
#### ubuntu_irc
|
| 148 |
+
```
|
| 149 |
+
{
|
| 150 |
+
'text': "#ubuntu 2004-07-05\n* Window 3\n* \tServer: [0] <None>\n* \tScreen: 0x817e90c\n* \tGeometry Info: [0 11 0 11 11 11] \n* \tCO, LI are [94 49] \n* \tCurrent channel: #ubuntu\n* \tQuery User: <None> \n*\tPrompt: <None>\n* \tSecond status line is OFF\n* \tSplit line is ON triple is OFF\n* \tLogging is ON\n* \tLogfile is irclogs/ubuntu.log\n* \tNotification is OFF\n* \tHold mode is OFF\n* \tWindow level is NONE\n* \tLastlog level is ALL\n* \tNotify level is ALL\n<mdz> lifeless: using tla effectively for all packages in Warty requ...",
|
| 151 |
+
'meta': "{'channel': 'ubuntu', 'month': 7}"
|
| 152 |
+
}
|
| 153 |
+
```
|
| 154 |
+
|
| 155 |
+
#### uspto
|
| 156 |
+
```
|
| 157 |
+
{
|
| 158 |
+
'text': "1. Field of the Invention\nIn an extensive plant breeding program, Grant Merrill, originator and now deceased, originated a large number of new and distinct varieties of fruit trees, and which included the herein-claimed variety of peach tree. Such plant breeding program was undertaken in originator's experimental orchard located near Exeter, Tulare County, Calif.\n2. Prior Varieties\nAmong the existent varieties of peach trees which were known to originator, particular reference is made to Gemfree (U.S. Plant Pat. No. 1,409) and June Lady (U.S. Plant Pat. No. 3,022) hereinafter mentioned for the purpose of comparison.",
|
| 159 |
+
'meta': "{'bibliographic_information': {'Patent Number': 'PP0049700', 'Series Code': '6', 'Application Number': '2845415', 'Application Type': '6', 'Art unit': '337', 'Application Filing Date': '19810720', 'Title of Invention': 'Peach tree (A3-10)', 'Issue Date': '19830104', 'Number of Claims': '1', 'Exemplary Claim Number(s)': '1', 'Primary Examiner': 'Bagwill; Robert E.', 'Number of Drawing Sheets': '1', 'Number of figures': '1'}, 'source_file': 'https://bulkdata.uspto.gov/data/patent/grant/redbook/fulltext/1983/pftaps19830104_wk01.zip', 'abstract': 'A peach tree which is large, vigorous, and spreading; foliated with large, lanceolate leaves having a finely serrate margin, a petiole of medium length and thickness, and medium size, reniform glands; blooms from medium size, conic, plump, pubescent buds; the flowers, medium in blooming period compared with other varieties, being of medium size, and pink; and is a regular and very productive bearer of medium but variable size, round truncate, clingstone fruit having yellow skin substantially overspread with red, yellow flesh mottled with red adjacent the skin, and an amber stone.', 'classifications': [{'OCL': ['Plt', '43'], 'EDF': ['3'], 'ICL': ['A01H', '503'], 'FSC': ['Plt'], 'FSS': ['43']}], 'inventors': [{'inventor name': 'Merrill, deceased; Grant', 'Street': '325 Breese Ave.', 'City': 'late of Red Bluff', 'State': 'CA'}, {'inventor name': 'Merrill, executrix; by Lucile B.', 'Street': '325 Breese Ave.', 'City': 'Red Bluff', 'State': 'CA', 'Zip code': '96080'}]}"
|
| 160 |
+
}
|
| 161 |
+
```
|
| 162 |
+
|
| 163 |
+
### Data Fields
|
| 164 |
+
|
| 165 |
+
#### all
|
| 166 |
+
|
| 167 |
+
- `text` (str): Text.
|
| 168 |
+
- `meta` (dict): Metadata of the data instance with keys:
|
| 169 |
+
- pile_set_name: Name of the subset.
|
| 170 |
+
|
| 171 |
+
#### enron_emails
|
| 172 |
+
|
| 173 |
+
- `text` (str): Text.
|
| 174 |
+
- `meta` (str): Metadata of the data instance.
|
| 175 |
+
|
| 176 |
+
#### europarl
|
| 177 |
+
|
| 178 |
+
- `text` (str): Text.
|
| 179 |
+
- `meta` (str): Metadata of the data instance with: language.
|
| 180 |
+
|
| 181 |
+
#### free_law
|
| 182 |
+
|
| 183 |
+
- `text` (str): Text.
|
| 184 |
+
- `meta` (str): Metadata of the data instance with: case_ID, case_jurisdiction, date_created.
|
| 185 |
+
|
| 186 |
+
#### hacker_news
|
| 187 |
+
|
| 188 |
+
- `text` (str): Text.
|
| 189 |
+
- `meta` (str): Metadata of the data instance with: id.
|
| 190 |
+
|
| 191 |
+
#### nih_exporter
|
| 192 |
+
|
| 193 |
+
- `text` (str): Text.
|
| 194 |
+
- `meta` (str): Metadata of the data instance with: APPLICATION_ID.
|
| 195 |
+
|
| 196 |
+
#### pubmed
|
| 197 |
+
|
| 198 |
+
- `text` (str): Text.
|
| 199 |
+
- `meta` (str): Metadata of the data instance with: pmid, language.
|
| 200 |
+
|
| 201 |
+
#### pubmed_central
|
| 202 |
+
|
| 203 |
+
- `text` (str): Text.
|
| 204 |
+
- `meta` (str): Metadata of the data instance with: ID of the data instance.
|
| 205 |
+
|
| 206 |
+
#### ubuntu_irc
|
| 207 |
+
|
| 208 |
+
- `text` (str): Text.
|
| 209 |
+
- `meta` (str): Metadata of the data instance with: channel, month.
|
| 210 |
+
|
| 211 |
+
#### uspto
|
| 212 |
+
|
| 213 |
+
- `text` (str): Text.
|
| 214 |
+
- `meta` (str): Metadata of the data instance with: bibliographic_information, source_file, abstract, classifications,
|
| 215 |
+
inventors.
|
| 216 |
+
|
| 217 |
+
### Data Splits
|
| 218 |
+
|
| 219 |
+
The "all" configuration is composed of 3 splits: train, validation and test.
|
| 220 |
+
|
| 221 |
+
## Dataset Creation
|
| 222 |
+
|
| 223 |
+
### Curation Rationale
|
| 224 |
+
|
| 225 |
+
[More Information Needed]
|
| 226 |
+
|
| 227 |
+
### Source Data
|
| 228 |
+
|
| 229 |
+
#### Initial Data Collection and Normalization
|
| 230 |
+
|
| 231 |
+
[More Information Needed]
|
| 232 |
+
|
| 233 |
+
#### Who are the source language producers?
|
| 234 |
+
|
| 235 |
+
[More Information Needed]
|
| 236 |
+
|
| 237 |
+
### Annotations
|
| 238 |
+
|
| 239 |
+
#### Annotation process
|
| 240 |
+
|
| 241 |
+
[More Information Needed]
|
| 242 |
+
|
| 243 |
+
#### Who are the annotators?
|
| 244 |
+
|
| 245 |
+
[More Information Needed]
|
| 246 |
+
|
| 247 |
+
### Personal and Sensitive Information
|
| 248 |
+
|
| 249 |
+
[More Information Needed]
|
| 250 |
+
|
| 251 |
+
## Considerations for Using the Data
|
| 252 |
+
|
| 253 |
+
### Social Impact of Dataset
|
| 254 |
+
|
| 255 |
+
[More Information Needed]
|
| 256 |
+
|
| 257 |
+
### Discussion of Biases
|
| 258 |
+
|
| 259 |
+
[More Information Needed]
|
| 260 |
+
|
| 261 |
+
### Other Known Limitations
|
| 262 |
+
|
| 263 |
+
[More Information Needed]
|
| 264 |
+
|
| 265 |
+
## Additional Information
|
| 266 |
+
|
| 267 |
+
### Dataset Curators
|
| 268 |
+
|
| 269 |
+
[More Information Needed]
|
| 270 |
+
|
| 271 |
+
### Licensing Information
|
| 272 |
+
|
| 273 |
+
Please refer to the specific license depending on the subset you use:
|
| 274 |
+
- PubMed Central: [MIT License](https://github.com/EleutherAI/pile-pubmedcentral/blob/master/LICENSE)
|
| 275 |
+
|
| 276 |
+
### Citation Information
|
| 277 |
+
|
| 278 |
+
```
|
| 279 |
+
@misc{gao2020pile,
|
| 280 |
+
title={The Pile: An 800GB Dataset of Diverse Text for Language Modeling},
|
| 281 |
+
author={Leo Gao and Stella Biderman and Sid Black and Laurence Golding and Travis Hoppe and Charles Foster and Jason Phang and Horace He and Anish Thite and Noa Nabeshima and Shawn Presser and Connor Leahy},
|
| 282 |
+
year={2020},
|
| 283 |
+
eprint={2101.00027},
|
| 284 |
+
archivePrefix={arXiv},
|
| 285 |
+
primaryClass={cs.CL}
|
| 286 |
+
}
|
| 287 |
+
```
|
| 288 |
+
|
| 289 |
+
### Contributions
|
| 290 |
+
|
| 291 |
+
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
huggingface_dataset/Dataset_Card/larrylawl_multilexnorm.md
ADDED
|
@@ -0,0 +1,98 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: cc-by-4.0
|
| 3 |
+
task_categories:
|
| 4 |
+
- text-generation
|
| 5 |
+
language:
|
| 6 |
+
- en
|
| 7 |
+
- da
|
| 8 |
+
- de
|
| 9 |
+
- es
|
| 10 |
+
- hr
|
| 11 |
+
- it
|
| 12 |
+
- nl
|
| 13 |
+
- sl
|
| 14 |
+
- sr
|
| 15 |
+
- tr
|
| 16 |
+
- id
|
| 17 |
+
size_categories:
|
| 18 |
+
- 100K<n<1M
|
| 19 |
+
---
|
| 20 |
+
|
| 21 |
+
|
| 22 |
+
# Dataset Card Creation Guide
|
| 23 |
+
|
| 24 |
+
## Table of Contents
|
| 25 |
+
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
|
| 26 |
+
- [Table of Contents](#table-of-contents)
|
| 27 |
+
- [Dataset Description](#dataset-description)
|
| 28 |
+
- [Dataset Summary](#dataset-summary)
|
| 29 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 30 |
+
- [Languages](#languages)
|
| 31 |
+
- [Dataset Structure](#dataset-structure)
|
| 32 |
+
- [Data Instances](#data-instances)
|
| 33 |
+
- [Data Fields](#data-fields)
|
| 34 |
+
- [Data Splits](#data-splits)
|
| 35 |
+
- [Dataset Creation](#dataset-creation)
|
| 36 |
+
- [Curation Rationale](#curation-rationale)
|
| 37 |
+
- [Source Data](#source-data)
|
| 38 |
+
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
|
| 39 |
+
- [Who are the source language producers?](#who-are-the-source-language-producers)
|
| 40 |
+
- [Annotations](#annotations)
|
| 41 |
+
- [Annotation process](#annotation-process)
|
| 42 |
+
- [Who are the annotators?](#who-are-the-annotators)
|
| 43 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 44 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 45 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 46 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 47 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 48 |
+
- [Additional Information](#additional-information)
|
| 49 |
+
- [Dataset Curators](#dataset-curators)
|
| 50 |
+
- [Licensing Information](#licensing-information)
|
| 51 |
+
- [Citation Information](#citation-information)
|
| 52 |
+
- [Contributions](#contributions)
|
| 53 |
+
|
| 54 |
+
## Dataset Description
|
| 55 |
+
|
| 56 |
+
- **Homepage:** [http://noisy-text.github.io/2021/multi-lexnorm.html]()
|
| 57 |
+
- **Paper:** [https://aclanthology.org/2021.wnut-1.55/]()
|
| 58 |
+
|
| 59 |
+
### Dataset Summary
|
| 60 |
+
|
| 61 |
+
This is the huggingface version of the MultiLexnorm dataset.
|
| 62 |
+
|
| 63 |
+
I'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.
|
| 64 |
+
|
| 65 |
+
|
| 66 |
+
### Citation Information
|
| 67 |
+
```
|
| 68 |
+
@inproceedings{van-der-goot-etal-2021-multilexnorm,
|
| 69 |
+
title = "{M}ulti{L}ex{N}orm: A Shared Task on Multilingual Lexical Normalization",
|
| 70 |
+
author = {van der Goot, Rob and
|
| 71 |
+
Ramponi, Alan and
|
| 72 |
+
Zubiaga, Arkaitz and
|
| 73 |
+
Plank, Barbara and
|
| 74 |
+
Muller, Benjamin and
|
| 75 |
+
San Vicente Roncal, I{\~n}aki and
|
| 76 |
+
Ljube{\v{s}}i{\'c}, Nikola and
|
| 77 |
+
{\c{C}}etino{\u{g}}lu, {\"O}zlem and
|
| 78 |
+
Mahendra, Rahmad and
|
| 79 |
+
{\c{C}}olako{\u{g}}lu, Talha and
|
| 80 |
+
Baldwin, Timothy and
|
| 81 |
+
Caselli, Tommaso and
|
| 82 |
+
Sidorenko, Wladimir},
|
| 83 |
+
booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)",
|
| 84 |
+
month = nov,
|
| 85 |
+
year = "2021",
|
| 86 |
+
address = "Online",
|
| 87 |
+
publisher = "Association for Computational Linguistics",
|
| 88 |
+
url = "https://aclanthology.org/2021.wnut-1.55",
|
| 89 |
+
doi = "10.18653/v1/2021.wnut-1.55",
|
| 90 |
+
pages = "493--509",
|
| 91 |
+
abstract = "Lexical normalization is the task of transforming an utterance into its standardized form. This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation. Such variation is typical for social media on which information is shared in a multitude of ways, including diverse languages and code-switching. Since the seminal work of Han and Baldwin (2011) a decade ago, lexical normalization has attracted attention in English and multiple other languages. However, there exists a lack of a common benchmark for comparison of systems across languages with a homogeneous data and evaluation setup. The MultiLexNorm shared task sets out to fill this gap. We provide the largest publicly available multilingual lexical normalization benchmark including 13 language variants. We propose a homogenized evaluation setup with both intrinsic and extrinsic evaluation. As extrinsic evaluation, we use dependency parsing and part-of-speech tagging with adapted evaluation metrics (a-LAS, a-UAS, and a-POS) to account for alignment discrepancies. The shared task hosted at W-NUT 2021 attracted 9 participants and 18 submissions. The results show that neural normalization systems outperform the previous state-of-the-art system by a large margin. Downstream parsing and part-of-speech tagging performance is positively affected but to varying degrees, with improvements of up to 1.72 a-LAS, 0.85 a-UAS, and 1.54 a-POS for the winning system.",
|
| 92 |
+
}
|
| 93 |
+
```
|
| 94 |
+
|
| 95 |
+
|
| 96 |
+
### Contributions
|
| 97 |
+
|
| 98 |
+
Thanks to [@larrylawl](https://github.com/larrylawl) for adding this dataset.
|
huggingface_dataset/Dataset_Card/nateraw_kitti.md
ADDED
|
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- found
|
| 4 |
+
language_creators:
|
| 5 |
+
- crowdsourced
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- unknown
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
pretty_name: Kitti
|
| 13 |
+
size_categories:
|
| 14 |
+
- 1K<n<10K
|
| 15 |
+
task_categories:
|
| 16 |
+
- object-detection
|
| 17 |
+
task_ids:
|
| 18 |
+
- object-detection
|
| 19 |
+
---
|
| 20 |
+
|
| 21 |
+
# Dataset Card for Kitti
|
| 22 |
+
|
| 23 |
+
The [Kitti](http://www.cvlibs.net/datasets/kitti/eval_object.php) dataset.
|
| 24 |
+
|
| 25 |
+
The Kitti object detection and object orientation estimation benchmark consists of 7481 training images and 7518 test images, comprising a total of 80.256 labeled objects
|
huggingface_dataset/Dataset_Card/noahshinn024_ts-code2td.md
ADDED
|
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
---
|
| 4 |
+
|
| 5 |
+
## Dataset Description
|
| 6 |
+
A dataset of pairs of TypeScript code to appropriate type declarations.
|
| 7 |
+
|
| 8 |
+
## Language
|
| 9 |
+
TypeScript only.
|
| 10 |
+
|
| 11 |
+
## To Load
|
| 12 |
+
```python
|
| 13 |
+
from datasets import load_dataset
|
| 14 |
+
|
| 15 |
+
load_dataset("noahshinn024/ts-code2td")
|
| 16 |
+
```
|
| 17 |
+
|
| 18 |
+
## Distribution of type declaration code lengths
|
| 19 |
+
- uses the tokenizer from [bigcode/santacoder](https://huggingface.co/bigcode/santacoder)
|
| 20 |
+
|
huggingface_dataset/Dataset_Card/pcoloc_autotrain-data-trackerlora_less_data.md
ADDED
|
@@ -0,0 +1,64 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
{}
|
| 3 |
+
|
| 4 |
+
---
|
| 5 |
+
# AutoTrain Dataset for project: trackerlora_less_data
|
| 6 |
+
|
| 7 |
+
## Dataset Description
|
| 8 |
+
|
| 9 |
+
This dataset has been automatically processed by AutoTrain for project trackerlora_less_data.
|
| 10 |
+
|
| 11 |
+
### Languages
|
| 12 |
+
|
| 13 |
+
The BCP-47 code for the dataset's language is unk.
|
| 14 |
+
|
| 15 |
+
## Dataset Structure
|
| 16 |
+
|
| 17 |
+
### Data Instances
|
| 18 |
+
|
| 19 |
+
A sample from this dataset looks as follows:
|
| 20 |
+
|
| 21 |
+
```json
|
| 22 |
+
[
|
| 23 |
+
{
|
| 24 |
+
"id": 444,
|
| 25 |
+
"feat_rssi": -113.0,
|
| 26 |
+
"feat_snr": -9.25,
|
| 27 |
+
"feat_spreading_factor": 7,
|
| 28 |
+
"feat_potencia": 14,
|
| 29 |
+
"target": 308.0
|
| 30 |
+
},
|
| 31 |
+
{
|
| 32 |
+
"id": 144,
|
| 33 |
+
"feat_rssi": -77.0,
|
| 34 |
+
"feat_snr": 8.800000190734863,
|
| 35 |
+
"feat_spreading_factor": 7,
|
| 36 |
+
"feat_potencia": 14,
|
| 37 |
+
"target": 126.0
|
| 38 |
+
}
|
| 39 |
+
]
|
| 40 |
+
```
|
| 41 |
+
|
| 42 |
+
### Dataset Fields
|
| 43 |
+
|
| 44 |
+
The dataset has the following fields (also called "features"):
|
| 45 |
+
|
| 46 |
+
```json
|
| 47 |
+
{
|
| 48 |
+
"id": "Value(dtype='int64', id=None)",
|
| 49 |
+
"feat_rssi": "Value(dtype='float64', id=None)",
|
| 50 |
+
"feat_snr": "Value(dtype='float64', id=None)",
|
| 51 |
+
"feat_spreading_factor": "Value(dtype='int64', id=None)",
|
| 52 |
+
"feat_potencia": "Value(dtype='int64', id=None)",
|
| 53 |
+
"target": "Value(dtype='float32', id=None)"
|
| 54 |
+
}
|
| 55 |
+
```
|
| 56 |
+
|
| 57 |
+
### Dataset Splits
|
| 58 |
+
|
| 59 |
+
This dataset is split into a train and validation split. The split sizes are as follow:
|
| 60 |
+
|
| 61 |
+
| Split name | Num samples |
|
| 62 |
+
| ------------ | ------------------- |
|
| 63 |
+
| train | 139 |
|
| 64 |
+
| valid | 40 |
|
huggingface_dataset/Dataset_Card/qgallouedec_prj_gia_dataset_metaworld_assembly_v2_1111.md
ADDED
|
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
library_name: gia
|
| 3 |
+
tags:
|
| 4 |
+
- deep-reinforcement-learning
|
| 5 |
+
- reinforcement-learning
|
| 6 |
+
- gia
|
| 7 |
+
- multi-task
|
| 8 |
+
- multi-modal
|
| 9 |
+
- imitation-learning
|
| 10 |
+
- offline-reinforcement-learning
|
| 11 |
+
---
|
| 12 |
+
|
| 13 |
+
An imitation learning environment for the assembly-v2 environment, sample for the policy assembly-v2
|
| 14 |
+
|
| 15 |
+
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
|
| 16 |
+
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
## Load dataset
|
| 21 |
+
|
| 22 |
+
First, clone it with
|
| 23 |
+
|
| 24 |
+
```sh
|
| 25 |
+
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_assembly_v2_1111
|
| 26 |
+
```
|
| 27 |
+
|
| 28 |
+
Then, load it with
|
| 29 |
+
|
| 30 |
+
```python
|
| 31 |
+
import numpy as np
|
| 32 |
+
dataset = np.load("prj_gia_dataset_metaworld_assembly_v2_1111/dataset.npy", allow_pickle=True).item()
|
| 33 |
+
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
|
| 34 |
+
```
|
| 35 |
+
|
| 36 |
+
|
huggingface_dataset/Dataset_Card/ulysses-camara_ulysses-ner-br.md
ADDED
|
@@ -0,0 +1,150 @@
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|
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|
|
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|
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|
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|
|
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|
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|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
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|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators: []
|
| 3 |
+
language_creators: []
|
| 4 |
+
language:
|
| 5 |
+
- pt
|
| 6 |
+
license: []
|
| 7 |
+
multilinguality:
|
| 8 |
+
- monolingual
|
| 9 |
+
pretty_name: UlyssesNER-br
|
| 10 |
+
size_categories:
|
| 11 |
+
- 10K<n<100K
|
| 12 |
+
source_datasets: []
|
| 13 |
+
task_categories:
|
| 14 |
+
- token-classification
|
| 15 |
+
task_ids:
|
| 16 |
+
- named-entity-recognition
|
| 17 |
+
---
|
| 18 |
+
|
| 19 |
+
# Dataset Card for UlyssesNER-Br
|
| 20 |
+
|
| 21 |
+
## Table of Contents
|
| 22 |
+
- [Table of Contents](#table-of-contents)
|
| 23 |
+
- [Dataset Description](#dataset-description)
|
| 24 |
+
- [Dataset Summary](#dataset-summary)
|
| 25 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 26 |
+
- [Languages](#languages)
|
| 27 |
+
- [Dataset Structure](#dataset-structure)
|
| 28 |
+
- [Data Instances](#data-instances)
|
| 29 |
+
- [Data Fields](#data-fields)
|
| 30 |
+
- [Data Splits](#data-splits)
|
| 31 |
+
- [Dataset Creation](#dataset-creation)
|
| 32 |
+
- [Curation Rationale](#curation-rationale)
|
| 33 |
+
- [Source Data](#source-data)
|
| 34 |
+
- [Annotations](#annotations)
|
| 35 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 36 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 37 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 38 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 39 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 40 |
+
- [Additional Information](#additional-information)
|
| 41 |
+
- [Dataset Curators](#dataset-curators)
|
| 42 |
+
- [Licensing Information](#licensing-information)
|
| 43 |
+
- [Citation Information](#citation-information)
|
| 44 |
+
- [Contributions](#contributions)
|
| 45 |
+
|
| 46 |
+
## Dataset Description
|
| 47 |
+
|
| 48 |
+
- **Homepage:** [Convenio-Camara-dos-Deputados/ulyssesner-br-propor](https://github.com/Convenio-Camara-dos-Deputados/ulyssesner-br-propor)
|
| 49 |
+
- **Repository:** [Convenio-Camara-dos-Deputados/ulyssesner-br-propor](https://github.com/Convenio-Camara-dos-Deputados/ulyssesner-br-propor)
|
| 50 |
+
- **Paper:** [UlyssesNER-Br: a Corpus of Brazilian Legislative Documents for Named Entity Recognition](https://link.springer.com/chapter/10.1007/978-3-030-98305-5_1)
|
| 51 |
+
- **Leaderboard:**
|
| 52 |
+
- **Point of Contact:**
|
| 53 |
+
|
| 54 |
+
### Dataset Summary
|
| 55 |
+
|
| 56 |
+
[More Information Needed]
|
| 57 |
+
|
| 58 |
+
### Supported Tasks and Leaderboards
|
| 59 |
+
|
| 60 |
+
[More Information Needed]
|
| 61 |
+
|
| 62 |
+
### Languages
|
| 63 |
+
|
| 64 |
+
Portuguese (Brazil).
|
| 65 |
+
|
| 66 |
+
## Dataset Structure
|
| 67 |
+
|
| 68 |
+
### Data Instances
|
| 69 |
+
|
| 70 |
+
[More Information Needed]
|
| 71 |
+
|
| 72 |
+
### Data Fields
|
| 73 |
+
|
| 74 |
+
[More Information Needed]
|
| 75 |
+
|
| 76 |
+
### Data Splits
|
| 77 |
+
|
| 78 |
+
[More Information Needed]
|
| 79 |
+
|
| 80 |
+
## Dataset Creation
|
| 81 |
+
|
| 82 |
+
### Curation Rationale
|
| 83 |
+
|
| 84 |
+
[More Information Needed]
|
| 85 |
+
|
| 86 |
+
### Source Data
|
| 87 |
+
|
| 88 |
+
#### Initial Data Collection and Normalization
|
| 89 |
+
|
| 90 |
+
[More Information Needed]
|
| 91 |
+
|
| 92 |
+
#### Who are the source language producers?
|
| 93 |
+
|
| 94 |
+
[More Information Needed]
|
| 95 |
+
|
| 96 |
+
### Annotations
|
| 97 |
+
|
| 98 |
+
#### Annotation process
|
| 99 |
+
|
| 100 |
+
[More Information Needed]
|
| 101 |
+
|
| 102 |
+
#### Who are the annotators?
|
| 103 |
+
|
| 104 |
+
[More Information Needed]
|
| 105 |
+
|
| 106 |
+
### Personal and Sensitive Information
|
| 107 |
+
|
| 108 |
+
[More Information Needed]
|
| 109 |
+
|
| 110 |
+
## Considerations for Using the Data
|
| 111 |
+
|
| 112 |
+
### Social Impact of Dataset
|
| 113 |
+
|
| 114 |
+
[More Information Needed]
|
| 115 |
+
|
| 116 |
+
### Discussion of Biases
|
| 117 |
+
|
| 118 |
+
[More Information Needed]
|
| 119 |
+
|
| 120 |
+
### Other Known Limitations
|
| 121 |
+
|
| 122 |
+
[More Information Needed]
|
| 123 |
+
|
| 124 |
+
## Additional Information
|
| 125 |
+
|
| 126 |
+
### Dataset Curators
|
| 127 |
+
|
| 128 |
+
[More Information Needed]
|
| 129 |
+
|
| 130 |
+
### Licensing Information
|
| 131 |
+
|
| 132 |
+
[More Information Needed]
|
| 133 |
+
|
| 134 |
+
### Citation Information
|
| 135 |
+
|
| 136 |
+
```
|
| 137 |
+
@inproceedings{UlyssesNER-Br,
|
| 138 |
+
title={UlyssesNER-Br: A Corpus of Brazilian Legislative Documents for Named Entity Recognition},
|
| 139 |
+
author={Albuquerque, Hidelberg O. and Costa, Rosimeire and Silvestre, Gabriel and Souza, Ellen and da Silva, Nádia F. F. and Vitório, Douglas and Moriyama, Gyovana and Martins, Lucas and Soezima, Luiza and Nunes, Augusto and Siqueira, Felipe and Tarrega, João P. and Beinotti, Joao V. and Dias, Marcio and Silva, Matheus and Gardini, Miguel and Silva, Vinicius and de Carvalho, André C. P. L. F. and Oliveira, Adriano L. I.},
|
| 140 |
+
booktitle={Computational Processing of the Portuguese Language},
|
| 141 |
+
year={2022},
|
| 142 |
+
publisher={Springer International Publishing},
|
| 143 |
+
isbn={978-3-030-98305-5},
|
| 144 |
+
doi={https://doi.org/10.1007/978-3-030-98305-5_1}
|
| 145 |
+
}
|
| 146 |
+
```
|
| 147 |
+
|
| 148 |
+
### Contributions
|
| 149 |
+
|
| 150 |
+
Thanks to [@augusnunes](https://github.com/augusnunes) for adding this dataset.
|