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- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-squad_v2-a5d9cc45-11645552.md +35 -0
- huggingface_dataset/Dataset_Card/dferndz_cSQuAD2.md +89 -0
- huggingface_dataset/Dataset_Card/huggingartists_rocket.md +204 -0
- huggingface_dataset/Dataset_Card/income_scifact-top-20-gen-queries.md +510 -0
- huggingface_dataset/Dataset_Card/lj_speech.md +263 -0
- huggingface_dataset/Dataset_Card/masakhane_masakhaner2.md +264 -0
- huggingface_dataset/Dataset_Card/mcemilg_laion2B-multi-turkish-subset.md +72 -0
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- huggingface_dataset/Dataset_Card/trojblue_public_data.md +12 -0
huggingface_dataset/Dataset_Card/3ee_regularization-space.md
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---
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license: mit
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tags:
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- stable-diffusion
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- regularization-images
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- text-to-image
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- image-to-image
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- dreambooth
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- class-instance
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- preservation-loss-training
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---
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# Space Regularization Images
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A collection of regularization & class instance datasets of space for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training.
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huggingface_dataset/Dataset_Card/BeIR_beir-corpus.md
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| 1 |
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---
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| 2 |
+
annotations_creators: []
|
| 3 |
+
language_creators: []
|
| 4 |
+
language:
|
| 5 |
+
- en
|
| 6 |
+
license:
|
| 7 |
+
- cc-by-sa-4.0
|
| 8 |
+
multilinguality:
|
| 9 |
+
- monolingual
|
| 10 |
+
paperswithcode_id: beir
|
| 11 |
+
pretty_name: BEIR Benchmark
|
| 12 |
+
size_categories:
|
| 13 |
+
msmarco:
|
| 14 |
+
- 1M<n<10M
|
| 15 |
+
trec-covid:
|
| 16 |
+
- 100k<n<1M
|
| 17 |
+
nfcorpus:
|
| 18 |
+
- 1K<n<10K
|
| 19 |
+
nq:
|
| 20 |
+
- 1M<n<10M
|
| 21 |
+
hotpotqa:
|
| 22 |
+
- 1M<n<10M
|
| 23 |
+
fiqa:
|
| 24 |
+
- 10K<n<100K
|
| 25 |
+
arguana:
|
| 26 |
+
- 1K<n<10K
|
| 27 |
+
touche-2020:
|
| 28 |
+
- 100K<n<1M
|
| 29 |
+
cqadupstack:
|
| 30 |
+
- 100K<n<1M
|
| 31 |
+
quora:
|
| 32 |
+
- 100K<n<1M
|
| 33 |
+
dbpedia:
|
| 34 |
+
- 1M<n<10M
|
| 35 |
+
scidocs:
|
| 36 |
+
- 10K<n<100K
|
| 37 |
+
fever:
|
| 38 |
+
- 1M<n<10M
|
| 39 |
+
climate-fever:
|
| 40 |
+
- 1M<n<10M
|
| 41 |
+
scifact:
|
| 42 |
+
- 1K<n<10K
|
| 43 |
+
source_datasets: []
|
| 44 |
+
task_categories:
|
| 45 |
+
- text-retrieval
|
| 46 |
+
- zero-shot-retrieval
|
| 47 |
+
- information-retrieval
|
| 48 |
+
- zero-shot-information-retrieval
|
| 49 |
+
task_ids:
|
| 50 |
+
- passage-retrieval
|
| 51 |
+
- entity-linking-retrieval
|
| 52 |
+
- fact-checking-retrieval
|
| 53 |
+
- tweet-retrieval
|
| 54 |
+
- citation-prediction-retrieval
|
| 55 |
+
- duplication-question-retrieval
|
| 56 |
+
- argument-retrieval
|
| 57 |
+
- news-retrieval
|
| 58 |
+
- biomedical-information-retrieval
|
| 59 |
+
- question-answering-retrieval
|
| 60 |
+
---
|
| 61 |
+
|
| 62 |
+
# 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 |
+
- 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 |
+
- 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/Gr3en_OperaDa3Soldi.md
ADDED
|
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
annotations_creators:
|
| 2 |
+
- found
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
language_creators:
|
| 6 |
+
- found
|
| 7 |
+
license:
|
| 8 |
+
- artistic-2.0
|
| 9 |
+
multilinguality:
|
| 10 |
+
- monolingual
|
| 11 |
+
pretty_name: a dataset of Opera da Tre Soldi by Berliner Ensemble
|
| 12 |
+
size_categories:
|
| 13 |
+
- n<1K
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
tags: []
|
| 17 |
+
task_categories:
|
| 18 |
+
- text-to-image
|
| 19 |
+
task_ids: []
|
huggingface_dataset/Dataset_Card/Nicky0007_titulos_noticias_rcn_clasificadas.md
ADDED
|
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
task_categories:
|
| 3 |
+
- token-classification
|
| 4 |
+
language:
|
| 5 |
+
- es
|
| 6 |
+
size_categories:
|
| 7 |
+
- 1K<n<10K
|
| 8 |
+
---
|
| 9 |
+
# Dataset Card for Dataset Name
|
| 10 |
+
titulos_noticias_rcn_clasificadas
|
| 11 |
+
## Dataset Description
|
| 12 |
+
Se tomo las noticias de la pagina de RCN y se clasifico los titulos por ['salud' 'tecnologia' 'colombia' 'economia' 'deportes']
|
| 13 |
+
|
| 14 |
+
salud= 1805 datos,
|
| 15 |
+
tecnologia= 1805 datos,
|
| 16 |
+
colombia= 1805 datos,
|
| 17 |
+
economia= 1805 datos,
|
| 18 |
+
deportes= 1805 datos,
|
| 19 |
+
|
| 20 |
+
|
| 21 |
+
Para dar un total de 9030 filas.
|
| 22 |
+
|
| 23 |
+
pagina: https://www.noticiasrcn.com/
|
| 24 |
+
|
| 25 |
+
- **Homepage:**
|
| 26 |
+
- **Repository:**
|
| 27 |
+
- **Point of Contact:**
|
| 28 |
+
|
| 29 |
+
### Languages
|
| 30 |
+
Español
|
| 31 |
+
|
| 32 |
+
## Dataset Structure
|
| 33 |
+
text, label, url
|
huggingface_dataset/Dataset_Card/Poulpidot_FrenchHateSpeechSuperset.md
ADDED
|
@@ -0,0 +1,88 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: unknown
|
| 3 |
+
---
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
### FrenchHateSpeechSuperset
|
| 7 |
+
|
| 8 |
+
This dataset is a superset of multiple datasets including hate speech, harasment, sexist, racist, etc...messages from various platforms.
|
| 9 |
+
|
| 10 |
+
Included datasets :
|
| 11 |
+
|
| 12 |
+
- MLMA dataset
|
| 13 |
+
- CAA dataset
|
| 14 |
+
- FTR dataset
|
| 15 |
+
- "An Annotated Corpus for Sexism Detection in French Tweets" dataset
|
| 16 |
+
- UC-Berkeley-Measuring-Hate-Speech dataset (translated from english*)
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
#### References
|
| 20 |
+
|
| 21 |
+
```
|
| 22 |
+
@inproceedings{chiril2020annotated,
|
| 23 |
+
title={An Annotated Corpus for Sexism Detection in French Tweets},
|
| 24 |
+
author={Chiril, Patricia and Moriceau, V{\'e}ronique and Benamara, Farah and Mari, Alda and Origgi, Gloria and Coulomb-Gully, Marl{\`e}ne},
|
| 25 |
+
booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
|
| 26 |
+
pages={1397--1403},
|
| 27 |
+
year={2020}
|
| 28 |
+
}
|
| 29 |
+
```
|
| 30 |
+
|
| 31 |
+
```
|
| 32 |
+
@inproceedings{ousidhoum-etal-multilingual-hate-speech-2019,
|
| 33 |
+
title = "Multilingual and Multi-Aspect Hate Speech Analysis",
|
| 34 |
+
author = "Ousidhoum, Nedjma
|
| 35 |
+
and Lin, Zizheng
|
| 36 |
+
and Zhang, Hongming
|
| 37 |
+
and Song, Yangqiu
|
| 38 |
+
and Yeung, Dit-Yan",
|
| 39 |
+
booktitle = "Proceedings of EMNLP",
|
| 40 |
+
year = "2019",
|
| 41 |
+
publisher = "Association for Computational Linguistics",
|
| 42 |
+
}
|
| 43 |
+
```
|
| 44 |
+
|
| 45 |
+
```
|
| 46 |
+
Vanetik, N.; Mimoun, E. Detection of Racist Language in French Tweets. Information 2022, 13, 318. https://doi.org/10.3390/info13070318
|
| 47 |
+
```
|
| 48 |
+
|
| 49 |
+
```
|
| 50 |
+
@article{kennedy2020constructing,
|
| 51 |
+
title={Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application},
|
| 52 |
+
author={Kennedy, Chris J and Bacon, Geoff and Sahn, Alexander and von Vacano, Claudia},
|
| 53 |
+
journal={arXiv preprint arXiv:2009.10277},
|
| 54 |
+
year={2020}
|
| 55 |
+
}
|
| 56 |
+
```
|
| 57 |
+
|
| 58 |
+
```
|
| 59 |
+
Anaïs Ollagnier, Elena Cabrio, Serena Villata, Catherine Blaya. CyberAgressionAdo-v1: a Dataset of Annotated Online Aggressions in French Collected through a Role-playing Game. Language Resources and Evaluation Conference, Jun 2022, Marseille, France. ⟨hal-03765860⟩
|
| 60 |
+
```
|
| 61 |
+
|
| 62 |
+
### Translation
|
| 63 |
+
|
| 64 |
+
French datasets for hate speech are quite rare. To augment current dataset, messages from other languages (english only for now) have been integrated.
|
| 65 |
+
To integrate other languages dataset, MT model were used and manually selected for each dataset.
|
| 66 |
+
|
| 67 |
+
- UC-Berkeley-Measuring-Hate-Speech dataset : Abelll/marian-finetuned-kde4-en-to-fr
|
| 68 |
+
|
| 69 |
+
### Language verification
|
| 70 |
+
|
| 71 |
+
Since MT models are not perfect, some messages are not entirely translated or not translated at all.
|
| 72 |
+
To check for obvious errors in pipeline, a general language detection model is used to prune non french texts.
|
| 73 |
+
|
| 74 |
+
Language detection model : papluca/xlm-roberta-base-language-detection
|
| 75 |
+
|
| 76 |
+
### Annotation
|
| 77 |
+
|
| 78 |
+
Since "hate speech" dimension is highly subjective, and datasets comes with different annotations types, a conventional labeling stategy is required.
|
| 79 |
+
|
| 80 |
+
Each sample is annotated with "0" if negative sample and "1" if positive sample.
|
| 81 |
+
|
| 82 |
+
### Filtering rules :
|
| 83 |
+
|
| 84 |
+
- FTR dataset : [wip]
|
| 85 |
+
- MLMA dataset : [wip]
|
| 86 |
+
- CAA dataset : [wip]
|
| 87 |
+
- "Annotated Corpus" dataset : [wip]
|
| 88 |
+
- UC-Berkeley Measuring Hate Speech dataset : average hate_speech_score > 0 -> 1
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-cnn_dailymail-3.0.0-5bee1b-2343673799.md
ADDED
|
@@ -0,0 +1,33 @@
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|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- cnn_dailymail
|
| 8 |
+
eval_info:
|
| 9 |
+
task: summarization
|
| 10 |
+
model: sshleifer/distilbart-xsum-1-1
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: cnn_dailymail
|
| 13 |
+
dataset_config: 3.0.0
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: article
|
| 17 |
+
target: highlights
|
| 18 |
+
---
|
| 19 |
+
# Dataset Card for AutoTrain Evaluator
|
| 20 |
+
|
| 21 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 22 |
+
|
| 23 |
+
* Task: Summarization
|
| 24 |
+
* Model: sshleifer/distilbart-xsum-1-1
|
| 25 |
+
* Dataset: cnn_dailymail
|
| 26 |
+
* Config: 3.0.0
|
| 27 |
+
* Split: test
|
| 28 |
+
|
| 29 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 30 |
+
|
| 31 |
+
## Contributions
|
| 32 |
+
|
| 33 |
+
Thanks to [@Buckeyes2019](https://huggingface.co/Buckeyes2019) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__feed-top_vi-71f14a-2175469968.md
ADDED
|
@@ -0,0 +1,34 @@
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|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- futin/feed
|
| 8 |
+
eval_info:
|
| 9 |
+
task: text_zero_shot_classification
|
| 10 |
+
model: facebook/opt-125m
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: futin/feed
|
| 13 |
+
dataset_config: top_vi
|
| 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: facebook/opt-125m
|
| 26 |
+
* Dataset: futin/feed
|
| 27 |
+
* Config: top_vi
|
| 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 [@futin](https://huggingface.co/futin) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__guess-vi-d44dbe-2087167153.md
ADDED
|
@@ -0,0 +1,34 @@
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|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- futin/guess
|
| 8 |
+
eval_info:
|
| 9 |
+
task: text_zero_shot_classification
|
| 10 |
+
model: bigscience/bloom-1b1
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: futin/guess
|
| 13 |
+
dataset_config: vi
|
| 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: bigscience/bloom-1b1
|
| 26 |
+
* Dataset: futin/guess
|
| 27 |
+
* Config: vi
|
| 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 [@futin](https://huggingface.co/futin) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267103.md
ADDED
|
@@ -0,0 +1,34 @@
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|
|
|
|
|
|
|
|
|
| 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-6.7b_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-6.7b_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/autoevaluate_autoeval-staging-eval-project-squad_v2-a5d9cc45-11645552.md
ADDED
|
@@ -0,0 +1,35 @@
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- squad_v2
|
| 8 |
+
eval_info:
|
| 9 |
+
task: extractive_question_answering
|
| 10 |
+
model: nbroad/deberta-v3-xsmall-squad2
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: squad_v2
|
| 13 |
+
dataset_config: squad_v2
|
| 14 |
+
dataset_split: validation
|
| 15 |
+
col_mapping:
|
| 16 |
+
context: context
|
| 17 |
+
question: question
|
| 18 |
+
answers-text: answers.text
|
| 19 |
+
answers-answer_start: answers.answer_start
|
| 20 |
+
---
|
| 21 |
+
# Dataset Card for AutoTrain Evaluator
|
| 22 |
+
|
| 23 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 24 |
+
|
| 25 |
+
* Task: Question Answering
|
| 26 |
+
* Model: nbroad/deberta-v3-xsmall-squad2
|
| 27 |
+
* Dataset: squad_v2
|
| 28 |
+
* Config: squad_v2
|
| 29 |
+
* Split: validation
|
| 30 |
+
|
| 31 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 32 |
+
|
| 33 |
+
## Contributions
|
| 34 |
+
|
| 35 |
+
Thanks to [@nbroad](https://huggingface.co/nbroad) for evaluating this model.
|
huggingface_dataset/Dataset_Card/dferndz_cSQuAD2.md
ADDED
|
@@ -0,0 +1,89 @@
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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 |
+
- expert-generated
|
| 4 |
+
language:
|
| 5 |
+
- en
|
| 6 |
+
language_creators:
|
| 7 |
+
- other
|
| 8 |
+
license:
|
| 9 |
+
- apache-2.0
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
pretty_name: cSQuAD2
|
| 13 |
+
size_categories: []
|
| 14 |
+
source_datasets: []
|
| 15 |
+
tags: []
|
| 16 |
+
task_categories:
|
| 17 |
+
- question-answering
|
| 18 |
+
task_ids: []
|
| 19 |
+
---
|
| 20 |
+
|
| 21 |
+
# Dataset Card for cSQuAD2
|
| 22 |
+
|
| 23 |
+
## Table of Contents
|
| 24 |
+
- [Table of Contents](#table-of-contents)
|
| 25 |
+
- [Dataset Description](#dataset-description)
|
| 26 |
+
- [Dataset Summary](#dataset-summary)
|
| 27 |
+
- [Supported Tasks](#supported-tasks)
|
| 28 |
+
- [Languages](#languages)
|
| 29 |
+
- [Dataset Structure](#dataset-structure)
|
| 30 |
+
- [Data Instances](#data-instances)
|
| 31 |
+
- [Data Fields](#data-fields)
|
| 32 |
+
- [Data Splits](#data-splits)
|
| 33 |
+
- [Additional Information](#additional-information)
|
| 34 |
+
- [Licensing Information](#licensing-information)
|
| 35 |
+
- [Citation Information](#citation-information)
|
| 36 |
+
|
| 37 |
+
## Dataset Description
|
| 38 |
+
|
| 39 |
+
- **Homepage:**
|
| 40 |
+
- **Repository:**
|
| 41 |
+
- **Paper:**
|
| 42 |
+
- **Leaderboard:**
|
| 43 |
+
- **Point of Contact:**
|
| 44 |
+
|
| 45 |
+
### Dataset Summary
|
| 46 |
+
|
| 47 |
+
A contrast set to evaluate models trained on SQUAD on out-of-domain data.
|
| 48 |
+
|
| 49 |
+
### Supported Tasks
|
| 50 |
+
|
| 51 |
+
Evaluate question-answering
|
| 52 |
+
|
| 53 |
+
### Languages
|
| 54 |
+
|
| 55 |
+
English
|
| 56 |
+
|
| 57 |
+
## Dataset Structure
|
| 58 |
+
|
| 59 |
+
### Data Instances
|
| 60 |
+
|
| 61 |
+
Dataset contains 40 instances
|
| 62 |
+
|
| 63 |
+
### Data Fields
|
| 64 |
+
| Field | Description |
|
| 65 |
+
|----------|--------------------------------------------------
|
| 66 |
+
| id | Id of document containing context |
|
| 67 |
+
| title | Title of the document |
|
| 68 |
+
| context | The context of the question |
|
| 69 |
+
| question | The question to answer |
|
| 70 |
+
| answers | A list of possible answers from the context |
|
| 71 |
+
| answer_start | The index in context where the answer starts |
|
| 72 |
+
|
| 73 |
+
### Data Splits
|
| 74 |
+
|
| 75 |
+
A single `test` split is provided
|
| 76 |
+
|
| 77 |
+
## Dataset Creation
|
| 78 |
+
|
| 79 |
+
Dataset was created from Wikipedia articles
|
| 80 |
+
|
| 81 |
+
## Additional Information
|
| 82 |
+
|
| 83 |
+
### Licensing Information
|
| 84 |
+
|
| 85 |
+
Apache 2.0 license
|
| 86 |
+
|
| 87 |
+
### Citation Information
|
| 88 |
+
|
| 89 |
+
TODO: add citations
|
huggingface_dataset/Dataset_Card/huggingartists_rocket.md
ADDED
|
@@ -0,0 +1,204 @@
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|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
tags:
|
| 5 |
+
- huggingartists
|
| 6 |
+
- lyrics
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Dataset Card for "huggingartists/rocket"
|
| 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:** 0.424035 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/0fb709925134799103886db5e722ef73.1000x1000x1.jpg')">
|
| 47 |
+
</div>
|
| 48 |
+
</div>
|
| 49 |
+
<a href="https://huggingface.co/huggingartists/rocket">
|
| 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">ROCKET</div>
|
| 53 |
+
<a href="https://genius.com/artists/rocket">
|
| 54 |
+
<div style="text-align: center; font-size: 14px;">@rocket</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/rocket).
|
| 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/rocket")
|
| 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 |
+
|134| -| -|
|
| 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/rocket")
|
| 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/income_scifact-top-20-gen-queries.md
ADDED
|
@@ -0,0 +1,510 @@
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|
| 1 |
+
---
|
| 2 |
+
annotations_creators: []
|
| 3 |
+
language_creators: []
|
| 4 |
+
language:
|
| 5 |
+
- en
|
| 6 |
+
license:
|
| 7 |
+
- cc-by-sa-4.0
|
| 8 |
+
multilinguality:
|
| 9 |
+
- monolingual
|
| 10 |
+
paperswithcode_id: beir
|
| 11 |
+
pretty_name: BEIR Benchmark
|
| 12 |
+
size_categories:
|
| 13 |
+
msmarco:
|
| 14 |
+
- 1M<n<10M
|
| 15 |
+
trec-covid:
|
| 16 |
+
- 100k<n<1M
|
| 17 |
+
nfcorpus:
|
| 18 |
+
- 1K<n<10K
|
| 19 |
+
nq:
|
| 20 |
+
- 1M<n<10M
|
| 21 |
+
hotpotqa:
|
| 22 |
+
- 1M<n<10M
|
| 23 |
+
fiqa:
|
| 24 |
+
- 10K<n<100K
|
| 25 |
+
arguana:
|
| 26 |
+
- 1K<n<10K
|
| 27 |
+
touche-2020:
|
| 28 |
+
- 100K<n<1M
|
| 29 |
+
cqadupstack:
|
| 30 |
+
- 100K<n<1M
|
| 31 |
+
quora:
|
| 32 |
+
- 100K<n<1M
|
| 33 |
+
dbpedia:
|
| 34 |
+
- 1M<n<10M
|
| 35 |
+
scidocs:
|
| 36 |
+
- 10K<n<100K
|
| 37 |
+
fever:
|
| 38 |
+
- 1M<n<10M
|
| 39 |
+
climate-fever:
|
| 40 |
+
- 1M<n<10M
|
| 41 |
+
scifact:
|
| 42 |
+
- 1K<n<10K
|
| 43 |
+
source_datasets: []
|
| 44 |
+
task_categories:
|
| 45 |
+
- text-retrieval
|
| 46 |
+
---
|
| 47 |
+
|
| 48 |
+
# NFCorpus: 20 generated queries (BEIR Benchmark)
|
| 49 |
+
|
| 50 |
+
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
|
| 51 |
+
|
| 52 |
+
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
|
| 53 |
+
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
|
| 54 |
+
- Questions generated: 20
|
| 55 |
+
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
|
| 56 |
+
|
| 57 |
+
|
| 58 |
+
Below contains the old dataset card for the BEIR benchmark.
|
| 59 |
+
|
| 60 |
+
|
| 61 |
+
# Dataset Card for BEIR Benchmark
|
| 62 |
+
|
| 63 |
+
## Table of Contents
|
| 64 |
+
- [Dataset Description](#dataset-description)
|
| 65 |
+
- [Dataset Summary](#dataset-summary)
|
| 66 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 67 |
+
- [Languages](#languages)
|
| 68 |
+
- [Dataset Structure](#dataset-structure)
|
| 69 |
+
- [Data Instances](#data-instances)
|
| 70 |
+
- [Data Fields](#data-fields)
|
| 71 |
+
- [Data Splits](#data-splits)
|
| 72 |
+
- [Dataset Creation](#dataset-creation)
|
| 73 |
+
- [Curation Rationale](#curation-rationale)
|
| 74 |
+
- [Source Data](#source-data)
|
| 75 |
+
- [Annotations](#annotations)
|
| 76 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 77 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 78 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 79 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 80 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 81 |
+
- [Additional Information](#additional-information)
|
| 82 |
+
- [Dataset Curators](#dataset-curators)
|
| 83 |
+
- [Licensing Information](#licensing-information)
|
| 84 |
+
- [Citation Information](#citation-information)
|
| 85 |
+
- [Contributions](#contributions)
|
| 86 |
+
|
| 87 |
+
## Dataset Description
|
| 88 |
+
|
| 89 |
+
- **Homepage:** https://github.com/UKPLab/beir
|
| 90 |
+
- **Repository:** https://github.com/UKPLab/beir
|
| 91 |
+
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
|
| 92 |
+
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
|
| 93 |
+
- **Point of Contact:** nandan.thakur@uwaterloo.ca
|
| 94 |
+
|
| 95 |
+
### Dataset Summary
|
| 96 |
+
|
| 97 |
+
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
|
| 98 |
+
|
| 99 |
+
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
|
| 100 |
+
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
|
| 101 |
+
- 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/)
|
| 102 |
+
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
|
| 103 |
+
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
|
| 104 |
+
- 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/)
|
| 105 |
+
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
|
| 106 |
+
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
|
| 107 |
+
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
|
| 108 |
+
|
| 109 |
+
All these datasets have been preprocessed and can be used for your experiments.
|
| 110 |
+
|
| 111 |
+
|
| 112 |
+
```python
|
| 113 |
+
|
| 114 |
+
```
|
| 115 |
+
|
| 116 |
+
### Supported Tasks and Leaderboards
|
| 117 |
+
|
| 118 |
+
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.
|
| 119 |
+
|
| 120 |
+
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
|
| 121 |
+
|
| 122 |
+
### Languages
|
| 123 |
+
|
| 124 |
+
All tasks are in English (`en`).
|
| 125 |
+
|
| 126 |
+
## Dataset Structure
|
| 127 |
+
|
| 128 |
+
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
|
| 129 |
+
- `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...."}`
|
| 130 |
+
- `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?"}`
|
| 131 |
+
- `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`
|
| 132 |
+
|
| 133 |
+
### Data Instances
|
| 134 |
+
|
| 135 |
+
A high level example of any beir dataset:
|
| 136 |
+
|
| 137 |
+
```python
|
| 138 |
+
corpus = {
|
| 139 |
+
"doc1" : {
|
| 140 |
+
"title": "Albert Einstein",
|
| 141 |
+
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
|
| 142 |
+
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
|
| 143 |
+
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
|
| 144 |
+
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
|
| 145 |
+
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
|
| 146 |
+
of the photoelectric effect', a pivotal step in the development of quantum theory."
|
| 147 |
+
},
|
| 148 |
+
"doc2" : {
|
| 149 |
+
"title": "", # Keep title an empty string if not present
|
| 150 |
+
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
|
| 151 |
+
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
|
| 152 |
+
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
|
| 153 |
+
},
|
| 154 |
+
}
|
| 155 |
+
|
| 156 |
+
queries = {
|
| 157 |
+
"q1" : "Who developed the mass-energy equivalence formula?",
|
| 158 |
+
"q2" : "Which beer is brewed with a large proportion of wheat?"
|
| 159 |
+
}
|
| 160 |
+
|
| 161 |
+
qrels = {
|
| 162 |
+
"q1" : {"doc1": 1},
|
| 163 |
+
"q2" : {"doc2": 1},
|
| 164 |
+
}
|
| 165 |
+
```
|
| 166 |
+
|
| 167 |
+
### Data Fields
|
| 168 |
+
|
| 169 |
+
Examples from all configurations have the following features:
|
| 170 |
+
|
| 171 |
+
### Corpus
|
| 172 |
+
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
|
| 173 |
+
- `_id`: a `string` feature representing the unique document id
|
| 174 |
+
- `title`: a `string` feature, denoting the title of the document.
|
| 175 |
+
- `text`: a `string` feature, denoting the text of the document.
|
| 176 |
+
|
| 177 |
+
### Queries
|
| 178 |
+
- `queries`: a `dict` feature representing the query, made up of:
|
| 179 |
+
- `_id`: a `string` feature representing the unique query id
|
| 180 |
+
- `text`: a `string` feature, denoting the text of the query.
|
| 181 |
+
|
| 182 |
+
### Qrels
|
| 183 |
+
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
|
| 184 |
+
- `_id`: a `string` feature representing the query id
|
| 185 |
+
- `_id`: a `string` feature, denoting the document id.
|
| 186 |
+
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
|
| 187 |
+
|
| 188 |
+
|
| 189 |
+
### Data Splits
|
| 190 |
+
|
| 191 |
+
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
|
| 192 |
+
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
|
| 193 |
+
| 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`` |
|
| 194 |
+
| 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`` |
|
| 195 |
+
| 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`` |
|
| 196 |
+
| 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) |
|
| 197 |
+
| 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`` |
|
| 198 |
+
| 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`` |
|
| 199 |
+
| 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`` |
|
| 200 |
+
| 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) |
|
| 201 |
+
| 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) |
|
| 202 |
+
| 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`` |
|
| 203 |
+
| 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`` |
|
| 204 |
+
| 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`` |
|
| 205 |
+
| 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`` |
|
| 206 |
+
| 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`` |
|
| 207 |
+
| 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`` |
|
| 208 |
+
| 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`` |
|
| 209 |
+
| 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`` |
|
| 210 |
+
| 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`` |
|
| 211 |
+
| 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) |
|
| 212 |
+
|
| 213 |
+
|
| 214 |
+
## Dataset Creation
|
| 215 |
+
|
| 216 |
+
### Curation Rationale
|
| 217 |
+
|
| 218 |
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[Needs More Information]
|
| 219 |
+
|
| 220 |
+
### Source Data
|
| 221 |
+
|
| 222 |
+
#### Initial Data Collection and Normalization
|
| 223 |
+
|
| 224 |
+
[Needs More Information]
|
| 225 |
+
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| 226 |
+
#### Who are the source language producers?
|
| 227 |
+
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| 228 |
+
[Needs More Information]
|
| 229 |
+
|
| 230 |
+
### Annotations
|
| 231 |
+
|
| 232 |
+
#### Annotation process
|
| 233 |
+
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+
[Needs More Information]
|
| 235 |
+
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| 236 |
+
#### Who are the annotators?
|
| 237 |
+
|
| 238 |
+
[Needs More Information]
|
| 239 |
+
|
| 240 |
+
### Personal and Sensitive Information
|
| 241 |
+
|
| 242 |
+
[Needs More Information]
|
| 243 |
+
|
| 244 |
+
## Considerations for Using the Data
|
| 245 |
+
|
| 246 |
+
### Social Impact of Dataset
|
| 247 |
+
|
| 248 |
+
[Needs More Information]
|
| 249 |
+
|
| 250 |
+
### Discussion of Biases
|
| 251 |
+
|
| 252 |
+
[Needs More Information]
|
| 253 |
+
|
| 254 |
+
### Other Known Limitations
|
| 255 |
+
|
| 256 |
+
[Needs More Information]
|
| 257 |
+
|
| 258 |
+
## Additional Information
|
| 259 |
+
|
| 260 |
+
### Dataset Curators
|
| 261 |
+
|
| 262 |
+
[Needs More Information]
|
| 263 |
+
|
| 264 |
+
### Licensing Information
|
| 265 |
+
|
| 266 |
+
[Needs More Information]
|
| 267 |
+
|
| 268 |
+
### Citation Information
|
| 269 |
+
|
| 270 |
+
Cite as:
|
| 271 |
+
```
|
| 272 |
+
@inproceedings{
|
| 273 |
+
thakur2021beir,
|
| 274 |
+
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
|
| 275 |
+
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
|
| 276 |
+
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
|
| 277 |
+
year={2021},
|
| 278 |
+
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
|
| 279 |
+
}
|
| 280 |
+
```
|
| 281 |
+
|
| 282 |
+
### Contributions
|
| 283 |
+
|
| 284 |
+
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
|
| 285 |
+
|
| 286 |
+
|
| 287 |
+
# Dataset Card for BEIR Benchmark
|
| 288 |
+
|
| 289 |
+
## Table of Contents
|
| 290 |
+
- [Dataset Description](#dataset-description)
|
| 291 |
+
- [Dataset Summary](#dataset-summary)
|
| 292 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 293 |
+
- [Languages](#languages)
|
| 294 |
+
- [Dataset Structure](#dataset-structure)
|
| 295 |
+
- [Data Instances](#data-instances)
|
| 296 |
+
- [Data Fields](#data-fields)
|
| 297 |
+
- [Data Splits](#data-splits)
|
| 298 |
+
- [Dataset Creation](#dataset-creation)
|
| 299 |
+
- [Curation Rationale](#curation-rationale)
|
| 300 |
+
- [Source Data](#source-data)
|
| 301 |
+
- [Annotations](#annotations)
|
| 302 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 303 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 304 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 305 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 306 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 307 |
+
- [Additional Information](#additional-information)
|
| 308 |
+
- [Dataset Curators](#dataset-curators)
|
| 309 |
+
- [Licensing Information](#licensing-information)
|
| 310 |
+
- [Citation Information](#citation-information)
|
| 311 |
+
- [Contributions](#contributions)
|
| 312 |
+
|
| 313 |
+
## Dataset Description
|
| 314 |
+
|
| 315 |
+
- **Homepage:** https://github.com/UKPLab/beir
|
| 316 |
+
- **Repository:** https://github.com/UKPLab/beir
|
| 317 |
+
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
|
| 318 |
+
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
|
| 319 |
+
- **Point of Contact:** nandan.thakur@uwaterloo.ca
|
| 320 |
+
|
| 321 |
+
### Dataset Summary
|
| 322 |
+
|
| 323 |
+
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
|
| 324 |
+
|
| 325 |
+
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
|
| 326 |
+
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
|
| 327 |
+
- 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/)
|
| 328 |
+
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
|
| 329 |
+
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
|
| 330 |
+
- 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/)
|
| 331 |
+
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
|
| 332 |
+
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
|
| 333 |
+
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
|
| 334 |
+
|
| 335 |
+
All these datasets have been preprocessed and can be used for your experiments.
|
| 336 |
+
|
| 337 |
+
|
| 338 |
+
```python
|
| 339 |
+
|
| 340 |
+
```
|
| 341 |
+
|
| 342 |
+
### Supported Tasks and Leaderboards
|
| 343 |
+
|
| 344 |
+
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.
|
| 345 |
+
|
| 346 |
+
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
|
| 347 |
+
|
| 348 |
+
### Languages
|
| 349 |
+
|
| 350 |
+
All tasks are in English (`en`).
|
| 351 |
+
|
| 352 |
+
## Dataset Structure
|
| 353 |
+
|
| 354 |
+
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
|
| 355 |
+
- `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...."}`
|
| 356 |
+
- `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?"}`
|
| 357 |
+
- `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`
|
| 358 |
+
|
| 359 |
+
### Data Instances
|
| 360 |
+
|
| 361 |
+
A high level example of any beir dataset:
|
| 362 |
+
|
| 363 |
+
```python
|
| 364 |
+
corpus = {
|
| 365 |
+
"doc1" : {
|
| 366 |
+
"title": "Albert Einstein",
|
| 367 |
+
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
|
| 368 |
+
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
|
| 369 |
+
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
|
| 370 |
+
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
|
| 371 |
+
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
|
| 372 |
+
of the photoelectric effect', a pivotal step in the development of quantum theory."
|
| 373 |
+
},
|
| 374 |
+
"doc2" : {
|
| 375 |
+
"title": "", # Keep title an empty string if not present
|
| 376 |
+
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
|
| 377 |
+
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
|
| 378 |
+
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
|
| 379 |
+
},
|
| 380 |
+
}
|
| 381 |
+
|
| 382 |
+
queries = {
|
| 383 |
+
"q1" : "Who developed the mass-energy equivalence formula?",
|
| 384 |
+
"q2" : "Which beer is brewed with a large proportion of wheat?"
|
| 385 |
+
}
|
| 386 |
+
|
| 387 |
+
qrels = {
|
| 388 |
+
"q1" : {"doc1": 1},
|
| 389 |
+
"q2" : {"doc2": 1},
|
| 390 |
+
}
|
| 391 |
+
```
|
| 392 |
+
|
| 393 |
+
### Data Fields
|
| 394 |
+
|
| 395 |
+
Examples from all configurations have the following features:
|
| 396 |
+
|
| 397 |
+
### Corpus
|
| 398 |
+
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
|
| 399 |
+
- `_id`: a `string` feature representing the unique document id
|
| 400 |
+
- `title`: a `string` feature, denoting the title of the document.
|
| 401 |
+
- `text`: a `string` feature, denoting the text of the document.
|
| 402 |
+
|
| 403 |
+
### Queries
|
| 404 |
+
- `queries`: a `dict` feature representing the query, made up of:
|
| 405 |
+
- `_id`: a `string` feature representing the unique query id
|
| 406 |
+
- `text`: a `string` feature, denoting the text of the query.
|
| 407 |
+
|
| 408 |
+
### Qrels
|
| 409 |
+
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
|
| 410 |
+
- `_id`: a `string` feature representing the query id
|
| 411 |
+
- `_id`: a `string` feature, denoting the document id.
|
| 412 |
+
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
|
| 413 |
+
|
| 414 |
+
|
| 415 |
+
### Data Splits
|
| 416 |
+
|
| 417 |
+
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
|
| 418 |
+
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
|
| 419 |
+
| 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`` |
|
| 420 |
+
| 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`` |
|
| 421 |
+
| 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`` |
|
| 422 |
+
| 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) |
|
| 423 |
+
| 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`` |
|
| 424 |
+
| 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`` |
|
| 425 |
+
| 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`` |
|
| 426 |
+
| 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) |
|
| 427 |
+
| 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) |
|
| 428 |
+
| 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`` |
|
| 429 |
+
| 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`` |
|
| 430 |
+
| 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`` |
|
| 431 |
+
| 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`` |
|
| 432 |
+
| 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`` |
|
| 433 |
+
| 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`` |
|
| 434 |
+
| 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`` |
|
| 435 |
+
| 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`` |
|
| 436 |
+
| 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`` |
|
| 437 |
+
| 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) |
|
| 438 |
+
|
| 439 |
+
|
| 440 |
+
## Dataset Creation
|
| 441 |
+
|
| 442 |
+
### Curation Rationale
|
| 443 |
+
|
| 444 |
+
[Needs More Information]
|
| 445 |
+
|
| 446 |
+
### Source Data
|
| 447 |
+
|
| 448 |
+
#### Initial Data Collection and Normalization
|
| 449 |
+
|
| 450 |
+
[Needs More Information]
|
| 451 |
+
|
| 452 |
+
#### Who are the source language producers?
|
| 453 |
+
|
| 454 |
+
[Needs More Information]
|
| 455 |
+
|
| 456 |
+
### Annotations
|
| 457 |
+
|
| 458 |
+
#### Annotation process
|
| 459 |
+
|
| 460 |
+
[Needs More Information]
|
| 461 |
+
|
| 462 |
+
#### Who are the annotators?
|
| 463 |
+
|
| 464 |
+
[Needs More Information]
|
| 465 |
+
|
| 466 |
+
### Personal and Sensitive Information
|
| 467 |
+
|
| 468 |
+
[Needs More Information]
|
| 469 |
+
|
| 470 |
+
## Considerations for Using the Data
|
| 471 |
+
|
| 472 |
+
### Social Impact of Dataset
|
| 473 |
+
|
| 474 |
+
[Needs More Information]
|
| 475 |
+
|
| 476 |
+
### Discussion of Biases
|
| 477 |
+
|
| 478 |
+
[Needs More Information]
|
| 479 |
+
|
| 480 |
+
### Other Known Limitations
|
| 481 |
+
|
| 482 |
+
[Needs More Information]
|
| 483 |
+
|
| 484 |
+
## Additional Information
|
| 485 |
+
|
| 486 |
+
### Dataset Curators
|
| 487 |
+
|
| 488 |
+
[Needs More Information]
|
| 489 |
+
|
| 490 |
+
### Licensing Information
|
| 491 |
+
|
| 492 |
+
[Needs More Information]
|
| 493 |
+
|
| 494 |
+
### Citation Information
|
| 495 |
+
|
| 496 |
+
Cite as:
|
| 497 |
+
```
|
| 498 |
+
@inproceedings{
|
| 499 |
+
thakur2021beir,
|
| 500 |
+
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
|
| 501 |
+
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
|
| 502 |
+
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
|
| 503 |
+
year={2021},
|
| 504 |
+
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
|
| 505 |
+
}
|
| 506 |
+
```
|
| 507 |
+
|
| 508 |
+
### Contributions
|
| 509 |
+
|
| 510 |
+
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
|
huggingface_dataset/Dataset_Card/lj_speech.md
ADDED
|
@@ -0,0 +1,263 @@
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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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|
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|
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|
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|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language_creators:
|
| 5 |
+
- found
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- unlicense
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
paperswithcode_id: ljspeech
|
| 13 |
+
pretty_name: LJ Speech
|
| 14 |
+
size_categories:
|
| 15 |
+
- 10K<n<100K
|
| 16 |
+
source_datasets:
|
| 17 |
+
- original
|
| 18 |
+
task_categories:
|
| 19 |
+
- automatic-speech-recognition
|
| 20 |
+
task_ids: []
|
| 21 |
+
train-eval-index:
|
| 22 |
+
- config: main
|
| 23 |
+
task: automatic-speech-recognition
|
| 24 |
+
task_id: speech_recognition
|
| 25 |
+
splits:
|
| 26 |
+
train_split: train
|
| 27 |
+
col_mapping:
|
| 28 |
+
file: path
|
| 29 |
+
text: text
|
| 30 |
+
metrics:
|
| 31 |
+
- type: wer
|
| 32 |
+
name: WER
|
| 33 |
+
- type: cer
|
| 34 |
+
name: CER
|
| 35 |
+
dataset_info:
|
| 36 |
+
features:
|
| 37 |
+
- name: id
|
| 38 |
+
dtype: string
|
| 39 |
+
- name: audio
|
| 40 |
+
dtype:
|
| 41 |
+
audio:
|
| 42 |
+
sampling_rate: 22050
|
| 43 |
+
- name: file
|
| 44 |
+
dtype: string
|
| 45 |
+
- name: text
|
| 46 |
+
dtype: string
|
| 47 |
+
- name: normalized_text
|
| 48 |
+
dtype: string
|
| 49 |
+
config_name: main
|
| 50 |
+
splits:
|
| 51 |
+
- name: train
|
| 52 |
+
num_bytes: 4667022
|
| 53 |
+
num_examples: 13100
|
| 54 |
+
download_size: 2748572632
|
| 55 |
+
dataset_size: 4667022
|
| 56 |
+
---
|
| 57 |
+
|
| 58 |
+
# Dataset Card for lj_speech
|
| 59 |
+
|
| 60 |
+
## Table of Contents
|
| 61 |
+
- [Dataset Description](#dataset-description)
|
| 62 |
+
- [Dataset Summary](#dataset-summary)
|
| 63 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 64 |
+
- [Languages](#languages)
|
| 65 |
+
- [Dataset Structure](#dataset-structure)
|
| 66 |
+
- [Data Instances](#data-instances)
|
| 67 |
+
- [Data Fields](#data-fields)
|
| 68 |
+
- [Data Splits](#data-splits)
|
| 69 |
+
- [Dataset Creation](#dataset-creation)
|
| 70 |
+
- [Curation Rationale](#curation-rationale)
|
| 71 |
+
- [Source Data](#source-data)
|
| 72 |
+
- [Annotations](#annotations)
|
| 73 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 74 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 75 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 76 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 77 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 78 |
+
- [Additional Information](#additional-information)
|
| 79 |
+
- [Dataset Curators](#dataset-curators)
|
| 80 |
+
- [Licensing Information](#licensing-information)
|
| 81 |
+
- [Citation Information](#citation-information)
|
| 82 |
+
- [Contributions](#contributions)
|
| 83 |
+
|
| 84 |
+
## Dataset Description
|
| 85 |
+
|
| 86 |
+
- **Homepage:** [The LJ Speech Dataset](https://keithito.com/LJ-Speech-Dataset/)
|
| 87 |
+
- **Repository:** [N/A]
|
| 88 |
+
- **Paper:** [N/A]
|
| 89 |
+
- **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech)
|
| 90 |
+
- **Point of Contact:** [Keith Ito](mailto:kito@kito.us)
|
| 91 |
+
|
| 92 |
+
### Dataset Summary
|
| 93 |
+
|
| 94 |
+
This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.
|
| 95 |
+
|
| 96 |
+
The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain.
|
| 97 |
+
|
| 98 |
+
### Supported Tasks and Leaderboards
|
| 99 |
+
|
| 100 |
+
The dataset can be used to train a model for Automatic Speech Recognition (ASR) or Text-to-Speech (TTS).
|
| 101 |
+
- `other:automatic-speech-recognition`: An ASR model is presented with an audio file and asked to transcribe the audio file to written text.
|
| 102 |
+
The most common ASR evaluation metric is the word error rate (WER).
|
| 103 |
+
- `other:text-to-speech`: A TTS model is given a written text in natural language and asked to generate a speech audio file.
|
| 104 |
+
A reasonable evaluation metric is the mean opinion score (MOS) of audio quality.
|
| 105 |
+
The dataset has an active leaderboard which can be found at https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech
|
| 106 |
+
|
| 107 |
+
### Languages
|
| 108 |
+
|
| 109 |
+
The transcriptions and audio are in English.
|
| 110 |
+
|
| 111 |
+
## Dataset Structure
|
| 112 |
+
|
| 113 |
+
### Data Instances
|
| 114 |
+
|
| 115 |
+
A data point comprises the path to the audio file, called `file` and its transcription, called `text`.
|
| 116 |
+
A normalized version of the text is also provided.
|
| 117 |
+
|
| 118 |
+
```
|
| 119 |
+
{
|
| 120 |
+
'id': 'LJ002-0026',
|
| 121 |
+
'file': '/datasets/downloads/extracted/05bfe561f096e4c52667e3639af495226afe4e5d08763f2d76d069e7a453c543/LJSpeech-1.1/wavs/LJ002-0026.wav',
|
| 122 |
+
'audio': {'path': '/datasets/downloads/extracted/05bfe561f096e4c52667e3639af495226afe4e5d08763f2d76d069e7a453c543/LJSpeech-1.1/wavs/LJ002-0026.wav',
|
| 123 |
+
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346,
|
| 124 |
+
0.00091553, 0.00085449], dtype=float32),
|
| 125 |
+
'sampling_rate': 22050},
|
| 126 |
+
'text': 'in the three years between 1813 and 1816,'
|
| 127 |
+
'normalized_text': 'in the three years between eighteen thirteen and eighteen sixteen,',
|
| 128 |
+
}
|
| 129 |
+
```
|
| 130 |
+
|
| 131 |
+
Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 22050 Hz.
|
| 132 |
+
|
| 133 |
+
### Data Fields
|
| 134 |
+
|
| 135 |
+
- id: unique id of the data sample.
|
| 136 |
+
|
| 137 |
+
- file: a path to the downloaded audio file in .wav format.
|
| 138 |
+
|
| 139 |
+
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
|
| 140 |
+
|
| 141 |
+
- text: the transcription of the audio file.
|
| 142 |
+
|
| 143 |
+
- normalized_text: the transcription with numbers, ordinals, and monetary units expanded into full words.
|
| 144 |
+
|
| 145 |
+
### Data Splits
|
| 146 |
+
|
| 147 |
+
The dataset is not pre-split. Some statistics:
|
| 148 |
+
|
| 149 |
+
- Total Clips: 13,100
|
| 150 |
+
- Total Words: 225,715
|
| 151 |
+
- Total Characters: 1,308,678
|
| 152 |
+
- Total Duration: 23:55:17
|
| 153 |
+
- Mean Clip Duration: 6.57 sec
|
| 154 |
+
- Min Clip Duration: 1.11 sec
|
| 155 |
+
- Max Clip Duration: 10.10 sec
|
| 156 |
+
- Mean Words per Clip: 17.23
|
| 157 |
+
- Distinct Words: 13,821
|
| 158 |
+
|
| 159 |
+
## Dataset Creation
|
| 160 |
+
|
| 161 |
+
### Curation Rationale
|
| 162 |
+
|
| 163 |
+
[Needs More Information]
|
| 164 |
+
|
| 165 |
+
### Source Data
|
| 166 |
+
|
| 167 |
+
#### Initial Data Collection and Normalization
|
| 168 |
+
|
| 169 |
+
This dataset consists of excerpts from the following works:
|
| 170 |
+
|
| 171 |
+
- Morris, William, et al. Arts and Crafts Essays. 1893.
|
| 172 |
+
- Griffiths, Arthur. The Chronicles of Newgate, Vol. 2. 1884.
|
| 173 |
+
- Roosevelt, Franklin D. The Fireside Chats of Franklin Delano Roosevelt. 1933-42.
|
| 174 |
+
- Harland, Marion. Marion Harland's Cookery for Beginners. 1893.
|
| 175 |
+
- Rolt-Wheeler, Francis. The Science - History of the Universe, Vol. 5: Biology. 1910.
|
| 176 |
+
- Banks, Edgar J. The Seven Wonders of the Ancient World. 1916.
|
| 177 |
+
- President's Commission on the Assassination of President Kennedy. Report of the President's Commission on the Assassination of President Kennedy. 1964.
|
| 178 |
+
|
| 179 |
+
Some details about normalization:
|
| 180 |
+
- The normalized transcription has the numbers, ordinals, and monetary units expanded into full words (UTF-8)
|
| 181 |
+
- 19 of the transcriptions contain non-ASCII characters (for example, LJ016-0257 contains "raison d'être").
|
| 182 |
+
- The following abbreviations appear in the text. They may be expanded as follows:
|
| 183 |
+
|
| 184 |
+
| Abbreviation | Expansion |
|
| 185 |
+
|--------------|-----------|
|
| 186 |
+
| Mr. | Mister |
|
| 187 |
+
| Mrs. | Misess (*) |
|
| 188 |
+
| Dr. | Doctor |
|
| 189 |
+
| No. | Number |
|
| 190 |
+
| St. | Saint |
|
| 191 |
+
| Co. | Company |
|
| 192 |
+
| Jr. | Junior |
|
| 193 |
+
| Maj. | Major |
|
| 194 |
+
| Gen. | General |
|
| 195 |
+
| Drs. | Doctors |
|
| 196 |
+
| Rev. | Reverend |
|
| 197 |
+
| Lt. | Lieutenant |
|
| 198 |
+
| Hon. | Honorable |
|
| 199 |
+
| Sgt. | Sergeant |
|
| 200 |
+
| Capt. | Captain |
|
| 201 |
+
| Esq. | Esquire |
|
| 202 |
+
| Ltd. | Limited |
|
| 203 |
+
| Col. | Colonel |
|
| 204 |
+
| Ft. | Fort |
|
| 205 |
+
(*) there's no standard expansion for "Mrs."
|
| 206 |
+
|
| 207 |
+
#### Who are the source language producers?
|
| 208 |
+
|
| 209 |
+
[Needs More Information]
|
| 210 |
+
|
| 211 |
+
### Annotations
|
| 212 |
+
|
| 213 |
+
#### Annotation process
|
| 214 |
+
|
| 215 |
+
- The audio clips range in length from approximately 1 second to 10 seconds. They were segmented automatically based on silences in the recording. Clip boundaries generally align with sentence or clause boundaries, but not always.
|
| 216 |
+
- The text was matched to the audio manually, and a QA pass was done to ensure that the text accurately matched the words spoken in the audio.
|
| 217 |
+
|
| 218 |
+
#### Who are the annotators?
|
| 219 |
+
|
| 220 |
+
Recordings by Linda Johnson from LibriVox. Alignment and annotation by Keith Ito.
|
| 221 |
+
|
| 222 |
+
### Personal and Sensitive Information
|
| 223 |
+
|
| 224 |
+
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
|
| 225 |
+
|
| 226 |
+
## Considerations for Using the Data
|
| 227 |
+
|
| 228 |
+
### Social Impact of Dataset
|
| 229 |
+
|
| 230 |
+
[Needs More Information]
|
| 231 |
+
|
| 232 |
+
### Discussion of Biases
|
| 233 |
+
|
| 234 |
+
[Needs More Information]
|
| 235 |
+
|
| 236 |
+
### Other Known Limitations
|
| 237 |
+
|
| 238 |
+
- The original LibriVox recordings were distributed as 128 kbps MP3 files. As a result, they may contain artifacts introduced by the MP3 encoding.
|
| 239 |
+
|
| 240 |
+
## Additional Information
|
| 241 |
+
|
| 242 |
+
### Dataset Curators
|
| 243 |
+
|
| 244 |
+
The dataset was initially created by Keith Ito and Linda Johnson.
|
| 245 |
+
|
| 246 |
+
### Licensing Information
|
| 247 |
+
|
| 248 |
+
Public Domain ([LibriVox](https://librivox.org/pages/public-domain/))
|
| 249 |
+
|
| 250 |
+
### Citation Information
|
| 251 |
+
|
| 252 |
+
```
|
| 253 |
+
@misc{ljspeech17,
|
| 254 |
+
author = {Keith Ito and Linda Johnson},
|
| 255 |
+
title = {The LJ Speech Dataset},
|
| 256 |
+
howpublished = {\url{https://keithito.com/LJ-Speech-Dataset/}},
|
| 257 |
+
year = 2017
|
| 258 |
+
}
|
| 259 |
+
```
|
| 260 |
+
|
| 261 |
+
### Contributions
|
| 262 |
+
|
| 263 |
+
Thanks to [@anton-l](https://github.com/anton-l) for adding this dataset.
|
huggingface_dataset/Dataset_Card/masakhane_masakhaner2.md
ADDED
|
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|
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|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language:
|
| 5 |
+
- bm
|
| 6 |
+
- bbj
|
| 7 |
+
- ee
|
| 8 |
+
- fon
|
| 9 |
+
- ha
|
| 10 |
+
- ig
|
| 11 |
+
- rw
|
| 12 |
+
- lg
|
| 13 |
+
- luo
|
| 14 |
+
- mos
|
| 15 |
+
- ny
|
| 16 |
+
- pcm
|
| 17 |
+
- sn
|
| 18 |
+
- sw
|
| 19 |
+
- tn
|
| 20 |
+
- tw
|
| 21 |
+
- wo
|
| 22 |
+
- xh
|
| 23 |
+
- yo
|
| 24 |
+
- zu
|
| 25 |
+
language_creators:
|
| 26 |
+
- expert-generated
|
| 27 |
+
license:
|
| 28 |
+
- afl-3.0
|
| 29 |
+
multilinguality:
|
| 30 |
+
- multilingual
|
| 31 |
+
pretty_name: masakhaner2.0
|
| 32 |
+
size_categories:
|
| 33 |
+
- 1K<n<10K
|
| 34 |
+
source_datasets:
|
| 35 |
+
- original
|
| 36 |
+
tags:
|
| 37 |
+
- ner
|
| 38 |
+
- masakhaner
|
| 39 |
+
- masakhane
|
| 40 |
+
task_categories:
|
| 41 |
+
- token-classification
|
| 42 |
+
task_ids:
|
| 43 |
+
- named-entity-recognition
|
| 44 |
+
|
| 45 |
+
---
|
| 46 |
+
|
| 47 |
+
|
| 48 |
+
# Dataset Card for [Dataset Name]
|
| 49 |
+
|
| 50 |
+
## Table of Contents
|
| 51 |
+
- [Table of Contents](#table-of-contents)
|
| 52 |
+
- [Dataset Description](#dataset-description)
|
| 53 |
+
- [Dataset Summary](#dataset-summary)
|
| 54 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 55 |
+
- [Languages](#languages)
|
| 56 |
+
- [Dataset Structure](#dataset-structure)
|
| 57 |
+
- [Data Instances](#data-instances)
|
| 58 |
+
- [Data Fields](#data-fields)
|
| 59 |
+
- [Data Splits](#data-splits)
|
| 60 |
+
- [Dataset Creation](#dataset-creation)
|
| 61 |
+
- [Curation Rationale](#curation-rationale)
|
| 62 |
+
- [Source Data](#source-data)
|
| 63 |
+
- [Annotations](#annotations)
|
| 64 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 65 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 66 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 67 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 68 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 69 |
+
- [Additional Information](#additional-information)
|
| 70 |
+
- [Dataset Curators](#dataset-curators)
|
| 71 |
+
- [Licensing Information](#licensing-information)
|
| 72 |
+
- [Citation Information](#citation-information)
|
| 73 |
+
- [Contributions](#contributions)
|
| 74 |
+
|
| 75 |
+
## Dataset Description
|
| 76 |
+
|
| 77 |
+
- **Homepage:** [homepage](https://github.com/masakhane-io/masakhane-ner)
|
| 78 |
+
- **Repository:** [github](https://github.com/masakhane-io/masakhane-ner)
|
| 79 |
+
- **Paper:** [paper](https://arxiv.org/abs/2103.11811)
|
| 80 |
+
- **Point of Contact:** [Masakhane](https://www.masakhane.io/) or didelani@lsv.uni-saarland.de
|
| 81 |
+
|
| 82 |
+
### Dataset Summary
|
| 83 |
+
|
| 84 |
+
MasakhaNER 2.0 is the largest publicly available high-quality dataset for named entity recognition (NER) in 20 African languages created by the Masakhane community.
|
| 85 |
+
|
| 86 |
+
Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. Example:
|
| 87 |
+
|
| 88 |
+
[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
|
| 89 |
+
|
| 90 |
+
MasakhaNER 2.0 is a named entity dataset consisting of PER, ORG, LOC, and DATE entities annotated by Masakhane for 20 African languages
|
| 91 |
+
|
| 92 |
+
The train/validation/test sets are available for all the 20 languages.
|
| 93 |
+
|
| 94 |
+
For more details see https://arxiv.org/abs/2210.12391
|
| 95 |
+
|
| 96 |
+
|
| 97 |
+
### Supported Tasks and Leaderboards
|
| 98 |
+
|
| 99 |
+
[More Information Needed]
|
| 100 |
+
|
| 101 |
+
- `named-entity-recognition`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better). A named entity is correct only if it is an exact match of the corresponding entity in the data.
|
| 102 |
+
|
| 103 |
+
### Languages
|
| 104 |
+
|
| 105 |
+
There are 20 languages available :
|
| 106 |
+
- Bambara (bam)
|
| 107 |
+
- Ghomala (bbj)
|
| 108 |
+
- Ewe (ewe)
|
| 109 |
+
- Fon (fon)
|
| 110 |
+
- Hausa (hau)
|
| 111 |
+
- Igbo (ibo)
|
| 112 |
+
- Kinyarwanda (kin)
|
| 113 |
+
- Luganda (lug)
|
| 114 |
+
- Dholuo (luo)
|
| 115 |
+
- Mossi (mos)
|
| 116 |
+
- Chichewa (nya)
|
| 117 |
+
- Nigerian Pidgin
|
| 118 |
+
- chShona (sna)
|
| 119 |
+
- Kiswahili (swą)
|
| 120 |
+
- Setswana (tsn)
|
| 121 |
+
- Twi (twi)
|
| 122 |
+
- Wolof (wol)
|
| 123 |
+
- isiXhosa (xho)
|
| 124 |
+
- Yorùbá (yor)
|
| 125 |
+
- isiZulu (zul)
|
| 126 |
+
|
| 127 |
+
## Dataset Structure
|
| 128 |
+
|
| 129 |
+
### Data Instances
|
| 130 |
+
|
| 131 |
+
The examples look like this for Yorùbá:
|
| 132 |
+
|
| 133 |
+
```
|
| 134 |
+
from datasets import load_dataset
|
| 135 |
+
data = load_dataset('masakhane/masakhaner2', 'yor')
|
| 136 |
+
|
| 137 |
+
# Please, specify the language code
|
| 138 |
+
|
| 139 |
+
# A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
|
| 140 |
+
{'id': '0',
|
| 141 |
+
'ner_tags': [B-DATE, I-DATE, 0, 0, 0, 0, 0, B-PER, I-PER, I-PER, O, O, O, O],
|
| 142 |
+
'tokens': ['Wákàtí', 'méje', 'ti', 'ré', 'kọjá', 'lọ', 'tí', 'Luis', 'Carlos', 'Díaz', 'ti', 'di', 'awati', '.']
|
| 143 |
+
}
|
| 144 |
+
```
|
| 145 |
+
|
| 146 |
+
### Data Fields
|
| 147 |
+
|
| 148 |
+
- `id`: id of the sample
|
| 149 |
+
- `tokens`: the tokens of the example text
|
| 150 |
+
- `ner_tags`: the NER tags of each token
|
| 151 |
+
|
| 152 |
+
The NER tags correspond to this list:
|
| 153 |
+
```
|
| 154 |
+
"O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE",
|
| 155 |
+
```
|
| 156 |
+
|
| 157 |
+
In the NER tags, a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and dates & time (DATE).
|
| 158 |
+
|
| 159 |
+
It is assumed that named entities are non-recursive and non-overlapping. In case a named entity is embedded in another named entity usually, only the top level entity is marked.
|
| 160 |
+
|
| 161 |
+
### Data Splits
|
| 162 |
+
|
| 163 |
+
For all languages, there are three splits.
|
| 164 |
+
|
| 165 |
+
The original splits were named `train`, `dev` and `test` and they correspond to the `train`, `validation` and `test` splits.
|
| 166 |
+
|
| 167 |
+
The splits have the following sizes :
|
| 168 |
+
|
| 169 |
+
| Language | train | validation | test |
|
| 170 |
+
|-----------------|------:|-----------:|------:|
|
| 171 |
+
| Bambara | 4463 | 638 | 1274 |
|
| 172 |
+
| Ghomala | 3384 | 483 | 966 |
|
| 173 |
+
| Ewe | 3505 | 501 | 1001 |
|
| 174 |
+
| Fon. | 4343 | 621 | 1240 |
|
| 175 |
+
| Hausa | 5716 | 816 | 1633 |
|
| 176 |
+
| Igbo | 7634 | 1090 | 2181 |
|
| 177 |
+
| Kinyarwanda | 7825 | 1118 | 2235 |
|
| 178 |
+
| Luganda | 4942 | 706 | 1412 |
|
| 179 |
+
| Luo | 5161 | 737 | 1474 |
|
| 180 |
+
| Mossi | 4532 | 648 | 1613 |
|
| 181 |
+
| Nigerian-Pidgin | 5646 | 806 | 1294 |
|
| 182 |
+
| Chichewa | 6250 | 893 | 1785 |
|
| 183 |
+
| chiShona | 6207 | 887 | 1773 |
|
| 184 |
+
| Kiswahili | 6593 | 942 | 1883 |
|
| 185 |
+
| Setswana | 3289 | 499 | 996 |
|
| 186 |
+
| Akan/Twi | 4240 | 605 | 1211 |
|
| 187 |
+
| Wolof | 4593 | 656 | 1312 |
|
| 188 |
+
| isiXhosa | 5718 | 817 | 1633 |
|
| 189 |
+
| Yoruba | 6877 | 983 | 1964 |
|
| 190 |
+
| isiZulu | 5848 | 836 | 1670 |
|
| 191 |
+
|
| 192 |
+
## Dataset Creation
|
| 193 |
+
|
| 194 |
+
### Curation Rationale
|
| 195 |
+
|
| 196 |
+
The dataset was introduced to introduce new resources to 20 languages that were under-served for natural language processing.
|
| 197 |
+
|
| 198 |
+
[More Information Needed]
|
| 199 |
+
|
| 200 |
+
### Source Data
|
| 201 |
+
|
| 202 |
+
The source of the data is from the news domain, details can be found here https://arxiv.org/abs/2210.12391
|
| 203 |
+
|
| 204 |
+
#### Initial Data Collection and Normalization
|
| 205 |
+
|
| 206 |
+
The articles were word-tokenized, information on the exact pre-processing pipeline is unavailable.
|
| 207 |
+
|
| 208 |
+
#### Who are the source language producers?
|
| 209 |
+
|
| 210 |
+
The source language was produced by journalists and writers employed by the news agency and newspaper mentioned above.
|
| 211 |
+
|
| 212 |
+
### Annotations
|
| 213 |
+
|
| 214 |
+
#### Annotation process
|
| 215 |
+
|
| 216 |
+
Details can be found here https://arxiv.org/abs/2103.11811
|
| 217 |
+
|
| 218 |
+
#### Who are the annotators?
|
| 219 |
+
|
| 220 |
+
Annotators were recruited from [Masakhane](https://www.masakhane.io/)
|
| 221 |
+
|
| 222 |
+
### Personal and Sensitive Information
|
| 223 |
+
|
| 224 |
+
The data is sourced from newspaper source and only contains mentions of public figures or individuals
|
| 225 |
+
|
| 226 |
+
## Considerations for Using the Data
|
| 227 |
+
|
| 228 |
+
### Social Impact of Dataset
|
| 229 |
+
[More Information Needed]
|
| 230 |
+
|
| 231 |
+
|
| 232 |
+
### Discussion of Biases
|
| 233 |
+
[More Information Needed]
|
| 234 |
+
|
| 235 |
+
|
| 236 |
+
### Other Known Limitations
|
| 237 |
+
|
| 238 |
+
Users should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains.
|
| 239 |
+
|
| 240 |
+
## Additional Information
|
| 241 |
+
|
| 242 |
+
### Dataset Curators
|
| 243 |
+
|
| 244 |
+
|
| 245 |
+
### Licensing Information
|
| 246 |
+
|
| 247 |
+
The licensing status of the data is CC 4.0 Non-Commercial
|
| 248 |
+
|
| 249 |
+
### Citation Information
|
| 250 |
+
|
| 251 |
+
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
|
| 252 |
+
```
|
| 253 |
+
@article{Adelani2022MasakhaNER2A,
|
| 254 |
+
title={MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition},
|
| 255 |
+
author={David Ifeoluwa Adelani and Graham Neubig and Sebastian Ruder and Shruti Rijhwani and Michael Beukman and Chester Palen-Michel and Constantine Lignos and Jesujoba Oluwadara Alabi and Shamsuddeen Hassan Muhammad and Peter Nabende and Cheikh M. Bamba Dione and Andiswa Bukula and Rooweither Mabuya and Bonaventure F. P. Dossou and Blessing K. Sibanda and Happy Buzaaba and Jonathan Mukiibi and Godson Kalipe and Derguene Mbaye and Amelia Taylor and Fatoumata Kabore and Chris C. Emezue and Anuoluwapo Aremu and Perez Ogayo and Catherine W. Gitau and Edwin Munkoh-Buabeng and Victoire Memdjokam Koagne and Allahsera Auguste Tapo and Tebogo Macucwa and Vukosi Marivate and Elvis Mboning and Tajuddeen R. Gwadabe and Tosin P. Adewumi and Orevaoghene Ahia and Joyce Nakatumba-Nabende and Neo L. Mokono and Ignatius M Ezeani and Chiamaka Ijeoma Chukwuneke and Mofetoluwa Adeyemi and Gilles Hacheme and Idris Abdulmumin and Odunayo Ogundepo and Oreen Yousuf and Tatiana Moteu Ngoli and Dietrich Klakow},
|
| 256 |
+
journal={ArXiv},
|
| 257 |
+
year={2022},
|
| 258 |
+
volume={abs/2210.12391}
|
| 259 |
+
}
|
| 260 |
+
```
|
| 261 |
+
|
| 262 |
+
### Contributions
|
| 263 |
+
|
| 264 |
+
Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset.
|
huggingface_dataset/Dataset_Card/mcemilg_laion2B-multi-turkish-subset.md
ADDED
|
@@ -0,0 +1,72 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- crowdsourced
|
| 4 |
+
language:
|
| 5 |
+
- tr
|
| 6 |
+
language_creators:
|
| 7 |
+
- crowdsourced
|
| 8 |
+
license:
|
| 9 |
+
- cc-by-4.0
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
pretty_name: 'laion2B-multi-turkish-subset'
|
| 13 |
+
size_categories:
|
| 14 |
+
- 10M<n<100M
|
| 15 |
+
task_categories:
|
| 16 |
+
- text-to-image
|
| 17 |
+
- image-to-text
|
| 18 |
+
---
|
| 19 |
+
|
| 20 |
+
# Dataset Card for laion2B-multi-turkish-subset
|
| 21 |
+
|
| 22 |
+
## Dataset Description
|
| 23 |
+
|
| 24 |
+
- **Homepage:** [laion-5b](https://laion.ai/blog/laion-5b/)
|
| 25 |
+
- **Huggingface:** [laion/laion2B-multi](https://huggingface.co/datasets/laion/laion2B-multi)
|
| 26 |
+
- **Point of Contact:** [mcemilg](mailto:mcg@mcemilg.dev)
|
| 27 |
+
|
| 28 |
+
### Dataset Summary
|
| 29 |
+
|
| 30 |
+
[LAION-5B](https://laion.ai/blog/laion-5b/) is a large scale openly accessible image-text dataset contains text from multiple languages. This is a Turkish subset data of [laion/laion2B-multi](https://huggingface.co/datasets/laion/laion2B-multi). It's compatible to be used with [image2dataset](https://github.com/rom1504/img2dataset) to fetch the images at scale.
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
### Data Structure
|
| 34 |
+
|
| 35 |
+
```python
|
| 36 |
+
DatasetDict({
|
| 37 |
+
train: Dataset({
|
| 38 |
+
features: ['SAMPLE_ID', 'URL', 'TEXT', 'HEIGHT', 'WIDTH', 'LICENSE', 'LANGUAGE', 'NSFW', 'similarity'],
|
| 39 |
+
num_rows: 34638627
|
| 40 |
+
})
|
| 41 |
+
})
|
| 42 |
+
```
|
| 43 |
+
|
| 44 |
+
```python
|
| 45 |
+
{
|
| 46 |
+
'SAMPLE_ID': Value(dtype='int64', id=None),
|
| 47 |
+
'URL': Value(dtype='string', id=None),
|
| 48 |
+
'TEXT': Value(dtype='string', id=None),
|
| 49 |
+
'HEIGHT': Value(dtype='int64', id=None),
|
| 50 |
+
'WIDTH': Value(dtype='int64', id=None),
|
| 51 |
+
'LICENSE': Value(dtype='string', id=None),
|
| 52 |
+
'LANGUAGE': Value(dtype='string', id=None),
|
| 53 |
+
'NSFW': Value(dtype='string', id=None),
|
| 54 |
+
'similarity': Value(dtype='float64', id=None)
|
| 55 |
+
}
|
| 56 |
+
```
|
| 57 |
+
|
| 58 |
+
|
| 59 |
+
### Notes
|
| 60 |
+
|
| 61 |
+
The data was basically processed to drop non-Turkish and irrelevant texts before published. Both [FastText](https://fasttext.cc/docs/en/language-identification.html) and [langdetect](https://pypi.org/project/langdetect/) libraries were used to identify if the text is Turkish or not. The cleaning process can be summarized as follows:
|
| 62 |
+
|
| 63 |
+
- replace \"\"\" with empty str
|
| 64 |
+
- remove URLs in texts
|
| 65 |
+
- Drop if both FastText and LangDetect are highly confident with there is no Turkish in text.
|
| 66 |
+
- Drop empty text fields.
|
| 67 |
+
|
| 68 |
+
### License
|
| 69 |
+
CC-BY-4.0
|
| 70 |
+
|
| 71 |
+
|
| 72 |
+
|
huggingface_dataset/Dataset_Card/pcuenq_lsun-bedrooms.md
ADDED
|
@@ -0,0 +1,48 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
dataset_info:
|
| 3 |
+
features:
|
| 4 |
+
- name: image
|
| 5 |
+
dtype: image
|
| 6 |
+
splits:
|
| 7 |
+
- name: train
|
| 8 |
+
num_bytes: 4450242498.020249
|
| 9 |
+
num_examples: 287968
|
| 10 |
+
- name: test
|
| 11 |
+
num_bytes: 234247797.33875093
|
| 12 |
+
num_examples: 15157
|
| 13 |
+
download_size: 4756942293
|
| 14 |
+
dataset_size: 4684490295.359
|
| 15 |
+
license: mit
|
| 16 |
+
---
|
| 17 |
+
|
| 18 |
+
# Dataset Card for "lsun-bedrooms"
|
| 19 |
+
|
| 20 |
+
This is a 20% sample of the bedrooms category in [`LSUN`](https://github.com/fyu/lsun), uploaded as a dataset for convenience.
|
| 21 |
+
|
| 22 |
+
The license for _this compilation only_ is MIT. The data retains the same license as the original dataset.
|
| 23 |
+
|
| 24 |
+
This is (roughly) the code that was used to upload this dataset:
|
| 25 |
+
|
| 26 |
+
```Python
|
| 27 |
+
import os
|
| 28 |
+
import shutil
|
| 29 |
+
|
| 30 |
+
from miniai.imports import *
|
| 31 |
+
from miniai.diffusion import *
|
| 32 |
+
|
| 33 |
+
from datasets import load_dataset
|
| 34 |
+
|
| 35 |
+
path_data = Path('data')
|
| 36 |
+
path_data.mkdir(exist_ok=True)
|
| 37 |
+
path = path_data/'bedroom'
|
| 38 |
+
|
| 39 |
+
url = 'https://s3.amazonaws.com/fast-ai-imageclas/bedroom.tgz'
|
| 40 |
+
if not path.exists():
|
| 41 |
+
path_zip = fc.urlsave(url, path_data)
|
| 42 |
+
shutil.unpack_archive('data/bedroom.tgz', 'data')
|
| 43 |
+
|
| 44 |
+
dataset = load_dataset("imagefolder", data_dir="data/bedroom")
|
| 45 |
+
dataset = dataset.remove_columns('label')
|
| 46 |
+
dataset = dataset['train'].train_test_split(test_size=0.05)
|
| 47 |
+
dataset.push_to_hub("pcuenq/lsun-bedrooms")
|
| 48 |
+
```
|
huggingface_dataset/Dataset_Card/peterhsu_github-issues.md
ADDED
|
@@ -0,0 +1,139 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
annotations_creators:
|
| 2 |
+
- no-annotation
|
| 3 |
+
language_creators:
|
| 4 |
+
- found
|
| 5 |
+
languages:
|
| 6 |
+
- en
|
| 7 |
+
licenses:
|
| 8 |
+
- unknown
|
| 9 |
+
multilinguality:
|
| 10 |
+
- monolingual
|
| 11 |
+
pretty_name: Practice
|
| 12 |
+
size_categories:
|
| 13 |
+
- unknown
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
task_categories:
|
| 17 |
+
- text-classification
|
| 18 |
+
- text-retrieval
|
| 19 |
+
task_ids:
|
| 20 |
+
- multi-class-classification
|
| 21 |
+
- multi-label-classification
|
| 22 |
+
- document-retrieval
|
| 23 |
+
|
| 24 |
+
# Dataset Card for [Needs More Information]
|
| 25 |
+
|
| 26 |
+
## Table of Contents
|
| 27 |
+
- [Dataset Description](#dataset-description)
|
| 28 |
+
- [Dataset Summary](#dataset-summary)
|
| 29 |
+
- [Supported Tasks](#supported-tasks-and-leaderboards)
|
| 30 |
+
- [Languages](#languages)
|
| 31 |
+
- [Dataset Structure](#dataset-structure)
|
| 32 |
+
- [Data Instances](#data-instances)
|
| 33 |
+
- [Data Fields](#data-instances)
|
| 34 |
+
- [Data Splits](#data-instances)
|
| 35 |
+
- [Dataset Creation](#dataset-creation)
|
| 36 |
+
- [Curation Rationale](#curation-rationale)
|
| 37 |
+
- [Source Data](#source-data)
|
| 38 |
+
- [Annotations](#annotations)
|
| 39 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 40 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 41 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 42 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 43 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 44 |
+
- [Additional Information](#additional-information)
|
| 45 |
+
- [Dataset Curators](#dataset-curators)
|
| 46 |
+
- [Licensing Information](#licensing-information)
|
| 47 |
+
- [Citation Information](#citation-information)
|
| 48 |
+
|
| 49 |
+
## Dataset Description
|
| 50 |
+
|
| 51 |
+
- **Homepage:** https://huggingface.co/datasets/peterhsu/
|
| 52 |
+
- **Repository:** github-issues
|
| 53 |
+
- **Paper:** [Needs More Information]
|
| 54 |
+
- **Leaderboard:** [Needs More Information]
|
| 55 |
+
- **Point of Contact:** [Needs More Information]
|
| 56 |
+
|
| 57 |
+
### Dataset Summary
|
| 58 |
+
|
| 59 |
+
For Practice
|
| 60 |
+
|
| 61 |
+
### Supported Tasks and Leaderboards
|
| 62 |
+
|
| 63 |
+
Classification
|
| 64 |
+
|
| 65 |
+
### Languages
|
| 66 |
+
|
| 67 |
+
en
|
| 68 |
+
|
| 69 |
+
## Dataset Structure
|
| 70 |
+
|
| 71 |
+
### Data Instances
|
| 72 |
+
|
| 73 |
+
[Needs More Information]
|
| 74 |
+
|
| 75 |
+
### Data Fields
|
| 76 |
+
|
| 77 |
+
[Needs More Information]
|
| 78 |
+
|
| 79 |
+
### Data Splits
|
| 80 |
+
|
| 81 |
+
train
|
| 82 |
+
|
| 83 |
+
## Dataset Creation
|
| 84 |
+
|
| 85 |
+
### Curation Rationale
|
| 86 |
+
|
| 87 |
+
[Needs More Information]
|
| 88 |
+
|
| 89 |
+
### Source Data
|
| 90 |
+
|
| 91 |
+
#### Initial Data Collection and Normalization
|
| 92 |
+
|
| 93 |
+
[Needs More Information]
|
| 94 |
+
|
| 95 |
+
#### Who are the source language producers?
|
| 96 |
+
|
| 97 |
+
[Needs More Information]
|
| 98 |
+
|
| 99 |
+
### Annotations
|
| 100 |
+
|
| 101 |
+
#### Annotation process
|
| 102 |
+
|
| 103 |
+
[Needs More Information]
|
| 104 |
+
|
| 105 |
+
#### Who are the annotators?
|
| 106 |
+
|
| 107 |
+
[Needs More Information]
|
| 108 |
+
|
| 109 |
+
### Personal and Sensitive Information
|
| 110 |
+
|
| 111 |
+
[Needs More Information]
|
| 112 |
+
|
| 113 |
+
## Considerations for Using the Data
|
| 114 |
+
|
| 115 |
+
### Social Impact of Dataset
|
| 116 |
+
|
| 117 |
+
[Needs More Information]
|
| 118 |
+
|
| 119 |
+
### Discussion of Biases
|
| 120 |
+
|
| 121 |
+
[Needs More Information]
|
| 122 |
+
|
| 123 |
+
### Other Known Limitations
|
| 124 |
+
|
| 125 |
+
[Needs More Information]
|
| 126 |
+
|
| 127 |
+
## Additional Information
|
| 128 |
+
|
| 129 |
+
### Dataset Curators
|
| 130 |
+
|
| 131 |
+
[Needs More Information]
|
| 132 |
+
|
| 133 |
+
### Licensing Information
|
| 134 |
+
|
| 135 |
+
[Needs More Information]
|
| 136 |
+
|
| 137 |
+
### Citation Information
|
| 138 |
+
|
| 139 |
+
[Needs More Information]
|
huggingface_dataset/Dataset_Card/taln-ls2n_inspec.md
ADDED
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|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- unknown
|
| 4 |
+
language_creators:
|
| 5 |
+
- unknown
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- unknown
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
task_categories:
|
| 13 |
+
- text-mining
|
| 14 |
+
- text-generation
|
| 15 |
+
task_ids:
|
| 16 |
+
- keyphrase-generation
|
| 17 |
+
- keyphrase-extraction
|
| 18 |
+
size_categories:
|
| 19 |
+
- 1K<n<10K
|
| 20 |
+
pretty_name: Inspec
|
| 21 |
+
---
|
| 22 |
+
|
| 23 |
+
# Inspec Benchmark Dataset for Keyphrase Generation
|
| 24 |
+
|
| 25 |
+
## About
|
| 26 |
+
|
| 27 |
+
Inspec is a dataset for benchmarking keyphrase extraction and generation models.
|
| 28 |
+
The dataset is composed of 2,000 abstracts of scientific papers collected from the [Inspec database](https://www.theiet.org/resources/inspec/).
|
| 29 |
+
Keyphrases were annotated by professional indexers in an uncontrolled setting (that is, not limited to thesaurus entries).
|
| 30 |
+
Details about the inspec dataset can be found in the original paper [(Hulth, 2003)][hulth-2003].
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
Reference (indexer-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in [(Boudin and Gallina, 2021)][boudin-2021].
|
| 34 |
+
|
| 35 |
+
Text pre-processing (tokenization) is carried out using `spacy` (`en_core_web_sm` model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token).
|
| 36 |
+
Stemming (Porter's stemmer implementation provided in `nltk`) is applied before reference keyphrases are matched against the source text.
|
| 37 |
+
Details about the process can be found in `prmu.py`.
|
| 38 |
+
|
| 39 |
+
## Content and statistics
|
| 40 |
+
|
| 41 |
+
The dataset is divided into the following three splits:
|
| 42 |
+
|
| 43 |
+
| Split | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
|
| 44 |
+
| :--------- | ----------: | -----: | -----------: | --------: | ----------: | ------: | -------: |
|
| 45 |
+
| Train | 1,000 | 141.7 | 9.79 | 78.00 | 9.85 | 6.22 | 5.93 |
|
| 46 |
+
| Validation | 500 | 132.2 | 9.15 | 77.96 | 9.82 | 6.75 | 5.47 |
|
| 47 |
+
| Test | 500 | 134.8 | 9.83 | 78.70 | 9.92 | 6.48 | 4.91 |
|
| 48 |
+
|
| 49 |
+
The following data fields are available :
|
| 50 |
+
|
| 51 |
+
- **id**: unique identifier of the document.
|
| 52 |
+
- **title**: title of the document.
|
| 53 |
+
- **abstract**: abstract of the document.
|
| 54 |
+
- **keyphrases**: list of reference keyphrases.
|
| 55 |
+
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
|
| 56 |
+
|
| 57 |
+
## References
|
| 58 |
+
|
| 59 |
+
- (Hulth, 2003) Anette Hulth. 2003.
|
| 60 |
+
[Improved automatic keyword extraction given more linguistic knowledge](https://aclanthology.org/W03-1028).
|
| 61 |
+
In Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pages 216-223.
|
| 62 |
+
- (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021.
|
| 63 |
+
[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://aclanthology.org/2021.naacl-main.330/).
|
| 64 |
+
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
|
| 65 |
+
|
| 66 |
+
[hulth-2003]: https://aclanthology.org/W03-1028/
|
| 67 |
+
[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/
|
huggingface_dataset/Dataset_Card/trojblue_public_data.md
ADDED
|
@@ -0,0 +1,12 @@
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|
|
| 1 |
+
---
|
| 2 |
+
license: bigscience-openrail-m
|
| 3 |
+
---
|
| 4 |
+
|
| 5 |
+
kndSet & kndSet_good_only: 宵崎奏 (~160p; 原图)
|
| 6 |
+
|
| 7 |
+
yada_train_v1: ai生成图片, 含bad anatomy tagging (1024*1560; 原图)
|
| 8 |
+
|
| 9 |
+
onimai:
|
| 10 |
+
- danbooru + wd tags, 按概率排序后去重:
|
| 11 |
+
- `onii-chan wa oshimai!` → `onimai`
|
| 12 |
+
- `oyama mahiro`, `hozuki kaede`, `oyama mihari`
|