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c219307f7fd35f295dcd0cdf4cc94cd949158b30 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-6.7b
* Dataset: mathemakitten/winobias_antistereotype_dev
* Config: mathemakitten--winobias_antistereotype_dev
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-7776e8-1573055858 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-27T15:14:32+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_dev"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-6.7b", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_dev", "dataset_config": "mathemakitten--winobias_antistereotype_dev", "dataset_split": "validation", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}} | 2022-09-27T15:30:46+00:00 |
4596f8cd06aa6f0fc71957d2e6a1f33c8664b643 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-1.3b
* Dataset: mathemakitten/winobias_antistereotype_dev
* Config: mathemakitten--winobias_antistereotype_dev
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-e92f99-1572955856 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-27T15:14:33+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_dev"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-1.3b", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_dev", "dataset_config": "mathemakitten--winobias_antistereotype_dev", "dataset_split": "validation", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}} | 2022-09-27T15:17:41+00:00 |
fba43e6d568abcfdab87ffe3068571fd21dca450 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-13b
* Dataset: mathemakitten/winobias_antistereotype_dev
* Config: mathemakitten--winobias_antistereotype_dev
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-7776e8-1573055859 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-27T15:14:34+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_dev"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-13b", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_dev", "dataset_config": "mathemakitten--winobias_antistereotype_dev", "dataset_split": "validation", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}} | 2022-09-27T15:43:28+00:00 |
25a3771e345e9226611b04bc2bd695eaebad972e | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-2.7b
* Dataset: mathemakitten/winobias_antistereotype_dev
* Config: mathemakitten--winobias_antistereotype_dev
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-e92f99-1572955857 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-27T15:14:36+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_dev"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-2.7b", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_dev", "dataset_config": "mathemakitten--winobias_antistereotype_dev", "dataset_split": "validation", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}} | 2022-09-27T15:19:55+00:00 |
36506bf4050ad3043e111c1812be9c557b238954 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-30b
* Dataset: mathemakitten/winobias_antistereotype_dev
* Config: mathemakitten--winobias_antistereotype_dev
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-7776e8-1573055860 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-27T15:14:36+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_dev"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-30b", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_dev", "dataset_config": "mathemakitten--winobias_antistereotype_dev", "dataset_split": "validation", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}} | 2022-09-27T16:25:03+00:00 |
2afaf26908533ee079a8fe1fb7d36c595b8d7176 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-350m
* Dataset: mathemakitten/winobias_antistereotype_dev
* Config: mathemakitten--winobias_antistereotype_dev
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-e92f99-1572955855 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-27T15:14:38+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_dev"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-350m", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_dev", "dataset_config": "mathemakitten--winobias_antistereotype_dev", "dataset_split": "validation", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}} | 2022-09-27T15:15:50+00:00 |
e17a8195959cef8071410fd7fa8c4130a16a3a72 |
# Dataset Card for "tner/wikiann"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/P17-1178/](https://aclanthology.org/P17-1178/)
- **Dataset:** WikiAnn
- **Domain:** Wikipedia
- **Number of Entity:** 3
### Dataset Summary
WikiAnn NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
- Entity Types: `LOC`, `ORG`, `PER`
## Dataset Structure
### Data Instances
An example of `train` of `ja` looks as follows.
```
{
'tokens': ['#', '#', 'ユ', 'リ', 'ウ', 'ス', '・', 'ベ', 'ー', 'リ', 'ッ', 'ク', '#', '1', '9','9','9'],
'tags': [6, 6, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6]
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/wikiann/raw/main/dataset/label.json).
```python
{
"B-LOC": 0,
"B-ORG": 1,
"B-PER": 2,
"I-LOC": 3,
"I-ORG": 4,
"I-PER": 5,
"O": 6
}
```
### Data Splits
| language | train | validation | test |
|:-------------|--------:|-------------:|-------:|
| ace | 100 | 100 | 100 |
| bg | 20000 | 10000 | 10000 |
| da | 20000 | 10000 | 10000 |
| fur | 100 | 100 | 100 |
| ilo | 100 | 100 | 100 |
| lij | 100 | 100 | 100 |
| mzn | 100 | 100 | 100 |
| qu | 100 | 100 | 100 |
| su | 100 | 100 | 100 |
| vi | 20000 | 10000 | 10000 |
| af | 5000 | 1000 | 1000 |
| bh | 100 | 100 | 100 |
| de | 20000 | 10000 | 10000 |
| fy | 1000 | 1000 | 1000 |
| io | 100 | 100 | 100 |
| lmo | 100 | 100 | 100 |
| nap | 100 | 100 | 100 |
| rm | 100 | 100 | 100 |
| sv | 20000 | 10000 | 10000 |
| vls | 100 | 100 | 100 |
| als | 100 | 100 | 100 |
| bn | 10000 | 1000 | 1000 |
| diq | 100 | 100 | 100 |
| ga | 1000 | 1000 | 1000 |
| is | 1000 | 1000 | 1000 |
| ln | 100 | 100 | 100 |
| nds | 100 | 100 | 100 |
| ro | 20000 | 10000 | 10000 |
| sw | 1000 | 1000 | 1000 |
| vo | 100 | 100 | 100 |
| am | 100 | 100 | 100 |
| bo | 100 | 100 | 100 |
| dv | 100 | 100 | 100 |
| gan | 100 | 100 | 100 |
| it | 20000 | 10000 | 10000 |
| lt | 10000 | 10000 | 10000 |
| ne | 100 | 100 | 100 |
| ru | 20000 | 10000 | 10000 |
| szl | 100 | 100 | 100 |
| wa | 100 | 100 | 100 |
| an | 1000 | 1000 | 1000 |
| br | 1000 | 1000 | 1000 |
| el | 20000 | 10000 | 10000 |
| gd | 100 | 100 | 100 |
| ja | 20000 | 10000 | 10000 |
| lv | 10000 | 10000 | 10000 |
| nl | 20000 | 10000 | 10000 |
| rw | 100 | 100 | 100 |
| ta | 15000 | 1000 | 1000 |
| war | 100 | 100 | 100 |
| ang | 100 | 100 | 100 |
| bs | 15000 | 1000 | 1000 |
| eml | 100 | 100 | 100 |
| gl | 15000 | 10000 | 10000 |
| jbo | 100 | 100 | 100 |
| map-bms | 100 | 100 | 100 |
| nn | 20000 | 1000 | 1000 |
| sa | 100 | 100 | 100 |
| te | 1000 | 1000 | 1000 |
| wuu | 100 | 100 | 100 |
| ar | 20000 | 10000 | 10000 |
| ca | 20000 | 10000 | 10000 |
| en | 20000 | 10000 | 10000 |
| gn | 100 | 100 | 100 |
| jv | 100 | 100 | 100 |
| mg | 100 | 100 | 100 |
| no | 20000 | 10000 | 10000 |
| sah | 100 | 100 | 100 |
| tg | 100 | 100 | 100 |
| xmf | 100 | 100 | 100 |
| arc | 100 | 100 | 100 |
| cbk-zam | 100 | 100 | 100 |
| eo | 15000 | 10000 | 10000 |
| gu | 100 | 100 | 100 |
| ka | 10000 | 10000 | 10000 |
| mhr | 100 | 100 | 100 |
| nov | 100 | 100 | 100 |
| scn | 100 | 100 | 100 |
| th | 20000 | 10000 | 10000 |
| yi | 100 | 100 | 100 |
| arz | 100 | 100 | 100 |
| cdo | 100 | 100 | 100 |
| es | 20000 | 10000 | 10000 |
| hak | 100 | 100 | 100 |
| kk | 1000 | 1000 | 1000 |
| mi | 100 | 100 | 100 |
| oc | 100 | 100 | 100 |
| sco | 100 | 100 | 100 |
| tk | 100 | 100 | 100 |
| yo | 100 | 100 | 100 |
| as | 100 | 100 | 100 |
| ce | 100 | 100 | 100 |
| et | 15000 | 10000 | 10000 |
| he | 20000 | 10000 | 10000 |
| km | 100 | 100 | 100 |
| min | 100 | 100 | 100 |
| or | 100 | 100 | 100 |
| sd | 100 | 100 | 100 |
| tl | 10000 | 1000 | 1000 |
| zea | 100 | 100 | 100 |
| ast | 1000 | 1000 | 1000 |
| ceb | 100 | 100 | 100 |
| eu | 10000 | 10000 | 10000 |
| hi | 5000 | 1000 | 1000 |
| kn | 100 | 100 | 100 |
| mk | 10000 | 1000 | 1000 |
| os | 100 | 100 | 100 |
| sh | 20000 | 10000 | 10000 |
| tr | 20000 | 10000 | 10000 |
| zh-classical | 100 | 100 | 100 |
| ay | 100 | 100 | 100 |
| ckb | 1000 | 1000 | 1000 |
| ext | 100 | 100 | 100 |
| hr | 20000 | 10000 | 10000 |
| ko | 20000 | 10000 | 10000 |
| ml | 10000 | 1000 | 1000 |
| pa | 100 | 100 | 100 |
| si | 100 | 100 | 100 |
| tt | 1000 | 1000 | 1000 |
| zh-min-nan | 100 | 100 | 100 |
| az | 10000 | 1000 | 1000 |
| co | 100 | 100 | 100 |
| fa | 20000 | 10000 | 10000 |
| hsb | 100 | 100 | 100 |
| ksh | 100 | 100 | 100 |
| mn | 100 | 100 | 100 |
| pdc | 100 | 100 | 100 |
| simple | 20000 | 1000 | 1000 |
| ug | 100 | 100 | 100 |
| zh-yue | 20000 | 10000 | 10000 |
| ba | 100 | 100 | 100 |
| crh | 100 | 100 | 100 |
| fi | 20000 | 10000 | 10000 |
| hu | 20000 | 10000 | 10000 |
| ku | 100 | 100 | 100 |
| mr | 5000 | 1000 | 1000 |
| pl | 20000 | 10000 | 10000 |
| sk | 20000 | 10000 | 10000 |
| uk | 20000 | 10000 | 10000 |
| zh | 20000 | 10000 | 10000 |
| bar | 100 | 100 | 100 |
| cs | 20000 | 10000 | 10000 |
| fiu-vro | 100 | 100 | 100 |
| hy | 15000 | 1000 | 1000 |
| ky | 100 | 100 | 100 |
| ms | 20000 | 1000 | 1000 |
| pms | 100 | 100 | 100 |
| sl | 15000 | 10000 | 10000 |
| ur | 20000 | 1000 | 1000 |
| bat-smg | 100 | 100 | 100 |
| csb | 100 | 100 | 100 |
| fo | 100 | 100 | 100 |
| ia | 100 | 100 | 100 |
| la | 5000 | 1000 | 1000 |
| mt | 100 | 100 | 100 |
| pnb | 100 | 100 | 100 |
| so | 100 | 100 | 100 |
| uz | 1000 | 1000 | 1000 |
| be-x-old | 5000 | 1000 | 1000 |
| cv | 100 | 100 | 100 |
| fr | 20000 | 10000 | 10000 |
| id | 20000 | 10000 | 10000 |
| lb | 5000 | 1000 | 1000 |
| mwl | 100 | 100 | 100 |
| ps | 100 | 100 | 100 |
| sq | 5000 | 1000 | 1000 |
| vec | 100 | 100 | 100 |
| be | 15000 | 1000 | 1000 |
| cy | 10000 | 1000 | 1000 |
| frr | 100 | 100 | 100 |
| ig | 100 | 100 | 100 |
| li | 100 | 100 | 100 |
| my | 100 | 100 | 100 |
| pt | 20000 | 10000 | 10000 |
| sr | 20000 | 10000 | 10000 |
| vep | 100 | 100 | 100 |
### Citation Information
```
@inproceedings{pan-etal-2017-cross,
title = "Cross-lingual Name Tagging and Linking for 282 Languages",
author = "Pan, Xiaoman and
Zhang, Boliang and
May, Jonathan and
Nothman, Joel and
Knight, Kevin and
Ji, Heng",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1178",
doi = "10.18653/v1/P17-1178",
pages = "1946--1958",
abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.",
}
``` | tner/wikiann | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:multilingual",
"size_categories:10K<100k",
"language:ace",
"language:bg",
"language:da",
"language:fur",
"language:ilo",
"language:lij",
"language:mzn",
"language:qu",
"language:su",
"language:vi",
"language:af",
"language:bh",
"language:de",
"language:fy",
"language:io",
"language:lmo",
"language:nap",
"language:rm",
"language:sv",
"language:vls",
"language:als",
"language:bn",
"language:diq",
"language:ga",
"language:is",
"language:ln",
"language:nds",
"language:ro",
"language:sw",
"language:vo",
"language:am",
"language:bo",
"language:dv",
"language:gan",
"language:it",
"language:lt",
"language:ne",
"language:ru",
"language:szl",
"language:wa",
"language:an",
"language:br",
"language:el",
"language:gd",
"language:ja",
"language:lv",
"language:nl",
"language:rw",
"language:ta",
"language:war",
"language:ang",
"language:bs",
"language:eml",
"language:gl",
"language:jbo",
"language:nn",
"language:sa",
"language:te",
"language:wuu",
"language:ar",
"language:ca",
"language:en",
"language:gn",
"language:jv",
"language:mg",
"language:no",
"language:sah",
"language:tg",
"language:xmf",
"language:arc",
"language:eo",
"language:gu",
"language:ka",
"language:mhr",
"language:nov",
"language:scn",
"language:th",
"language:yi",
"language:arz",
"language:cdo",
"language:es",
"language:hak",
"language:kk",
"language:mi",
"language:oc",
"language:sco",
"language:tk",
"language:yo",
"language:as",
"language:ce",
"language:et",
"language:he",
"language:km",
"language:min",
"language:or",
"language:sd",
"language:tl",
"language:zea",
"language:ast",
"language:ceb",
"language:eu",
"language:hi",
"language:kn",
"language:mk",
"language:os",
"language:sh",
"language:tr",
"language:ay",
"language:ckb",
"language:ext",
"language:hr",
"language:ko",
"language:ml",
"language:pa",
"language:si",
"language:tt",
"language:az",
"language:co",
"language:fa",
"language:hsb",
"language:ksh",
"language:mn",
"language:pdc",
"language:ug",
"language:ba",
"language:crh",
"language:fi",
"language:hu",
"language:ku",
"language:mr",
"language:pl",
"language:sk",
"language:uk",
"language:zh",
"language:bar",
"language:cs",
"language:hy",
"language:ky",
"language:ms",
"language:pms",
"language:sl",
"language:ur",
"language:csb",
"language:fo",
"language:ia",
"language:la",
"language:mt",
"language:pnb",
"language:so",
"language:uz",
"language:cv",
"language:fr",
"language:id",
"language:lb",
"language:mwl",
"language:ps",
"language:sq",
"language:vec",
"language:be",
"language:cy",
"language:frr",
"language:ig",
"language:li",
"language:my",
"language:pt",
"language:sr",
"region:us"
] | 2022-09-27T15:22:58+00:00 | {"language": ["ace", "bg", "da", "fur", "ilo", "lij", "mzn", "qu", "su", "vi", "af", "bh", "de", "fy", "io", "lmo", "nap", "rm", "sv", "vls", "als", "bn", "diq", "ga", "is", "ln", "nds", "ro", "sw", "vo", "am", "bo", "dv", "gan", "it", "lt", "ne", "ru", "szl", "wa", "an", "br", "el", "gd", "ja", "lv", "nl", "rw", "ta", "war", "ang", "bs", "eml", "gl", "jbo", "nn", "sa", "te", "wuu", "ar", "ca", "en", "gn", "jv", "mg", false, "sah", "tg", "xmf", "arc", "eo", "gu", "ka", "mhr", "nov", "scn", "th", "yi", "arz", "cdo", "es", "hak", "kk", "mi", "oc", "sco", "tk", "yo", "as", "ce", "et", "he", "km", "min", "or", "sd", "tl", "zea", "ast", "ceb", "eu", "hi", "kn", "mk", "os", "sh", "tr", "ay", "ckb", "ext", "hr", "ko", "ml", "pa", "si", "tt", "az", "co", "fa", "hsb", "ksh", "mn", "pdc", "ug", "ba", "crh", "fi", "hu", "ku", "mr", "pl", "sk", "uk", "zh", "bar", "cs", "hy", "ky", "ms", "pms", "sl", "ur", "csb", "fo", "ia", "la", "mt", "pnb", "so", "uz", "cv", "fr", "id", "lb", "mwl", "ps", "sq", "vec", "be", "cy", "frr", "ig", "li", "my", "pt", "sr"], "multilinguality": ["multilingual"], "size_categories": ["10K<100k"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "WikiAnn"} | 2022-09-27T17:39:42+00:00 |
b04b5ce4ae52ba21af979685fe68bbf29782951a | ErikSihab/erik-sihab | [
"region:us"
] | 2022-09-27T15:54:55+00:00 | {} | 2022-09-27T22:14:07+00:00 |
|
a7b32d401cdb057eb9a204e471db642abe3058ab | freddyaboulton/gradio-reviews | [
"license:mit",
"region:us"
] | 2022-09-27T16:49:12+00:00 | {"license": "mit"} | 2023-11-03T19:13:19+00:00 |
|
ce7483a909a7b68ddc02920087462355f7680057 |
# Dataset Card for "tner/wikineural"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/2021.findings-emnlp.215/](https://aclanthology.org/2021.findings-emnlp.215/)
- **Dataset:** WikiNeural
- **Domain:** Wikipedia
- **Number of Entity:** 16
### Dataset Summary
WikiAnn NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
- Entity Types: `PER`, `LOC`, `ORG`, `ANIM`, `BIO`, `CEL`, `DIS`, `EVE`, `FOOD`, `INST`, `MEDIA`, `PLANT`, `MYTH`, `TIME`, `VEHI`, `MISC`
## Dataset Structure
### Data Instances
An example of `train` of `de` looks as follows.
```
{
'tokens': [ "Dieses", "wiederum", "basierte", "auf", "dem", "gleichnamigen", "Roman", "von", "Noël", "Calef", "." ],
'tags': [ 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0 ]
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/wikineural/raw/main/dataset/label.json).
```python
{
"O": 0,
"B-PER": 1,
"I-PER": 2,
"B-LOC": 3,
"I-LOC": 4,
"B-ORG": 5,
"I-ORG": 6,
"B-ANIM": 7,
"I-ANIM": 8,
"B-BIO": 9,
"I-BIO": 10,
"B-CEL": 11,
"I-CEL": 12,
"B-DIS": 13,
"I-DIS": 14,
"B-EVE": 15,
"I-EVE": 16,
"B-FOOD": 17,
"I-FOOD": 18,
"B-INST": 19,
"I-INST": 20,
"B-MEDIA": 21,
"I-MEDIA": 22,
"B-PLANT": 23,
"I-PLANT": 24,
"B-MYTH": 25,
"I-MYTH": 26,
"B-TIME": 27,
"I-TIME": 28,
"B-VEHI": 29,
"I-VEHI": 30,
"B-MISC": 31,
"I-MISC": 32
}
```
### Data Splits
| language | train | validation | test |
|:-----------|--------:|-------------:|-------:|
| de | 98640 | 12330 | 12372 |
| en | 92720 | 11590 | 11597 |
| es | 76320 | 9540 | 9618 |
| fr | 100800 | 12600 | 12678 |
| it | 88400 | 11050 | 11069 |
| nl | 83680 | 10460 | 10547 |
| pl | 108160 | 13520 | 13585 |
| pt | 80560 | 10070 | 10160 |
| ru | 92320 | 11540 | 11580 |
### Citation Information
```
@inproceedings{tedeschi-etal-2021-wikineural-combined,
title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}",
author = "Tedeschi, Simone and
Maiorca, Valentino and
Campolungo, Niccol{\`o} and
Cecconi, Francesco and
Navigli, Roberto",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.215",
doi = "10.18653/v1/2021.findings-emnlp.215",
pages = "2521--2533",
abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.",
}
``` | tner/wikineural | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:multilingual",
"size_categories:10K<100k",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:nl",
"language:pl",
"language:pt",
"language:ru",
"region:us"
] | 2022-09-27T16:56:40+00:00 | {"language": ["de", "en", "es", "fr", "it", "nl", "pl", "pt", "ru"], "multilinguality": ["multilingual"], "size_categories": ["10K<100k"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "WikiNeural"} | 2022-09-27T18:46:37+00:00 |
3c9285ea8a531da6066ac04bb17394bc8e8ca3b6 | # Dataset Card for "pip"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | open-source-metrics/pip | [
"region:us"
] | 2022-09-27T17:19:45+00:00 | {"dataset_info": {"features": [{"name": "day", "dtype": "string"}, {"name": "num_downloads", "dtype": "int64"}], "splits": [{"name": "gradio", "num_bytes": 27742, "num_examples": 1261}, {"name": "safetensors", "num_bytes": 9812, "num_examples": 446}, {"name": "optimum", "num_bytes": 19360, "num_examples": 880}, {"name": "evaluate", "num_bytes": 16346, "num_examples": 743}, {"name": "huggingface_hub", "num_bytes": 25256, "num_examples": 1148}, {"name": "pytorch_image_models", "num_bytes": 27742, "num_examples": 1261}, {"name": "accelerate", "num_bytes": 24376, "num_examples": 1108}, {"name": "tokenizers", "num_bytes": 27742, "num_examples": 1261}, {"name": "transformers", "num_bytes": 28424, "num_examples": 1292}, {"name": "peft", "num_bytes": 8602, "num_examples": 391}, {"name": "diffusers", "num_bytes": 13750, "num_examples": 625}, {"name": "datasets", "num_bytes": 24376, "num_examples": 1108}], "download_size": 148060, "dataset_size": 253528}, "configs": [{"config_name": "default", "data_files": [{"split": "accelerate", "path": "data/accelerate-*"}, {"split": "datasets", "path": "data/datasets-*"}, {"split": "diffusers", "path": "data/diffusers-*"}, {"split": "evaluate", "path": "data/evaluate-*"}, {"split": "gradio", "path": "data/gradio-*"}, {"split": "huggingface_hub", "path": "data/huggingface_hub-*"}, {"split": "optimum", "path": "data/optimum-*"}, {"split": "peft", "path": "data/peft-*"}, {"split": "pytorch_image_models", "path": "data/pytorch_image_models-*"}, {"split": "safetensors", "path": "data/safetensors-*"}, {"split": "tokenizers", "path": "data/tokenizers-*"}, {"split": "transformers", "path": "data/transformers-*"}]}]} | 2024-02-15T11:18:27+00:00 |
533a80b990626e7984be36fbfeb2371c425b2a27 | chunkeduptube/chunkis | [
"license:artistic-2.0",
"region:us"
] | 2022-09-27T17:21:14+00:00 | {"license": "artistic-2.0"} | 2022-09-27T17:26:34+00:00 |
|
facdfd1c6f139820e44b5dd7b341d056fbe2044e |
# Dataset Card for "tner/multinerd"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/2022.findings-naacl.60/](https://aclanthology.org/2022.findings-naacl.60/)
- **Dataset:** MultiNERD
- **Domain:** Wikipedia, WikiNews
- **Number of Entity:** 18
### Dataset Summary
MultiNERD NER benchmark dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
- Entity Types: `PER`, `LOC`, `ORG`, `ANIM`, `BIO`, `CEL`, `DIS`, `EVE`, `FOOD`, `INST`, `MEDIA`, `PLANT`, `MYTH`, `TIME`, `VEHI`, `MISC`, `SUPER`, `PHY`
## Dataset Structure
### Data Instances
An example of `train` of `de` looks as follows.
```
{
'tokens': [ "Die", "Blätter", "des", "Huflattichs", "sind", "leicht", "mit", "den", "sehr", "ähnlichen", "Blättern", "der", "Weißen", "Pestwurz", "(", "\"", "Petasites", "albus", "\"", ")", "zu", "verwechseln", "." ],
'tags': [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0 ]
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/multinerd/raw/main/dataset/label.json).
```python
{
"O": 0,
"B-PER": 1,
"I-PER": 2,
"B-LOC": 3,
"I-LOC": 4,
"B-ORG": 5,
"I-ORG": 6,
"B-ANIM": 7,
"I-ANIM": 8,
"B-BIO": 9,
"I-BIO": 10,
"B-CEL": 11,
"I-CEL": 12,
"B-DIS": 13,
"I-DIS": 14,
"B-EVE": 15,
"I-EVE": 16,
"B-FOOD": 17,
"I-FOOD": 18,
"B-INST": 19,
"I-INST": 20,
"B-MEDIA": 21,
"I-MEDIA": 22,
"B-PLANT": 23,
"I-PLANT": 24,
"B-MYTH": 25,
"I-MYTH": 26,
"B-TIME": 27,
"I-TIME": 28,
"B-VEHI": 29,
"I-VEHI": 30,
"B-SUPER": 31,
"I-SUPER": 32,
"B-PHY": 33,
"I-PHY": 34
}
```
### Data Splits
| language | test |
|:-----------|-------:|
| de | 156792 |
| en | 164144 |
| es | 173189 |
| fr | 176185 |
| it | 181927 |
| nl | 171711 |
| pl | 194965 |
| pt | 177565 |
| ru | 82858 |
### Citation Information
```
@inproceedings{tedeschi-navigli-2022-multinerd,
title = "{M}ulti{NERD}: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)",
author = "Tedeschi, Simone and
Navigli, Roberto",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.60",
doi = "10.18653/v1/2022.findings-naacl.60",
pages = "801--812",
abstract = "Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications. Current datasets for NER focus mainly on coarse-grained entity types, tend to consider a single textual genre and to cover a narrow set of languages, thus limiting the general applicability of NER systems.In this work, we design a new methodology for automatically producing NER annotations, and address the aforementioned limitations by introducing a novel dataset that covers 10 languages, 15 NER categories and 2 textual genres.We also introduce a manually-annotated test set, and extensively evaluate the quality of our novel dataset on both this new test set and standard benchmarks for NER.In addition, in our dataset, we include: i) disambiguation information to enable the development of multilingual entity linking systems, and ii) image URLs to encourage the creation of multimodal systems.We release our dataset at https://github.com/Babelscape/multinerd.",
}
``` | tner/multinerd | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:multilingual",
"size_categories:<10K",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:nl",
"language:pl",
"language:pt",
"language:ru",
"region:us"
] | 2022-09-27T18:13:36+00:00 | {"language": ["de", "en", "es", "fr", "it", "nl", "pl", "pt", "ru"], "multilinguality": ["multilingual"], "size_categories": ["<10K"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "MultiNERD"} | 2022-09-27T18:48:40+00:00 |
dfd59f85a7256d183b215f86b8ad1c8a8bdc6ec3 | LucaBlight/Kheiron | [
"license:afl-3.0",
"region:us"
] | 2022-09-27T19:36:17+00:00 | {"license": "afl-3.0"} | 2022-09-27T19:36:17+00:00 |
|
ebbb3a2ae953c0a73ab3db40e849c6c23a82542a |
---
Sample
---
- 6900 transcripts
- 44 churches
- timeframe: 2010-2022
- Denomination: Unitarian Universalist, USA
---
Dataset structure
---
- church (church name or website)
- source (mp3 file)
- text
- sentences (count)
- errors (number of sentences skipped because could not understand audio, or just long pauses skipped)
- duration (in seconds)
---
Dataset creation
---
- see notebook in files
| marcmaxmeister/unitarian-universalist-sermons | [
"license:mit",
"region:us"
] | 2022-09-27T21:11:20+00:00 | {"license": "mit"} | 2022-09-28T20:04:16+00:00 |
6947305648990c358f904def2a18cc3cc62fd4c0 |
The code is provided under a Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. Under the license, the code is provided royalty free for non-commercial purposes only. The code may be covered by patents and if you want to use the code for commercial purposes, please contact us for a different license.
This dataset is a pre-processed small sample of the Waymo Open Motion Dataset intended for illustration purposes only.
| jmercat/risk_biased_dataset | [
"license:cc-by-nc-4.0",
"region:us"
] | 2022-09-27T21:35:21+00:00 | {"license": "cc-by-nc-4.0"} | 2023-08-01T18:08:31+00:00 |
01982dd3e03603a1e07e2c2d9ad30d0a5a722e95 | Zavek/Contradictory-xnli | [
"license:other",
"region:us"
] | 2022-09-27T23:49:35+00:00 | {"license": "other"} | 2022-09-28T00:37:20+00:00 |
|
e01e8edff5797a78f34c568ecab33a64794842f2 | zyznull/msmarco-passage-ranking | [
"license:apache-2.0",
"region:us"
] | 2022-09-28T01:29:39+00:00 | {"license": "apache-2.0"} | 2022-09-28T02:30:10+00:00 |
|
074942294995d8a21a045f2dcafcb9dd19966991 | zyznull/msmarco-passage-corpus | [
"license:mit",
"region:us"
] | 2022-09-28T05:15:51+00:00 | {"license": "mit"} | 2023-01-09T08:16:28+00:00 |
|
b7b9168a7ce51714c0914a4ac7c8511abc3d82c3 | dhruvs00/datahogyaset | [
"license:openrail",
"region:us"
] | 2022-09-28T05:46:48+00:00 | {"license": "openrail"} | 2022-09-28T05:46:48+00:00 |
|
5e92c47f62e3a16dc4b38ed70aa8841eacb22514 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
paperswithcode_id: acronym-identification
pretty_name: datahogyas
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- token-classification
task_ids:
- part-of-speech
train-eval-index:
- col_mapping:
labels: tags
tokens: tokens
config: default
splits:
eval_split: test
task: token-classification
task_id: entity_extraction
--- | dhruvs00/datahogyas | [
"region:us"
] | 2022-09-28T05:47:21+00:00 | {} | 2022-09-28T07:08:02+00:00 |
5e2a11d9729621f0375b6ccd1114d335c6ee1b94 | # Dummy Dataset for AutoTrain Benchmark
This dataset contains dummy data that's needed to create AutoTrain projects for benchmarks like [RAFT](https://huggingface.co/spaces/ought/raft-leaderboard). See [here](https://github.com/huggingface/hf_benchmarks) for more details. | autoevaluator/benchmark-dummy-data | [
"region:us"
] | 2022-09-28T06:57:08+00:00 | {} | 2022-11-18T13:19:56+00:00 |
519a29f2934a650967d7c6c99f4c53ed99e083d0 | # dureader
数据来自DuReader-Retreval数据集，这里是[原始地址](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval)。
> 本数据集只用作学术研究使用。如果本仓库涉及侵权行为，会立即删除。
| zyznull/dureader-retrieval-corpus | [
"license:apache-2.0",
"region:us"
] | 2022-09-28T07:03:03+00:00 | {"license": "apache-2.0"} | 2023-01-03T08:05:06+00:00 |
f33c72ade15f98638f3598a9ca4ac989d21f699e |
All eight of datasets in ESC can be downloaded and prepared in just a single line of code through the Hugging Face Datasets library:
```python
from datasets import load_dataset
librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech", split="train")
```
- `"esc-benchmark"`: the repository namespace. This is fixed for all ESC datasets.
- `"librispeech"`: the dataset name. This can be changed to any of any one of the eight datasets in ESC to download that dataset.
- `split="train"`: the split. Set this to one of train/validation/test to generate a specific split. Omit the `split` argument to generate all splits for a dataset.
The datasets are full prepared, such that the audio and transcription files can be used directly in training/evaluation scripts.
## Dataset Information
A data point can be accessed by indexing the dataset object loaded through `load_dataset`:
```python
print(librispeech[0])
```
A typical data point comprises the path to the audio file and its transcription. Also included is information of the dataset from which the sample derives and a unique identifier name:
```python
{
'dataset': 'librispeech',
'audio': {'path': '/home/esc-bencher/.cache/huggingface/datasets/downloads/extracted/d2da1969fe9e7d06661b5dc370cf2e3c119a14c35950045bcb76243b264e4f01/374-180298-0000.flac',
'array': array([ 7.01904297e-04, 7.32421875e-04, 7.32421875e-04, ...,
-2.74658203e-04, -1.83105469e-04, -3.05175781e-05]),
'sampling_rate': 16000},
'text': 'chapter sixteen i might have told you of the beginning of this liaison in a few lines but i wanted you to see every step by which we came i to agree to whatever marguerite wished',
'id': '374-180298-0000'
}
```
### Data Fields
- `dataset`: name of the ESC dataset from which the sample is taken.
- `audio`: a dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `text`: the transcription of the audio file.
- `id`: unique id of the data sample.
### Data Preparation
#### Audio
The audio for all ESC datasets is segmented into sample lengths suitable for training ASR systems. The Hugging Face datasets library decodes audio files on the fly, reading the segments and converting them to a Python arrays. Consequently, no further preparation of the audio is required to be used in training/evaluation scripts.
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]`.
#### Transcriptions
The transcriptions corresponding to each audio file are provided in their 'error corrected' format. No transcription pre-processing is applied to the text, only necessary 'error correction' steps such as removing junk tokens (_<unk>_) or converting symbolic punctuation to spelled out form (_<comma>_ to _,_). As such, no further preparation of the transcriptions is required to be used in training/evaluation scripts.
Transcriptions are provided for training and validation splits. The transcriptions are **not** provided for the test splits. The ESC benchmark requires you to generate predictions for the test sets and upload them to https://huggingface.co/spaces/esc-benchmark/esc for scoring.
### Access
All eight of the datasets in ESC are accessible and licensing is freely available. Three of the ESC datasets have specific terms of usage that must be agreed to before using the data. To do so, fill in the access forms on the specific datasets' pages:
* Common Voice: https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0
* GigaSpeech: https://huggingface.co/datasets/speechcolab/gigaspeech
* SPGISpeech: https://huggingface.co/datasets/kensho/spgispeech
## LibriSpeech
The LibriSpeech corpus is a standard large-scale corpus for assessing ASR systems. It consists of approximately 1,000 hours of narrated audiobooks from the [LibriVox](https://librivox.org) project. It is licensed under CC-BY-4.0.
Example Usage:
```python
librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech")
```
Train/validation splits:
- `train` (combination of `train.clean.100`, `train.clean.360` and `train.other.500`)
- `validation.clean`
- `validation.other`
Test splits:
- `test.clean`
- `test.other`
Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument:
```python
librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech", subconfig="clean.100")
```
- `clean.100`: 100 hours of training data from the 'clean' subset
- `clean.360`: 360 hours of training data from the 'clean' subset
- `other.500`: 500 hours of training data from the 'other' subset
## Common Voice
Common Voice is a series of crowd-sourced open-licensed speech datasets where speakers record text from Wikipedia in various languages. The English subset of contains approximately 1,400 hours of audio data from speakers of various nationalities, accents and different recording conditions. It is licensed under CC0-1.0.
Example usage:
```python
common_voice = load_dataset("esc-benchmark/esc-datasets", "common_voice", use_auth_token=True)
```
Training/validation splits:
- `train`
- `validation`
Test splits:
- `test`
## VoxPopuli
VoxPopuli s a large-scale multilingual speech corpus consisting of political data sourced from 2009-2020 European Parliament event recordings. The English subset contains approximately 550 hours of speech largely from non-native English speakers. It is licensed under CC0.
Example usage:
```python
voxpopuli = load_dataset("esc-benchmark/esc-datasets", "voxpopuli")
```
Training/validation splits:
- `train`
- `validation`
Test splits:
- `test`
## TED-LIUM
TED-LIUM consists of English-language TED Talk conference videos covering a range of different cultural, political, and academic topics. It contains approximately 450 hours of transcribed speech data. It is licensed under CC-BY-NC-ND 3.0.
Example usage:
```python
tedlium = load_dataset("esc-benchmark/esc-datasets", "tedlium")
```
Training/validation splits:
- `train`
- `validation`
Test splits:
- `test`
## GigaSpeech
GigaSpeech is a multi-domain English speech recognition corpus created from audiobooks, podcasts and YouTube. We provide the large train set (2,500 hours) and the standard validation and test splits. It is licensed under apache-2.0.
Example usage:
```python
gigaspeech = load_dataset("esc-benchmark/esc-datasets", "gigaspeech", use_auth_token=True)
```
Training/validation splits:
- `train` (`l` subset of training data (2,500 h))
- `validation`
Test splits:
- `test`
Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument:
```python
gigaspeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech", subconfig="xs", use_auth_token=True)
```
- `xs`: extra-small subset of training data (10 h)
- `s`: small subset of training data (250 h)
- `m`: medium subset of training data (1,000 h)
- `xl`: extra-large subset of training data (10,000 h)
## SPGISpeech
SPGISpeech consists of company earnings calls that have been manually transcribed by S&P Global, Inc according to a professional style guide. We provide the large train set (5,000 hours) and the standard validation and test splits. It is licensed under a Kensho user agreement.
Loading the dataset requires authorization.
Example usage:
```python
spgispeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech", use_auth_token=True)
```
Training/validation splits:
- `train` (`l` subset of training data (~5,000 h))
- `validation`
Test splits:
- `test`
Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument:
```python
spgispeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech", subconfig="s", use_auth_token=True)
```
- `s`: small subset of training data (~200 h)
- `m`: medium subset of training data (~1,000 h)
## Earnings-22
Earnings-22 is a 119-hour corpus of English-language earnings calls collected from global companies, with speakers of many different nationalities and accents. It is licensed under CC-BY-SA-4.0.
Example usage:
```python
earnings22 = load_dataset("esc-benchmark/esc-datasets", "earnings22")
```
Training/validation splits:
- `train`
- `validation`
Test splits:
- `test`
## AMI
The AMI Meeting Corpus consists of 100 hours of meeting recordings from multiple recording devices synced to a common timeline. It is licensed under CC-BY-4.0.
Example usage:
```python
ami = load_dataset("esc-benchmark/esc-datasets", "ami")
```
Training/validation splits:
- `train`
- `validation`
Test splits:
- `test`
| esc-benchmark/esc-datasets | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
"source_datasets:original",
"source_datasets:extended|librispeech_asr",
"source_datasets:extended|common_voice",
"language:en",
"license:cc-by-4.0",
"license:apache-2.0",
"license:cc0-1.0",
"license:cc-by-nc-3.0",
"license:other",
"asr",
"benchmark",
"speech",
"esc",
"region:us"
] | 2022-09-28T07:40:04+00:00 | {"annotations_creators": ["expert-generated", "crowdsourced", "machine-generated"], "language_creators": ["crowdsourced", "expert-generated"], "language": ["en"], "license": ["cc-by-4.0", "apache-2.0", "cc0-1.0", "cc-by-nc-3.0", "other"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M", "1M<n<10M"], "source_datasets": ["original", "extended|librispeech_asr", "extended|common_voice"], "task_categories": ["automatic-speech-recognition"], "task_ids": [], "pretty_name": "esc-datasets", "tags": ["asr", "benchmark", "speech", "esc"], "extra_gated_prompt": "Three of the ESC datasets have specific terms of usage that must be agreed to before using the data. \nTo do so, fill in the access forms on the specific datasets' pages:\n * Common Voice: https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0\n * GigaSpeech: https://huggingface.co/datasets/speechcolab/gigaspeech\n * SPGISpeech: https://huggingface.co/datasets/kensho/spgispeech", "extra_gated_fields": {"I hereby confirm that I have registered on the original Common Voice page and agree to not attempt to determine the identity of speakers in the Common Voice dataset": "checkbox", "I hereby confirm that I have accepted the terms of usages on GigaSpeech page": "checkbox", "I hereby confirm that I have accepted the terms of usages on SPGISpeech page": "checkbox"}} | 2022-10-14T13:30:30+00:00 |
70ae446852c18cf146a29082a2acf66e74609cd8 | # dureader
数据来自DuReader-Retreval数据集，这里是[原始地址](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval)。
> 本数据集只用作学术研究使用。如果本仓库涉及侵权行为，会立即删除。 | zyznull/dureader-retrieval-ranking | [
"license:apache-2.0",
"region:us"
] | 2022-09-28T08:00:20+00:00 | {"license": "apache-2.0"} | 2023-01-03T08:05:57+00:00 |
e7da52d27ed5301d1f0f4c7359c04f95befbada5 | mayjestro/LittleHodler | [
"license:c-uda",
"region:us"
] | 2022-09-28T08:30:12+00:00 | {"license": "c-uda"} | 2022-09-28T13:30:31+00:00 |
|
0d792180b9349c544a2ea220de6b72f78611fb17 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-cnn
* Dataset: big_patent
* Config: g
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@jonesdaniel](https://huggingface.co/jonesdaniel) for evaluating this model. | autoevaluate/autoeval-eval-big_patent-g-9d42aa-1581555947 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-28T08:54:38+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["big_patent"], "eval_info": {"task": "summarization", "model": "facebook/bart-large-cnn", "metrics": ["perplexity"], "dataset_name": "big_patent", "dataset_config": "g", "dataset_split": "validation", "col_mapping": {"text": "description", "target": "abstract"}}} | 2022-09-28T10:15:24+00:00 |
c801dc186b40a532c5820b4662570390da90431b | # Dataset Card for "tacred"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://nlp.stanford.edu/projects/tacred](https://nlp.stanford.edu/projects/tacred)
- **Paper:** [Position-aware Attention and Supervised Data Improve Slot Filling](https://aclanthology.org/D17-1004/)
- **Point of Contact:** See [https://nlp.stanford.edu/projects/tacred/](https://nlp.stanford.edu/projects/tacred/)
- **Size of downloaded dataset files:** 62.3 MB
- **Size of the generated dataset:** 139.2 MB
- **Total amount of disk used:** 201.5 MB
### Dataset Summary
The TAC Relation Extraction Dataset (TACRED) is a large-scale relation extraction dataset with 106,264 examples built over newswire and web text from the corpus used in the yearly TAC Knowledge Base Population (TAC KBP) challenges. Examples in TACRED cover 41 relation types as used in the TAC KBP challenges (e.g., per:schools_attended
and org:members) or are labeled as no_relation if no defined relation is held. These examples are created by combining available human annotations from the TAC
KBP challenges and crowdsourcing. Please see [Stanford's EMNLP paper](https://nlp.stanford.edu/pubs/zhang2017tacred.pdf), or their [EMNLP slides](https://nlp.stanford.edu/projects/tacred/files/position-emnlp2017.pdf) for full details.
Note:
- There is currently a [label-corrected version](https://github.com/DFKI-NLP/tacrev) of the TACRED dataset, which you should consider using instead of
the original version released in 2017. For more details on this new version, see the [TACRED Revisited paper](https://aclanthology.org/2020.acl-main.142/)
published at ACL 2020.
- There is also a [relabeled and pruned version](https://github.com/gstoica27/Re-TACRED) of the TACRED dataset.
For more details on this new version, see the [Re-TACRED paper](https://arxiv.org/abs/2104.08398)
published at ACL 2020.
This repository provides all three versions of the dataset as BuilderConfigs - `'original'`, `'revisited'` and `'re-tacred'`.
Simply set the `name` parameter in the `load_dataset` method in order to choose a specific version. The original TACRED is loaded per default.
### Supported Tasks and Leaderboards
- **Tasks:** Relation Classification
- **Leaderboards:** [https://paperswithcode.com/sota/relation-extraction-on-tacred](https://paperswithcode.com/sota/relation-extraction-on-tacred)
### Languages
The language in the dataset is English.
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 62.3 MB
- **Size of the generated dataset:** 139.2 MB
- **Total amount of disk used:** 201.5 MB
An example of 'train' looks as follows:
```json
{
"id": "61b3a5c8c9a882dcfcd2",
"docid": "AFP_ENG_20070218.0019.LDC2009T13",
"relation": "org:founded_by",
"token": ["Tom", "Thabane", "resigned", "in", "October", "last", "year", "to", "form", "the", "All", "Basotho", "Convention", "-LRB-", "ABC", "-RRB-", ",", "crossing", "the", "floor", "with", "17", "members", "of", "parliament", ",", "causing", "constitutional", "monarch", "King", "Letsie", "III", "to", "dissolve", "parliament", "and", "call", "the", "snap", "election", "."],
"subj_start": 10,
"subj_end": 13,
"obj_start": 0,
"obj_end": 2,
"subj_type": "ORGANIZATION",
"obj_type": "PERSON",
"stanford_pos": ["NNP", "NNP", "VBD", "IN", "NNP", "JJ", "NN", "TO", "VB", "DT", "DT", "NNP", "NNP", "-LRB-", "NNP", "-RRB-", ",", "VBG", "DT", "NN", "IN", "CD", "NNS", "IN", "NN", ",", "VBG", "JJ", "NN", "NNP", "NNP", "NNP", "TO", "VB", "NN", "CC", "VB", "DT", "NN", "NN", "."],
"stanford_ner": ["PERSON", "PERSON", "O", "O", "DATE", "DATE", "DATE", "O", "O", "O", "O", "O", "O", "O", "ORGANIZATION", "O", "O", "O", "O", "O", "O", "NUMBER", "O", "O", "O", "O", "O", "O", "O", "O", "PERSON", "PERSON", "O", "O", "O", "O", "O", "O", "O", "O", "O"],
"stanford_head": [2, 3, 0, 5, 3, 7, 3, 9, 3, 13, 13, 13, 9, 15, 13, 15, 3, 3, 20, 18, 23, 23, 18, 25, 23, 3, 3, 32, 32, 32, 32, 27, 34, 27, 34, 34, 34, 40, 40, 37, 3],
"stanford_deprel": ["compound", "nsubj", "ROOT", "case", "nmod", "amod", "nmod:tmod", "mark", "xcomp", "det", "compound", "compound", "dobj", "punct", "appos", "punct", "punct", "xcomp", "det", "dobj", "case", "nummod", "nmod", "case", "nmod", "punct", "xcomp", "amod", "compound", "compound", "compound", "dobj", "mark", "xcomp", "dobj", "cc", "conj", "det", "compound", "dobj", "punct"]
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: the instance id of this sentence, a `string` feature.
- `docid`: the TAC KBP document id of this sentence, a `string` feature.
- `token`: the list of tokens of this sentence, obtained with the StanfordNLP toolkit, a `list` of `string` features.
- `relation`: the relation label of this instance, a `string` classification label.
- `subj_start`: the 0-based index of the start token of the relation subject mention, an `ìnt` feature.
- `subj_end`: the 0-based index of the end token of the relation subject mention, exclusive, an `ìnt` feature.
- `subj_type`: the NER type of the subject mention, among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `string` feature.
- `obj_start`: the 0-based index of the start token of the relation object mention, an `ìnt` feature.
- `obj_end`: the 0-based index of the end token of the relation object mention, exclusive, an `ìnt` feature.
- `obj_type`: the NER type of the object mention, among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `string` feature.
- `stanford_pos`: the part-of-speech tag per token. the NER type of the subject mention, among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `list` of `string` features.
- `stanford_ner`: the NER tags of tokens (IO-Scheme), among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `list` of `string` features.
- `stanford_deprel`: the Stanford dependency relation tag per token, a `list` of `string` features.
- `stanford_head`: the head (source) token index (0-based) for the dependency relation per token. The root token has a head index of -1, a `list` of `int` features.
### Data Splits
To miminize dataset bias, TACRED is stratified across years in which the TAC KBP challenge was run:
| | Train | Dev | Test |
| ----- | ------ | ----- | ---- |
| TACRED | 68,124 (TAC KBP 2009-2012) | 22,631 (TAC KBP 2013) | 15,509 (TAC KBP 2014) |
| Re-TACRED | 58,465 (TAC KBP 2009-2012) | 19,584 (TAC KBP 2013) | 13,418 (TAC KBP 2014) |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
See the Stanford paper and the Tacred Revisited paper, plus their appendices.
To ensure that models trained on TACRED are not biased towards predicting false positives on real-world text,
all sampled sentences where no relation was found between the mention pairs were fully annotated to be negative examples. As a result, 79.5% of the examples
are labeled as no_relation.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
To respect the copyright of the underlying TAC KBP corpus, TACRED is released via the
Linguistic Data Consortium ([LDC License](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf)).
You can download TACRED from the [LDC TACRED webpage](https://catalog.ldc.upenn.edu/LDC2018T24).
If you are an LDC member, the access will be free; otherwise, an access fee of $25 is needed.
### Citation Information
The original dataset:
```
@inproceedings{zhang2017tacred,
author = {Zhang, Yuhao and Zhong, Victor and Chen, Danqi and Angeli, Gabor and Manning, Christopher D.},
booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)},
title = {Position-aware Attention and Supervised Data Improve Slot Filling},
url = {https://nlp.stanford.edu/pubs/zhang2017tacred.pdf},
pages = {35--45},
year = {2017}
}
```
For the revised version (`"revisited"`), please also cite:
```
@inproceedings{alt-etal-2020-tacred,
title = "{TACRED} Revisited: A Thorough Evaluation of the {TACRED} Relation Extraction Task",
author = "Alt, Christoph and
Gabryszak, Aleksandra and
Hennig, Leonhard",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.142",
doi = "10.18653/v1/2020.acl-main.142",
pages = "1558--1569",
}
```
For the relabeled version (`"re-tacred"`), please also cite:
```
@inproceedings{DBLP:conf/aaai/StoicaPP21,
author = {George Stoica and
Emmanouil Antonios Platanios and
Barnab{\'{a}}s P{\'{o}}czos},
title = {Re-TACRED: Addressing Shortcomings of the {TACRED} Dataset},
booktitle = {Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI}
2021, Thirty-Third Conference on Innovative Applications of Artificial
Intelligence, {IAAI} 2021, The Eleventh Symposium on Educational Advances
in Artificial Intelligence, {EAAI} 2021, Virtual Event, February 2-9,
2021},
pages = {13843--13850},
publisher = {{AAAI} Press},
year = {2021},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/17631},
}
```
### Contributions
Thanks to [@dfki-nlp](https://github.com/dfki-nlp) and [@phucdev](https://github.com/phucdev) for adding this dataset.
| DFKI-SLT/tacred | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other",
"language:en",
"license:other",
"relation extraction",
"arxiv:2104.08398",
"region:us"
] | 2022-09-28T09:02:34+00:00 | {"annotations_creators": ["crowdsourced", "expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended|other"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "pretty_name": "The TAC Relation Extraction Dataset, TACRED Revisited and Re-TACRED", "tags": ["relation extraction"]} | 2023-05-17T11:55:00+00:00 |
812fc620b27eb25e0a3b85699e631e01e407c7dd | awkwardneutrino/daniellismore-01 | [
"license:openrail",
"region:us"
] | 2022-09-28T09:14:03+00:00 | {"license": "openrail"} | 2022-09-28T09:25:12+00:00 |
|
c385a6a9a7c200cde48d6b7ed171e9187db8c99a | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- other
multilinguality:
- monolingual
pretty_name: disTD
task_categories:
- token-classification
task_ids:
- disfluency-detection
dataset_info:
features:
- name: tokens
sequence: string
- name: isDisf
sequence:
class_label:
names:
0: O
1: B_RM
2: I_RM
3: B_RP
4: I_RP
5: IP
config_name: disTD
# Dataset Card for myds
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
dataset for Tunisian dialect
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
tuanisian arabic dialect
## Dataset Structure
### Data Instances
Size of downloaded dataset files: 4.63 MB
Size of the generated dataset: 9.78 MB
Total amount of disk used: 14.41 MB
### Data Fields
dsfsergrth
### Data Splits
rtsert
## Dataset Creation
### Curation Rationale
link
### Source Data
#### Initial Data Collection and Normalization
kink
#### Who are the source language producers?
link
### Annotations
#### Annotation process
tool
#### Who are the annotators?
me
### Personal and Sensitive Information
ok
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] | EmnaBou/tokenDS | [
"region:us"
] | 2022-09-28T10:34:05+00:00 | {} | 2022-11-30T11:32:39+00:00 |
91e996a3d990bddbd4c554f54ebe821afc978fb9 |
# UD_Catalan-AnCora
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Website:** https://github.com/UniversalDependencies/UD_Catalan-AnCora
- **Point of Contact:** [Daniel Zeman]([email protected])
### Dataset Summary
This dataset is composed of the annotations from the [AnCora corpus](http://clic.ub.edu/corpus/), projected on the [Universal Dependencies treebank](https://universaldependencies.org/). We use the POS annotations of this corpus as part of the [Catalan Language Understanding Benchmark (CLUB)](https://club.aina.bsc.es/).
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">CC Attribution 4.0 International License</a>.
### Supported Tasks and Leaderboards
POS tagging
### Languages
The dataset is in Catalan (`ca-ES`)
## Dataset Structure
### Data Instances
Three conllu files.
Annotations are encoded in plain text files (UTF-8, normalized to NFC, using only the LF character as line break, including an LF character at the end of file) with three types of lines:
1) Word lines containing the annotation of a word/token in 10 fields separated by single tab characters (see below).
2) Blank lines marking sentence boundaries.
3) Comment lines starting with hash (#).
### Data Fields
Word lines contain the following fields:
1) ID: Word index, integer starting at 1 for each new sentence; may be a range for multiword tokens; may be a decimal number for empty nodes (decimal numbers can be lower than 1 but must be greater than 0).
2) FORM: Word form or punctuation symbol.
3) LEMMA: Lemma or stem of word form.
4) UPOS: Universal part-of-speech tag.
5) XPOS: Language-specific part-of-speech tag; underscore if not available.
6) FEATS: List of morphological features from the universal feature inventory or from a defined language-specific extension; underscore if not available.
7) HEAD: Head of the current word, which is either a value of ID or zero (0).
8) DEPREL: Universal dependency relation to the HEAD (root iff HEAD = 0) or a defined language-specific subtype of one.
9) DEPS: Enhanced dependency graph in the form of a list of head-deprel pairs.
10) MISC: Any other annotation.
From: [https://universaldependencies.org](https://universaldependencies.org/guidelines.html)
### Data Splits
- ca_ancora-ud-train.conllu
- ca_ancora-ud-dev.conllu
- ca_ancora-ud-test.conllu
## Dataset Creation
### Curation Rationale
[N/A]
### Source Data
- [UD_Catalan-AnCora](https://github.com/UniversalDependencies/UD_Catalan-AnCora)
#### Initial Data Collection and Normalization
The original annotation was done in a constituency framework as a part of the [AnCora project](http://clic.ub.edu/corpus/) at the University of Barcelona. It was converted to dependencies by the [Universal Dependencies team](https://universaldependencies.org/) and used in the CoNLL 2009 shared task. The CoNLL 2009 version was later converted to HamleDT and to Universal Dependencies.
For more information on the AnCora project, visit the [AnCora site](http://clic.ub.edu/corpus/).
To learn about the Universal Dependences, visit the webpage [https://universaldependencies.org](https://universaldependencies.org)
#### Who are the source language producers?
For more information on the AnCora corpus and its sources, visit the [AnCora site](http://clic.ub.edu/corpus/).
### Annotations
#### Annotation process
For more information on the first AnCora annotation, visit the [AnCora site](http://clic.ub.edu/corpus/).
#### Who are the annotators?
For more information on the AnCora annotation team, visit the [AnCora site](http://clic.ub.edu/corpus/).
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
### Licensing Information
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">CC Attribution 4.0 International License</a>.
### Citation Information
The following paper must be cited when using this corpus:
Taulé, M., M.A. Martí, M. Recasens (2008) 'Ancora: Multilevel Annotated Corpora for Catalan and Spanish', Proceedings of 6th International Conference on Language Resources and Evaluation. Marrakesh (Morocco).
To cite the Universal Dependencies project:
Rueter, J. (Creator), Erina, O. (Contributor), Klementeva, J. (Contributor), Ryabov, I. (Contributor), Tyers, F. M. (Contributor), Zeman, D. (Contributor), Nivre, J. (Creator) (15 Nov 2020). Universal Dependencies version 2.7 Erzya JR. Universal Dependencies Consortium.
| projecte-aina/UD_Catalan-AnCora | [
"task_categories:token-classification",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"language:ca",
"license:cc-by-4.0",
"region:us"
] | 2022-09-28T10:51:06+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ca"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": [], "source_datasets": [], "task_categories": ["token-classification"], "task_ids": ["part-of-speech"], "pretty_name": "UD_Catalan-AnCora", "tags": []} | 2023-11-25T06:31:40+00:00 |
8a5e23f6ffbd1b55efaf0ffe6322f985fe859bf2 |
# Dataset Card for xP3
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/bigscience-workshop/xmtf
- **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
- **Point of Contact:** [Niklas Muennighoff](mailto:[email protected])
### Dataset Summary
> xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot.
- **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3). We provide this version to save processing time and ease reproducibility.
- **Languages:** 46 (Can be extended by [recreating with more splits](https://github.com/bigscience-workshop/xmtf#create-xp3))
- **xP3 Dataset Family:**
<table>
<tr>
<th>Name</th>
<th>Explanation</th>
<th>Example models</th>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/Muennighoff/xP3x>xP3x</a></t>
<td>Mixture of 17 tasks in 277 languages with English prompts</td>
<td>WIP - Join us at Project Aya @<a href=https://cohere.for.ai/>C4AI</a> to help!</td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t>
<td>Mixture of 13 training tasks in 46 languages with English prompts</td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t>
<td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td>
<td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t>
<td>xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td>
<td></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t>
<td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t>
<td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td>
<td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td>
</tr>
</table>
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"inputs": "Oración 1: Fue académico en literatura metafísica, teología y ciencias clásicas.\Oración 2: Fue académico en literatura metafísica, teología y ciencia clásica.\nPregunta: ¿La oración 1 parafrasea la oración 2? ¿Si o no?",
"targets": "Sí"
}
```
### Data Fields
The data fields are the same among all splits:
- `inputs`: the natural language input fed to the model
- `targets`: the natural language target that the model has to generate
### Data Splits
The below table summarizes sizes per language (computed from the `merged_{lang}.jsonl` files). Due to languages like `tw` only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage. We machine-translated prompts for monolingual datasets, thus languages with only crosslingual datasets (e.g. Translation) do not have non-English prompts. Languages without non-English prompts are equivalent to [xP3](https://huggingface.co/datasets/bigscience/xP3).
|Language|Kilobytes|%|Samples|%|Non-English prompts|
|--------|------:|-:|---:|-:|-:|
|tw|106288|0.11|265071|0.33| |
|bm|107056|0.11|265180|0.33| |
|ak|108096|0.11|265071|0.33| |
|ca|110608|0.11|271191|0.34| |
|eu|113008|0.12|281199|0.35| |
|fon|113072|0.12|265063|0.33| |
|st|114080|0.12|265063|0.33| |
|ki|115040|0.12|265180|0.33| |
|tum|116032|0.12|265063|0.33| |
|wo|122560|0.13|365063|0.46| |
|ln|126304|0.13|365060|0.46| |
|as|156256|0.16|265063|0.33| |
|or|161472|0.17|265063|0.33| |
|kn|165456|0.17|265063|0.33| |
|ml|175040|0.18|265864|0.33| |
|rn|192992|0.2|318189|0.4| |
|nso|229712|0.24|915051|1.14| |
|tn|235536|0.24|915054|1.14| |
|lg|235936|0.24|915021|1.14| |
|rw|249360|0.26|915043|1.14| |
|ts|250256|0.26|915044|1.14| |
|sn|252496|0.26|865056|1.08| |
|xh|254672|0.26|915058|1.14| |
|zu|263712|0.27|915061|1.14| |
|ny|272128|0.28|915063|1.14| |
|ig|325440|0.33|950097|1.19|✅|
|yo|339664|0.35|913021|1.14|✅|
|ne|398144|0.41|315754|0.39|✅|
|pa|529632|0.55|339210|0.42|✅|
|sw|561392|0.58|1114439|1.39|✅|
|gu|566576|0.58|347499|0.43|✅|
|mr|674000|0.69|417269|0.52|✅|
|bn|854864|0.88|428725|0.54|✅|
|ta|943440|0.97|410633|0.51|✅|
|te|1384016|1.42|573354|0.72|✅|
|ur|1944416|2.0|855756|1.07|✅|
|vi|3113184|3.2|1667306|2.08|✅|
|code|4330752|4.46|2707724|3.38| |
|hi|4469712|4.6|1543441|1.93|✅|
|id|4538768|4.67|2582272|3.22|✅|
|zh|4604112|4.74|3571636|4.46|✅|
|ar|4703968|4.84|2148970|2.68|✅|
|fr|5558912|5.72|5055942|6.31|✅|
|pt|6130016|6.31|3562772|4.45|✅|
|es|7579424|7.8|5151349|6.43|✅|
|en|39252528|40.4|32740750|40.87| |
|total|97150128|100.0|80100816|100.0|✅|
## Dataset Creation
### Source Data
#### Training datasets
- Code Miscellaneous
- [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex)
- [Docstring Corpus](https://huggingface.co/datasets/teven/code_docstring_corpus)
- [GreatCode](https://huggingface.co/datasets/great_code)
- [State Changes](https://huggingface.co/datasets/Fraser/python-state-changes)
- Closed-book QA
- [Hotpot QA](https://huggingface.co/datasets/hotpot_qa)
- [Trivia QA](https://huggingface.co/datasets/trivia_qa)
- [Web Questions](https://huggingface.co/datasets/web_questions)
- [Wiki QA](https://huggingface.co/datasets/wiki_qa)
- Extractive QA
- [Adversarial QA](https://huggingface.co/datasets/adversarial_qa)
- [CMRC2018](https://huggingface.co/datasets/cmrc2018)
- [DRCD](https://huggingface.co/datasets/clue)
- [DuoRC](https://huggingface.co/datasets/duorc)
- [MLQA](https://huggingface.co/datasets/mlqa)
- [Quoref](https://huggingface.co/datasets/quoref)
- [ReCoRD](https://huggingface.co/datasets/super_glue)
- [ROPES](https://huggingface.co/datasets/ropes)
- [SQuAD v2](https://huggingface.co/datasets/squad_v2)
- [xQuAD](https://huggingface.co/datasets/xquad)
- TyDI QA
- [Primary](https://huggingface.co/datasets/khalidalt/tydiqa-primary)
- [Goldp](https://huggingface.co/datasets/khalidalt/tydiqa-goldp)
- Multiple-Choice QA
- [ARC](https://huggingface.co/datasets/ai2_arc)
- [C3](https://huggingface.co/datasets/c3)
- [CoS-E](https://huggingface.co/datasets/cos_e)
- [Cosmos](https://huggingface.co/datasets/cosmos)
- [DREAM](https://huggingface.co/datasets/dream)
- [MultiRC](https://huggingface.co/datasets/super_glue)
- [OpenBookQA](https://huggingface.co/datasets/openbookqa)
- [PiQA](https://huggingface.co/datasets/piqa)
- [QUAIL](https://huggingface.co/datasets/quail)
- [QuaRel](https://huggingface.co/datasets/quarel)
- [QuaRTz](https://huggingface.co/datasets/quartz)
- [QASC](https://huggingface.co/datasets/qasc)
- [RACE](https://huggingface.co/datasets/race)
- [SciQ](https://huggingface.co/datasets/sciq)
- [Social IQA](https://huggingface.co/datasets/social_i_qa)
- [Wiki Hop](https://huggingface.co/datasets/wiki_hop)
- [WiQA](https://huggingface.co/datasets/wiqa)
- Paraphrase Identification
- [MRPC](https://huggingface.co/datasets/super_glue)
- [PAWS](https://huggingface.co/datasets/paws)
- [PAWS-X](https://huggingface.co/datasets/paws-x)
- [QQP](https://huggingface.co/datasets/qqp)
- Program Synthesis
- [APPS](https://huggingface.co/datasets/codeparrot/apps)
- [CodeContests](https://huggingface.co/datasets/teven/code_contests)
- [JupyterCodePairs](https://huggingface.co/datasets/codeparrot/github-jupyter-text-code-pairs)
- [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp)
- [NeuralCodeSearch](https://huggingface.co/datasets/neural_code_search)
- [XLCoST](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code)
- Structure-to-text
- [Common Gen](https://huggingface.co/datasets/common_gen)
- [Wiki Bio](https://huggingface.co/datasets/wiki_bio)
- Sentiment
- [Amazon](https://huggingface.co/datasets/amazon_polarity)
- [App Reviews](https://huggingface.co/datasets/app_reviews)
- [IMDB](https://huggingface.co/datasets/imdb)
- [Rotten Tomatoes](https://huggingface.co/datasets/rotten_tomatoes)
- [Yelp](https://huggingface.co/datasets/yelp_review_full)
- Simplification
- [BiSECT](https://huggingface.co/datasets/GEM/BiSECT)
- Summarization
- [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail)
- [Gigaword](https://huggingface.co/datasets/gigaword)
- [MultiNews](https://huggingface.co/datasets/multi_news)
- [SamSum](https://huggingface.co/datasets/samsum)
- [Wiki-Lingua](https://huggingface.co/datasets/GEM/wiki_lingua)
- [XLSum](https://huggingface.co/datasets/GEM/xlsum)
- [XSum](https://huggingface.co/datasets/xsum)
- Topic Classification
- [AG News](https://huggingface.co/datasets/ag_news)
- [DBPedia](https://huggingface.co/datasets/dbpedia_14)
- [TNEWS](https://huggingface.co/datasets/clue)
- [TREC](https://huggingface.co/datasets/trec)
- [CSL](https://huggingface.co/datasets/clue)
- Translation
- [Flores-200](https://huggingface.co/datasets/Muennighoff/flores200)
- [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt)
- Word Sense disambiguation
- [WiC](https://huggingface.co/datasets/super_glue)
- [XL-WiC](https://huggingface.co/datasets/pasinit/xlwic)
#### Evaluation datasets (included in [xP3all](https://huggingface.co/datasets/bigscience/xP3all) except for NLI & HumanEval)
- Natural Language Inference (NLI)
- [ANLI](https://huggingface.co/datasets/anli)
- [CB](https://huggingface.co/datasets/super_glue)
- [RTE](https://huggingface.co/datasets/super_glue)
- [XNLI](https://huggingface.co/datasets/xnli)
- Coreference Resolution
- [Winogrande](https://huggingface.co/datasets/winogrande)
- [XWinograd](https://huggingface.co/datasets/Muennighoff/xwinograd)
- Program Synthesis
- [HumanEval](https://huggingface.co/datasets/openai_humaneval)
- Sentence Completion
- [COPA](https://huggingface.co/datasets/super_glue)
- [Story Cloze](https://huggingface.co/datasets/story_cloze)
- [XCOPA](https://huggingface.co/datasets/xcopa)
- [XStoryCloze](https://huggingface.co/datasets/Muennighoff/xstory_cloze)
## Additional Information
### Licensing Information
The dataset is released under Apache 2.0.
### Citation Information
```bibtex
@misc{muennighoff2022crosslingual,
title={Crosslingual Generalization through Multitask Finetuning},
author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel},
year={2022},
eprint={2211.01786},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding many prompts used in this dataset. | bigscience/xP3mt | [
"task_categories:other",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100M<n<1B",
"language:ak",
"language:ar",
"language:as",
"language:bm",
"language:bn",
"language:ca",
"language:code",
"language:en",
"language:es",
"language:eu",
"language:fon",
"language:fr",
"language:gu",
"language:hi",
"language:id",
"language:ig",
"language:ki",
"language:kn",
"language:lg",
"language:ln",
"language:ml",
"language:mr",
"language:ne",
"language:nso",
"language:ny",
"language:or",
"language:pa",
"language:pt",
"language:rn",
"language:rw",
"language:sn",
"language:st",
"language:sw",
"language:ta",
"language:te",
"language:tn",
"language:ts",
"language:tum",
"language:tw",
"language:ur",
"language:vi",
"language:wo",
"language:xh",
"language:yo",
"language:zh",
"language:zu",
"license:apache-2.0",
"arxiv:2211.01786",
"region:us"
] | 2022-09-28T11:36:00+00:00 | {"annotations_creators": ["expert-generated", "crowdsourced"], "language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zu"], "license": ["apache-2.0"], "multilinguality": ["multilingual"], "size_categories": ["100M<n<1B"], "task_categories": ["other"], "pretty_name": "xP3", "programming_language": ["C", "C++", "C#", "Go", "Java", "JavaScript", "Lua", "PHP", "Python", "Ruby", "Rust", "Scala", "TypeScript"]} | 2023-05-30T14:50:57+00:00 |
cf5adcceef86da1cf28e72987026fcabb357a54b | Nielser/minithresh | [
"license:afl-3.0",
"region:us"
] | 2022-09-28T12:25:52+00:00 | {"license": "afl-3.0"} | 2022-09-28T12:37:05+00:00 |
|
8b08f37958afaaf8b6afec45f6aa348167ea777f | sasha/stablediffusionbias | [
"license:cc-by-nc-4.0",
"region:us"
] | 2022-09-28T12:33:54+00:00 | {"license": "cc-by-nc-4.0"} | 2022-09-28T12:33:54+00:00 |
|
c5c81300c6eed75b0c2fba9e702ec21039d9a961 | ankitkupadhyay/XNLI | [
"license:apache-2.0",
"region:us"
] | 2022-09-28T12:47:21+00:00 | {"license": "apache-2.0"} | 2022-09-28T18:27:00+00:00 |
|
dda37a4cbf1f2cee6d752d6bc501f03c53d90317 | OMGSAMUELRBR/Test47236 | [
"license:gpl-3.0",
"region:us"
] | 2022-09-28T14:08:59+00:00 | {"license": "gpl-3.0"} | 2022-09-28T14:08:59+00:00 |
|
097422ac9004c632e11f3a0dcd52fca53226f85d | NobuLuis/zeein | [
"license:other",
"region:us"
] | 2022-09-28T14:18:04+00:00 | {"license": "other"} | 2022-09-28T14:21:04+00:00 |
|
d03aafe26e3255f043e10bdf4d1d098c9f0707d1 | eround/MyFace | [
"region:us"
] | 2022-09-28T14:21:35+00:00 | {} | 2022-09-28T22:24:40+00:00 |
|
ee9293bbaae6d3604d2774b49e2cc93aaa10f585 | macfarrut/macfarrut | [
"license:openrail",
"region:us"
] | 2022-09-28T14:23:47+00:00 | {"license": "openrail"} | 2022-09-28T14:29:14+00:00 |
|
67141dfcd78fdce1b716624fe853988f3997b3de | MrContext/DREAMCONTEXT | [
"region:us"
] | 2022-09-28T14:35:34+00:00 | {} | 2022-09-28T14:54:13+00:00 |
|
38849a0521e548dd30f944f0e09f1799edf90415 | semiller206/semiller206 | [
"license:openrail",
"region:us"
] | 2022-09-28T14:47:30+00:00 | {"license": "openrail"} | 2022-09-30T19:01:06+00:00 |
|
cc06d31cd266a978219b212ba00e72eb0ad14d4c | a | CANUTO/images | [
"region:us"
] | 2022-09-28T14:54:45+00:00 | {} | 2022-09-28T15:00:43+00:00 |
da95ff05f257074ed9be9c5706d9570e2f9ae7c2 | fersebas/Fer | [
"region:us"
] | 2022-09-28T15:00:06+00:00 | {} | 2022-10-05T18:10:55+00:00 |
|
4e531582d091467f2f3c4de4e530d0f9733314b5 | MrProcastinador/CHOLO | [
"region:us"
] | 2022-09-28T15:07:21+00:00 | {} | 2022-09-28T15:07:58+00:00 |
|
2729379a3f4648fdee939b5e501e3dc2789d27e5 | khalidx199/k199 | [
"license:apache-2.0",
"region:us"
] | 2022-09-28T15:47:43+00:00 | {"license": "apache-2.0"} | 2022-09-28T15:49:21+00:00 |
|
e85d8a286079ca576ea7d8820dfd0f20f57dbef5 | almost/test | [
"license:afl-3.0",
"region:us"
] | 2022-09-28T15:51:34+00:00 | {"license": "afl-3.0"} | 2022-09-28T15:51:34+00:00 |
|
74c2e9f15ecd969d74ae3f82749c26d10268190a | PCScreen/Thomaz_Junior | [
"license:unknown",
"region:us"
] | 2022-09-28T15:54:08+00:00 | {"license": "unknown"} | 2022-09-28T15:57:51+00:00 |
|
e38cf8f0d16cdefbe65415f8173812f68b24108f | kashif/tourism-monthly-batch | [
"license:cc",
"region:us"
] | 2022-09-28T16:08:10+00:00 | {"license": "cc"} | 2022-09-28T16:29:04+00:00 |
|
ed89518500ea14c7cf8208d1e82f16bf5abdd07c | alx-ai/noggles_inversion | [
"license:cc0-1.0",
"region:us"
] | 2022-09-28T16:28:06+00:00 | {"license": "cc0-1.0"} | 2022-09-28T16:30:23+00:00 |
|
d0a11f31e2c40f1da8060c3377289514669606d6 | marcosfevre/images | [
"license:cc-by-4.0",
"region:us"
] | 2022-09-28T16:59:45+00:00 | {"license": "cc-by-4.0"} | 2022-09-28T18:42:07+00:00 |
|
d965544df7c29b63d21cd188684998673e726467 | CarlosMachucaFotografia/Imagenesmias | [
"region:us"
] | 2022-09-28T17:26:34+00:00 | {} | 2022-09-28T17:38:45+00:00 |
|
9a76277bcbb403d82f84201035723d3d7bd600c7 | JosephEudave/Stabledifussion-dreambooth | [
"license:other",
"region:us"
] | 2022-09-28T17:34:33+00:00 | {"license": "other"} | 2022-09-28T18:21:08+00:00 |
|
42b703eeb2f8b004158d0cb88752aaeca90eb439 | jurer/farias | [
"license:cc-by-4.0",
"region:us"
] | 2022-09-28T17:41:45+00:00 | {"license": "cc-by-4.0"} | 2022-09-28T17:51:07+00:00 |
|
e91596d78fb16f41a5b993e2db7d4345bca01d77 | #Training IA Model
Here are the images that i used to train an a SD model with "tiomonkey" concept | EltioMonkey/MonkeyTrain | [
"region:us"
] | 2022-09-28T17:52:43+00:00 | {} | 2022-09-29T16:44:43+00:00 |
d23b094346c5dbda1080a74bb2a24c18adbf7409 |
# Dataset Card for MultiPL-E
## Dataset Description
- **Homepage:** https://nuprl.github.io/MultiPL-E/
- **Repository:** https://github.com/nuprl/MultiPL-E
- **Paper:** https://ieeexplore.ieee.org/abstract/document/10103177
- **Point of Contact:** [email protected], [email protected], [email protected]
## Dataset Summary
MultiPL-E is a dataset for evaluating large language models for code
generation that supports 18 programming languages. It takes the OpenAI
"HumanEval" and the MBPP Python benchmarks and uses little compilers to
translate them to other languages. It is easy to add support for new languages
and benchmarks.
## Subsets
For most purposes, you should use the variations called *SRCDATA-LANG*, where
*SRCDATA* is either "humaneval" or "mbpp" and *LANG* is one of the supported
languages. We use the canonical file extension for each language to identify
the language, e.g., "py" for Python, "cpp" for C++, "lua" for Lua, and so on.
We also provide a few other variations:
- *SRCDATA-LANG-keep* is the same as *SRCDATA-LANG*, but the text of the prompt
is totally unchanged. If the original prompt had Python doctests, they remain
as Python instead of being translated to *LANG*. If the original prompt had
Python-specific terminology, e.g., "list", it remains "list", instead of
being translated, e.g., to "vector" for C++.
- *SRCDATA-LANG-transform* transforms the doctests to *LANG* but leaves
the natural language text of the prompt unchanged.
- *SRCDATA-LANG-removed* removes the doctests from the prompt.
Note that MBPP does not have any doctests, so the "removed" and "transform"
variations are not available for MBPP.
## Example
The following script uses the Salesforce/codegen model to generate Lua
and MultiPL-E to produce a script with unit tests for luaunit.
```python
import datasets
from transformers import AutoTokenizer, AutoModelForCausalLM
LANG = "lua"
MODEL_NAME = "Salesforce/codegen-350M-multi"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).half().cuda()
problems = datasets.load_dataset("nuprl/MultiPL-E", f"humaneval-{LANG}")
def stop_at_stop_token(decoded_string, problem):
"""
Truncates the output at stop tokens, taking care to skip the prompt
which may have stop tokens.
"""
min_stop_index = len(decoded_string)
for stop_token in problem["stop_tokens"]:
stop_index = decoded_string.find(stop_token)
if stop_index != -1 and stop_index > len(problem["prompt"]) and stop_index < min_stop_index:
min_stop_index = stop_index
return decoded_string[:min_stop_index]
for problem in problems["test"]:
input_ids = tokenizer(
problem["prompt"],
return_tensors="pt",
).input_ids.cuda()
generated_ids = model.generate(
input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id + 2
)
truncated_string = stop_at_stop_token(tokenizer.decode(generated_ids[0]), problem)
filename = problem["name"] + "." + LANG
with open(filename, "w") as f:
print(f"Created {filename}")
f.write(truncated_string)
f.write("\n")
f.write(problem["tests"])
``` | nuprl/MultiPL-E | [
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"source_datasets:extended|openai_humaneval",
"source_datasets:extended|mbpp",
"language:en",
"license:mit",
"region:us"
] | 2022-09-28T18:20:07+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated", "expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original", "extended|openai_humaneval", "extended|mbpp"], "task_categories": [], "task_ids": [], "pretty_name": "MultiPLE-E", "tags": [], "dataset_info": [{"config_name": "cpp-keep", "features": [{"name": "name", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "doctests", "dtype": "string"}, {"name": "original", "dtype": "string"}, {"name": "prompt_terminology", "dtype": "string"}, {"name": "tests", "dtype": "string"}, {"name": "stop_tokens", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 217792, "num_examples": 161}], "download_size": 248493, "dataset_size": 217792}, {"config_name": "cpp-transform", "features": [{"name": "name", "dtype": 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34326d1ee26cafea5e2ac83b0f3b5308de2077c0 | bastiankase/dianakreuz | [
"license:openrail",
"region:us"
] | 2022-09-28T18:38:10+00:00 | {"license": "openrail"} | 2022-09-29T17:07:05+00:00 |
|
53f065e69993fb412774efb69e933fec782970e4 | LuisPerezT/Fotos | [
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"region:us"
] | 2022-09-28T18:42:55+00:00 | {"license": "openrail"} | 2022-09-28T20:27:29+00:00 |
|
cda2e3de3397cb59cb0eed606c2179e780e66663 | Grim421/testing | [
"license:afl-3.0",
"region:us"
] | 2022-09-28T18:51:20+00:00 | {"license": "afl-3.0"} | 2022-09-28T18:51:56+00:00 |
|
5c9e80ea311d9ab56264265b77ed06a1d32bcef0 |
# Cannabis Licenses
<!-- FIXME:
<div align="center" style="text-align:center; margin-top:1rem; margin-bottom: 1rem;">
<img style="max-height:365px;width:100%;max-width:720px;" alt="" src="analysis/figures/cannabis-licenses-map.png">
</div> -->
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Data Collection and Normalization](#data-collection-and-normalization)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [License](#license)
- [Citation](#citation)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** <https://github.com/cannlytics/cannlytics>
- **Repository:** <https://huggingface.co/datasets/cannlytics/cannabis_licenses>
- **Point of Contact:** <[email protected]>
### Dataset Summary
**Cannabis Licenses** is a collection of cannabis license data for each state with permitted adult-use cannabis. The dataset also includes a sub-dataset, `all`, that includes all licenses.
## Dataset Structure
The dataset is partitioned into 18 subsets for each state and the aggregate.
| State | Code | Status |
|-------|------|--------|
| [All](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/all) | `all` | ✅ |
| [Alaska](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ak) | `ak` | ✅ |
| [Arizona](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/az) | `az` | ✅ |
| [California](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ca) | `ca` | ✅ |
| [Colorado](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/co) | `co` | ✅ |
| [Connecticut](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ct) | `ct` | ✅ |
| [Delaware](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/de) | `md` | ✅ |
| [Illinois](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/il) | `il` | ✅ |
| [Maine](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/me) | `me` | ✅ |
| [Maryland](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/md) | `md` | ✅ |
| [Massachusetts](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ma) | `ma` | ✅ |
| [Michigan](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/mi) | `mi` | ✅ |
| [Missouri](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/mo) | `mo` | ✅ |
| [Montana](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/mt) | `mt` | ✅ |
| [Nevada](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/nv) | `nv` | ✅ |
| [New Jersey](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/nj) | `nj` | ✅ |
| [New Mexico](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/nm) | `nm` | ✅ |
| [New York](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ny) | `ny` | ✅ |
| [Oregon](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/or) | `or` | ✅ |
| [Rhode Island](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ri) | `ri` | ✅ |
| [Vermont](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/vt) | `vt` | ✅ |
| Virginia | `va` | ⏳ Expected 2024 |
| [Washington](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/wa) | `wa` | ✅ |
The following states have issued medical cannabis licenses, but are not (yet) included in the dataset:
- Alabama
- Arkansas
- District of Columbia (D.C.)
- Florida
- Kentucky (2024)
- Louisiana
- Minnesota
- Mississippi
- New Hampshire
- North Dakota
- Ohio
- Oklahoma
- Pennsylvania
- South Dakota
- Utah
- West Virginia
### Data Instances
You can load the licenses for each state. For example:
```py
from datasets import load_dataset
# Get the licenses for a specific state.
dataset = load_dataset('cannlytics/cannabis_licenses', 'all')
data = dataset['data']
```
### Data Fields
Below is a non-exhaustive list of fields, used to standardize the various data that are encountered, that you may expect to find for each observation.
| Field | Example | Description |
|-------|-----|-------------|
| `id` | `"1046"` | A state-unique ID for the license. |
| `license_number` | `"C10-0000423-LIC"` | A unique license number. |
| `license_status` | `"Active"` | The status of the license. Only licenses that are active are included. |
| `license_status_date` | `"2022-04-20T00:00"` | The date the status was assigned, an ISO-formatted date if present. |
| `license_term` | `"Provisional"` | The term for the license. |
| `license_type` | `"Commercial - Retailer"` | The type of business license. |
| `license_designation` | `"Adult-Use and Medicinal"` | A state-specific classification for the license. |
| `issue_date` | `"2019-07-15T00:00:00"` | An issue date for the license, an ISO-formatted date if present. |
| `expiration_date` | `"2023-07-14T00:00:00"` | An expiration date for the license, an ISO-formatted date if present. |
| `licensing_authority_id` | `"BCC"` | A unique ID for the state licensing authority. |
| `licensing_authority` | `"Bureau of Cannabis Control (BCC)"` | The state licensing authority. |
| `business_legal_name` | `"Movocan"` | The legal name of the business that owns the license. |
| `business_dba_name` | `"Movocan"` | The name the license is doing business as. |
| `business_owner_name` | `"redacted"` | The name of the owner of the license. |
| `business_structure` | `"Corporation"` | The structure of the business that owns the license. |
| `activity` | `"Pending Inspection"` | Any relevant license activity. |
| `premise_street_address` | `"1632 Gateway Rd"` | The street address of the business. |
| `premise_city` | `"Calexico"` | The city of the business. |
| `premise_state` | `"CA"` | The state abbreviation of the business. |
| `premise_county` | `"Imperial"` | The county of the business. |
| `premise_zip_code` | `"92231"` | The zip code of the business. |
| `business_email` | `"[email protected]"` | The business email of the license. |
| `business_phone` | `"(555) 555-5555"` | The business phone of the license. |
| `business_website` | `"cannlytics.com"` | The business website of the license. |
| `parcel_number` | `"A42"` | An ID for the business location. |
| `premise_latitude` | `32.69035693` | The latitude of the business. |
| `premise_longitude` | `-115.38987552` | The longitude of the business. |
| `data_refreshed_date` | `"2022-09-21T12:16:33.3866667"` | An ISO-formatted time when the license data was updated. |
### Data Splits
The data is split into subsets by state. You can retrieve all licenses by requesting the `all` subset.
```py
from datasets import load_dataset
# Get all cannabis licenses.
dataset = load_dataset('cannlytics/cannabis_licenses', 'all')
data = dataset['data']
```
## Dataset Creation
### Curation Rationale
Data about organizations operating in the cannabis industry for each state is valuable for research.
### Source Data
| State | Data Source URL |
|-------|-----------------|
| Alaska | <https://www.commerce.alaska.gov/abc/marijuana/Home/licensesearch> |
| Arizona | <https://azcarecheck.azdhs.gov/s/?licenseType=null> |
| California | <https://search.cannabis.ca.gov/> |
| Colorado | <https://sbg.colorado.gov/med/licensed-facilities> |
| Connecticut | <https://portal.ct.gov/DCP/Medical-Marijuana-Program/Connecticut-Medical-Marijuana-Dispensary-Facilities> |
| Delaware | <https://dhss.delaware.gov/dhss/dph/hsp/medmarcc.html> |
| Illinois | <https://www.idfpr.com/LicenseLookup/AdultUseDispensaries.pdf> |
| Maine | <https://www.maine.gov/dafs/ocp/open-data/adult-use> |
| Maryland | <https://mmcc.maryland.gov/Pages/Dispensaries.aspx> |
| Massachusetts | <https://masscannabiscontrol.com/open-data/data-catalog/> |
| Michigan | <https://michigan.maps.arcgis.com/apps/webappviewer/index.html?id=cd5a1a76daaf470b823a382691c0ff60> |
| Missouri | <https://health.mo.gov/safety/cannabis/licensed-facilities.php> |
| Montana | <https://mtrevenue.gov/cannabis/#CannabisLicenses> |
| Nevada | <https://ccb.nv.gov/list-of-licensees/> |
| New Jersey | <https://data.nj.gov/stories/s/ggm4-mprw> |
| New Mexico | <https://nmrldlpi.force.com/bcd/s/public-search-license?division=CCD&language=en_US> |
| New York | <https://cannabis.ny.gov/licensing> |
| Oregon | <https://www.oregon.gov/olcc/marijuana/pages/recreational-marijuana-licensing.aspx> |
| Rhode Island | <https://dbr.ri.gov/office-cannabis-regulation/compassion-centers/licensed-compassion-centers> |
| Vermont | <https://ccb.vermont.gov/licenses> |
| Washington | <https://lcb.wa.gov/records/frequently-requested-lists> |
### Data Collection and Normalization
In the `algorithms` directory, you can find the algorithms used for data collection. You can use these algorithms to recreate the dataset. First, you will need to clone the repository:
```
git clone https://huggingface.co/datasets/cannlytics/cannabis_licenses
```
You can then install the algorithm Python (3.9+) requirements:
```
cd cannabis_licenses
pip install -r requirements.txt
```
Then you can run all of the data-collection algorithms:
```
python algorithms/main.py
```
Or you can run each algorithm individually. For example:
```
python algorithms/get_licenses_ny.py
```
### Personal and Sensitive Information
This dataset includes names of individuals, public addresses, and contact information for cannabis licensees. It is important to take care to use these data points in a legal manner.
## Considerations for Using the Data
### Social Impact of Dataset
Arguably, there is substantial social impact that could result from the study of permitted adult-use cannabis, therefore, researchers and data consumers alike should take the utmost care in the use of this dataset.
### Discussion of Biases
Cannlytics is a for-profit data and analytics company that primarily serves cannabis businesses. The data are not randomly collected and thus sampling bias should be taken into consideration.
### Other Known Limitations
The data is for adult-use cannabis licenses. It would be valuable to include medical cannabis licenses too.
## Additional Information
### Dataset Curators
Curated by [🔥Cannlytics](https://cannlytics.com)<br>
<[email protected]>
### License
```
Copyright (c) 2022-2023 Cannlytics and the Cannabis Data Science Team
The files associated with this dataset are licensed under a
Creative Commons Attribution 4.0 International license.
You can share, copy and modify this dataset so long as you give
appropriate credit, provide a link to the CC BY license, and
indicate if changes were made, but you may not do so in a way
that suggests the rights holder has endorsed you or your use of
the dataset. Note that further permission may be required for
any content within the dataset that is identified as belonging
to a third party.
```
### Citation
Please cite the following if you use the code examples in your research:
```bibtex
@misc{cannlytics2023,
title={Cannabis Data Science},
author={Skeate, Keegan and O'Sullivan-Sutherland, Candace},
journal={https://github.com/cannlytics/cannabis-data-science},
year={2023}
}
```
### Contributions
Thanks to [🔥Cannlytics](https://cannlytics.com), [@candy-o](https://github.com/candy-o), [@hcadeaux](https://huggingface.co/hcadeaux), [@keeganskeate](https://github.com/keeganskeate), and the entire [Cannabis Data Science Team](https://meetup.com/cannabis-data-science/members) for their contributions.
| cannlytics/cannabis_licenses | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"license:cc-by-4.0",
"cannabis",
"licenses",
"region:us"
] | 2022-09-28T18:52:23+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "license": ["cc-by-4.0"], "pretty_name": "cannabis_licenses", "tags": ["cannabis", "licenses"]} | 2023-09-30T13:23:05+00:00 |
3562204543b81d961ccef05e11e3d69011fe5104 | # ****Dataset Card for tathagata****
# **I-Dataset Summary**
tathagata.txt is a dataset based on summaries of major Buddhist, Hindu and Advaita texts such as:
- Diamond Sutra
- Lankavatara Sutra
- Sri Nisargadatta Maharaj quotes
- Quotes from the Bhagavad Gita
This dataset was used to train this model https://huggingface.co/radm/rugpt3medium-tathagata
# **II-Languages**
The texts in the dataset are in Russian (ru). | radm/tathagata | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:ru",
"license:apache-2.0",
"text_generation",
"quotes",
"region:us"
] | 2022-09-28T18:55:18+00:00 | {"annotations_creators": ["found"], "language_creators": ["found"], "language": ["ru"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "tathagata", "tags": ["text_generation", "quotes"]} | 2022-09-28T19:20:13+00:00 |
bc637e0366cdba0bf5cd9542b4cb6ed819d925b7 | valluvera/gemma | [
"license:other",
"region:us"
] | 2022-09-28T19:01:58+00:00 | {"license": "other"} | 2022-09-28T19:12:34+00:00 |
|
9d61249c9d960863eeefff485280129c7c0b1e44 | bjornsing/PCG-signals | [
"license:cc-by-4.0",
"region:us"
] | 2022-09-28T19:41:44+00:00 | {"license": "cc-by-4.0"} | 2022-09-28T19:44:06+00:00 |
|
c10a50d07a444af455999711419682ae9d6dba15 | thewalkerdenton/Canny | [
"license:apache-2.0",
"region:us"
] | 2022-09-28T19:57:32+00:00 | {"license": "apache-2.0"} | 2022-09-28T20:02:20+00:00 |
|
57e5044606ea180cd495a3c301c25a19fde3d7ff | rousses/imagine | [
"license:other",
"region:us"
] | 2022-09-28T20:46:38+00:00 | {"license": "other"} | 2022-09-28T21:16:15+00:00 |
|
cc27350c690c3bf84e52554a42e7e6af62d917c3 | Franmg/Fotos | [
"region:us"
] | 2022-09-28T21:32:24+00:00 | {} | 2022-09-28T21:37:01+00:00 |
|
1786207ffebfbe62211179fccbd4d0566ace37a9 | This textual inversion has been trained on WaifuDiffusion v1.2 (`[45dee52b]`). This will probably not work well with the standard Stable Diffusion model.
# How to use (with webui)
- create `embeddings` folder in the root directory of the webui
- paste the .bin in there
**keyword: `<marine>`** | cattoroboto/waifudiffusion-marine-textual-inversion | [
"region:us"
] | 2022-09-28T22:30:57+00:00 | {} | 2022-09-28T23:06:45+00:00 |
048a873dc8ee97644ef250ff3e5fdec23e635a68 | AmliArt/face | [
"license:unknown",
"region:us"
] | 2022-09-28T22:42:04+00:00 | {"license": "unknown"} | 2022-09-28T22:55:28+00:00 |
|
774d821c1bb64c62c0eef7204ff19776946d9892 | Jonnyck/myself | [
"license:other",
"region:us"
] | 2022-09-28T22:46:41+00:00 | {"license": "other"} | 2022-09-28T23:14:28+00:00 |
|
ff362105035ab3d6251d4fd0dbb65bb826d3e357 | Limbicnation/pixelart | [
"license:artistic-2.0",
"region:us"
] | 2022-09-28T23:03:03+00:00 | {"license": "artistic-2.0"} | 2022-09-28T23:03:03+00:00 |
|
6fd8ede7dbde80c793cf5a335a3f5ccf431f9890 | JorgeAcevedx/portrait | [
"license:afl-3.0",
"region:us"
] | 2022-09-28T23:17:43+00:00 | {"license": "afl-3.0"} | 2022-09-28T23:17:43+00:00 |
|
0c52d74f1f27559051c13c40bcbdc0ea22e5dac9 | Pitagorak/Yo | [
"license:other",
"region:us"
] | 2022-09-29T00:03:00+00:00 | {"license": "other"} | 2022-10-01T03:21:10+00:00 |
|
337ec38c58a30812c0944d807f5acdc1f86f4bc3 | # Info
> This is a repository for anime regularization. If you wish to contribute to the dataset, contact me at naotsue#9786. I will add them to the dataset and update it.
# Criteria
> 512x512
> No excessive deformations
> Vaguely resembles an anime artstyle
# Contribution Leaderboard
> 1. bWm_nubby: 5838 images
> 2. naotsue: 888 images
 | waifu-research-department/regularization | [
"license:mit",
"region:us"
] | 2022-09-29T01:09:44+00:00 | {"license": "mit"} | 2022-09-29T21:00:10+00:00 |
1b3d125486d7fa6f77402af5339516a157984177 | vfx/dh | [
"region:us"
] | 2022-09-29T01:25:08+00:00 | {} | 2022-09-29T02:03:14+00:00 |
|
501e676071e2bde888b80b52227f0aedc4f82d81 | Brayant115/yo | [
"license:apache-2.0",
"region:us"
] | 2022-09-29T01:47:35+00:00 | {"license": "apache-2.0"} | 2022-09-29T01:47:35+00:00 |
|
f2c96e0553b980a0f6d6660dac79b7c8b2e8b0a7 | Xitari/soyyo | [
"license:artistic-2.0",
"region:us"
] | 2022-09-29T02:20:05+00:00 | {"license": "artistic-2.0"} | 2022-09-29T02:49:31+00:00 |
|
0277cb91dfecc95f779b25bbd9223bc770b276e1 | leizu/face1 | [
"license:openrail",
"region:us"
] | 2022-09-29T03:33:47+00:00 | {"license": "openrail"} | 2022-09-29T03:33:47+00:00 |
|
b4adca9c6281d8076dcd2f1d30d83f991cdca1ec | sudapop/test | [
"license:afl-3.0",
"region:us"
] | 2022-09-29T03:49:58+00:00 | {"license": "afl-3.0"} | 2022-09-29T03:51:38+00:00 |
|
fb9b4efc3c14b039c5012ad7d7de29bca88e4a0b | ruffusplay/ajolote | [
"license:openrail",
"region:us"
] | 2022-09-29T04:27:47+00:00 | {"license": "openrail"} | 2022-09-29T04:27:47+00:00 |
|
3fbc3b7f095455dcbfa990c7cd9840bca953aceb | ruffusplay/ajolote2 | [
"license:openrail",
"region:us"
] | 2022-09-29T04:30:34+00:00 | {"license": "openrail"} | 2022-09-29T04:30:34+00:00 |
|
d821a66d1ea7e1a1c3d0f41d2b214d53af651cde | ruffusplay/ajo | [
"license:c-uda",
"region:us"
] | 2022-09-29T04:31:46+00:00 | {"license": "c-uda"} | 2022-09-29T04:31:46+00:00 |
|
2066097f3c1e270598bdeb8376f45e4d55bfdeb3 | Metalistenia/daniel | [
"license:openrail",
"region:us"
] | 2022-09-29T04:36:11+00:00 | {"license": "openrail"} | 2022-09-29T04:54:18+00:00 |
|
4d7946ef7f0c5ff5e261e384db8015dfe8e417cb |
# Dataset Card for EurlexResources: A Corpus Covering the Largest EURLEX Resources
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** [GitHub](https://github.com/JoelNiklaus/LegalDatasets/tree/main/pretrain/eurlex)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:[email protected])
### Dataset Summary
This dataset contains large text resources (~179GB in total) from EURLEX that can be used for pretraining language models.
Use the dataset like this:
```python
from datasets import load_dataset
config = "de_caselaw" # {lang}_{resource}
dataset = load_dataset("joelito/eurlex_resources", config, split='train', streaming=True)
```
### Supported Tasks and Leaderboards
The dataset supports the task of masked language modeling.
### Languages
The following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv
## Dataset Structure
### Data Instances
The file format is jsonl.xz and there is one split available ("train").
The following resource types are supported: caselaw, decision, directive, intagr, proposal, recommendation, regulation
More information about the resource types can be found here:
- Caselaw: [EU](https://eur-lex.europa.eu/collection/eu-law/eu-case-law.html)
- Decision: [EU](https://eur-lex.europa.eu/EN/legal-content/summary/european-union-decisions.html), [Wikipedia](https://en.wikipedia.org/wiki/Decision_(European_Union))
- Directive: [EU](https://european-union.europa.eu/institutions-law-budget/law/types-legislation_en), [Wikipedia](https://en.wikipedia.org/wiki/Directive_(European_Union))
- Recommendation: [EU](https://eur-lex.europa.eu/EN/legal-content/glossary/recommendation.html), [Wikipedia](https://en.wikipedia.org/wiki/Recommendation_(European_Union))
- Regulation: [EU](https://european-union.europa.eu/institutions-law-budget/law/types-legislation_en), [Wikipedia](https://en.wikipedia.org/wiki/Regulation_(European_Union))
- Intagr: [EU](https://eur-lex.europa.eu/collection/eu-law/inter-agree.html), [Wikipedia](https://en.wikipedia.org/wiki/Treaties_of_the_European_Union)
- Proposal: No resource found
| Source | Size (MB) | Words | Documents | Words/Document |
|:-------------------|------------:|------------:|------------:|-----------------:|
| all_all | 180668 | 12106556233 | 8306749 | 1457 |
| all_caselaw | 34939 | 3413551598 | 2487794 | 1372 |
| all_decision | 28519 | 1698585620 | 1267402 | 1340 |
| all_directive | 4786 | 368577940 | 104187 | 3537 |
| all_intagr | 11421 | 743271516 | 274485 | 2707 |
| all_proposal | 26526 | 2087989530 | 702392 | 2972 |
| all_recommendation | 1886 | 164979037 | 80277 | 2055 |
| all_regulation | 72590 | 3629600992 | 3390212 | 1070 |
| bg_all | 7819 | 398067053 | 348691 | 1141 |
| bg_caselaw | 1588 | 109749174 | 104434 | 1050 |
| bg_decision | 1248 | 58817972 | 54075 | 1087 |
| bg_directive | 263 | 15731608 | 4388 | 3585 |
| bg_intagr | 603 | 31292848 | 11581 | 2702 |
| bg_proposal | 1083 | 60674956 | 29251 | 2074 |
| bg_recommendation | 89 | 5588991 | 3321 | 1682 |
| bg_regulation | 2943 | 116211504 | 141641 | 820 |
| cs_all | 8360 | 471961631 | 449793 | 1049 |
| cs_caselaw | 1163 | 110005022 | 104519 | 1052 |
| cs_decision | 1102 | 58921128 | 54075 | 1089 |
| cs_directive | 186 | 13951134 | 4388 | 3179 |
| cs_intagr | 449 | 28106332 | 11581 | 2426 |
| cs_proposal | 840 | 61838692 | 29252 | 2113 |
| cs_recommendation | 64 | 5416549 | 3323 | 1630 |
| cs_regulation | 4557 | 193722774 | 242655 | 798 |
| da_all | 8932 | 671484862 | 332500 | 2019 |
| da_caselaw | 1746 | 185589641 | 88234 | 2103 |
| da_decision | 1356 | 89498535 | 54085 | 1654 |
| da_directive | 207 | 17525792 | 4388 | 3994 |
| da_intagr | 506 | 35596169 | 11582 | 3073 |
| da_proposal | 1399 | 119759476 | 29257 | 4093 |
| da_recommendation | 100 | 9463897 | 3352 | 2823 |
| da_regulation | 3618 | 214051352 | 141602 | 1511 |
| de_all | 9607 | 695512401 | 348290 | 1996 |
| de_caselaw | 1930 | 193232441 | 104228 | 1853 |
| de_decision | 1449 | 93688222 | 53980 | 1735 |
| de_directive | 218 | 17337760 | 4385 | 3953 |
| de_intagr | 531 | 36791153 | 11580 | 3177 |
| de_proposal | 1556 | 126987454 | 29219 | 4346 |
| de_recommendation | 109 | 9608034 | 3318 | 2895 |
| de_regulation | 3813 | 217867337 | 141580 | 1538 |
| el_all | 12469 | 696216541 | 349667 | 1991 |
| el_caselaw | 2951 | 202027703 | 105138 | 1921 |
| el_decision | 1823 | 94919886 | 54150 | 1752 |
| el_directive | 321 | 19411959 | 4390 | 4421 |
| el_intagr | 701 | 38965777 | 11584 | 3363 |
| el_proposal | 2085 | 128005737 | 29290 | 4370 |
| el_recommendation | 145 | 9344866 | 3357 | 2783 |
| el_regulation | 4443 | 203540613 | 141758 | 1435 |
| en_all | 9217 | 769465561 | 348641 | 2207 |
| en_caselaw | 1846 | 222891827 | 104422 | 2134 |
| en_decision | 1504 | 114626013 | 54054 | 2120 |
| en_directive | 204 | 18860876 | 4388 | 4298 |
| en_intagr | 499 | 39029843 | 11581 | 3370 |
| en_proposal | 1538 | 140781768 | 29242 | 4814 |
| en_recommendation | 97 | 10091809 | 3320 | 3039 |
| en_regulation | 3530 | 223183425 | 141634 | 1575 |
| es_all | 8588 | 725125274 | 348443 | 2081 |
| es_caselaw | 1870 | 220621730 | 104312 | 2115 |
| es_decision | 1334 | 98163499 | 54001 | 1817 |
| es_directive | 221 | 21484479 | 4385 | 4899 |
| es_intagr | 516 | 41841805 | 11581 | 3612 |
| es_proposal | 1366 | 133674486 | 29224 | 4574 |
| es_recommendation | 82 | 8864018 | 3319 | 2670 |
| es_regulation | 3199 | 200475257 | 141621 | 1415 |
| et_all | 6090 | 328068754 | 349615 | 938 |
| et_caselaw | 1074 | 93096396 | 105111 | 885 |
| et_decision | 1069 | 50752324 | 54159 | 937 |
| et_directive | 177 | 11555930 | 4390 | 2632 |
| et_intagr | 436 | 24018147 | 11584 | 2073 |
| et_proposal | 810 | 51600852 | 29283 | 1762 |
| et_recommendation | 61 | 4451369 | 3355 | 1326 |
| et_regulation | 2464 | 92593736 | 141733 | 653 |
| fi_all | 7346 | 404265224 | 349633 | 1156 |
| fi_caselaw | 1596 | 126525296 | 105119 | 1203 |
| fi_decision | 1227 | 59659475 | 54163 | 1101 |
| fi_directive | 204 | 12766491 | 4389 | 2908 |
| fi_intagr | 463 | 25392311 | 11584 | 2192 |
| fi_proposal | 1075 | 69198401 | 29288 | 2362 |
| fi_recommendation | 73 | 5070392 | 3356 | 1510 |
| fi_regulation | 2707 | 105652858 | 141734 | 745 |
| fr_all | 9937 | 828959218 | 348295 | 2380 |
| fr_caselaw | 2158 | 246262666 | 104228 | 2362 |
| fr_decision | 1473 | 108648744 | 53981 | 2012 |
| fr_directive | 222 | 20308801 | 4385 | 4631 |
| fr_intagr | 536 | 41986012 | 11580 | 3625 |
| fr_proposal | 1592 | 149134298 | 29218 | 5104 |
| fr_recommendation | 112 | 11510415 | 3318 | 3469 |
| fr_regulation | 3845 | 251108282 | 141585 | 1773 |
| ga_all | 1028 | 65030095 | 349778 | 185 |
| ga_caselaw | 11 | 696305 | 105205 | 6 |
| ga_decision | 87 | 4415457 | 54189 | 81 |
| ga_directive | 18 | 1512027 | 4390 | 344 |
| ga_intagr | 19 | 1820723 | 11586 | 157 |
| ga_proposal | 289 | 26106889 | 29298 | 891 |
| ga_recommendation | 10 | 902390 | 3361 | 268 |
| ga_regulation | 594 | 29576304 | 141749 | 208 |
| hr_all | 4594 | 258816068 | 348691 | 742 |
| hr_caselaw | 617 | 62432734 | 104434 | 597 |
| hr_decision | 596 | 31911903 | 54075 | 590 |
| hr_directive | 156 | 10855913 | 4388 | 2474 |
| hr_intagr | 450 | 24962086 | 11581 | 2155 |
| hr_proposal | 552 | 33437815 | 29251 | 1143 |
| hr_recommendation | 40 | 3612247 | 3321 | 1087 |
| hr_regulation | 2183 | 91603370 | 141641 | 646 |
| hu_all | 6653 | 375253894 | 349605 | 1073 |
| hu_caselaw | 1278 | 110179375 | 105144 | 1047 |
| hu_decision | 1147 | 57108172 | 54156 | 1054 |
| hu_directive | 200 | 13568304 | 4389 | 3091 |
| hu_intagr | 470 | 27258501 | 11586 | 2352 |
| hu_proposal | 912 | 60882750 | 29291 | 2078 |
| hu_recommendation | 70 | 5312868 | 3357 | 1582 |
| hu_regulation | 2576 | 100943924 | 141682 | 712 |
| it_all | 9586 | 768605772 | 333631 | 2303 |
| it_caselaw | 1889 | 206117726 | 89560 | 2301 |
| it_decision | 1445 | 102848859 | 53983 | 1905 |
| it_directive | 217 | 19687773 | 4385 | 4489 |
| it_intagr | 528 | 40134330 | 11580 | 3465 |
| it_proposal | 1533 | 140713925 | 29218 | 4816 |
| it_recommendation | 109 | 10923431 | 3318 | 3292 |
| it_regulation | 3865 | 248179728 | 141587 | 1752 |
| lt_all | 6400 | 364361783 | 200565 | 1816 |
| lt_caselaw | 1137 | 101808706 | 105477 | 965 |
| lt_decision | 1096 | 55850308 | 21990 | 2539 |
| lt_directive | 185 | 13078983 | 3239 | 4037 |
| lt_intagr | 452 | 27009631 | 7481 | 3610 |
| lt_proposal | 850 | 58553579 | 29272 | 2000 |
| lt_recommendation | 64 | 5121089 | 3363 | 1522 |
| lt_regulation | 2617 | 102939487 | 29743 | 3460 |
| lv_all | 6349 | 363239195 | 349919 | 1038 |
| lv_caselaw | 1153 | 103456811 | 105242 | 983 |
| lv_decision | 1103 | 55512944 | 54224 | 1023 |
| lv_directive | 186 | 13023024 | 4392 | 2965 |
| lv_intagr | 452 | 26693107 | 11630 | 2295 |
| lv_proposal | 96 | 58176216 | 29298 | 1985 |
| lv_recommendation | 64 | 5074494 | 3361 | 1509 |
| lv_regulation | 2545 | 101302599 | 141772 | 714 |
| mt_all | 6540 | 367834815 | 350292 | 1050 |
| mt_caselaw | 1164 | 100423543 | 105479 | 952 |
| mt_decision | 1109 | 55239141 | 54280 | 1017 |
| mt_directive | 203 | 14355266 | 4392 | 3268 |
| mt_intagr | 470 | 27701991 | 11675 | 2372 |
| mt_proposal | 878 | 59749277 | 29274 | 2041 |
| mt_recommendation | 65 | 5039600 | 3363 | 1498 |
| mt_regulation | 2650 | 105325997 | 141829 | 742 |
| nl_all | 9586 | 770312808 | 349407 | 2204 |
| nl_caselaw | 1847 | 206271837 | 105005 | 1964 |
| nl_decision | 1456 | 104060901 | 54152 | 1921 |
| nl_directive | 217 | 19529361 | 4388 | 4450 |
| nl_intagr | 529 | 40247634 | 11584 | 3474 |
| nl_proposal | 1540 | 141258274 | 29279 | 4824 |
| nl_recommendation | 111 | 11002405 | 3355 | 3279 |
| nl_regulation | 3886 | 247942396 | 141644 | 1750 |
| pl_all | 6677 | 406648795 | 350349 | 1160 |
| pl_caselaw | 1231 | 115824759 | 105479 | 1098 |
| pl_decision | 1125 | 60407576 | 54287 | 1112 |
| pl_directive | 197 | 14672157 | 4392 | 3340 |
| pl_intagr | 466 | 28543668 | 11680 | 2443 |
| pl_proposal | 886 | 64728230 | 29317 | 2207 |
| pl_recommendation | 68 | 5769893 | 3363 | 1715 |
| pl_regulation | 2703 | 116702512 | 141831 | 822 |
| pt_all | 8450 | 675152149 | 348449 | 1937 |
| pt_caselaw | 1763 | 198084937 | 104312 | 1898 |
| pt_decision | 1327 | 93278293 | 54007 | 1727 |
| pt_directive | 217 | 19831549 | 4385 | 4522 |
| pt_intagr | 504 | 37999753 | 11581 | 3281 |
| pt_proposal | 1361 | 127461782 | 29224 | 4361 |
| pt_recommendation | 81 | 8396661 | 3319 | 2529 |
| pt_regulation | 3197 | 190099174 | 141621 | 1342 |
| ro_all | 6315 | 415038571 | 350300 | 1184 |
| ro_caselaw | 1110 | 114780999 | 105516 | 1087 |
| ro_decision | 1047 | 59479553 | 54281 | 1095 |
| ro_directive | 206 | 16101628 | 4392 | 3666 |
| ro_intagr | 481 | 31497000 | 11675 | 2697 |
| ro_proposal | 805 | 62130419 | 29274 | 2122 |
| ro_recommendation | 63 | 5977913 | 3363 | 1777 |
| ro_regulation | 2603 | 125071059 | 141799 | 882 |
| sk_all | 6484 | 392235510 | 350570 | 1118 |
| sk_caselaw | 1160 | 110125141 | 105608 | 1042 |
| sk_decision | 1111 | 59576875 | 54349 | 1096 |
| sk_directive | 188 | 14132755 | 4393 | 3217 |
| sk_intagr | 458 | 28298155 | 11676 | 2423 |
| sk_proposal | 859 | 63726047 | 29290 | 2175 |
| sk_recommendation | 66 | 5654790 | 3364 | 1680 |
| sk_regulation | 2642 | 110721747 | 141890 | 780 |
| sl_all | 6222 | 394814289 | 350574 | 1126 |
| sl_caselaw | 1071 | 111238184 | 105608 | 1053 |
| sl_decision | 1075 | 59454906 | 54349 | 1093 |
| sl_directive | 176 | 13908097 | 4393 | 3165 |
| sl_intagr | 441 | 28239078 | 11676 | 2418 |
| sl_proposal | 812 | 63391970 | 29290 | 2164 |
| sl_recommendation | 62 | 5628775 | 3364 | 1673 |
| sl_regulation | 2585 | 112953279 | 141894 | 796 |
| sv_all | 7419 | 500085970 | 351051 | 1424 |
| sv_caselaw | 1585 | 162108645 | 105980 | 1529 |
| sv_decision | 1213 | 71744934 | 54357 | 1319 |
| sv_directive | 195 | 15386273 | 4393 | 3502 |
| sv_intagr | 463 | 29845462 | 11676 | 2556 |
| sv_proposal | 1059 | 86016237 | 29292 | 2936 |
| sv_recommendation | 79 | 7152141 | 3366 | 2124 |
| sv_regulation | 2825 | 127832278 | 141987 | 900 |
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The data has been downloaded using the R package [eurlex](https://cran.r-project.org/web/packages/eurlex/vignettes/eurlexpkg.html) between June and August 2022.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
[see also the legal notice](https://eur-lex.europa.eu/content/legal-notice/legal-notice.html)
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.
| joelniklaus/eurlex_resources | [
"task_categories:fill-mask",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:ga",
"language:hr",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"license:cc-by-4.0",
"region:us"
] | 2022-09-29T06:35:34+00:00 | {"annotations_creators": ["other"], "language_creators": ["found"], "language": ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["fill-mask"], "pretty_name": "EurlexResources: A Corpus Covering the Largest EURLEX Resources"} | 2023-05-10T07:04:28+00:00 |
a9873510cff4ae717264cf96e403b4ac71548080 | pablohorch/miFaceHorch | [
"region:us"
] | 2022-09-29T06:42:27+00:00 | {} | 2022-09-29T06:42:48+00:00 |
|
b5776b60b9d42f79b41260579d0e7d3420b045ee | Algp123/seansimon | [
"license:cc",
"region:us"
] | 2022-09-29T07:04:40+00:00 | {"license": "cc"} | 2022-09-29T07:06:44+00:00 |
|
e5f041fc5d507821b395ff746d57f97818bd8db1 |
# Dataset Card for Weakly supervised AG News Dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .
The Weakly supervised AG News Dataset was created by Team 44 of FSDL 2022 course with the only purpose of experimenting with weak supervision techniques. It was assumed that only the labels of the original test set and 20% of the training set were available. The labels in the training set were obtained by creating weak labels with LFs and denoising them with Snorkel's label model.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
text: a string feature
label: a classification label, with possible values including World (0), Sports (1), Business (2), Sci/Tech (3).
### Data Splits
- Training set with probabilistic labels from weak supervision: 37340
- Unlabeled data: 58660
- Validation set: 24000
- Test set: 7600
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to Xiang Zhang ([email protected]) for adding this dataset to the HF Dataset Hub. | bergr7/weakly_supervised_ag_news | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|ag_news",
"language:en",
"region:us"
] | 2022-09-29T07:43:34+00:00 | {"annotations_creators": [], "language_creators": ["other"], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|ag_news"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "pretty_name": "Weakly supervised AG News Dataset", "tags": []} | 2022-10-06T11:51:52+00:00 |
f6323032886e971c842c7b0b5b9f3592e6e2bd0a | Ces images de nuages sont divisées en 2 classes, les cirrus et les cumulus.
These cloud images are divided into 2 classes, cirrus and cumulus. | Doudou69/Cloud_Recognition | [
"region:us"
] | 2022-09-29T08:48:44+00:00 | {} | 2022-09-29T09:19:04+00:00 |
e6fb52c53dc1e653addb69adfa0113d171f221ab | Fhantomchaos/testing | [
"license:afl-3.0",
"region:us"
] | 2022-09-29T08:52:08+00:00 | {"license": "afl-3.0"} | 2022-09-29T08:53:27+00:00 |
|
aa6c355c4ac69c8e28fe1db0a5b5c194839328aa | liuweihug/da | [
"license:openrail",
"region:us"
] | 2022-09-29T08:56:08+00:00 | {"license": "openrail"} | 2022-09-29T08:56:08+00:00 |
|
402ac2dfe0a7d2e2353f93ef0fde8e40f59a21fa | HansHansHansHans/me | [
"license:unlicense",
"region:us"
] | 2022-09-29T09:13:43+00:00 | {"license": "unlicense"} | 2022-09-29T09:54:38+00:00 |
|
81b731b90a2a11229c78e6791d0d8c1ccf6833d4 | # Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
| merkalo-ziri/vsosh2022 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:ru",
"license:other",
"region:us"
] | 2022-09-29T09:35:38+00:00 | {"annotations_creators": ["found"], "language_creators": ["found"], "language": ["ru"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": [], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "vsosh_dataset", "tags": []} | 2022-09-29T10:02:34+00:00 |
e214dad7ae9dd678a2f01c9220d45d42c94c8f91 |
# Dataset Card for MC4_Legal: A Corpus Covering the Legal Part of MC4 for European Languages
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** [GitHub](https://github.com/JoelNiklaus/LegalDatasets/tree/main/pretrain/mc4_legal)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:[email protected])
### Dataset Summary
This dataset contains large text resources (~133GB in total) from mc4 filtered for legal data that can be used for pretraining language models.
Use the dataset like this:
```python
from datasets import load_dataset
dataset = load_dataset("joelito/mc4_legal", "de", split='train', streaming=True)
```
### Supported Tasks and Leaderboards
The dataset supports the task of masked language modeling.
### Languages
The following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv
## Dataset Structure
### Data Instances
The file format is jsonl.xz and there is one split available ("train").
| Source | Size (MB) | Words | Documents | Words/Document |
|:---------|------------:|------------:|------------:|-----------------:|
| all | 448980 | 28599300521 | 9873288 | 2896 |
| bg | 57 | 2390349 | 379 | 6306 |
| cs | 31005 | 1840827375 | 677796 | 2715 |
| da | 162 | 10466716 | 3231 | 3239 |
| de | 105739 | 6184578784 | 3164461 | 1954 |
| el | 30 | 1155977 | 307 | 3765 |
| en | 13734 | 966539309 | 359283 | 2690 |
| es | 132053 | 9058939804 | 2281888 | 3969 |
| et | 2059 | 110198368 | 49987 | 2204 |
| fi | 1270 | 62799074 | 44875 | 1399 |
| fr | 30878 | 2117306229 | 598983 | 3534 |
| ga | 1 | 32772 | 8 | 4096 |
| hu | 4677 | 244911748 | 58857 | 4161 |
| it | 46957 | 3053920779 | 990823 | 3082 |
| lt | 156 | 9142223 | 1529 | 5979 |
| lv | 1 | 58702 | 16 | 3668 |
| mt | 65 | 3479869 | 731 | 4760 |
| nl | 326 | 21962633 | 6875 | 3194 |
| pl | 37950 | 2235839721 | 827641 | 2701 |
| pt | 20120 | 1338147828 | 382173 | 3501 |
| ro | 8816 | 551372510 | 136513 | 4038 |
| sk | 5850 | 349265172 | 130701 | 2672 |
| sl | 1742 | 107493024 | 32574 | 3299 |
| sv | 5332 | 328471555 | 123657 | 2656 |
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
The dataset was created by filtering mc4 for legal data.
We used terms indicating legal citations to get the texts.
Note that this dataset can be quite noisy, and the quality is not known.
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.
| joelniklaus/mc4_legal | [
"task_categories:fill-mask",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:ga",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"license:cc-by-4.0",
"region:us"
] | 2022-09-29T09:53:01+00:00 | {"annotations_creators": ["other"], "language_creators": ["found"], "language": ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["fill-mask"], "pretty_name": "MC4_Legal: A Corpus Covering the Legal Part of MC4 for European Languages"} | 2023-03-20T23:24:13+00:00 |
d5ed1a1b69fc5d8f027273a4686fc3bff6c6c05f | INAI/svet | [
"region:us"
] | 2022-09-29T11:18:27+00:00 | {} | 2022-09-29T11:36:43+00:00 |
|
d8c978c8b79d61393b9036a9bf09e76a83b39345 | DannyHane/test | [
"region:us"
] | 2022-09-29T12:28:38+00:00 | {} | 2022-09-29T12:43:52+00:00 |
|
f400ef054edf219b2529b673de34ff6c49f9ac9c |
# Dataset Card for AISegment.cn - Matting Human datasets
## Table of Contents
- [Dataset Card for AISegment.cn - Matting Human datasets](#dataset-card-for-aisegmentcn---matting-human-datasets)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Structure](#dataset-structure)
- [Licensing Information](#licensing-information)
## Dataset Description
Quoting the [dataset's github](https://github.com/aisegmentcn/matting_human_datasets) (translated by Apple Translator):
> This dataset is currently the largest portrait matting dataset, containing 34,427 images and corresponding matting results.
> The data set was marked by the high quality of Beijing Play Star Convergence Technology Co. Ltd., and the portrait soft segmentation model trained using this data set has been commercialized.
> The original images in the dataset are from `Flickr`, `Baidu`, and `Taobao`. After face detection and area cropping, a half-length portrait of 600\*800 was generated.
> The clip_img directory is a half-length portrait image in the format jpg; the matting directory is the corresponding matting file (convenient to confirm the matting quality), the format is png, you should first extract the alpha map from the png image before training.
- **Repository:** [aisegmentcn/matting_human_datasets](https://github.com/aisegmentcn/matting_human_datasets)
## Dataset Structure
```text
└── data/
├── clip_img/
│ └── {group-id}/
│ └── clip_{subgroup-id}/
│ └── {group-id}-{img-id}.jpg
└── matting/
└── {group-id}/
└── matting_{subgroup-id}/
└── {group-id}-{img-id}.png
```
The input `data/clip_img/1803151818/clip_00000000/1803151818-00000003.jpg` matches the label `data/matting/1803151818/matting_00000000/1803151818-00000003.png`
### Licensing Information
See authors [Github](https://github.com/aisegmentcn/matting_human_datasets)
| fredguth/aisegmentcn-matting-human | [
"task_categories:image-segmentation",
"task_ids:semantic-segmentation",
"annotations_creators:Beijing Wanxing Convergence Technology Co",
"size_categories:10K<n<100K",
"license:mit",
"binary",
"aisegment.cn",
"region:us"
] | 2022-09-29T12:32:40+00:00 | {"annotations_creators": ["Beijing Wanxing Convergence Technology Co"], "license": ["mit"], "size_categories": ["10K<n<100K"], "task_categories": ["image-segmentation"], "task_ids": ["semantic-segmentation"], "pretty_name": "aisegmentcn-matting-human", "tags": ["binary", "aisegment.cn"]} | 2022-09-29T14:18:42+00:00 |
3293876da7c613c9e5c603411139d2c8933319e5 | airnicco8/umls_sent_trans | [
"license:gpl-3.0",
"region:us"
] | 2022-09-29T13:04:52+00:00 | {"license": "gpl-3.0"} | 2022-09-29T13:04:52+00:00 |
|
a80bf0644d4149cbe69d2e57b0517c86975dd1fa | Gossher/GossherImages | [
"license:other",
"region:us"
] | 2022-09-29T13:35:18+00:00 | {"license": "other"} | 2022-09-29T13:51:25+00:00 |
|
d921ec7e349ce0d28daf30b2da9da5ee698bef0d |
# Dataset Card for MIRACL Corpus
## Dataset Description
* **Homepage:** http://miracl.ai
* **Repository:** https://github.com/project-miracl/miracl
* **Paper:** https://arxiv.org/abs/2210.09984
MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
This dataset contains the collection data of the 16 "known languages". The remaining 2 "surprise languages" will not be released until later.
The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage.
## Dataset Structure
Each retrieval unit contains three fields: `docid`, `title`, and `text`. Consider an example from the English corpus:
```
{
"docid": "39#0",
"title": "Albedo",
"text": "Albedo (meaning 'whiteness') is the measure of the diffuse reflection of solar radiation out of the total solar radiation received by an astronomical body (e.g. a planet like Earth). It is dimensionless and measured on a scale from 0 (corresponding to a black body that absorbs all incident radiation) to 1 (corresponding to a body that reflects all incident radiation)."
}
```
The `docid` has the schema `X#Y`, where all passages with the same `X` come from the same Wikipedia article, whereas `Y` denotes the passage within that article, numbered sequentially. The text field contains the text of the passage. The title field contains the name of the article the passage comes from.
The collection can be loaded using:
```
lang='ar' # or any of the 16 languages
miracl_corpus = datasets.load_dataset('miracl/miracl-corpus', lang)['train']
for doc in miracl_corpus:
docid = doc['docid']
title = doc['title']
text = doc['text']
```
## Dataset Statistics and Links
The following table contains the number of passage and Wikipedia articles in the collection of each language, along with the links to the datasets and raw Wikipedia dumps.
| Language | # of Passages | # of Articles | Links | Raw Wiki Dump |
|:----------------|--------------:|--------------:|:------|:------|
| Arabic (ar) | 2,061,414 | 656,982 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ar) | [🌏](https://archive.org/download/arwiki-20190201/arwiki-20190201-pages-articles-multistream.xml.bz2)
| Bengali (bn) | 297,265 | 63,762 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-bn) | [🌏](https://archive.org/download/bnwiki-20190201/bnwiki-20190201-pages-articles-multistream.xml.bz2)
| English (en) | 32,893,221 | 5,758,285 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-en) | [🌏](https://archive.org/download/enwiki-20190201/enwiki-20190201-pages-articles-multistream.xml.bz2)
| Spanish (es) | 10,373,953 | 1,669,181 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-es) | [🌏](https://archive.org/download/eswiki-20220301/eswiki-20220301-pages-articles-multistream.xml.bz2)
| Persian (fa) | 2,207,172 | 857,827 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fa) | [🌏](https://archive.org/download/fawiki-20220301/fawiki-20220301-pages-articles-multistream.xml.bz2)
| Finnish (fi) | 1,883,509 | 447,815 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fi) | [🌏](https://archive.org/download/fiwiki-20190201/fiwiki-20190201-pages-articles-multistream.xml.bz2)
| French (fr) | 14,636,953 | 2,325,608 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fr) | [🌏](https://archive.org/download/frwiki-20220301/frwiki-20220301-pages-articles-multistream.xml.bz2)
| Hindi (hi) | 506,264 | 148,107 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-hi) | [🌏](https://archive.org/download/hiwiki-20220301/hiwiki-20220301-pages-articles-multistream.xml.bz2)
| Indonesian (id) | 1,446,315 | 446,330 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-id) | [🌏](https://archive.org/download/idwiki-20190201/idwiki-20190201-pages-articles-multistream.xml.bz2)
| Japanese (ja) | 6,953,614 | 1,133,444 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ja) | [🌏](https://archive.org/download/jawiki-20190201/jawiki-20190201-pages-articles-multistream.xml.bz2)
| Korean (ko) | 1,486,752 | 437,373 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ko) | [🌏](https://archive.org/download/kowiki-20190201/kowiki-20190201-pages-articles-multistream.xml.bz2)
| Russian (ru) | 9,543,918 | 1,476,045 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ru) | [🌏](https://archive.org/download/ruwiki-20190201/ruwiki-20190201-pages-articles-multistream.xml.bz2)
| Swahili (sw) | 131,924 | 47,793 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-sw) | [🌏](https://archive.org/download/swwiki-20190201/swwiki-20190201-pages-articles-multistream.xml.bz2)
| Telugu (te) | 518,079 | 66,353 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-te) | [🌏](https://archive.org/download/tewiki-20190201/tewiki-20190201-pages-articles-multistream.xml.bz2)
| Thai (th) | 542,166 | 128,179 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-th) | [🌏](https://archive.org/download/thwiki-20190101/thwiki-20190101-pages-articles-multistream.xml.bz2)
| Chinese (zh) | 4,934,368 | 1,246,389 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-zh) | [🌏](https://archive.org/download/zhwiki-20220301/zhwiki-20220301-pages-articles-multistream.xml.bz2)
| miracl/miracl-corpus | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:expert-generated",
"multilinguality:multilingual",
"language:ar",
"language:bn",
"language:en",
"language:es",
"language:fa",
"language:fi",
"language:fr",
"language:hi",
"language:id",
"language:ja",
"language:ko",
"language:ru",
"language:sw",
"language:te",
"language:th",
"language:zh",
"license:apache-2.0",
"arxiv:2210.09984",
"region:us"
] | 2022-09-29T13:49:58+00:00 | {"annotations_creators": ["expert-generated"], "language": ["ar", "bn", "en", "es", "fa", "fi", "fr", "hi", "id", "ja", "ko", "ru", "sw", "te", "th", "zh"], "license": ["apache-2.0"], "multilinguality": ["multilingual"], "size_categories": [], "source_datasets": [], "task_categories": ["text-retrieval"], "task_ids": ["document-retrieval"], "pretty_name": "MIRACL-corpus", "tags": []} | 2023-01-05T17:28:26+00:00 |
59ced5f474e574d107b1b669e745b047f33d2947 | riogerz/florz | [
"license:openrail",
"region:us"
] | 2022-09-29T13:54:13+00:00 | {"license": "openrail"} | 2022-09-29T13:54:13+00:00 |
|
aa4f6645451098df234769f89af1fcccd16d567f | ---
license: othera
| Shinadayu/test | [
"region:us"
] | 2022-09-29T14:19:40+00:00 | {} | 2022-09-29T14:21:16+00:00 |
6eb9f5c5ce5375d1620a1809cd1d0490d5318342 | KamiNoGi/pochi | [
"license:openrail",
"region:us"
] | 2022-09-29T14:29:52+00:00 | {"license": "openrail"} | 2022-09-29T14:39:50+00:00 |
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