Upload batch 65 (20 files, last=huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-joelito__brazilian_court_decisions-joelito__brazilian_c-4bed1b-1985466167.md)
Browse files- huggingface_dataset/Dataset_Card/101arrowz_vox_celeb.md +195 -0
- huggingface_dataset/Dataset_Card/AnanthZeke_naamapadam.md +256 -0
- huggingface_dataset/Dataset_Card/Datatang_Chinese_Children_Speech_Data_by_Microphone.md +125 -0
- huggingface_dataset/Dataset_Card/VESSL_Bored_Ape_NFT_text.md +34 -0
- huggingface_dataset/Dataset_Card/alkzar90_CC6204-Hackaton-Cub-Dataset.md +308 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__guess-en_3-fcaae9-2012466617.md +34 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-joelito__brazilian_court_decisions-joelito__brazilian_c-4bed1b-1985466167.md +33 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-cnn_dailymail-3.0.0-5863f2-15966190.md +33 -0
- huggingface_dataset/Dataset_Card/banking77.md +397 -0
- huggingface_dataset/Dataset_Card/bigbio_tmvar_v2.md +48 -0
- huggingface_dataset/Dataset_Card/brook_fullwiki-context.md +1 -0
- huggingface_dataset/Dataset_Card/codah.md +334 -0
- huggingface_dataset/Dataset_Card/gem.md +2105 -0
- huggingface_dataset/Dataset_Card/hebrew_projectbenyehuda.md +233 -0
- huggingface_dataset/Dataset_Card/huggingartists_veggietales.md +204 -0
- huggingface_dataset/Dataset_Card/irds_lotte_pooled_dev_search.md +50 -0
- huggingface_dataset/Dataset_Card/keremberke_pokemon-classification.md +89 -0
- huggingface_dataset/Dataset_Card/lbox_lbox_open.md +50 -0
- huggingface_dataset/Dataset_Card/molamin_Kinyarwanda_Engligh_Multilingual_ASR.md +14 -0
- huggingface_dataset/Dataset_Card/shibing624_source_code.md +179 -0
huggingface_dataset/Dataset_Card/101arrowz_vox_celeb.md
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| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- crowdsourced
|
| 4 |
+
language: []
|
| 5 |
+
language_creators:
|
| 6 |
+
- crowdsourced
|
| 7 |
+
license:
|
| 8 |
+
- cc-by-4.0
|
| 9 |
+
multilinguality:
|
| 10 |
+
- multilingual
|
| 11 |
+
pretty_name: VoxCeleb
|
| 12 |
+
size_categories:
|
| 13 |
+
- 1K<n<10K
|
| 14 |
+
- 10K<n<100K
|
| 15 |
+
- 100K<n<1M
|
| 16 |
+
source_datasets: []
|
| 17 |
+
tags: []
|
| 18 |
+
task_categories:
|
| 19 |
+
- automatic-speech-recognition
|
| 20 |
+
- audio-classification
|
| 21 |
+
- image-classification
|
| 22 |
+
task_ids:
|
| 23 |
+
- speaker-identification
|
| 24 |
+
---
|
| 25 |
+
|
| 26 |
+
# Dataset Card for VoxCeleb
|
| 27 |
+
|
| 28 |
+
## Table of Contents
|
| 29 |
+
- [Table of Contents](#table-of-contents)
|
| 30 |
+
- [Dataset Description](#dataset-description)
|
| 31 |
+
- [Dataset Summary](#dataset-summary)
|
| 32 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 33 |
+
- [Languages](#languages)
|
| 34 |
+
- [Dataset Structure](#dataset-structure)
|
| 35 |
+
- [Data Instances](#data-instances)
|
| 36 |
+
- [Data Fields](#data-fields)
|
| 37 |
+
- [Data Splits](#data-splits)
|
| 38 |
+
- [Dataset Creation](#dataset-creation)
|
| 39 |
+
- [Curation Rationale](#curation-rationale)
|
| 40 |
+
- [Source Data](#source-data)
|
| 41 |
+
- [Annotations](#annotations)
|
| 42 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 43 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 44 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 45 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 46 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 47 |
+
- [Additional Information](#additional-information)
|
| 48 |
+
- [Dataset Curators](#dataset-curators)
|
| 49 |
+
- [Licensing Information](#licensing-information)
|
| 50 |
+
- [Citation Information](#citation-information)
|
| 51 |
+
- [Contributions](#contributions)
|
| 52 |
+
|
| 53 |
+
## Dataset Description
|
| 54 |
+
|
| 55 |
+
### Dataset Summary
|
| 56 |
+
|
| 57 |
+
VoxCeleb is an audio-visual dataset consisting of short clips of human speech, extracted from interview videos uploaded to YouTube.
|
| 58 |
+
|
| 59 |
+
NOTE: Although this dataset can be automatically downloaded, you must manually request credentials to access it from the creators' website.
|
| 60 |
+
|
| 61 |
+
### Supported Tasks and Leaderboards
|
| 62 |
+
|
| 63 |
+
[More Information Needed]
|
| 64 |
+
|
| 65 |
+
### Languages
|
| 66 |
+
|
| 67 |
+
[More Information Needed]
|
| 68 |
+
|
| 69 |
+
## Dataset Structure
|
| 70 |
+
|
| 71 |
+
### Data Instances
|
| 72 |
+
|
| 73 |
+
Each datapoint has a path to the audio/video clip along with metadata about the speaker.
|
| 74 |
+
|
| 75 |
+
```
|
| 76 |
+
{
|
| 77 |
+
'file': '/datasets/downloads/extracted/[hash]/wav/id10271/_YimahVgI1A/00003.wav',
|
| 78 |
+
'file_format': 'wav',
|
| 79 |
+
'dataset_id': 'vox1',
|
| 80 |
+
'speaker_id': 'id10271',
|
| 81 |
+
'speaker_gender': 'm',
|
| 82 |
+
'speaker_name': 'Ed_Westwick',
|
| 83 |
+
'speaker_nationality': 'UK',
|
| 84 |
+
'video_id': '_YimahVgI1A',
|
| 85 |
+
'clip_id': '00003',
|
| 86 |
+
'audio': {
|
| 87 |
+
'path': '/datasets/downloads/extracted/[hash]/wav/id10271/_YimahVgI1A/00003.wav',
|
| 88 |
+
'array': array([...], dtype=float32),
|
| 89 |
+
'sampling_rate': 16000
|
| 90 |
+
}
|
| 91 |
+
}
|
| 92 |
+
```
|
| 93 |
+
|
| 94 |
+
### Data Fields
|
| 95 |
+
|
| 96 |
+
Each row includes the following fields:
|
| 97 |
+
- `file`: The path to the audio/video clip
|
| 98 |
+
- `file_format`: The file format in which the clip is stored (e.g. `wav`, `aac`, `mp4`)
|
| 99 |
+
- `dataset_id`: The ID of the dataset this clip is from (`vox1`, `vox2`)
|
| 100 |
+
- `speaker_id`: The ID of the speaker in this clip
|
| 101 |
+
- `speaker_gender`: The gender of the speaker (`m`/`f`)
|
| 102 |
+
- `speaker_name` (VoxCeleb1 only): The full name of the speaker in the clip
|
| 103 |
+
- `speaker_nationality` (VoxCeleb1 only): The speaker's country of origin
|
| 104 |
+
- `video_id`: The ID of the video from which this clip was taken
|
| 105 |
+
- `clip_index`: The index of the clip for this specific video
|
| 106 |
+
- `audio` (Audio dataset only): The audio signal data
|
| 107 |
+
|
| 108 |
+
### Data Splits
|
| 109 |
+
|
| 110 |
+
The dataset has a predefined dev set and test set. The dev set has been renamed to a "train" split.
|
| 111 |
+
|
| 112 |
+
## Dataset Creation
|
| 113 |
+
|
| 114 |
+
### Curation Rationale
|
| 115 |
+
|
| 116 |
+
[More Information Needed]
|
| 117 |
+
|
| 118 |
+
### Source Data
|
| 119 |
+
|
| 120 |
+
#### Initial Data Collection and Normalization
|
| 121 |
+
|
| 122 |
+
[More Information Needed]
|
| 123 |
+
|
| 124 |
+
#### Who are the source language producers?
|
| 125 |
+
|
| 126 |
+
[More Information Needed]
|
| 127 |
+
|
| 128 |
+
### Annotations
|
| 129 |
+
|
| 130 |
+
#### Annotation process
|
| 131 |
+
|
| 132 |
+
[More Information Needed]
|
| 133 |
+
|
| 134 |
+
#### Who are the annotators?
|
| 135 |
+
|
| 136 |
+
[More Information Needed]
|
| 137 |
+
|
| 138 |
+
### Personal and Sensitive Information
|
| 139 |
+
|
| 140 |
+
The dataset includes recordings of clips (mostly of celebrities and public figures) from public YouTube videos. The names of speakers in VoxCeleb1 are provided.
|
| 141 |
+
|
| 142 |
+
## Considerations for Using the Data
|
| 143 |
+
|
| 144 |
+
### Social Impact of Dataset
|
| 145 |
+
|
| 146 |
+
[More Information Needed]
|
| 147 |
+
|
| 148 |
+
### Discussion of Biases
|
| 149 |
+
|
| 150 |
+
[More Information Needed]
|
| 151 |
+
|
| 152 |
+
### Other Known Limitations
|
| 153 |
+
|
| 154 |
+
[More Information Needed]
|
| 155 |
+
|
| 156 |
+
## Additional Information
|
| 157 |
+
|
| 158 |
+
### Dataset Curators
|
| 159 |
+
|
| 160 |
+
[More Information Needed]
|
| 161 |
+
|
| 162 |
+
### Licensing Information
|
| 163 |
+
|
| 164 |
+
[More Information Needed]
|
| 165 |
+
|
| 166 |
+
### Citation Information
|
| 167 |
+
|
| 168 |
+
The VoxCeleb authors request that anyone who uses VoxCeleb1 or VoxCeleb2 includes the following three citations:
|
| 169 |
+
```
|
| 170 |
+
@Article{Nagrani19,
|
| 171 |
+
author = "Arsha Nagrani and Joon~Son Chung and Weidi Xie and Andrew Zisserman",
|
| 172 |
+
title = "Voxceleb: Large-scale speaker verification in the wild",
|
| 173 |
+
journal = "Computer Science and Language",
|
| 174 |
+
year = "2019",
|
| 175 |
+
publisher = "Elsevier",
|
| 176 |
+
}
|
| 177 |
+
|
| 178 |
+
@InProceedings{Chung18b,
|
| 179 |
+
author = "Chung, J.~S. and Nagrani, A. and Zisserman, A.",
|
| 180 |
+
title = "VoxCeleb2: Deep Speaker Recognition",
|
| 181 |
+
booktitle = "INTERSPEECH",
|
| 182 |
+
year = "2018",
|
| 183 |
+
}
|
| 184 |
+
|
| 185 |
+
@InProceedings{Nagrani17,
|
| 186 |
+
author = "Nagrani, A. and Chung, J.~S. and Zisserman, A.",
|
| 187 |
+
title = "VoxCeleb: a large-scale speaker identification dataset",
|
| 188 |
+
booktitle = "INTERSPEECH",
|
| 189 |
+
year = "2017",
|
| 190 |
+
}
|
| 191 |
+
```
|
| 192 |
+
|
| 193 |
+
### Contributions
|
| 194 |
+
|
| 195 |
+
Thanks to [@101arrowz](https://github.com/101arrowz) for adding this dataset.
|
huggingface_dataset/Dataset_Card/AnanthZeke_naamapadam.md
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|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- machine-generated
|
| 4 |
+
language_creators:
|
| 5 |
+
- machine-generated
|
| 6 |
+
language:
|
| 7 |
+
- as
|
| 8 |
+
- bn
|
| 9 |
+
- gu
|
| 10 |
+
- hi
|
| 11 |
+
- kn
|
| 12 |
+
- ml
|
| 13 |
+
- mr
|
| 14 |
+
- or
|
| 15 |
+
- pa
|
| 16 |
+
- ta
|
| 17 |
+
- te
|
| 18 |
+
license:
|
| 19 |
+
- cc0-1.0
|
| 20 |
+
multilinguality:
|
| 21 |
+
- multilingual
|
| 22 |
+
pretty_name: naamapadam
|
| 23 |
+
size_categories:
|
| 24 |
+
- 1M<n<10M
|
| 25 |
+
source_datasets:
|
| 26 |
+
- original
|
| 27 |
+
task_categories:
|
| 28 |
+
- token-classification
|
| 29 |
+
task_ids:
|
| 30 |
+
- named-entity-recognition
|
| 31 |
+
---
|
| 32 |
+
|
| 33 |
+
# Dataset Card for naamapadam
|
| 34 |
+
|
| 35 |
+
## Table of Contents
|
| 36 |
+
- [Dataset Description](#dataset-description)
|
| 37 |
+
- [Dataset Summary](#dataset-summary)
|
| 38 |
+
- [Supported Tasks](#supported-tasks-and-leaderboards)
|
| 39 |
+
- [Languages](#languages)
|
| 40 |
+
- [Dataset Structure](#dataset-structure)
|
| 41 |
+
- [Data Instances](#data-instances)
|
| 42 |
+
- [Data Fields](#data-instances)
|
| 43 |
+
- [Data Splits](#data-instances)
|
| 44 |
+
- [Dataset Creation](#dataset-creation)
|
| 45 |
+
- [Curation Rationale](#curation-rationale)
|
| 46 |
+
- [Source Data](#source-data)
|
| 47 |
+
- [Annotations](#annotations)
|
| 48 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 49 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 50 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 51 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 52 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 53 |
+
- [Additional Information](#additional-information)
|
| 54 |
+
- [Dataset Curators](#dataset-curators)
|
| 55 |
+
- [Licensing Information](#licensing-information)
|
| 56 |
+
- [Citation Information](#citation-information)
|
| 57 |
+
|
| 58 |
+
## Dataset Description
|
| 59 |
+
|
| 60 |
+
- **Homepage:** [Needs More Information]
|
| 61 |
+
- **Repository:** https://github.com/AI4Bharat/indicner
|
| 62 |
+
- **Paper:** [Needs More Information]
|
| 63 |
+
- **Leaderboard:** [Needs More Information]
|
| 64 |
+
- **Point of Contact:** Anoop Kunchukuttan
|
| 65 |
+
|
| 66 |
+
### Dataset Summary
|
| 67 |
+
|
| 68 |
+
Naamapadam is the largest publicly available Named Entity Annotated dataset for 11 Indic languages. This corpora was created by projecting named entities from English side to the Indic language side of the English-Indic languages parallel corpus. The dataset additionally contains manually labelled test set for 8 Indic languages containing 500-1000 sentences.
|
| 69 |
+
|
| 70 |
+
|
| 71 |
+
### Supported Tasks and Leaderboards
|
| 72 |
+
|
| 73 |
+
**Tasks:** NER on Indian languages.
|
| 74 |
+
|
| 75 |
+
**Leaderboards:** Currently there is no Leaderboard for this dataset.
|
| 76 |
+
|
| 77 |
+
### Languages
|
| 78 |
+
- `Assamese (as)`
|
| 79 |
+
- `Bengali (bn)`
|
| 80 |
+
- `Gujarati (gu)`
|
| 81 |
+
- `Kannada (kn)`
|
| 82 |
+
- `Hindi (hi)`
|
| 83 |
+
- `Malayalam (ml)`
|
| 84 |
+
- `Marathi (mr)`
|
| 85 |
+
- `Oriya (or)`
|
| 86 |
+
- `Punjabi (pa)`
|
| 87 |
+
- `Tamil (ta)`
|
| 88 |
+
- `Telugu (te)`
|
| 89 |
+
|
| 90 |
+
## Dataset Structure
|
| 91 |
+
|
| 92 |
+
### Data Instances
|
| 93 |
+
|
| 94 |
+
{'words': ['उन्हेनें', 'शिकांगों','में','बोरोडिन','की','पत्नी','को','तथा','वाशिंगटन','में','रूसी','व्यापार','संघ','को','पैसे','भेजे','।'],
|
| 95 |
+
'ner': [0, 3, 0, 1, 0, 0, 0, 0, 3, 0, 5, 6, 6, 0, 0, 0, 0],
|
| 96 |
+
}
|
| 97 |
+
|
| 98 |
+
### Data Fields
|
| 99 |
+
|
| 100 |
+
- `words`: Raw tokens in the dataset.
|
| 101 |
+
- `ner`: the NER tags for this dataset.
|
| 102 |
+
|
| 103 |
+
### Data Splits
|
| 104 |
+
(to be updated, see paper for correct numbers)
|
| 105 |
+
|
| 106 |
+
| Language | Train | Validation | Test |
|
| 107 |
+
|---:|---:|---:|---:|
|
| 108 |
+
| as | 10266 | 52 | 51 |
|
| 109 |
+
| bn | 961679 | 4859 | 607 |
|
| 110 |
+
| gu | 472845 | 2389 | 50 |
|
| 111 |
+
| hi | 985787 | 13460 | 437 |
|
| 112 |
+
| kn | 471763 | 2381 | 1019 |
|
| 113 |
+
| ml | 716652 | 3618 | 974 |
|
| 114 |
+
| mr | 455248 | 2300 | 1080 |
|
| 115 |
+
| or | 196793 | 993 | 994 |
|
| 116 |
+
| pa | 463534 | 2340 | 2342 |
|
| 117 |
+
| ta | 497882 | 2795 | 49 |
|
| 118 |
+
| te | 507741 | 2700 | 53 |
|
| 119 |
+
|
| 120 |
+
|
| 121 |
+
## Usage
|
| 122 |
+
|
| 123 |
+
You should have the 'datasets' packages installed to be able to use the :rocket: HuggingFace datasets repository. Please use the following command and install via pip:
|
| 124 |
+
|
| 125 |
+
```code
|
| 126 |
+
pip install datasets
|
| 127 |
+
```
|
| 128 |
+
|
| 129 |
+
To use the dataset, please use:<br/>
|
| 130 |
+
|
| 131 |
+
```python
|
| 132 |
+
from datasets import load_dataset
|
| 133 |
+
hiner = load_dataset('ai4bharat/naamapadam')
|
| 134 |
+
```
|
| 135 |
+
|
| 136 |
+
## Dataset Creation
|
| 137 |
+
We use the parallel corpus from the Samanantar Dataset between English and the 11 major Indian languages to create the NER dataset. We annotate the English portion of the parallel corpus with existing state-of-the-art NER model. We use word-level alignments learned from the parallel corpus to project the entity labels from English to the Indian language.
|
| 138 |
+
|
| 139 |
+
### Curation Rationale
|
| 140 |
+
|
| 141 |
+
naamapadam was built from [Samanantar dataset](https://indicnlp.ai4bharat.org/samanantar/). This dataset was built for the task of Named Entity Recognition in Indic languages. The dataset was introduced to introduce new resources to the Indic languages language that was under-served for Natural Language Processing.
|
| 142 |
+
|
| 143 |
+
### Source Data
|
| 144 |
+
|
| 145 |
+
[Samanantar dataset](https://indicnlp.ai4bharat.org/samanantar/)
|
| 146 |
+
|
| 147 |
+
#### Initial Data Collection and Normalization
|
| 148 |
+
|
| 149 |
+
[Needs More Information]
|
| 150 |
+
|
| 151 |
+
#### Who are the source language producers?
|
| 152 |
+
|
| 153 |
+
[Needs More Information]
|
| 154 |
+
|
| 155 |
+
### Annotations
|
| 156 |
+
|
| 157 |
+
#### Annotation process
|
| 158 |
+
|
| 159 |
+
NER annotations were done following the CoNLL-2003 guidelines.
|
| 160 |
+
|
| 161 |
+
#### Who are the annotators?
|
| 162 |
+
|
| 163 |
+
The annotations for the testset have been done by volunteers who are proficient in the respective languages. We would like to thank all the volunteers:
|
| 164 |
+
|
| 165 |
+
- Anil Mhaske
|
| 166 |
+
- Anoop Kunchukuttan
|
| 167 |
+
- Archana Mhaske
|
| 168 |
+
- Arnav Mhaske
|
| 169 |
+
- Gowtham Ramesh
|
| 170 |
+
- Harshit Kedia
|
| 171 |
+
- Nitin Kedia
|
| 172 |
+
- Rudramurthy V
|
| 173 |
+
- Sangeeta Rajagopal
|
| 174 |
+
- Sumanth Doddapaneni
|
| 175 |
+
- Vindhya DS
|
| 176 |
+
- Yash Madhani
|
| 177 |
+
- Kabir Ahuja
|
| 178 |
+
- Shallu Rani
|
| 179 |
+
- Armin Virk
|
| 180 |
+
|
| 181 |
+
### Personal and Sensitive Information
|
| 182 |
+
|
| 183 |
+
[Needs More Information]
|
| 184 |
+
|
| 185 |
+
## Considerations for Using the Data
|
| 186 |
+
|
| 187 |
+
### Social Impact of Dataset
|
| 188 |
+
|
| 189 |
+
The purpose of this dataset is to provide a large-scale Named Entity Recognition dataset for Indic languages. Since the information (data points) has been obtained from public resources, we do not think there is a negative social impact in releasing this data.
|
| 190 |
+
|
| 191 |
+
|
| 192 |
+
### Discussion of Biases
|
| 193 |
+
|
| 194 |
+
[Needs More Information]
|
| 195 |
+
|
| 196 |
+
### Other Known Limitations
|
| 197 |
+
|
| 198 |
+
[Needs More Information]
|
| 199 |
+
|
| 200 |
+
## Additional Information
|
| 201 |
+
|
| 202 |
+
### Dataset Curators
|
| 203 |
+
|
| 204 |
+
[Needs More Information]
|
| 205 |
+
|
| 206 |
+
### Licensing Information
|
| 207 |
+
|
| 208 |
+
<!-- <a rel="license" float="left" href="http://creativecommons.org/publicdomain/zero/1.0/">
|
| 209 |
+
<img src="https://licensebuttons.net/p/zero/1.0/88x31.png" style="border-style: none;" alt="CC0" width="100" />
|
| 210 |
+
<img src="https://mirrors.creativecommons.org/presskit/buttons/88x31/png/by.png" style="border-style: none;" alt="CC-BY" width="100" href="http://creativecommons.org/publicdomain/zero/1.0/"/>
|
| 211 |
+
</a>
|
| 212 |
+
<br/> -->
|
| 213 |
+
|
| 214 |
+
**CC0 License Statement**
|
| 215 |
+
<a rel="license" float="left" href="https://creativecommons.org/about/cclicenses/">
|
| 216 |
+
<img src="https://licensebuttons.net/p/zero/1.0/88x31.png" style="border-style: none;" alt="CC0" width="100"/>
|
| 217 |
+
</a>
|
| 218 |
+
<br>
|
| 219 |
+
<br>
|
| 220 |
+
- We do not own any of the text from which this data has been extracted.
|
| 221 |
+
- We license the actual packaging of the mined data under the [Creative Commons CC0 license (“no rights reserved”)](http://creativecommons.org/publicdomain/zero/1.0).
|
| 222 |
+
- To the extent possible under law, <a rel="dct:publisher" href="https://ai4bharat.iitm.ac.in/"> <span property="dct:title">AI4Bharat</span></a> has waived all copyright and related or neighboring rights to <span property="dct:title">Naamapadam</span> manually collected data and existing sources.
|
| 223 |
+
- This work is published from: India.
|
| 224 |
+
|
| 225 |
+
|
| 226 |
+
### Citation Information
|
| 227 |
+
|
| 228 |
+
If you are using the Naampadam corpus, please cite the following article:
|
| 229 |
+
```
|
| 230 |
+
@misc{mhaske2022naamapadam,
|
| 231 |
+
doi = {10.48550/ARXIV.2212.10168},
|
| 232 |
+
url = {https://arxiv.org/abs/2212.10168},
|
| 233 |
+
author = {Mhaske, Arnav and Kedia, Harshit and Doddapaneni, Sumanth and Khapra, Mitesh M. and Kumar, Pratyush and Murthy, Rudra and Kunchukuttan, Anoop},
|
| 234 |
+
title = {Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages}
|
| 235 |
+
publisher = {arXiv},
|
| 236 |
+
year = {2022},
|
| 237 |
+
}
|
| 238 |
+
```
|
| 239 |
+
|
| 240 |
+
<!-- Contributors -->
|
| 241 |
+
### Contributors
|
| 242 |
+
- Arnav Mhaske <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
|
| 243 |
+
- Harshit Kedia <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
|
| 244 |
+
- Sumanth Doddapaneni <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
|
| 245 |
+
- Mitesh M. Khapra <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
|
| 246 |
+
- Pratyush Kumar <sub> ([AI4Bharat](https://ai4bharat.org), [Microsoft](https://www.microsoft.com/en-in/), [IITM](https://www.iitm.ac.in)) </sub>
|
| 247 |
+
- Rudra Murthy <sub> ([AI4Bharat](https://ai4bharat.org), [IBM](https://www.ibm.com))</sub>
|
| 248 |
+
- Anoop Kunchukuttan <sub> ([AI4Bharat](https://ai4bharat.org), [Microsoft](https://www.microsoft.com/en-in/), [IITM](https://www.iitm.ac.in)) </sub>
|
| 249 |
+
|
| 250 |
+
This work is the outcome of a volunteer effort as part of the [AI4Bharat initiative](https://ai4bharat.iitm.ac.in).
|
| 251 |
+
|
| 252 |
+
|
| 253 |
+
<!-- Contact -->
|
| 254 |
+
### Contact
|
| 255 |
+
- Anoop Kunchukuttan ([anoop.kunchukuttan@gmail.com](mailto:anoop.kunchukuttan@gmail.com))
|
| 256 |
+
- Rudra Murthy V ([rmurthyv@in.ibm.com](mailto:rmurthyv@in.ibm.com))
|
huggingface_dataset/Dataset_Card/Datatang_Chinese_Children_Speech_Data_by_Microphone.md
ADDED
|
@@ -0,0 +1,125 @@
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
YAML tags:
|
| 3 |
+
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
|
| 4 |
+
---
|
| 5 |
+
|
| 6 |
+
# Dataset Card for Datatang/Chinese_Children_Speech_Data_by_Microphone
|
| 7 |
+
|
| 8 |
+
## Table of Contents
|
| 9 |
+
- [Table of Contents](#table-of-contents)
|
| 10 |
+
- [Dataset Description](#dataset-description)
|
| 11 |
+
- [Dataset Summary](#dataset-summary)
|
| 12 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 13 |
+
- [Languages](#languages)
|
| 14 |
+
- [Dataset Structure](#dataset-structure)
|
| 15 |
+
- [Data Instances](#data-instances)
|
| 16 |
+
- [Data Fields](#data-fields)
|
| 17 |
+
- [Data Splits](#data-splits)
|
| 18 |
+
- [Dataset Creation](#dataset-creation)
|
| 19 |
+
- [Curation Rationale](#curation-rationale)
|
| 20 |
+
- [Source Data](#source-data)
|
| 21 |
+
- [Annotations](#annotations)
|
| 22 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 23 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 24 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 25 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 26 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 27 |
+
- [Additional Information](#additional-information)
|
| 28 |
+
- [Dataset Curators](#dataset-curators)
|
| 29 |
+
- [Licensing Information](#licensing-information)
|
| 30 |
+
- [Citation Information](#citation-information)
|
| 31 |
+
- [Contributions](#contributions)
|
| 32 |
+
|
| 33 |
+
## Dataset Description
|
| 34 |
+
|
| 35 |
+
- **Homepage:** https://bit.ly/3y5Y5RK
|
| 36 |
+
- **Repository:**
|
| 37 |
+
- **Paper:**
|
| 38 |
+
- **Leaderboard:**
|
| 39 |
+
- **Point of Contact:**
|
| 40 |
+
|
| 41 |
+
### Dataset Summary
|
| 42 |
+
|
| 43 |
+
The data is recorded by 739 children from China through high-fidelity microphones, with a balanced male-female ratio. The recorded content of the data mainly comes from children's textbooks, children's storybooks, and numbers, which are in line with children's language usage habits. The recording environment is a relatively quiet indoor, the text is manually transferred with high accuracy.
|
| 44 |
+
|
| 45 |
+
For more details, please refer to the link: https://bit.ly/3y5Y5RK
|
| 46 |
+
|
| 47 |
+
### Supported Tasks and Leaderboards
|
| 48 |
+
|
| 49 |
+
[More Information Needed]automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
|
| 50 |
+
|
| 51 |
+
### Languages
|
| 52 |
+
Chinese
|
| 53 |
+
## Dataset Structure
|
| 54 |
+
|
| 55 |
+
### Data Instances
|
| 56 |
+
|
| 57 |
+
[More Information Needed]
|
| 58 |
+
|
| 59 |
+
### Data Fields
|
| 60 |
+
|
| 61 |
+
[More Information Needed]
|
| 62 |
+
|
| 63 |
+
### Data Splits
|
| 64 |
+
|
| 65 |
+
[More Information Needed]
|
| 66 |
+
|
| 67 |
+
## Dataset Creation
|
| 68 |
+
|
| 69 |
+
### Curation Rationale
|
| 70 |
+
|
| 71 |
+
[More Information Needed]
|
| 72 |
+
|
| 73 |
+
### Source Data
|
| 74 |
+
|
| 75 |
+
#### Initial Data Collection and Normalization
|
| 76 |
+
|
| 77 |
+
[More Information Needed]
|
| 78 |
+
|
| 79 |
+
#### Who are the source language producers?
|
| 80 |
+
|
| 81 |
+
[More Information Needed]
|
| 82 |
+
|
| 83 |
+
### Annotations
|
| 84 |
+
|
| 85 |
+
#### Annotation process
|
| 86 |
+
|
| 87 |
+
[More Information Needed]
|
| 88 |
+
|
| 89 |
+
#### Who are the annotators?
|
| 90 |
+
|
| 91 |
+
[More Information Needed]
|
| 92 |
+
|
| 93 |
+
### Personal and Sensitive Information
|
| 94 |
+
|
| 95 |
+
[More Information Needed]
|
| 96 |
+
|
| 97 |
+
## Considerations for Using the Data
|
| 98 |
+
|
| 99 |
+
### Social Impact of Dataset
|
| 100 |
+
|
| 101 |
+
[More Information Needed]
|
| 102 |
+
|
| 103 |
+
### Discussion of Biases
|
| 104 |
+
|
| 105 |
+
[More Information Needed]
|
| 106 |
+
|
| 107 |
+
### Other Known Limitations
|
| 108 |
+
|
| 109 |
+
[More Information Needed]
|
| 110 |
+
|
| 111 |
+
## Additional Information
|
| 112 |
+
|
| 113 |
+
### Dataset Curators
|
| 114 |
+
|
| 115 |
+
[More Information Needed]
|
| 116 |
+
|
| 117 |
+
### Licensing Information
|
| 118 |
+
|
| 119 |
+
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
|
| 120 |
+
|
| 121 |
+
### Citation Information
|
| 122 |
+
|
| 123 |
+
[More Information Needed]
|
| 124 |
+
|
| 125 |
+
### Contributions
|
huggingface_dataset/Dataset_Card/VESSL_Bored_Ape_NFT_text.md
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
|
| 4 |
+
task_categories:
|
| 5 |
+
- text-to-image
|
| 6 |
+
pretty_name: text_to_bayc_image
|
| 7 |
+
size_categories:
|
| 8 |
+
- 1K<n<10K
|
| 9 |
+
source_dataset: https://opensea.io/collection/boredapeyachtclub c 2021 Yuga Labs LLC
|
| 10 |
+
---
|
| 11 |
+
|
| 12 |
+
# Disclaimer
|
| 13 |
+
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright holder.
|
| 14 |
+
|
| 15 |
+
# How to use
|
| 16 |
+
|
| 17 |
+
```bash
|
| 18 |
+
from datasets import load_dataset
|
| 19 |
+
dataset = load_dataset("VESSL/Bored_Ape_NFT_text")
|
| 20 |
+
```
|
| 21 |
+
|
| 22 |
+
# Data Field
|
| 23 |
+
|
| 24 |
+
image = binary image file and path
|
| 25 |
+
text = auto generated prompt for image
|
| 26 |
+
|
| 27 |
+
# Citation & Information
|
| 28 |
+
@InProceedings{VESSL,\
|
| 29 |
+
author={Jinpil Choi}\
|
| 30 |
+
year=2023\
|
| 31 |
+
}
|
| 32 |
+
|
| 33 |
+
# Projects
|
| 34 |
+
https://github.com/vessl-ai/examples
|
huggingface_dataset/Dataset_Card/alkzar90_CC6204-Hackaton-Cub-Dataset.md
ADDED
|
@@ -0,0 +1,308 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
license:
|
| 5 |
+
- apache-2.0
|
| 6 |
+
pretty_name: CC6204-Hackaton-CUB200
|
| 7 |
+
size_categories:
|
| 8 |
+
- 10K<n<15K
|
| 9 |
+
source_datasets:
|
| 10 |
+
- extended|other
|
| 11 |
+
paperswithcode_id: cub-200-2011
|
| 12 |
+
task_categories:
|
| 13 |
+
- image-classification
|
| 14 |
+
- text-classification
|
| 15 |
+
task_ids:
|
| 16 |
+
- multi-class-image-classification
|
| 17 |
+
---
|
| 18 |
+
|
| 19 |
+
## Dataset Description
|
| 20 |
+
|
| 21 |
+
- **Homepage:** [CUB 200 2011](http://www.vision.caltech.edu/datasets/cub_200_2011/)
|
| 22 |
+
- **Repository:** [Caltech Vision Lab](http://www.vision.caltech.edu/datasets/cub_200_2011/)
|
| 23 |
+
- **Paper:** [The Caltech-UCSD Birds-200-2011 Dataset](https://authors.library.caltech.edu/27452/1/CUB_200_2011.pdf)
|
| 24 |
+
- **Leaderboard:** [Paperswithcode](https://paperswithcode.com/dataset/cub-200-2011)
|
| 25 |
+
- **Point of Contact:** [Catherine Wah](https://scholar.google.com/citations?user=rCDdLUsAAAAJ&hl=en)
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
# CC6204: Hackaton Deep Learning 2022
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
**Nota:** esta fue un actividad del curso CC6204: Deep Learning, Universidad de Chile, año 2022. Dictado por el profesor Iván Sipiran, material del curso [aquí](https://github.com/ivansipiran/CC6204-Deep-Learning).
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
En esta actividad intentaremos resolver un problema de clasificación multimodal. En un problema de clasificación multimodal, cada pieza de información viene en diferentes representaciones (imágenes, texto, audios, etc) y la idea es determinar cómo usar esos datos para un problema de clasificación.
|
| 35 |
+
En este caso trabajaremos con un dataset que contiene datos sobre especies de pájaros.
|
| 36 |
+
|
| 37 |
+
## Dataset
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
### Data Instances
|
| 41 |
+
|
| 42 |
+
Una muestra del _dataset_ se encuentra a continuación:
|
| 43 |
+
|
| 44 |
+
```
|
| 45 |
+
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=334x500 at 0x7F59DE348AF0>,
|
| 46 |
+
'description': 'this bird has a short orange bill, white breast and body and white eyes.\na medium sized bird with a orange bill and a black crown and white eyes\nthis white-breasted bird has a short, squat, orange bill, a black head and wings, and small white eyes above a white stripe.\nthis bird has a white breast, a black head, a short red beak, and webbed feet.\nthis bird is white with black on its neck and has a long, pointy beak.\nthis bird has wings that are black and has a white belly\nthis bird has wings that are black and has a long bill\nthis is a medium sized bird, with a white belly, and a grey head and wings, with a short yellow bill.\nthis bird is white and gray in color, and has a bright orange beak.\nthis bird has a blunt orange beak with mostly black above the neck, the belly is solid white.\n',
|
| 47 |
+
'label': 6,
|
| 48 |
+
'file_name': 'Parakeet_Auklet_0048_795980.jpg'}
|
| 49 |
+
```
|
| 50 |
+
|
| 51 |
+
### Data Fields
|
| 52 |
+
|
| 53 |
+
Cada instancia de datos tiene los siguientes campos:
|
| 54 |
+
|
| 55 |
+
- `image`: imagen RGB de un pájaro
|
| 56 |
+
- `description`: texto con 10 descripciones del pájaro en la foto, cada descripción esta separado por un salto de linea (i.e. `\n`)
|
| 57 |
+
- `label`: un número entero que representa el id de la especie a la que pertenece el pájaro
|
| 58 |
+
<details>
|
| 59 |
+
<summary>Id2String</summary>
|
| 60 |
+
```bash
|
| 61 |
+
1 001.Black_footed_Albatross
|
| 62 |
+
2 002.Laysan_Albatross
|
| 63 |
+
3 003.Sooty_Albatross
|
| 64 |
+
4 004.Groove_billed_Ani
|
| 65 |
+
5 005.Crested_Auklet
|
| 66 |
+
6 006.Least_Auklet
|
| 67 |
+
7 007.Parakeet_Auklet
|
| 68 |
+
8 008.Rhinoceros_Auklet
|
| 69 |
+
9 009.Brewer_Blackbird
|
| 70 |
+
10 010.Red_winged_Blackbird
|
| 71 |
+
11 011.Rusty_Blackbird
|
| 72 |
+
12 012.Yellow_headed_Blackbird
|
| 73 |
+
13 013.Bobolink
|
| 74 |
+
14 014.Indigo_Bunting
|
| 75 |
+
15 015.Lazuli_Bunting
|
| 76 |
+
16 016.Painted_Bunting
|
| 77 |
+
17 017.Cardinal
|
| 78 |
+
18 018.Spotted_Catbird
|
| 79 |
+
19 019.Gray_Catbird
|
| 80 |
+
20 020.Yellow_breasted_Chat
|
| 81 |
+
21 021.Eastern_Towhee
|
| 82 |
+
22 022.Chuck_will_Widow
|
| 83 |
+
23 023.Brandt_Cormorant
|
| 84 |
+
24 024.Red_faced_Cormorant
|
| 85 |
+
25 025.Pelagic_Cormorant
|
| 86 |
+
26 026.Bronzed_Cowbird
|
| 87 |
+
27 027.Shiny_Cowbird
|
| 88 |
+
28 028.Brown_Creeper
|
| 89 |
+
29 029.American_Crow
|
| 90 |
+
30 030.Fish_Crow
|
| 91 |
+
31 031.Black_billed_Cuckoo
|
| 92 |
+
32 032.Mangrove_Cuckoo
|
| 93 |
+
33 033.Yellow_billed_Cuckoo
|
| 94 |
+
34 034.Gray_crowned_Rosy_Finch
|
| 95 |
+
35 035.Purple_Finch
|
| 96 |
+
36 036.Northern_Flicker
|
| 97 |
+
37 037.Acadian_Flycatcher
|
| 98 |
+
38 038.Great_Crested_Flycatcher
|
| 99 |
+
39 039.Least_Flycatcher
|
| 100 |
+
40 040.Olive_sided_Flycatcher
|
| 101 |
+
41 041.Scissor_tailed_Flycatcher
|
| 102 |
+
42 042.Vermilion_Flycatcher
|
| 103 |
+
43 043.Yellow_bellied_Flycatcher
|
| 104 |
+
44 044.Frigatebird
|
| 105 |
+
45 045.Northern_Fulmar
|
| 106 |
+
46 046.Gadwall
|
| 107 |
+
47 047.American_Goldfinch
|
| 108 |
+
48 048.European_Goldfinch
|
| 109 |
+
49 049.Boat_tailed_Grackle
|
| 110 |
+
50 050.Eared_Grebe
|
| 111 |
+
51 051.Horned_Grebe
|
| 112 |
+
52 052.Pied_billed_Grebe
|
| 113 |
+
53 053.Western_Grebe
|
| 114 |
+
54 054.Blue_Grosbeak
|
| 115 |
+
55 055.Evening_Grosbeak
|
| 116 |
+
56 056.Pine_Grosbeak
|
| 117 |
+
57 057.Rose_breasted_Grosbeak
|
| 118 |
+
58 058.Pigeon_Guillemot
|
| 119 |
+
59 059.California_Gull
|
| 120 |
+
60 060.Glaucous_winged_Gull
|
| 121 |
+
61 061.Heermann_Gull
|
| 122 |
+
62 062.Herring_Gull
|
| 123 |
+
63 063.Ivory_Gull
|
| 124 |
+
64 064.Ring_billed_Gull
|
| 125 |
+
65 065.Slaty_backed_Gull
|
| 126 |
+
66 066.Western_Gull
|
| 127 |
+
67 067.Anna_Hummingbird
|
| 128 |
+
68 068.Ruby_throated_Hummingbird
|
| 129 |
+
69 069.Rufous_Hummingbird
|
| 130 |
+
70 070.Green_Violetear
|
| 131 |
+
71 071.Long_tailed_Jaeger
|
| 132 |
+
72 072.Pomarine_Jaeger
|
| 133 |
+
73 073.Blue_Jay
|
| 134 |
+
74 074.Florida_Jay
|
| 135 |
+
75 075.Green_Jay
|
| 136 |
+
76 076.Dark_eyed_Junco
|
| 137 |
+
77 077.Tropical_Kingbird
|
| 138 |
+
78 078.Gray_Kingbird
|
| 139 |
+
79 079.Belted_Kingfisher
|
| 140 |
+
80 080.Green_Kingfisher
|
| 141 |
+
81 081.Pied_Kingfisher
|
| 142 |
+
82 082.Ringed_Kingfisher
|
| 143 |
+
83 083.White_breasted_Kingfisher
|
| 144 |
+
84 084.Red_legged_Kittiwake
|
| 145 |
+
85 085.Horned_Lark
|
| 146 |
+
86 086.Pacific_Loon
|
| 147 |
+
87 087.Mallard
|
| 148 |
+
88 088.Western_Meadowlark
|
| 149 |
+
89 089.Hooded_Merganser
|
| 150 |
+
90 090.Red_breasted_Merganser
|
| 151 |
+
91 091.Mockingbird
|
| 152 |
+
92 092.Nighthawk
|
| 153 |
+
93 093.Clark_Nutcracker
|
| 154 |
+
94 094.White_breasted_Nuthatch
|
| 155 |
+
95 095.Baltimore_Oriole
|
| 156 |
+
96 096.Hooded_Oriole
|
| 157 |
+
97 097.Orchard_Oriole
|
| 158 |
+
98 098.Scott_Oriole
|
| 159 |
+
99 099.Ovenbird
|
| 160 |
+
100 100.Brown_Pelican
|
| 161 |
+
101 101.White_Pelican
|
| 162 |
+
102 102.Western_Wood_Pewee
|
| 163 |
+
103 103.Sayornis
|
| 164 |
+
104 104.American_Pipit
|
| 165 |
+
105 105.Whip_poor_Will
|
| 166 |
+
106 106.Horned_Puffin
|
| 167 |
+
107 107.Common_Raven
|
| 168 |
+
108 108.White_necked_Raven
|
| 169 |
+
109 109.American_Redstart
|
| 170 |
+
110 110.Geococcyx
|
| 171 |
+
111 111.Loggerhead_Shrike
|
| 172 |
+
112 112.Great_Grey_Shrike
|
| 173 |
+
113 113.Baird_Sparrow
|
| 174 |
+
114 114.Black_throated_Sparrow
|
| 175 |
+
115 115.Brewer_Sparrow
|
| 176 |
+
116 116.Chipping_Sparrow
|
| 177 |
+
117 117.Clay_colored_Sparrow
|
| 178 |
+
118 118.House_Sparrow
|
| 179 |
+
119 119.Field_Sparrow
|
| 180 |
+
120 120.Fox_Sparrow
|
| 181 |
+
121 121.Grasshopper_Sparrow
|
| 182 |
+
122 122.Harris_Sparrow
|
| 183 |
+
123 123.Henslow_Sparrow
|
| 184 |
+
124 124.Le_Conte_Sparrow
|
| 185 |
+
125 125.Lincoln_Sparrow
|
| 186 |
+
126 126.Nelson_Sharp_tailed_Sparrow
|
| 187 |
+
127 127.Savannah_Sparrow
|
| 188 |
+
128 128.Seaside_Sparrow
|
| 189 |
+
129 129.Song_Sparrow
|
| 190 |
+
130 130.Tree_Sparrow
|
| 191 |
+
131 131.Vesper_Sparrow
|
| 192 |
+
132 132.White_crowned_Sparrow
|
| 193 |
+
133 133.White_throated_Sparrow
|
| 194 |
+
134 134.Cape_Glossy_Starling
|
| 195 |
+
135 135.Bank_Swallow
|
| 196 |
+
136 136.Barn_Swallow
|
| 197 |
+
137 137.Cliff_Swallow
|
| 198 |
+
138 138.Tree_Swallow
|
| 199 |
+
139 139.Scarlet_Tanager
|
| 200 |
+
140 140.Summer_Tanager
|
| 201 |
+
141 141.Artic_Tern
|
| 202 |
+
142 142.Black_Tern
|
| 203 |
+
143 143.Caspian_Tern
|
| 204 |
+
144 144.Common_Tern
|
| 205 |
+
145 145.Elegant_Tern
|
| 206 |
+
146 146.Forsters_Tern
|
| 207 |
+
147 147.Least_Tern
|
| 208 |
+
148 148.Green_tailed_Towhee
|
| 209 |
+
149 149.Brown_Thrasher
|
| 210 |
+
150 150.Sage_Thrasher
|
| 211 |
+
151 151.Black_capped_Vireo
|
| 212 |
+
152 152.Blue_headed_Vireo
|
| 213 |
+
153 153.Philadelphia_Vireo
|
| 214 |
+
154 154.Red_eyed_Vireo
|
| 215 |
+
155 155.Warbling_Vireo
|
| 216 |
+
156 156.White_eyed_Vireo
|
| 217 |
+
157 157.Yellow_throated_Vireo
|
| 218 |
+
158 158.Bay_breasted_Warbler
|
| 219 |
+
159 159.Black_and_white_Warbler
|
| 220 |
+
160 160.Black_throated_Blue_Warbler
|
| 221 |
+
161 161.Blue_winged_Warbler
|
| 222 |
+
162 162.Canada_Warbler
|
| 223 |
+
163 163.Cape_May_Warbler
|
| 224 |
+
164 164.Cerulean_Warbler
|
| 225 |
+
165 165.Chestnut_sided_Warbler
|
| 226 |
+
166 166.Golden_winged_Warbler
|
| 227 |
+
167 167.Hooded_Warbler
|
| 228 |
+
168 168.Kentucky_Warbler
|
| 229 |
+
169 169.Magnolia_Warbler
|
| 230 |
+
170 170.Mourning_Warbler
|
| 231 |
+
171 171.Myrtle_Warbler
|
| 232 |
+
172 172.Nashville_Warbler
|
| 233 |
+
173 173.Orange_crowned_Warbler
|
| 234 |
+
174 174.Palm_Warbler
|
| 235 |
+
175 175.Pine_Warbler
|
| 236 |
+
176 176.Prairie_Warbler
|
| 237 |
+
177 177.Prothonotary_Warbler
|
| 238 |
+
178 178.Swainson_Warbler
|
| 239 |
+
179 179.Tennessee_Warbler
|
| 240 |
+
180 180.Wilson_Warbler
|
| 241 |
+
181 181.Worm_eating_Warbler
|
| 242 |
+
182 182.Yellow_Warbler
|
| 243 |
+
183 183.Northern_Waterthrush
|
| 244 |
+
184 184.Louisiana_Waterthrush
|
| 245 |
+
185 185.Bohemian_Waxwing
|
| 246 |
+
186 186.Cedar_Waxwing
|
| 247 |
+
187 187.American_Three_toed_Woodpecker
|
| 248 |
+
188 188.Pileated_Woodpecker
|
| 249 |
+
189 189.Red_bellied_Woodpecker
|
| 250 |
+
190 190.Red_cockaded_Woodpecker
|
| 251 |
+
191 191.Red_headed_Woodpecker
|
| 252 |
+
192 192.Downy_Woodpecker
|
| 253 |
+
193 193.Bewick_Wren
|
| 254 |
+
194 194.Cactus_Wren
|
| 255 |
+
195 195.Carolina_Wren
|
| 256 |
+
196 196.House_Wren
|
| 257 |
+
197 197.Marsh_Wren
|
| 258 |
+
198 198.Rock_Wren
|
| 259 |
+
199 199.Winter_Wren
|
| 260 |
+
200 200.Common_Yellowthroat
|
| 261 |
+
```
|
| 262 |
+
</details>
|
| 263 |
+
- `file_name`: nombre del archivo que tiene la imagen
|
| 264 |
+
|
| 265 |
+
### Data Splits
|
| 266 |
+
|
| 267 |
+
| |train| test|
|
| 268 |
+
|------------------|----:|----:|
|
| 269 |
+
|# de observaciones|5994 |5794 |
|
| 270 |
+
|
| 271 |
+
## Problema
|
| 272 |
+
|
| 273 |
+
El problema consiste en entrenar un modelo que clasifique instancias del dataset CUB de la mejor manera posible. Algunas preguntas que podrían guiar nuestro desarrollo son:
|
| 274 |
+
|
| 275 |
+
* Se podrá obtener un buen _performance_ de clasificación solo usando las imágenes del dataset? Este tipo de problema sería el clásico problema de clasificar imágenes.
|
| 276 |
+
* Se podrá obtener un buen _performance_ de clasificación solo usando los textos del dataset? Este tipo de problema sería el clásico problema de clasificar texto.
|
| 277 |
+
* Se podrá obtener un mejor _performance_ si combino la información en un modelo multimodal? Cómo construyo un modelo multimodal que reciba una imagen y un texto y clasifique la instancia con su respectiva especie? Hint: piense en cómo una red neuronal (la que sea) es simplemente una función que recibe un dato y genera una representación de alto nivel (vector característico) de ese dato. Una red CNN podría hacerse cargo de calcular la representación de una imagen y una red RNN podría hacerse cargo de calcular la representación del texto. Finalmente concateno ambas representaciones y entreno un MLP final que hace la clasificación.
|
| 278 |
+
|
| 279 |
+
## Experimentación
|
| 280 |
+
|
| 281 |
+
Como el dataset es grande y los recursos de computación son muy limitados, una estrategia para hacer los experimentos es tomar una muestra más pequeña de datos para ir probando las ideas. Para esta estrategia, éstas son dos ideas válidas:
|
| 282 |
+
|
| 283 |
+
* Tomar menos instancias por cada clase para el desarrollo y solo dejar el dataset final para hacer el entrenamiento final y la evaluación final con testing.
|
| 284 |
+
* Tomar menos clases para el desarrollo inicial y solo dejar el dataset final para hacer el entrenamiento final y la evaluación final con testing.
|
| 285 |
+
|
| 286 |
+
Ambas estrategias nos permiten lidiar con los recursos limitados que tenemos, pero cuáles son sus ventajas o desventajas? Si usas alguna de estas estrategias, puedes comentar este punto en tu desarrollo final.
|
| 287 |
+
|
| 288 |
+
## Métrica de Evaluación
|
| 289 |
+
|
| 290 |
+
La métrica que se debe reportar es el accuracy en conjunto de test.
|
| 291 |
+
|
| 292 |
+
## Citation Information
|
| 293 |
+
|
| 294 |
+
Sitio web del [_dataset_ CUB200](http://www.vision.caltech.edu/datasets/cub_200_2011/), y reporte técnico [aquí](https://authors.library.caltech.edu/27452/1/CUB_200_2011.pdf).
|
| 295 |
+
|
| 296 |
+
```
|
| 297 |
+
@techreport{WahCUB_200_2011,
|
| 298 |
+
Title = The Caltech-UCSD Birds-200-2011 Dataset,
|
| 299 |
+
Author = {Wah, C. and Branson, S. and Welinder, P. and Perona, P. and Belongie, S.},
|
| 300 |
+
Year = {2011}
|
| 301 |
+
Institution = {California Institute of Technology},
|
| 302 |
+
Number = {CNS-TR-2011-001}
|
| 303 |
+
}
|
| 304 |
+
```
|
| 305 |
+
|
| 306 |
+
## Contributions
|
| 307 |
+
|
| 308 |
+
Creación y adaptación del material de la actividad en un Hugging Face dataset por Cristóbal Alcázar.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__guess-en_3-fcaae9-2012466617.md
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- futin/guess
|
| 8 |
+
eval_info:
|
| 9 |
+
task: text_zero_shot_classification
|
| 10 |
+
model: facebook/opt-125m
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: futin/guess
|
| 13 |
+
dataset_config: en_3
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: text
|
| 17 |
+
classes: classes
|
| 18 |
+
target: target
|
| 19 |
+
---
|
| 20 |
+
# Dataset Card for AutoTrain Evaluator
|
| 21 |
+
|
| 22 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 23 |
+
|
| 24 |
+
* Task: Zero-Shot Text Classification
|
| 25 |
+
* Model: facebook/opt-125m
|
| 26 |
+
* Dataset: futin/guess
|
| 27 |
+
* Config: en_3
|
| 28 |
+
* Split: test
|
| 29 |
+
|
| 30 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 31 |
+
|
| 32 |
+
## Contributions
|
| 33 |
+
|
| 34 |
+
Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-joelito__brazilian_court_decisions-joelito__brazilian_c-4bed1b-1985466167.md
ADDED
|
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- joelito/brazilian_court_decisions
|
| 8 |
+
eval_info:
|
| 9 |
+
task: multi_class_classification
|
| 10 |
+
model: Luciano/bertimbau-base-finetuned-brazilian_court_decisions
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: joelito/brazilian_court_decisions
|
| 13 |
+
dataset_config: joelito--brazilian_court_decisions
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: decision_description
|
| 17 |
+
target: judgment_label
|
| 18 |
+
---
|
| 19 |
+
# Dataset Card for AutoTrain Evaluator
|
| 20 |
+
|
| 21 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 22 |
+
|
| 23 |
+
* Task: Multi-class Text Classification
|
| 24 |
+
* Model: Luciano/bertimbau-base-finetuned-brazilian_court_decisions
|
| 25 |
+
* Dataset: joelito/brazilian_court_decisions
|
| 26 |
+
* Config: joelito--brazilian_court_decisions
|
| 27 |
+
* Split: test
|
| 28 |
+
|
| 29 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 30 |
+
|
| 31 |
+
## Contributions
|
| 32 |
+
|
| 33 |
+
Thanks to [@Luciano](https://huggingface.co/Luciano) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-cnn_dailymail-3.0.0-5863f2-15966190.md
ADDED
|
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- cnn_dailymail
|
| 8 |
+
eval_info:
|
| 9 |
+
task: summarization
|
| 10 |
+
model: SamuelAllen123/t5-efficient-large-nl36_fine_tuned_for_sum
|
| 11 |
+
metrics: ['rouge', 'accuracy']
|
| 12 |
+
dataset_name: cnn_dailymail
|
| 13 |
+
dataset_config: 3.0.0
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: article
|
| 17 |
+
target: highlights
|
| 18 |
+
---
|
| 19 |
+
# Dataset Card for AutoTrain Evaluator
|
| 20 |
+
|
| 21 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 22 |
+
|
| 23 |
+
* Task: Summarization
|
| 24 |
+
* Model: SamuelAllen123/t5-efficient-large-nl36_fine_tuned_for_sum
|
| 25 |
+
* Dataset: cnn_dailymail
|
| 26 |
+
* Config: 3.0.0
|
| 27 |
+
* Split: test
|
| 28 |
+
|
| 29 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 30 |
+
|
| 31 |
+
## Contributions
|
| 32 |
+
|
| 33 |
+
Thanks to [@samuelallen123](https://huggingface.co/samuelallen123) for evaluating this model.
|
huggingface_dataset/Dataset_Card/banking77.md
ADDED
|
@@ -0,0 +1,397 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language_creators:
|
| 5 |
+
- expert-generated
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- cc-by-4.0
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- 10K<n<100K
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
task_categories:
|
| 17 |
+
- text-classification
|
| 18 |
+
task_ids:
|
| 19 |
+
- intent-classification
|
| 20 |
+
- multi-class-classification
|
| 21 |
+
pretty_name: BANKING77
|
| 22 |
+
dataset_info:
|
| 23 |
+
features:
|
| 24 |
+
- name: text
|
| 25 |
+
dtype: string
|
| 26 |
+
- name: label
|
| 27 |
+
dtype:
|
| 28 |
+
class_label:
|
| 29 |
+
names:
|
| 30 |
+
'0': activate_my_card
|
| 31 |
+
'1': age_limit
|
| 32 |
+
'2': apple_pay_or_google_pay
|
| 33 |
+
'3': atm_support
|
| 34 |
+
'4': automatic_top_up
|
| 35 |
+
'5': balance_not_updated_after_bank_transfer
|
| 36 |
+
'6': balance_not_updated_after_cheque_or_cash_deposit
|
| 37 |
+
'7': beneficiary_not_allowed
|
| 38 |
+
'8': cancel_transfer
|
| 39 |
+
'9': card_about_to_expire
|
| 40 |
+
'10': card_acceptance
|
| 41 |
+
'11': card_arrival
|
| 42 |
+
'12': card_delivery_estimate
|
| 43 |
+
'13': card_linking
|
| 44 |
+
'14': card_not_working
|
| 45 |
+
'15': card_payment_fee_charged
|
| 46 |
+
'16': card_payment_not_recognised
|
| 47 |
+
'17': card_payment_wrong_exchange_rate
|
| 48 |
+
'18': card_swallowed
|
| 49 |
+
'19': cash_withdrawal_charge
|
| 50 |
+
'20': cash_withdrawal_not_recognised
|
| 51 |
+
'21': change_pin
|
| 52 |
+
'22': compromised_card
|
| 53 |
+
'23': contactless_not_working
|
| 54 |
+
'24': country_support
|
| 55 |
+
'25': declined_card_payment
|
| 56 |
+
'26': declined_cash_withdrawal
|
| 57 |
+
'27': declined_transfer
|
| 58 |
+
'28': direct_debit_payment_not_recognised
|
| 59 |
+
'29': disposable_card_limits
|
| 60 |
+
'30': edit_personal_details
|
| 61 |
+
'31': exchange_charge
|
| 62 |
+
'32': exchange_rate
|
| 63 |
+
'33': exchange_via_app
|
| 64 |
+
'34': extra_charge_on_statement
|
| 65 |
+
'35': failed_transfer
|
| 66 |
+
'36': fiat_currency_support
|
| 67 |
+
'37': get_disposable_virtual_card
|
| 68 |
+
'38': get_physical_card
|
| 69 |
+
'39': getting_spare_card
|
| 70 |
+
'40': getting_virtual_card
|
| 71 |
+
'41': lost_or_stolen_card
|
| 72 |
+
'42': lost_or_stolen_phone
|
| 73 |
+
'43': order_physical_card
|
| 74 |
+
'44': passcode_forgotten
|
| 75 |
+
'45': pending_card_payment
|
| 76 |
+
'46': pending_cash_withdrawal
|
| 77 |
+
'47': pending_top_up
|
| 78 |
+
'48': pending_transfer
|
| 79 |
+
'49': pin_blocked
|
| 80 |
+
'50': receiving_money
|
| 81 |
+
'51': Refund_not_showing_up
|
| 82 |
+
'52': request_refund
|
| 83 |
+
'53': reverted_card_payment?
|
| 84 |
+
'54': supported_cards_and_currencies
|
| 85 |
+
'55': terminate_account
|
| 86 |
+
'56': top_up_by_bank_transfer_charge
|
| 87 |
+
'57': top_up_by_card_charge
|
| 88 |
+
'58': top_up_by_cash_or_cheque
|
| 89 |
+
'59': top_up_failed
|
| 90 |
+
'60': top_up_limits
|
| 91 |
+
'61': top_up_reverted
|
| 92 |
+
'62': topping_up_by_card
|
| 93 |
+
'63': transaction_charged_twice
|
| 94 |
+
'64': transfer_fee_charged
|
| 95 |
+
'65': transfer_into_account
|
| 96 |
+
'66': transfer_not_received_by_recipient
|
| 97 |
+
'67': transfer_timing
|
| 98 |
+
'68': unable_to_verify_identity
|
| 99 |
+
'69': verify_my_identity
|
| 100 |
+
'70': verify_source_of_funds
|
| 101 |
+
'71': verify_top_up
|
| 102 |
+
'72': virtual_card_not_working
|
| 103 |
+
'73': visa_or_mastercard
|
| 104 |
+
'74': why_verify_identity
|
| 105 |
+
'75': wrong_amount_of_cash_received
|
| 106 |
+
'76': wrong_exchange_rate_for_cash_withdrawal
|
| 107 |
+
splits:
|
| 108 |
+
- name: train
|
| 109 |
+
num_bytes: 715036
|
| 110 |
+
num_examples: 10003
|
| 111 |
+
- name: test
|
| 112 |
+
num_bytes: 204014
|
| 113 |
+
num_examples: 3080
|
| 114 |
+
download_size: 1079034
|
| 115 |
+
dataset_size: 919050
|
| 116 |
+
train-eval-index:
|
| 117 |
+
- config: default
|
| 118 |
+
task: text-classification
|
| 119 |
+
task_id: multi_class_classification
|
| 120 |
+
splits:
|
| 121 |
+
train_split: train
|
| 122 |
+
eval_split: test
|
| 123 |
+
col_mapping:
|
| 124 |
+
text: text
|
| 125 |
+
label: target
|
| 126 |
+
metrics:
|
| 127 |
+
- type: accuracy
|
| 128 |
+
name: Accuracy
|
| 129 |
+
- type: f1
|
| 130 |
+
name: F1 macro
|
| 131 |
+
args:
|
| 132 |
+
average: macro
|
| 133 |
+
- type: f1
|
| 134 |
+
name: F1 micro
|
| 135 |
+
args:
|
| 136 |
+
average: micro
|
| 137 |
+
- type: f1
|
| 138 |
+
name: F1 weighted
|
| 139 |
+
args:
|
| 140 |
+
average: weighted
|
| 141 |
+
- type: precision
|
| 142 |
+
name: Precision macro
|
| 143 |
+
args:
|
| 144 |
+
average: macro
|
| 145 |
+
- type: precision
|
| 146 |
+
name: Precision micro
|
| 147 |
+
args:
|
| 148 |
+
average: micro
|
| 149 |
+
- type: precision
|
| 150 |
+
name: Precision weighted
|
| 151 |
+
args:
|
| 152 |
+
average: weighted
|
| 153 |
+
- type: recall
|
| 154 |
+
name: Recall macro
|
| 155 |
+
args:
|
| 156 |
+
average: macro
|
| 157 |
+
- type: recall
|
| 158 |
+
name: Recall micro
|
| 159 |
+
args:
|
| 160 |
+
average: micro
|
| 161 |
+
- type: recall
|
| 162 |
+
name: Recall weighted
|
| 163 |
+
args:
|
| 164 |
+
average: weighted
|
| 165 |
+
---
|
| 166 |
+
|
| 167 |
+
# Dataset Card for BANKING77
|
| 168 |
+
|
| 169 |
+
## Table of Contents
|
| 170 |
+
- [Dataset Description](#dataset-description)
|
| 171 |
+
- [Dataset Summary](#dataset-summary)
|
| 172 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 173 |
+
- [Languages](#languages)
|
| 174 |
+
- [Dataset Structure](#dataset-structure)
|
| 175 |
+
- [Data Instances](#data-instances)
|
| 176 |
+
- [Data Fields](#data-fields)
|
| 177 |
+
- [Data Splits](#data-splits)
|
| 178 |
+
- [Dataset Creation](#dataset-creation)
|
| 179 |
+
- [Curation Rationale](#curation-rationale)
|
| 180 |
+
- [Source Data](#source-data)
|
| 181 |
+
- [Annotations](#annotations)
|
| 182 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 183 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 184 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 185 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 186 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 187 |
+
- [Additional Information](#additional-information)
|
| 188 |
+
- [Dataset Curators](#dataset-curators)
|
| 189 |
+
- [Licensing Information](#licensing-information)
|
| 190 |
+
- [Citation Information](#citation-information)
|
| 191 |
+
- [Contributions](#contributions)
|
| 192 |
+
|
| 193 |
+
## Dataset Description
|
| 194 |
+
|
| 195 |
+
- **Homepage:** [Github](https://github.com/PolyAI-LDN/task-specific-datasets)
|
| 196 |
+
- **Repository:** [Github](https://github.com/PolyAI-LDN/task-specific-datasets)
|
| 197 |
+
- **Paper:** [ArXiv](https://arxiv.org/abs/2003.04807)
|
| 198 |
+
- **Leaderboard:**
|
| 199 |
+
- **Point of Contact:**
|
| 200 |
+
|
| 201 |
+
### Dataset Summary
|
| 202 |
+
|
| 203 |
+
Dataset composed of online banking queries annotated with their corresponding intents.
|
| 204 |
+
|
| 205 |
+
BANKING77 dataset provides a very fine-grained set of intents in a banking domain.
|
| 206 |
+
It comprises 13,083 customer service queries labeled with 77 intents.
|
| 207 |
+
It focuses on fine-grained single-domain intent detection.
|
| 208 |
+
|
| 209 |
+
### Supported Tasks and Leaderboards
|
| 210 |
+
|
| 211 |
+
Intent classification, intent detection
|
| 212 |
+
|
| 213 |
+
### Languages
|
| 214 |
+
|
| 215 |
+
English
|
| 216 |
+
|
| 217 |
+
## Dataset Structure
|
| 218 |
+
|
| 219 |
+
### Data Instances
|
| 220 |
+
|
| 221 |
+
An example of 'train' looks as follows:
|
| 222 |
+
```
|
| 223 |
+
{
|
| 224 |
+
'label': 11, # integer label corresponding to "card_arrival" intent
|
| 225 |
+
'text': 'I am still waiting on my card?'
|
| 226 |
+
}
|
| 227 |
+
```
|
| 228 |
+
|
| 229 |
+
### Data Fields
|
| 230 |
+
|
| 231 |
+
- `text`: a string feature.
|
| 232 |
+
- `label`: One of classification labels (0-76) corresponding to unique intents.
|
| 233 |
+
|
| 234 |
+
Intent names are mapped to `label` in the following way:
|
| 235 |
+
|
| 236 |
+
| label | intent (category) |
|
| 237 |
+
|---:|:-------------------------------------------------|
|
| 238 |
+
| 0 | activate_my_card |
|
| 239 |
+
| 1 | age_limit |
|
| 240 |
+
| 2 | apple_pay_or_google_pay |
|
| 241 |
+
| 3 | atm_support |
|
| 242 |
+
| 4 | automatic_top_up |
|
| 243 |
+
| 5 | balance_not_updated_after_bank_transfer |
|
| 244 |
+
| 6 | balance_not_updated_after_cheque_or_cash_deposit |
|
| 245 |
+
| 7 | beneficiary_not_allowed |
|
| 246 |
+
| 8 | cancel_transfer |
|
| 247 |
+
| 9 | card_about_to_expire |
|
| 248 |
+
| 10 | card_acceptance |
|
| 249 |
+
| 11 | card_arrival |
|
| 250 |
+
| 12 | card_delivery_estimate |
|
| 251 |
+
| 13 | card_linking |
|
| 252 |
+
| 14 | card_not_working |
|
| 253 |
+
| 15 | card_payment_fee_charged |
|
| 254 |
+
| 16 | card_payment_not_recognised |
|
| 255 |
+
| 17 | card_payment_wrong_exchange_rate |
|
| 256 |
+
| 18 | card_swallowed |
|
| 257 |
+
| 19 | cash_withdrawal_charge |
|
| 258 |
+
| 20 | cash_withdrawal_not_recognised |
|
| 259 |
+
| 21 | change_pin |
|
| 260 |
+
| 22 | compromised_card |
|
| 261 |
+
| 23 | contactless_not_working |
|
| 262 |
+
| 24 | country_support |
|
| 263 |
+
| 25 | declined_card_payment |
|
| 264 |
+
| 26 | declined_cash_withdrawal |
|
| 265 |
+
| 27 | declined_transfer |
|
| 266 |
+
| 28 | direct_debit_payment_not_recognised |
|
| 267 |
+
| 29 | disposable_card_limits |
|
| 268 |
+
| 30 | edit_personal_details |
|
| 269 |
+
| 31 | exchange_charge |
|
| 270 |
+
| 32 | exchange_rate |
|
| 271 |
+
| 33 | exchange_via_app |
|
| 272 |
+
| 34 | extra_charge_on_statement |
|
| 273 |
+
| 35 | failed_transfer |
|
| 274 |
+
| 36 | fiat_currency_support |
|
| 275 |
+
| 37 | get_disposable_virtual_card |
|
| 276 |
+
| 38 | get_physical_card |
|
| 277 |
+
| 39 | getting_spare_card |
|
| 278 |
+
| 40 | getting_virtual_card |
|
| 279 |
+
| 41 | lost_or_stolen_card |
|
| 280 |
+
| 42 | lost_or_stolen_phone |
|
| 281 |
+
| 43 | order_physical_card |
|
| 282 |
+
| 44 | passcode_forgotten |
|
| 283 |
+
| 45 | pending_card_payment |
|
| 284 |
+
| 46 | pending_cash_withdrawal |
|
| 285 |
+
| 47 | pending_top_up |
|
| 286 |
+
| 48 | pending_transfer |
|
| 287 |
+
| 49 | pin_blocked |
|
| 288 |
+
| 50 | receiving_money |
|
| 289 |
+
| 51 | Refund_not_showing_up |
|
| 290 |
+
| 52 | request_refund |
|
| 291 |
+
| 53 | reverted_card_payment? |
|
| 292 |
+
| 54 | supported_cards_and_currencies |
|
| 293 |
+
| 55 | terminate_account |
|
| 294 |
+
| 56 | top_up_by_bank_transfer_charge |
|
| 295 |
+
| 57 | top_up_by_card_charge |
|
| 296 |
+
| 58 | top_up_by_cash_or_cheque |
|
| 297 |
+
| 59 | top_up_failed |
|
| 298 |
+
| 60 | top_up_limits |
|
| 299 |
+
| 61 | top_up_reverted |
|
| 300 |
+
| 62 | topping_up_by_card |
|
| 301 |
+
| 63 | transaction_charged_twice |
|
| 302 |
+
| 64 | transfer_fee_charged |
|
| 303 |
+
| 65 | transfer_into_account |
|
| 304 |
+
| 66 | transfer_not_received_by_recipient |
|
| 305 |
+
| 67 | transfer_timing |
|
| 306 |
+
| 68 | unable_to_verify_identity |
|
| 307 |
+
| 69 | verify_my_identity |
|
| 308 |
+
| 70 | verify_source_of_funds |
|
| 309 |
+
| 71 | verify_top_up |
|
| 310 |
+
| 72 | virtual_card_not_working |
|
| 311 |
+
| 73 | visa_or_mastercard |
|
| 312 |
+
| 74 | why_verify_identity |
|
| 313 |
+
| 75 | wrong_amount_of_cash_received |
|
| 314 |
+
| 76 | wrong_exchange_rate_for_cash_withdrawal |
|
| 315 |
+
|
| 316 |
+
### Data Splits
|
| 317 |
+
|
| 318 |
+
| Dataset statistics | Train | Test |
|
| 319 |
+
| --- | --- | --- |
|
| 320 |
+
| Number of examples | 10 003 | 3 080 |
|
| 321 |
+
| Average character length | 59.5 | 54.2 |
|
| 322 |
+
| Number of intents | 77 | 77 |
|
| 323 |
+
| Number of domains | 1 | 1 |
|
| 324 |
+
|
| 325 |
+
## Dataset Creation
|
| 326 |
+
|
| 327 |
+
### Curation Rationale
|
| 328 |
+
|
| 329 |
+
Previous intent detection datasets such as Web Apps, Ask Ubuntu, the Chatbot Corpus or SNIPS are limited to small number of classes (<10), which oversimplifies the intent detection task and does not emulate the true environment of commercial systems. Although there exist large scale *multi-domain* datasets ([HWU64](https://github.com/xliuhw/NLU-Evaluation-Data) and [CLINC150](https://github.com/clinc/oos-eval)), the examples per each domain may not sufficiently capture the full complexity of each domain as encountered "in the wild". This dataset tries to fill the gap and provides a very fine-grained set of intents in a *single-domain* i.e. **banking**. Its focus on fine-grained single-domain intent detection makes it complementary to the other two multi-domain datasets.
|
| 330 |
+
|
| 331 |
+
### Source Data
|
| 332 |
+
|
| 333 |
+
#### Initial Data Collection and Normalization
|
| 334 |
+
|
| 335 |
+
[More Information Needed]
|
| 336 |
+
|
| 337 |
+
#### Who are the source language producers?
|
| 338 |
+
|
| 339 |
+
[More Information Needed]
|
| 340 |
+
|
| 341 |
+
### Annotations
|
| 342 |
+
|
| 343 |
+
#### Annotation process
|
| 344 |
+
|
| 345 |
+
The dataset does not contain any additional annotations.
|
| 346 |
+
|
| 347 |
+
#### Who are the annotators?
|
| 348 |
+
|
| 349 |
+
[N/A]
|
| 350 |
+
|
| 351 |
+
### Personal and Sensitive Information
|
| 352 |
+
|
| 353 |
+
[N/A]
|
| 354 |
+
|
| 355 |
+
## Considerations for Using the Data
|
| 356 |
+
|
| 357 |
+
### Social Impact of Dataset
|
| 358 |
+
|
| 359 |
+
The purpose of this dataset it to help develop better intent detection systems.
|
| 360 |
+
|
| 361 |
+
Any comprehensive intent detection evaluation should involve both coarser-grained multi-domain datasets and a fine-grained single-domain dataset such as BANKING77.
|
| 362 |
+
|
| 363 |
+
### Discussion of Biases
|
| 364 |
+
|
| 365 |
+
[More Information Needed]
|
| 366 |
+
|
| 367 |
+
### Other Known Limitations
|
| 368 |
+
|
| 369 |
+
[More Information Needed]
|
| 370 |
+
|
| 371 |
+
## Additional Information
|
| 372 |
+
|
| 373 |
+
### Dataset Curators
|
| 374 |
+
|
| 375 |
+
[PolyAI](https://github.com/PolyAI-LDN)
|
| 376 |
+
|
| 377 |
+
### Licensing Information
|
| 378 |
+
|
| 379 |
+
Creative Commons Attribution 4.0 International
|
| 380 |
+
|
| 381 |
+
### Citation Information
|
| 382 |
+
|
| 383 |
+
```
|
| 384 |
+
@inproceedings{Casanueva2020,
|
| 385 |
+
author = {I{\~{n}}igo Casanueva and Tadas Temcinas and Daniela Gerz and Matthew Henderson and Ivan Vulic},
|
| 386 |
+
title = {Efficient Intent Detection with Dual Sentence Encoders},
|
| 387 |
+
year = {2020},
|
| 388 |
+
month = {mar},
|
| 389 |
+
note = {Data available at https://github.com/PolyAI-LDN/task-specific-datasets},
|
| 390 |
+
url = {https://arxiv.org/abs/2003.04807},
|
| 391 |
+
booktitle = {Proceedings of the 2nd Workshop on NLP for ConvAI - ACL 2020}
|
| 392 |
+
}
|
| 393 |
+
```
|
| 394 |
+
|
| 395 |
+
### Contributions
|
| 396 |
+
|
| 397 |
+
Thanks to [@dkajtoch](https://github.com/dkajtoch) for adding this dataset.
|
huggingface_dataset/Dataset_Card/bigbio_tmvar_v2.md
ADDED
|
@@ -0,0 +1,48 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
|
| 2 |
+
---
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
bigbio_language:
|
| 6 |
+
- English
|
| 7 |
+
license: unknown
|
| 8 |
+
multilinguality: monolingual
|
| 9 |
+
bigbio_license_shortname: UNKNOWN
|
| 10 |
+
pretty_name: tmVar v2
|
| 11 |
+
homepage: https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/
|
| 12 |
+
bigbio_pubmed: True
|
| 13 |
+
bigbio_public: True
|
| 14 |
+
bigbio_tasks:
|
| 15 |
+
- NAMED_ENTITY_RECOGNITION
|
| 16 |
+
- NAMED_ENTITY_DISAMBIGUATION
|
| 17 |
+
---
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
# Dataset Card for tmVar v2
|
| 21 |
+
|
| 22 |
+
## Dataset Description
|
| 23 |
+
|
| 24 |
+
- **Homepage:** https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/
|
| 25 |
+
- **Pubmed:** True
|
| 26 |
+
- **Public:** True
|
| 27 |
+
- **Tasks:** NER,NED
|
| 28 |
+
|
| 29 |
+
|
| 30 |
+
This dataset contains 158 PubMed articles manually annotated with mutation mentions of various kinds and dbsnp normalizations for each of them.
|
| 31 |
+
It can be used for NER tasks and NED tasks, This dataset has a single split
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
## Citation Information
|
| 35 |
+
|
| 36 |
+
```
|
| 37 |
+
@article{wei2018tmvar,
|
| 38 |
+
title={tmVar 2.0: integrating genomic variant information from literature with dbSNP and ClinVar for precision medicine},
|
| 39 |
+
author={Wei, Chih-Hsuan and Phan, Lon and Feltz, Juliana and Maiti, Rama and Hefferon, Tim and Lu, Zhiyong},
|
| 40 |
+
journal={Bioinformatics},
|
| 41 |
+
volume={34},
|
| 42 |
+
number={1},
|
| 43 |
+
pages={80--87},
|
| 44 |
+
year={2018},
|
| 45 |
+
publisher={Oxford University Press}
|
| 46 |
+
}
|
| 47 |
+
|
| 48 |
+
```
|
huggingface_dataset/Dataset_Card/brook_fullwiki-context.md
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
a fullwiki context for hotpot_qa
|
huggingface_dataset/Dataset_Card/codah.md
ADDED
|
@@ -0,0 +1,334 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- crowdsourced
|
| 4 |
+
language_creators:
|
| 5 |
+
- crowdsourced
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- unknown
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- 1K<n<10K
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
task_categories:
|
| 17 |
+
- question-answering
|
| 18 |
+
task_ids:
|
| 19 |
+
- multiple-choice-qa
|
| 20 |
+
paperswithcode_id: codah
|
| 21 |
+
pretty_name: COmmonsense Dataset Adversarially-authored by Humans
|
| 22 |
+
dataset_info:
|
| 23 |
+
- config_name: codah
|
| 24 |
+
features:
|
| 25 |
+
- name: id
|
| 26 |
+
dtype: int32
|
| 27 |
+
- name: question_category
|
| 28 |
+
dtype:
|
| 29 |
+
class_label:
|
| 30 |
+
names:
|
| 31 |
+
'0': Idioms
|
| 32 |
+
'1': Reference
|
| 33 |
+
'2': Polysemy
|
| 34 |
+
'3': Negation
|
| 35 |
+
'4': Quantitative
|
| 36 |
+
'5': Others
|
| 37 |
+
- name: question_propmt
|
| 38 |
+
dtype: string
|
| 39 |
+
- name: candidate_answers
|
| 40 |
+
sequence: string
|
| 41 |
+
- name: correct_answer_idx
|
| 42 |
+
dtype: int32
|
| 43 |
+
splits:
|
| 44 |
+
- name: train
|
| 45 |
+
num_bytes: 571208
|
| 46 |
+
num_examples: 2776
|
| 47 |
+
download_size: 485130
|
| 48 |
+
dataset_size: 571208
|
| 49 |
+
- config_name: fold_0
|
| 50 |
+
features:
|
| 51 |
+
- name: id
|
| 52 |
+
dtype: int32
|
| 53 |
+
- name: question_category
|
| 54 |
+
dtype:
|
| 55 |
+
class_label:
|
| 56 |
+
names:
|
| 57 |
+
'0': Idioms
|
| 58 |
+
'1': Reference
|
| 59 |
+
'2': Polysemy
|
| 60 |
+
'3': Negation
|
| 61 |
+
'4': Quantitative
|
| 62 |
+
'5': Others
|
| 63 |
+
- name: question_propmt
|
| 64 |
+
dtype: string
|
| 65 |
+
- name: candidate_answers
|
| 66 |
+
sequence: string
|
| 67 |
+
- name: correct_answer_idx
|
| 68 |
+
dtype: int32
|
| 69 |
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splits:
|
| 70 |
+
- name: train
|
| 71 |
+
num_bytes: 344912
|
| 72 |
+
num_examples: 1665
|
| 73 |
+
- name: validation
|
| 74 |
+
num_bytes: 114211
|
| 75 |
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num_examples: 556
|
| 76 |
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- name: test
|
| 77 |
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num_bytes: 112109
|
| 78 |
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num_examples: 555
|
| 79 |
+
download_size: 485130
|
| 80 |
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dataset_size: 571232
|
| 81 |
+
- config_name: fold_1
|
| 82 |
+
features:
|
| 83 |
+
- name: id
|
| 84 |
+
dtype: int32
|
| 85 |
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- name: question_category
|
| 86 |
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dtype:
|
| 87 |
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class_label:
|
| 88 |
+
names:
|
| 89 |
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'0': Idioms
|
| 90 |
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'1': Reference
|
| 91 |
+
'2': Polysemy
|
| 92 |
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'3': Negation
|
| 93 |
+
'4': Quantitative
|
| 94 |
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'5': Others
|
| 95 |
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- name: question_propmt
|
| 96 |
+
dtype: string
|
| 97 |
+
- name: candidate_answers
|
| 98 |
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sequence: string
|
| 99 |
+
- name: correct_answer_idx
|
| 100 |
+
dtype: int32
|
| 101 |
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splits:
|
| 102 |
+
- name: train
|
| 103 |
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num_bytes: 340990
|
| 104 |
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num_examples: 1665
|
| 105 |
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- name: validation
|
| 106 |
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num_bytes: 114211
|
| 107 |
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num_examples: 556
|
| 108 |
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- name: test
|
| 109 |
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num_bytes: 116031
|
| 110 |
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num_examples: 555
|
| 111 |
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download_size: 485130
|
| 112 |
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dataset_size: 571232
|
| 113 |
+
- config_name: fold_2
|
| 114 |
+
features:
|
| 115 |
+
- name: id
|
| 116 |
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dtype: int32
|
| 117 |
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- name: question_category
|
| 118 |
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dtype:
|
| 119 |
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class_label:
|
| 120 |
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names:
|
| 121 |
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'0': Idioms
|
| 122 |
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'1': Reference
|
| 123 |
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'2': Polysemy
|
| 124 |
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'3': Negation
|
| 125 |
+
'4': Quantitative
|
| 126 |
+
'5': Others
|
| 127 |
+
- name: question_propmt
|
| 128 |
+
dtype: string
|
| 129 |
+
- name: candidate_answers
|
| 130 |
+
sequence: string
|
| 131 |
+
- name: correct_answer_idx
|
| 132 |
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dtype: int32
|
| 133 |
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splits:
|
| 134 |
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- name: train
|
| 135 |
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num_bytes: 342293
|
| 136 |
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num_examples: 1665
|
| 137 |
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- name: validation
|
| 138 |
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num_bytes: 114211
|
| 139 |
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num_examples: 556
|
| 140 |
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- name: test
|
| 141 |
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num_bytes: 114728
|
| 142 |
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num_examples: 555
|
| 143 |
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download_size: 485130
|
| 144 |
+
dataset_size: 571232
|
| 145 |
+
- config_name: fold_3
|
| 146 |
+
features:
|
| 147 |
+
- name: id
|
| 148 |
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dtype: int32
|
| 149 |
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- name: question_category
|
| 150 |
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dtype:
|
| 151 |
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class_label:
|
| 152 |
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names:
|
| 153 |
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'0': Idioms
|
| 154 |
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'1': Reference
|
| 155 |
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'2': Polysemy
|
| 156 |
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'3': Negation
|
| 157 |
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'4': Quantitative
|
| 158 |
+
'5': Others
|
| 159 |
+
- name: question_propmt
|
| 160 |
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dtype: string
|
| 161 |
+
- name: candidate_answers
|
| 162 |
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sequence: string
|
| 163 |
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- name: correct_answer_idx
|
| 164 |
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dtype: int32
|
| 165 |
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splits:
|
| 166 |
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- name: train
|
| 167 |
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num_bytes: 342844
|
| 168 |
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num_examples: 1665
|
| 169 |
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- name: validation
|
| 170 |
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num_bytes: 114211
|
| 171 |
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num_examples: 556
|
| 172 |
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- name: test
|
| 173 |
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num_bytes: 114177
|
| 174 |
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num_examples: 555
|
| 175 |
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download_size: 485130
|
| 176 |
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dataset_size: 571232
|
| 177 |
+
- config_name: fold_4
|
| 178 |
+
features:
|
| 179 |
+
- name: id
|
| 180 |
+
dtype: int32
|
| 181 |
+
- name: question_category
|
| 182 |
+
dtype:
|
| 183 |
+
class_label:
|
| 184 |
+
names:
|
| 185 |
+
'0': Idioms
|
| 186 |
+
'1': Reference
|
| 187 |
+
'2': Polysemy
|
| 188 |
+
'3': Negation
|
| 189 |
+
'4': Quantitative
|
| 190 |
+
'5': Others
|
| 191 |
+
- name: question_propmt
|
| 192 |
+
dtype: string
|
| 193 |
+
- name: candidate_answers
|
| 194 |
+
sequence: string
|
| 195 |
+
- name: correct_answer_idx
|
| 196 |
+
dtype: int32
|
| 197 |
+
splits:
|
| 198 |
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- name: train
|
| 199 |
+
num_bytes: 342844
|
| 200 |
+
num_examples: 1665
|
| 201 |
+
- name: validation
|
| 202 |
+
num_bytes: 114177
|
| 203 |
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num_examples: 555
|
| 204 |
+
- name: test
|
| 205 |
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num_bytes: 114211
|
| 206 |
+
num_examples: 556
|
| 207 |
+
download_size: 485130
|
| 208 |
+
dataset_size: 571232
|
| 209 |
+
---
|
| 210 |
+
|
| 211 |
+
# Dataset Card for COmmonsense Dataset Adversarially-authored by Humans
|
| 212 |
+
|
| 213 |
+
## Table of Contents
|
| 214 |
+
- [Dataset Description](#dataset-description)
|
| 215 |
+
- [Dataset Summary](#dataset-summary)
|
| 216 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 217 |
+
- [Languages](#languages)
|
| 218 |
+
- [Dataset Structure](#dataset-structure)
|
| 219 |
+
- [Data Instances](#data-instances)
|
| 220 |
+
- [Data Fields](#data-fields)
|
| 221 |
+
- [Data Splits](#data-splits)
|
| 222 |
+
- [Dataset Creation](#dataset-creation)
|
| 223 |
+
- [Curation Rationale](#curation-rationale)
|
| 224 |
+
- [Source Data](#source-data)
|
| 225 |
+
- [Annotations](#annotations)
|
| 226 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 227 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 228 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 229 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 230 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 231 |
+
- [Additional Information](#additional-information)
|
| 232 |
+
- [Dataset Curators](#dataset-curators)
|
| 233 |
+
- [Licensing Information](#licensing-information)
|
| 234 |
+
- [Citation Information](#citation-information)
|
| 235 |
+
- [Contributions](#contributions)
|
| 236 |
+
|
| 237 |
+
## Dataset Description
|
| 238 |
+
|
| 239 |
+
- **Homepage:** [Add homepage URL here if available (unless it's a GitHub repository)]()
|
| 240 |
+
- **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]()
|
| 241 |
+
- **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]()
|
| 242 |
+
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
|
| 243 |
+
- **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]()
|
| 244 |
+
|
| 245 |
+
### Dataset Summary
|
| 246 |
+
|
| 247 |
+
[More Information Needed]
|
| 248 |
+
|
| 249 |
+
### Supported Tasks and Leaderboards
|
| 250 |
+
|
| 251 |
+
[More Information Needed]
|
| 252 |
+
|
| 253 |
+
### Languages
|
| 254 |
+
|
| 255 |
+
[More Information Needed]
|
| 256 |
+
|
| 257 |
+
## Dataset Structure
|
| 258 |
+
|
| 259 |
+
### Data Instances
|
| 260 |
+
|
| 261 |
+
[More Information Needed]
|
| 262 |
+
|
| 263 |
+
### Data Fields
|
| 264 |
+
|
| 265 |
+
[More Information Needed]
|
| 266 |
+
|
| 267 |
+
### Data Splits
|
| 268 |
+
|
| 269 |
+
[More Information Needed]
|
| 270 |
+
## Dataset Creation
|
| 271 |
+
|
| 272 |
+
### Curation Rationale
|
| 273 |
+
|
| 274 |
+
[More Information Needed]
|
| 275 |
+
|
| 276 |
+
### Source Data
|
| 277 |
+
|
| 278 |
+
[More Information Needed]
|
| 279 |
+
|
| 280 |
+
#### Initial Data Collection and Normalization
|
| 281 |
+
|
| 282 |
+
[More Information Needed]
|
| 283 |
+
|
| 284 |
+
#### Who are the source language producers?
|
| 285 |
+
|
| 286 |
+
[More Information Needed]
|
| 287 |
+
|
| 288 |
+
### Annotations
|
| 289 |
+
|
| 290 |
+
[More Information Needed]
|
| 291 |
+
|
| 292 |
+
#### Annotation process
|
| 293 |
+
|
| 294 |
+
[More Information Needed]
|
| 295 |
+
|
| 296 |
+
#### Who are the annotators?
|
| 297 |
+
|
| 298 |
+
[More Information Needed]
|
| 299 |
+
|
| 300 |
+
### Personal and Sensitive Information
|
| 301 |
+
|
| 302 |
+
[More Information Needed]
|
| 303 |
+
|
| 304 |
+
## Considerations for Using the Data
|
| 305 |
+
|
| 306 |
+
### Social Impact of Dataset
|
| 307 |
+
|
| 308 |
+
[More Information Needed]
|
| 309 |
+
|
| 310 |
+
### Discussion of Biases
|
| 311 |
+
|
| 312 |
+
[More Information Needed]
|
| 313 |
+
|
| 314 |
+
### Other Known Limitations
|
| 315 |
+
|
| 316 |
+
[More Information Needed]
|
| 317 |
+
|
| 318 |
+
## Additional Information
|
| 319 |
+
|
| 320 |
+
### Dataset Curators
|
| 321 |
+
|
| 322 |
+
[More Information Needed]
|
| 323 |
+
|
| 324 |
+
### Licensing Information
|
| 325 |
+
|
| 326 |
+
[More Information Needed]
|
| 327 |
+
|
| 328 |
+
### Citation Information
|
| 329 |
+
|
| 330 |
+
[More Information Needed]
|
| 331 |
+
|
| 332 |
+
### Contributions
|
| 333 |
+
|
| 334 |
+
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
|
huggingface_dataset/Dataset_Card/gem.md
ADDED
|
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| 1 |
+
---
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| 2 |
+
annotations_creators:
|
| 3 |
+
- crowdsourced
|
| 4 |
+
- found
|
| 5 |
+
language_creators:
|
| 6 |
+
- crowdsourced
|
| 7 |
+
- found
|
| 8 |
+
- machine-generated
|
| 9 |
+
language:
|
| 10 |
+
- cs
|
| 11 |
+
- de
|
| 12 |
+
- en
|
| 13 |
+
- es
|
| 14 |
+
- ru
|
| 15 |
+
- tr
|
| 16 |
+
- vi
|
| 17 |
+
license:
|
| 18 |
+
- other
|
| 19 |
+
multilinguality:
|
| 20 |
+
- monolingual
|
| 21 |
+
- multilingual
|
| 22 |
+
size_categories:
|
| 23 |
+
- 100K<n<1M
|
| 24 |
+
- 10K<n<100K
|
| 25 |
+
- 1K<n<10K
|
| 26 |
+
source_datasets:
|
| 27 |
+
- extended|other-vision-datasets
|
| 28 |
+
- original
|
| 29 |
+
task_categories:
|
| 30 |
+
- fill-mask
|
| 31 |
+
- summarization
|
| 32 |
+
- table-to-text
|
| 33 |
+
- tabular-to-text
|
| 34 |
+
- text-generation
|
| 35 |
+
- text2text-generation
|
| 36 |
+
task_ids:
|
| 37 |
+
- dialogue-modeling
|
| 38 |
+
- rdf-to-text
|
| 39 |
+
- news-articles-summarization
|
| 40 |
+
- text-simplification
|
| 41 |
+
paperswithcode_id: gem
|
| 42 |
+
pretty_name: GEM
|
| 43 |
+
configs:
|
| 44 |
+
- common_gen
|
| 45 |
+
- cs_restaurants
|
| 46 |
+
- dart
|
| 47 |
+
- e2e_nlg
|
| 48 |
+
- mlsum_de
|
| 49 |
+
- mlsum_es
|
| 50 |
+
- schema_guided_dialog
|
| 51 |
+
- totto
|
| 52 |
+
- web_nlg_en
|
| 53 |
+
- web_nlg_ru
|
| 54 |
+
- wiki_auto_asset_turk
|
| 55 |
+
- wiki_lingua_es_en
|
| 56 |
+
- wiki_lingua_ru_en
|
| 57 |
+
- wiki_lingua_tr_en
|
| 58 |
+
- wiki_lingua_vi_en
|
| 59 |
+
- xsum
|
| 60 |
+
tags:
|
| 61 |
+
- intent-to-text
|
| 62 |
+
- meaning-representation-to-text
|
| 63 |
+
- concepts-to-text
|
| 64 |
+
dataset_info:
|
| 65 |
+
- config_name: mlsum_de
|
| 66 |
+
features:
|
| 67 |
+
- name: gem_id
|
| 68 |
+
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|
| 69 |
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|
| 70 |
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| 71 |
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|
| 72 |
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|
| 73 |
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|
| 74 |
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dtype: string
|
| 75 |
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|
| 76 |
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dtype: string
|
| 77 |
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- name: title
|
| 78 |
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dtype: string
|
| 79 |
+
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|
| 80 |
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dtype: string
|
| 81 |
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|
| 82 |
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|
| 83 |
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| 84 |
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| 85 |
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| 86 |
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| 87 |
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| 90 |
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| 93 |
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| 96 |
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num_examples: 500
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| 98 |
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| 99 |
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| 102 |
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|
| 104 |
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| 105 |
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|
| 106 |
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|
| 107 |
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| 108 |
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| 147 |
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| 170 |
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| 171 |
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| 1315 |
+
- name: service
|
| 1316 |
+
dtype: string
|
| 1317 |
+
- name: turn_id
|
| 1318 |
+
dtype: int32
|
| 1319 |
+
- name: prompt
|
| 1320 |
+
dtype: string
|
| 1321 |
+
- name: target
|
| 1322 |
+
dtype: string
|
| 1323 |
+
- name: references
|
| 1324 |
+
list: string
|
| 1325 |
+
splits:
|
| 1326 |
+
- name: train
|
| 1327 |
+
num_bytes: 146648117
|
| 1328 |
+
num_examples: 164982
|
| 1329 |
+
- name: validation
|
| 1330 |
+
num_bytes: 9376504
|
| 1331 |
+
num_examples: 10000
|
| 1332 |
+
- name: test
|
| 1333 |
+
num_bytes: 10160596
|
| 1334 |
+
num_examples: 10000
|
| 1335 |
+
- name: challenge_train_sample
|
| 1336 |
+
num_bytes: 441326
|
| 1337 |
+
num_examples: 500
|
| 1338 |
+
- name: challenge_validation_sample
|
| 1339 |
+
num_bytes: 491492
|
| 1340 |
+
num_examples: 500
|
| 1341 |
+
- name: challenge_test_backtranslation
|
| 1342 |
+
num_bytes: 512834
|
| 1343 |
+
num_examples: 500
|
| 1344 |
+
- name: challenge_test_bfp02
|
| 1345 |
+
num_bytes: 529404
|
| 1346 |
+
num_examples: 500
|
| 1347 |
+
- name: challenge_test_bfp05
|
| 1348 |
+
num_bytes: 515151
|
| 1349 |
+
num_examples: 500
|
| 1350 |
+
- name: challenge_test_nopunc
|
| 1351 |
+
num_bytes: 509332
|
| 1352 |
+
num_examples: 500
|
| 1353 |
+
- name: challenge_test_scramble
|
| 1354 |
+
num_bytes: 514644
|
| 1355 |
+
num_examples: 500
|
| 1356 |
+
download_size: 17826468
|
| 1357 |
+
dataset_size: 169699400
|
| 1358 |
+
---
|
| 1359 |
+
|
| 1360 |
+
# Dataset Card for GEM
|
| 1361 |
+
|
| 1362 |
+
## Table of Contents
|
| 1363 |
+
- [Dataset Description](#dataset-description)
|
| 1364 |
+
- [Dataset Summary](#dataset-summary)
|
| 1365 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 1366 |
+
- [Languages](#languages)
|
| 1367 |
+
- [Dataset Structure](#dataset-structure)
|
| 1368 |
+
- [Data Instances](#data-instances)
|
| 1369 |
+
- [Data Fields](#data-fields)
|
| 1370 |
+
- [Data Splits](#data-splits)
|
| 1371 |
+
- [Dataset Creation](#dataset-creation)
|
| 1372 |
+
- [Curation Rationale](#curation-rationale)
|
| 1373 |
+
- [Source Data](#source-data)
|
| 1374 |
+
- [Annotations](#annotations)
|
| 1375 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 1376 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 1377 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 1378 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 1379 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 1380 |
+
- [Additional Information](#additional-information)
|
| 1381 |
+
- [Dataset Curators](#dataset-curators)
|
| 1382 |
+
- [Licensing Information](#licensing-information)
|
| 1383 |
+
- [Citation Information](#citation-information)
|
| 1384 |
+
- [Contributions](#contributions)
|
| 1385 |
+
|
| 1386 |
+
## Dataset Description
|
| 1387 |
+
|
| 1388 |
+
- **Homepage:** [https://gem-benchmark.github.io/](https://gem-benchmark.github.io/)
|
| 1389 |
+
- **Repository:**
|
| 1390 |
+
- **Paper:** [The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics](https://arxiv.org/abs/2102.01672)
|
| 1391 |
+
- **Point of Contact:** [Sebastian Gehrman](gehrmann@google.com)
|
| 1392 |
+
- **Size of downloaded dataset files:** 2084.23 MB
|
| 1393 |
+
- **Size of the generated dataset:** 3734.73 MB
|
| 1394 |
+
- **Total amount of disk used:** 5818.96 MB
|
| 1395 |
+
|
| 1396 |
+
### Dataset Summary
|
| 1397 |
+
|
| 1398 |
+
GEM is a benchmark environment for Natural Language Generation with a focus on its Evaluation,
|
| 1399 |
+
both through human annotations and automated Metrics.
|
| 1400 |
+
|
| 1401 |
+
GEM aims to:
|
| 1402 |
+
- measure NLG progress across 13 datasets spanning many NLG tasks and languages.
|
| 1403 |
+
- provide an in-depth analysis of data and models presented via data statements and challenge sets.
|
| 1404 |
+
- develop standards for evaluation of generated text using both automated and human metrics.
|
| 1405 |
+
|
| 1406 |
+
It is our goal to regularly update GEM and to encourage toward more inclusive practices in dataset development
|
| 1407 |
+
by extending existing data or developing datasets for additional languages.
|
| 1408 |
+
|
| 1409 |
+
You can find more complete information in the dataset cards for each of the subsets:
|
| 1410 |
+
- [CommonGen](https://gem-benchmark.com/data_cards/common_gen)
|
| 1411 |
+
- [Czech Restaurant](https://gem-benchmark.com/data_cards/cs_restaurants)
|
| 1412 |
+
- [DART](https://gem-benchmark.com/data_cards/dart)
|
| 1413 |
+
- [E2E](https://gem-benchmark.com/data_cards/e2e_nlg)
|
| 1414 |
+
- [MLSum](https://gem-benchmark.com/data_cards/mlsum)
|
| 1415 |
+
- [Schema-Guided Dialog](https://gem-benchmark.com/data_cards/schema_guided_dialog)
|
| 1416 |
+
- [WebNLG](https://gem-benchmark.com/data_cards/web_nlg)
|
| 1417 |
+
- [Wiki-Auto/ASSET/TURK](https://gem-benchmark.com/data_cards/wiki_auto_asset_turk)
|
| 1418 |
+
- [WikiLingua](https://gem-benchmark.com/data_cards/wiki_lingua)
|
| 1419 |
+
- [XSum](https://gem-benchmark.com/data_cards/xsum)
|
| 1420 |
+
|
| 1421 |
+
The subsets are organized by task:
|
| 1422 |
+
```
|
| 1423 |
+
{
|
| 1424 |
+
"summarization": {
|
| 1425 |
+
"mlsum": ["mlsum_de", "mlsum_es"],
|
| 1426 |
+
"wiki_lingua": ["wiki_lingua_es_en", "wiki_lingua_ru_en", "wiki_lingua_tr_en", "wiki_lingua_vi_en"],
|
| 1427 |
+
"xsum": ["xsum"],
|
| 1428 |
+
},
|
| 1429 |
+
"struct2text": {
|
| 1430 |
+
"common_gen": ["common_gen"],
|
| 1431 |
+
"cs_restaurants": ["cs_restaurants"],
|
| 1432 |
+
"dart": ["dart"],
|
| 1433 |
+
"e2e": ["e2e_nlg"],
|
| 1434 |
+
"totto": ["totto"],
|
| 1435 |
+
"web_nlg": ["web_nlg_en", "web_nlg_ru"],
|
| 1436 |
+
},
|
| 1437 |
+
"simplification": {
|
| 1438 |
+
"wiki_auto_asset_turk": ["wiki_auto_asset_turk"],
|
| 1439 |
+
},
|
| 1440 |
+
"dialog": {
|
| 1441 |
+
"schema_guided_dialog": ["schema_guided_dialog"],
|
| 1442 |
+
},
|
| 1443 |
+
}
|
| 1444 |
+
```
|
| 1445 |
+
|
| 1446 |
+
Each example has one `target` per example in its training set, and a set of `references` (with one or more items) in its validation and test set.
|
| 1447 |
+
|
| 1448 |
+
### Supported Tasks and Leaderboards
|
| 1449 |
+
|
| 1450 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1451 |
+
|
| 1452 |
+
### Languages
|
| 1453 |
+
|
| 1454 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1455 |
+
|
| 1456 |
+
## Dataset Structure
|
| 1457 |
+
|
| 1458 |
+
### Data Instances
|
| 1459 |
+
|
| 1460 |
+
#### common_gen
|
| 1461 |
+
|
| 1462 |
+
- **Size of downloaded dataset files:** 1.76 MB
|
| 1463 |
+
- **Size of the generated dataset:** 8.80 MB
|
| 1464 |
+
- **Total amount of disk used:** 10.56 MB
|
| 1465 |
+
|
| 1466 |
+
An example of `validation` looks as follows.
|
| 1467 |
+
```
|
| 1468 |
+
{'concept_set_id': 0,
|
| 1469 |
+
'concepts': ['field', 'look', 'stand'],
|
| 1470 |
+
'gem_id': 'common_gen-validation-0',
|
| 1471 |
+
'references': ['The player stood in the field looking at the batter.',
|
| 1472 |
+
'The coach stands along the field, looking at the goalkeeper.',
|
| 1473 |
+
'I stood and looked across the field, peacefully.',
|
| 1474 |
+
'Someone stands, looking around the empty field.'],
|
| 1475 |
+
'target': 'The player stood in the field looking at the batter.'}
|
| 1476 |
+
```
|
| 1477 |
+
|
| 1478 |
+
#### cs_restaurants
|
| 1479 |
+
|
| 1480 |
+
- **Size of downloaded dataset files:** 1.40 MB
|
| 1481 |
+
- **Size of the generated dataset:** 1.25 MB
|
| 1482 |
+
- **Total amount of disk used:** 2.64 MB
|
| 1483 |
+
|
| 1484 |
+
An example of `validation` looks as follows.
|
| 1485 |
+
```
|
| 1486 |
+
{'dialog_act': '?request(area)',
|
| 1487 |
+
'dialog_act_delexicalized': '?request(area)',
|
| 1488 |
+
'gem_id': 'cs_restaurants-validation-0',
|
| 1489 |
+
'references': ['Jakou lokalitu hledáte ?'],
|
| 1490 |
+
'target': 'Jakou lokalitu hledáte ?',
|
| 1491 |
+
'target_delexicalized': 'Jakou lokalitu hledáte ?'}
|
| 1492 |
+
```
|
| 1493 |
+
|
| 1494 |
+
#### dart
|
| 1495 |
+
|
| 1496 |
+
- **Size of downloaded dataset files:** 28.01 MB
|
| 1497 |
+
- **Size of the generated dataset:** 26.17 MB
|
| 1498 |
+
- **Total amount of disk used:** 54.18 MB
|
| 1499 |
+
|
| 1500 |
+
An example of `validation` looks as follows.
|
| 1501 |
+
```
|
| 1502 |
+
{'dart_id': 0,
|
| 1503 |
+
'gem_id': 'dart-validation-0',
|
| 1504 |
+
'references': ['A school from Mars Hill, North Carolina, joined in 1973.'],
|
| 1505 |
+
'subtree_was_extended': True,
|
| 1506 |
+
'target': 'A school from Mars Hill, North Carolina, joined in 1973.',
|
| 1507 |
+
'target_sources': ['WikiSQL_decl_sents'],
|
| 1508 |
+
'tripleset': [['Mars Hill College', 'JOINED', '1973'], ['Mars Hill College', 'LOCATION', 'Mars Hill, North Carolina']]}
|
| 1509 |
+
```
|
| 1510 |
+
|
| 1511 |
+
#### e2e_nlg
|
| 1512 |
+
|
| 1513 |
+
- **Size of downloaded dataset files:** 13.92 MB
|
| 1514 |
+
- **Size of the generated dataset:** 11.58 MB
|
| 1515 |
+
- **Total amount of disk used:** 25.50 MB
|
| 1516 |
+
|
| 1517 |
+
An example of `validation` looks as follows.
|
| 1518 |
+
```
|
| 1519 |
+
{'gem_id': 'e2e_nlg-validation-0',
|
| 1520 |
+
'meaning_representation': 'name[Alimentum], area[city centre], familyFriendly[no]',
|
| 1521 |
+
'references': ['There is a place in the city centre, Alimentum, that is not family-friendly.'],
|
| 1522 |
+
'target': 'There is a place in the city centre, Alimentum, that is not family-friendly.'}
|
| 1523 |
+
```
|
| 1524 |
+
|
| 1525 |
+
#### mlsum_de
|
| 1526 |
+
|
| 1527 |
+
- **Size of downloaded dataset files:** 331.27 MB
|
| 1528 |
+
- **Size of the generated dataset:** 907.00 MB
|
| 1529 |
+
- **Total amount of disk used:** 1238.27 MB
|
| 1530 |
+
|
| 1531 |
+
An example of `validation` looks as follows.
|
| 1532 |
+
```
|
| 1533 |
+
{'date': '00/04/2019',
|
| 1534 |
+
'gem_id': 'mlsum_de-validation-0',
|
| 1535 |
+
'references': ['In einer Kleinstadt auf der Insel Usedom war eine junge Frau tot in ihrer Wohnung gefunden worden. Nun stehen zwei Bekannte unter Verdacht.'],
|
| 1536 |
+
'target': 'In einer Kleinstadt auf der Insel Usedom war eine junge Frau tot in ihrer Wohnung gefunden worden. Nun stehen zwei Bekannte unter Verdacht.',
|
| 1537 |
+
'text': 'Kerzen und Blumen stehen vor dem Eingang eines Hauses, in dem eine 18-jährige Frau tot aufgefunden wurde. In einer Kleinstadt auf der Insel Usedom war eine junge Frau tot in ...',
|
| 1538 |
+
'title': 'Tod von 18-Jähriger auf Usedom: Zwei Festnahmen',
|
| 1539 |
+
'topic': 'panorama',
|
| 1540 |
+
'url': 'https://www.sueddeutsche.de/panorama/usedom-frau-tot-festnahme-verdaechtige-1.4412256'}
|
| 1541 |
+
```
|
| 1542 |
+
|
| 1543 |
+
#### mlsum_es
|
| 1544 |
+
|
| 1545 |
+
- **Size of downloaded dataset files:** 490.29 MB
|
| 1546 |
+
- **Size of the generated dataset:** 1253.63 MB
|
| 1547 |
+
- **Total amount of disk used:** 1743.92 MB
|
| 1548 |
+
|
| 1549 |
+
An example of `validation` looks as follows.
|
| 1550 |
+
```
|
| 1551 |
+
{'date': '05/01/2019',
|
| 1552 |
+
'gem_id': 'mlsum_es-validation-0',
|
| 1553 |
+
'references': ['El diseñador que dio carta de naturaleza al estilo genuinamente americano celebra el medio siglo de su marca entre grandes fastos y problemas financieros. Conectar con las nuevas generaciones es el regalo que precisa más que nunca'],
|
| 1554 |
+
'target': 'El diseñador que dio carta de naturaleza al estilo genuinamente americano celebra el medio siglo de su marca entre grandes fastos y problemas financieros. Conectar con las nuevas generaciones es el regalo que precisa más que nunca',
|
| 1555 |
+
'text': 'Un oso de peluche marcándose un heelflip de monopatín es todo lo que Ralph Lauren necesitaba esta Navidad. Estampado en un jersey de lana azul marino, supone la guinda que corona ...',
|
| 1556 |
+
'title': 'Ralph Lauren busca el secreto de la eterna juventud',
|
| 1557 |
+
'topic': 'elpais estilo',
|
| 1558 |
+
'url': 'http://elpais.com/elpais/2019/01/04/estilo/1546617396_933318.html'}
|
| 1559 |
+
```
|
| 1560 |
+
|
| 1561 |
+
#### schema_guided_dialog
|
| 1562 |
+
|
| 1563 |
+
- **Size of downloaded dataset files:** 8.24 MB
|
| 1564 |
+
- **Size of the generated dataset:** 43.66 MB
|
| 1565 |
+
- **Total amount of disk used:** 51.91 MB
|
| 1566 |
+
|
| 1567 |
+
An example of `validation` looks as follows.
|
| 1568 |
+
```
|
| 1569 |
+
{'dialog_acts': [{'act': 2, 'slot': 'song_name', 'values': ['Carnivore']}, {'act': 2, 'slot': 'playback_device', 'values': ['TV']}],
|
| 1570 |
+
'dialog_id': '10_00054',
|
| 1571 |
+
'gem_id': 'schema_guided_dialog-validation-0',
|
| 1572 |
+
'prompt': 'Yes, I would.',
|
| 1573 |
+
'references': ['Please confirm the song Carnivore on tv.'],
|
| 1574 |
+
'target': 'Please confirm the song Carnivore on tv.',
|
| 1575 |
+
'turn_id': 15}
|
| 1576 |
+
```
|
| 1577 |
+
|
| 1578 |
+
#### totto
|
| 1579 |
+
|
| 1580 |
+
- **Size of downloaded dataset files:** 179.03 MB
|
| 1581 |
+
- **Size of the generated dataset:** 722.88 MB
|
| 1582 |
+
- **Total amount of disk used:** 901.91 MB
|
| 1583 |
+
|
| 1584 |
+
An example of `validation` looks as follows.
|
| 1585 |
+
```
|
| 1586 |
+
{'example_id': '7391450717765563190',
|
| 1587 |
+
'gem_id': 'totto-validation-0',
|
| 1588 |
+
'highlighted_cells': [[3, 0], [3, 2], [3, 3]],
|
| 1589 |
+
'overlap_subset': 'True',
|
| 1590 |
+
'references': ['Daniel Henry Chamberlain was the 76th Governor of South Carolina from 1874.',
|
| 1591 |
+
'Daniel Henry Chamberlain was the 76th Governor of South Carolina, beginning in 1874.',
|
| 1592 |
+
'Daniel Henry Chamberlain was the 76th Governor of South Carolina who took office in 1874.'],
|
| 1593 |
+
'sentence_annotations': [{'final_sentence': 'Daniel Henry Chamberlain was the 76th Governor of South Carolina from 1874.',
|
| 1594 |
+
'original_sentence': 'Daniel Henry Chamberlain (June 23, 1835 – April 13, 1907) was an American planter, lawyer, author and the 76th Governor of South Carolina '
|
| 1595 |
+
'from 1874 until 1877.',
|
| 1596 |
+
'sentence_after_ambiguity': 'Daniel Henry Chamberlain was the 76th Governor of South Carolina from 1874.',
|
| 1597 |
+
'sentence_after_deletion': 'Daniel Henry Chamberlain was the 76th Governor of South Carolina from 1874.'},
|
| 1598 |
+
...
|
| 1599 |
+
],
|
| 1600 |
+
'table': [[{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': '#'},
|
| 1601 |
+
{'column_span': 2, 'is_header': True, 'row_span': 1, 'value': 'Governor'},
|
| 1602 |
+
{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Took Office'},
|
| 1603 |
+
{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Left Office'}],
|
| 1604 |
+
[{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': '74'},
|
| 1605 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '-'},
|
| 1606 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'Robert Kingston Scott'},
|
| 1607 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'July 6, 1868'}],
|
| 1608 |
+
...
|
| 1609 |
+
],
|
| 1610 |
+
'table_page_title': 'List of Governors of South Carolina',
|
| 1611 |
+
'table_section_text': 'Parties Democratic Republican',
|
| 1612 |
+
'table_section_title': 'Governors under the Constitution of 1868',
|
| 1613 |
+
'table_webpage_url': 'http://en.wikipedia.org/wiki/List_of_Governors_of_South_Carolina',
|
| 1614 |
+
'target': 'Daniel Henry Chamberlain was the 76th Governor of South Carolina from 1874.',
|
| 1615 |
+
'totto_id': 0}
|
| 1616 |
+
```
|
| 1617 |
+
|
| 1618 |
+
#### web_nlg_en
|
| 1619 |
+
|
| 1620 |
+
- **Size of downloaded dataset files:** 12.35 MB
|
| 1621 |
+
- **Size of the generated dataset:** 13.95 MB
|
| 1622 |
+
- **Total amount of disk used:** 26.29 MB
|
| 1623 |
+
|
| 1624 |
+
An example of `validation` looks as follows.
|
| 1625 |
+
```
|
| 1626 |
+
{'category': 'Airport',
|
| 1627 |
+
'gem_id': 'web_nlg_en-validation-0',
|
| 1628 |
+
'input': ['Aarhus | leader | Jacob_Bundsgaard'],
|
| 1629 |
+
'references': ['The leader of Aarhus is Jacob Bundsgaard.'],
|
| 1630 |
+
'target': 'The leader of Aarhus is Jacob Bundsgaard.',
|
| 1631 |
+
'webnlg_id': 'dev/Airport/1/Id1'}
|
| 1632 |
+
```
|
| 1633 |
+
|
| 1634 |
+
#### web_nlg_ru
|
| 1635 |
+
|
| 1636 |
+
- **Size of downloaded dataset files:** 7.28 MB
|
| 1637 |
+
- **Size of the generated dataset:** 8.02 MB
|
| 1638 |
+
- **Total amount of disk used:** 15.30 MB
|
| 1639 |
+
|
| 1640 |
+
An example of `validation` looks as follows.
|
| 1641 |
+
```
|
| 1642 |
+
{'category': 'Airport',
|
| 1643 |
+
'gem_id': 'web_nlg_ru-validation-0',
|
| 1644 |
+
'input': ['Punjab,_Pakistan | leaderTitle | Provincial_Assembly_of_the_Punjab'],
|
| 1645 |
+
'references': ['Пенджаб, Пакистан, возглавляется Провинциальной ассамблеей Пенджаба.', 'Пенджаб, Пакистан возглавляется Провинциальной ассамблеей Пенджаба.'],
|
| 1646 |
+
'target': 'Пенджаб, Пакистан, возглавляется Провинциальной ассамблеей Пенджаба.',
|
| 1647 |
+
'webnlg_id': 'dev/Airport/1/Id1'}
|
| 1648 |
+
```
|
| 1649 |
+
|
| 1650 |
+
#### wiki_auto_asset_turk
|
| 1651 |
+
|
| 1652 |
+
- **Size of downloaded dataset files:** 121.37 MB
|
| 1653 |
+
- **Size of the generated dataset:** 145.69 MB
|
| 1654 |
+
- **Total amount of disk used:** 267.07 MB
|
| 1655 |
+
|
| 1656 |
+
An example of `validation` looks as follows.
|
| 1657 |
+
```
|
| 1658 |
+
{'gem_id': 'wiki_auto_asset_turk-validation-0',
|
| 1659 |
+
'references': ['The Gandalf Awards honor excellent writing in in fantasy literature.'],
|
| 1660 |
+
'source': 'The Gandalf Awards, honoring achievement in fantasy literature, were conferred by the World Science Fiction Society annually from 1974 to 1981.',
|
| 1661 |
+
'source_id': '350_691837-1-0-0',
|
| 1662 |
+
'target': 'The Gandalf Awards honor excellent writing in in fantasy literature.',
|
| 1663 |
+
'target_id': '350_691837-0-0-0'}
|
| 1664 |
+
```
|
| 1665 |
+
|
| 1666 |
+
#### wiki_lingua_es_en
|
| 1667 |
+
|
| 1668 |
+
- **Size of downloaded dataset files:** 161.56 MB
|
| 1669 |
+
- **Size of the generated dataset:** 274.28 MB
|
| 1670 |
+
- **Total amount of disk used:** 435.84 MB
|
| 1671 |
+
|
| 1672 |
+
An example of `validation` looks as follows.
|
| 1673 |
+
```
|
| 1674 |
+
'references': ["Practice matted hair prevention from early in your cat's life. Make sure that your cat is grooming itself effectively. Keep a close eye on cats with long hair."],
|
| 1675 |
+
'source': 'Muchas personas presentan problemas porque no cepillaron el pelaje de sus gatos en una etapa temprana de su vida, ya que no lo consideraban necesario. Sin embargo, a medida que...',
|
| 1676 |
+
'target': "Practice matted hair prevention from early in your cat's life. Make sure that your cat is grooming itself effectively. Keep a close eye on cats with long hair."}
|
| 1677 |
+
```
|
| 1678 |
+
|
| 1679 |
+
#### wiki_lingua_ru_en
|
| 1680 |
+
|
| 1681 |
+
- **Size of downloaded dataset files:** 161.56 MB
|
| 1682 |
+
- **Size of the generated dataset:** 201.43 MB
|
| 1683 |
+
- **Total amount of disk used:** 362.99 MB
|
| 1684 |
+
|
| 1685 |
+
An example of `validation` looks as follows.
|
| 1686 |
+
```
|
| 1687 |
+
{'gem_id': 'wiki_lingua_ru_en-val-0',
|
| 1688 |
+
'references': ['Get immediate medical care if you notice signs of a complication. Undergo diagnostic tests to check for gallstones and complications. Ask your doctor about your treatment '
|
| 1689 |
+
'options.'],
|
| 1690 |
+
'source': 'И хотя, скорее всего, вам не о чем волноваться, следует незамедлительно обратиться к врачу, если вы подозреваете, что у вас возникло осложнение желчекаменной болезни. Это ...',
|
| 1691 |
+
'target': 'Get immediate medical care if you notice signs of a complication. Undergo diagnostic tests to check for gallstones and complications. Ask your doctor about your treatment '
|
| 1692 |
+
'options.'}
|
| 1693 |
+
```
|
| 1694 |
+
|
| 1695 |
+
#### wiki_lingua_tr_en
|
| 1696 |
+
|
| 1697 |
+
- **Size of downloaded dataset files:** 161.56 MB
|
| 1698 |
+
- **Size of the generated dataset:** 9.87 MB
|
| 1699 |
+
- **Total amount of disk used:** 171.42 MB
|
| 1700 |
+
|
| 1701 |
+
An example of `validation` looks as follows.
|
| 1702 |
+
```
|
| 1703 |
+
{'gem_id': 'wiki_lingua_tr_en-val-0',
|
| 1704 |
+
'references': ['Open Instagram. Go to the video you want to download. Tap ⋮. Tap Copy Link. Open Google Chrome. Tap the address bar. Go to the SaveFromWeb site. Tap the "Paste Instagram Video" text box. Tap and hold the text box. Tap PASTE. Tap Download. Download the video. Find the video on your Android.'],
|
| 1705 |
+
'source': 'Instagram uygulamasının çok renkli kamera şeklindeki simgesine dokun. Daha önce giriş yaptıysan Instagram haber kaynağı açılır. Giriş yapmadıysan istendiğinde e-posta adresini ...',
|
| 1706 |
+
'target': 'Open Instagram. Go to the video you want to download. Tap ⋮. Tap Copy Link. Open Google Chrome. Tap the address bar. Go to the SaveFromWeb site. Tap the "Paste Instagram Video" text box. Tap and hold the text box. Tap PASTE. Tap Download. Download the video. Find the video on your Android.'}
|
| 1707 |
+
```
|
| 1708 |
+
|
| 1709 |
+
#### wiki_lingua_vi_en
|
| 1710 |
+
|
| 1711 |
+
- **Size of downloaded dataset files:** 161.56 MB
|
| 1712 |
+
- **Size of the generated dataset:** 39.12 MB
|
| 1713 |
+
- **Total amount of disk used:** 200.68 MB
|
| 1714 |
+
|
| 1715 |
+
An example of `validation` looks as follows.
|
| 1716 |
+
```
|
| 1717 |
+
{'gem_id': 'wiki_lingua_vi_en-val-0',
|
| 1718 |
+
'references': ['Select the right time of year for planting the tree. You will usually want to plant your tree when it is dormant, or not flowering, during cooler or colder times of year.'],
|
| 1719 |
+
'source': 'Bạn muốn cung cấp cho cây cơ hội tốt nhất để phát triển và sinh tồn. Trồng cây đúng thời điểm trong năm chính là yếu tố then chốt. Thời điểm sẽ thay đổi phụ thuộc vào loài cây ...',
|
| 1720 |
+
'target': 'Select the right time of year for planting the tree. You will usually want to plant your tree when it is dormant, or not flowering, during cooler or colder times of year.'}
|
| 1721 |
+
```
|
| 1722 |
+
|
| 1723 |
+
#### xsum
|
| 1724 |
+
|
| 1725 |
+
- **Size of downloaded dataset files:** 243.08 MB
|
| 1726 |
+
- **Size of the generated dataset:** 67.40 MB
|
| 1727 |
+
- **Total amount of disk used:** 310.48 MB
|
| 1728 |
+
|
| 1729 |
+
An example of `validation` looks as follows.
|
| 1730 |
+
```
|
| 1731 |
+
{'document': 'Burberry reported pre-tax profits of £166m for the year to March. A year ago it made a loss of £16.1m, hit by charges at its Spanish operations.\n'
|
| 1732 |
+
'In the past year it has opened 21 new stores and closed nine. It plans to open 20-30 stores this year worldwide.\n'
|
| 1733 |
+
'The group has also focused on promoting the Burberry brand online...',
|
| 1734 |
+
'gem_id': 'xsum-validation-0',
|
| 1735 |
+
'references': ['Luxury fashion designer Burberry has returned to profit after opening new stores and spending more on online marketing'],
|
| 1736 |
+
'target': 'Luxury fashion designer Burberry has returned to profit after opening new stores and spending more on online marketing',
|
| 1737 |
+
'xsum_id': '10162122'}
|
| 1738 |
+
```
|
| 1739 |
+
|
| 1740 |
+
### Data Fields
|
| 1741 |
+
|
| 1742 |
+
The data fields are the same among all splits.
|
| 1743 |
+
|
| 1744 |
+
#### common_gen
|
| 1745 |
+
- `gem_id`: a `string` feature.
|
| 1746 |
+
- `concept_set_id`: a `int32` feature.
|
| 1747 |
+
- `concepts`: a `list` of `string` features.
|
| 1748 |
+
- `target`: a `string` feature.
|
| 1749 |
+
- `references`: a `list` of `string` features.
|
| 1750 |
+
|
| 1751 |
+
#### cs_restaurants
|
| 1752 |
+
- `gem_id`: a `string` feature.
|
| 1753 |
+
- `dialog_act`: a `string` feature.
|
| 1754 |
+
- `dialog_act_delexicalized`: a `string` feature.
|
| 1755 |
+
- `target_delexicalized`: a `string` feature.
|
| 1756 |
+
- `target`: a `string` feature.
|
| 1757 |
+
- `references`: a `list` of `string` features.
|
| 1758 |
+
|
| 1759 |
+
#### dart
|
| 1760 |
+
- `gem_id`: a `string` feature.
|
| 1761 |
+
- `dart_id`: a `int32` feature.
|
| 1762 |
+
- `tripleset`: a `list` of `string` features.
|
| 1763 |
+
- `subtree_was_extended`: a `bool` feature.
|
| 1764 |
+
- `target_sources`: a `list` of `string` features.
|
| 1765 |
+
- `target`: a `string` feature.
|
| 1766 |
+
- `references`: a `list` of `string` features.
|
| 1767 |
+
|
| 1768 |
+
#### e2e_nlg
|
| 1769 |
+
- `gem_id`: a `string` feature.
|
| 1770 |
+
- `meaning_representation`: a `string` feature.
|
| 1771 |
+
- `target`: a `string` feature.
|
| 1772 |
+
- `references`: a `list` of `string` features.
|
| 1773 |
+
|
| 1774 |
+
#### mlsum_de
|
| 1775 |
+
- `gem_id`: a `string` feature.
|
| 1776 |
+
- `text`: a `string` feature.
|
| 1777 |
+
- `topic`: a `string` feature.
|
| 1778 |
+
- `url`: a `string` feature.
|
| 1779 |
+
- `title`: a `string` feature.
|
| 1780 |
+
- `date`: a `string` feature.
|
| 1781 |
+
- `target`: a `string` feature.
|
| 1782 |
+
- `references`: a `list` of `string` features.
|
| 1783 |
+
|
| 1784 |
+
#### mlsum_es
|
| 1785 |
+
- `gem_id`: a `string` feature.
|
| 1786 |
+
- `text`: a `string` feature.
|
| 1787 |
+
- `topic`: a `string` feature.
|
| 1788 |
+
- `url`: a `string` feature.
|
| 1789 |
+
- `title`: a `string` feature.
|
| 1790 |
+
- `date`: a `string` feature.
|
| 1791 |
+
- `target`: a `string` feature.
|
| 1792 |
+
- `references`: a `list` of `string` features.
|
| 1793 |
+
|
| 1794 |
+
#### schema_guided_dialog
|
| 1795 |
+
- `gem_id`: a `string` feature.
|
| 1796 |
+
- `act`: a classification label, with possible values including `AFFIRM` (0), `AFFIRM_INTENT` (1), `CONFIRM` (2), `GOODBYE` (3), `INFORM` (4).
|
| 1797 |
+
- `slot`: a `string` feature.
|
| 1798 |
+
- `values`: a `list` of `string` features.
|
| 1799 |
+
- `dialog_id`: a `string` feature.
|
| 1800 |
+
- `turn_id`: a `int32` feature.
|
| 1801 |
+
- `prompt`: a `string` feature.
|
| 1802 |
+
- `target`: a `string` feature.
|
| 1803 |
+
- `references`: a `list` of `string` features.
|
| 1804 |
+
|
| 1805 |
+
#### totto
|
| 1806 |
+
- `gem_id`: a `string` feature.
|
| 1807 |
+
- `totto_id`: a `int32` feature.
|
| 1808 |
+
- `table_page_title`: a `string` feature.
|
| 1809 |
+
- `table_webpage_url`: a `string` feature.
|
| 1810 |
+
- `table_section_title`: a `string` feature.
|
| 1811 |
+
- `table_section_text`: a `string` feature.
|
| 1812 |
+
- `column_span`: a `int32` feature.
|
| 1813 |
+
- `is_header`: a `bool` feature.
|
| 1814 |
+
- `row_span`: a `int32` feature.
|
| 1815 |
+
- `value`: a `string` feature.
|
| 1816 |
+
- `highlighted_cells`: a `list` of `int32` features.
|
| 1817 |
+
- `example_id`: a `string` feature.
|
| 1818 |
+
- `original_sentence`: a `string` feature.
|
| 1819 |
+
- `sentence_after_deletion`: a `string` feature.
|
| 1820 |
+
- `sentence_after_ambiguity`: a `string` feature.
|
| 1821 |
+
- `final_sentence`: a `string` feature.
|
| 1822 |
+
- `overlap_subset`: a `string` feature.
|
| 1823 |
+
- `target`: a `string` feature.
|
| 1824 |
+
- `references`: a `list` of `string` features.
|
| 1825 |
+
|
| 1826 |
+
#### web_nlg_en
|
| 1827 |
+
- `gem_id`: a `string` feature.
|
| 1828 |
+
- `input`: a `list` of `string` features.
|
| 1829 |
+
- `target`: a `string` feature.
|
| 1830 |
+
- `references`: a `list` of `string` features.
|
| 1831 |
+
- `category`: a `string` feature.
|
| 1832 |
+
- `webnlg_id`: a `string` feature.
|
| 1833 |
+
|
| 1834 |
+
#### web_nlg_ru
|
| 1835 |
+
- `gem_id`: a `string` feature.
|
| 1836 |
+
- `input`: a `list` of `string` features.
|
| 1837 |
+
- `target`: a `string` feature.
|
| 1838 |
+
- `references`: a `list` of `string` features.
|
| 1839 |
+
- `category`: a `string` feature.
|
| 1840 |
+
- `webnlg_id`: a `string` feature.
|
| 1841 |
+
|
| 1842 |
+
#### wiki_auto_asset_turk
|
| 1843 |
+
- `gem_id`: a `string` feature.
|
| 1844 |
+
- `source_id`: a `string` feature.
|
| 1845 |
+
- `target_id`: a `string` feature.
|
| 1846 |
+
- `source`: a `string` feature.
|
| 1847 |
+
- `target`: a `string` feature.
|
| 1848 |
+
- `references`: a `list` of `string` features.
|
| 1849 |
+
|
| 1850 |
+
#### wiki_lingua_es_en
|
| 1851 |
+
- `gem_id`: a `string` feature.
|
| 1852 |
+
- `source`: a `string` feature.
|
| 1853 |
+
- `target`: a `string` feature.
|
| 1854 |
+
- `references`: a `list` of `string` features.
|
| 1855 |
+
|
| 1856 |
+
#### wiki_lingua_ru_en
|
| 1857 |
+
- `gem_id`: a `string` feature.
|
| 1858 |
+
- `source`: a `string` feature.
|
| 1859 |
+
- `target`: a `string` feature.
|
| 1860 |
+
- `references`: a `list` of `string` features.
|
| 1861 |
+
|
| 1862 |
+
#### wiki_lingua_tr_en
|
| 1863 |
+
- `gem_id`: a `string` feature.
|
| 1864 |
+
- `source`: a `string` feature.
|
| 1865 |
+
- `target`: a `string` feature.
|
| 1866 |
+
- `references`: a `list` of `string` features.
|
| 1867 |
+
|
| 1868 |
+
#### wiki_lingua_vi_en
|
| 1869 |
+
- `gem_id`: a `string` feature.
|
| 1870 |
+
- `source`: a `string` feature.
|
| 1871 |
+
- `target`: a `string` feature.
|
| 1872 |
+
- `references`: a `list` of `string` features.
|
| 1873 |
+
|
| 1874 |
+
#### xsum
|
| 1875 |
+
- `gem_id`: a `string` feature.
|
| 1876 |
+
- `xsum_id`: a `string` feature.
|
| 1877 |
+
- `document`: a `string` feature.
|
| 1878 |
+
- `target`: a `string` feature.
|
| 1879 |
+
- `references`: a `list` of `string` features.
|
| 1880 |
+
|
| 1881 |
+
### Data Splits
|
| 1882 |
+
|
| 1883 |
+
#### common_gen
|
| 1884 |
+
|
| 1885 |
+
| |train|validation|test|
|
| 1886 |
+
|----------|----:|---------:|---:|
|
| 1887 |
+
|common_gen|67389| 993|1497|
|
| 1888 |
+
|
| 1889 |
+
#### cs_restaurants
|
| 1890 |
+
|
| 1891 |
+
| |train|validation|test|
|
| 1892 |
+
|--------------|----:|---------:|---:|
|
| 1893 |
+
|cs_restaurants| 3569| 781| 842|
|
| 1894 |
+
|
| 1895 |
+
#### dart
|
| 1896 |
+
|
| 1897 |
+
| |train|validation|test|
|
| 1898 |
+
|----|----:|---------:|---:|
|
| 1899 |
+
|dart|62659| 2768|6959|
|
| 1900 |
+
|
| 1901 |
+
#### e2e_nlg
|
| 1902 |
+
|
| 1903 |
+
| |train|validation|test|
|
| 1904 |
+
|-------|----:|---------:|---:|
|
| 1905 |
+
|e2e_nlg|33525| 4299|4693|
|
| 1906 |
+
|
| 1907 |
+
#### mlsum_de
|
| 1908 |
+
|
| 1909 |
+
| |train |validation|test |
|
| 1910 |
+
|--------|-----:|---------:|----:|
|
| 1911 |
+
|mlsum_de|220748| 11392|10695|
|
| 1912 |
+
|
| 1913 |
+
#### mlsum_es
|
| 1914 |
+
|
| 1915 |
+
| |train |validation|test |
|
| 1916 |
+
|--------|-----:|---------:|----:|
|
| 1917 |
+
|mlsum_es|259886| 9977|13365|
|
| 1918 |
+
|
| 1919 |
+
#### schema_guided_dialog
|
| 1920 |
+
|
| 1921 |
+
| |train |validation|test |
|
| 1922 |
+
|--------------------|-----:|---------:|----:|
|
| 1923 |
+
|schema_guided_dialog|164982| 10000|10000|
|
| 1924 |
+
|
| 1925 |
+
#### totto
|
| 1926 |
+
|
| 1927 |
+
| |train |validation|test|
|
| 1928 |
+
|-----|-----:|---------:|---:|
|
| 1929 |
+
|totto|121153| 7700|7700|
|
| 1930 |
+
|
| 1931 |
+
#### web_nlg_en
|
| 1932 |
+
|
| 1933 |
+
| |train|validation|test|
|
| 1934 |
+
|----------|----:|---------:|---:|
|
| 1935 |
+
|web_nlg_en|35426| 1667|1779|
|
| 1936 |
+
|
| 1937 |
+
#### web_nlg_ru
|
| 1938 |
+
|
| 1939 |
+
| |train|validation|test|
|
| 1940 |
+
|----------|----:|---------:|---:|
|
| 1941 |
+
|web_nlg_ru|14630| 790|1102|
|
| 1942 |
+
|
| 1943 |
+
#### wiki_auto_asset_turk
|
| 1944 |
+
|
| 1945 |
+
| |train |validation|test_asset|test_turk|
|
| 1946 |
+
|--------------------|-----:|---------:|---------:|--------:|
|
| 1947 |
+
|wiki_auto_asset_turk|373801| 73249| 359| 359|
|
| 1948 |
+
|
| 1949 |
+
#### wiki_lingua_es_en
|
| 1950 |
+
|
| 1951 |
+
| |train|validation|test |
|
| 1952 |
+
|-----------------|----:|---------:|----:|
|
| 1953 |
+
|wiki_lingua_es_en|79515| 8835|19797|
|
| 1954 |
+
|
| 1955 |
+
#### wiki_lingua_ru_en
|
| 1956 |
+
|
| 1957 |
+
| |train|validation|test|
|
| 1958 |
+
|-----------------|----:|---------:|---:|
|
| 1959 |
+
|wiki_lingua_ru_en|36898| 4100|9094|
|
| 1960 |
+
|
| 1961 |
+
#### wiki_lingua_tr_en
|
| 1962 |
+
|
| 1963 |
+
| |train|validation|test|
|
| 1964 |
+
|-----------------|----:|---------:|---:|
|
| 1965 |
+
|wiki_lingua_tr_en| 3193| 355| 808|
|
| 1966 |
+
|
| 1967 |
+
#### wiki_lingua_vi_en
|
| 1968 |
+
|
| 1969 |
+
| |train|validation|test|
|
| 1970 |
+
|-----------------|----:|---------:|---:|
|
| 1971 |
+
|wiki_lingua_vi_en| 9206| 1023|2167|
|
| 1972 |
+
|
| 1973 |
+
#### xsum
|
| 1974 |
+
|
| 1975 |
+
| |train|validation|test|
|
| 1976 |
+
|----|----:|---------:|---:|
|
| 1977 |
+
|xsum|23206| 1117|1166|
|
| 1978 |
+
|
| 1979 |
+
## Dataset Creation
|
| 1980 |
+
|
| 1981 |
+
### Curation Rationale
|
| 1982 |
+
|
| 1983 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1984 |
+
|
| 1985 |
+
### Source Data
|
| 1986 |
+
|
| 1987 |
+
#### Initial Data Collection and Normalization
|
| 1988 |
+
|
| 1989 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1990 |
+
|
| 1991 |
+
#### Who are the source language producers?
|
| 1992 |
+
|
| 1993 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1994 |
+
|
| 1995 |
+
### Annotations
|
| 1996 |
+
|
| 1997 |
+
#### Annotation process
|
| 1998 |
+
|
| 1999 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 2000 |
+
|
| 2001 |
+
#### Who are the annotators?
|
| 2002 |
+
|
| 2003 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 2004 |
+
|
| 2005 |
+
### Personal and Sensitive Information
|
| 2006 |
+
|
| 2007 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 2008 |
+
|
| 2009 |
+
## Considerations for Using the Data
|
| 2010 |
+
|
| 2011 |
+
### Social Impact of Dataset
|
| 2012 |
+
|
| 2013 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 2014 |
+
|
| 2015 |
+
### Discussion of Biases
|
| 2016 |
+
|
| 2017 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 2018 |
+
|
| 2019 |
+
### Other Known Limitations
|
| 2020 |
+
|
| 2021 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 2022 |
+
|
| 2023 |
+
## Additional Information
|
| 2024 |
+
|
| 2025 |
+
### Dataset Curators
|
| 2026 |
+
|
| 2027 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 2028 |
+
|
| 2029 |
+
### Licensing Information
|
| 2030 |
+
|
| 2031 |
+
CC-BY-SA-4.0
|
| 2032 |
+
|
| 2033 |
+
### Citation Information
|
| 2034 |
+
|
| 2035 |
+
```
|
| 2036 |
+
@article{gem_benchmark,
|
| 2037 |
+
author = {Sebastian Gehrmann and
|
| 2038 |
+
Tosin P. Adewumi and
|
| 2039 |
+
Karmanya Aggarwal and
|
| 2040 |
+
Pawan Sasanka Ammanamanchi and
|
| 2041 |
+
Aremu Anuoluwapo and
|
| 2042 |
+
Antoine Bosselut and
|
| 2043 |
+
Khyathi Raghavi Chandu and
|
| 2044 |
+
Miruna{-}Adriana Clinciu and
|
| 2045 |
+
Dipanjan Das and
|
| 2046 |
+
Kaustubh D. Dhole and
|
| 2047 |
+
Wanyu Du and
|
| 2048 |
+
Esin Durmus and
|
| 2049 |
+
Ondrej Dusek and
|
| 2050 |
+
Chris Emezue and
|
| 2051 |
+
Varun Gangal and
|
| 2052 |
+
Cristina Garbacea and
|
| 2053 |
+
Tatsunori Hashimoto and
|
| 2054 |
+
Yufang Hou and
|
| 2055 |
+
Yacine Jernite and
|
| 2056 |
+
Harsh Jhamtani and
|
| 2057 |
+
Yangfeng Ji and
|
| 2058 |
+
Shailza Jolly and
|
| 2059 |
+
Dhruv Kumar and
|
| 2060 |
+
Faisal Ladhak and
|
| 2061 |
+
Aman Madaan and
|
| 2062 |
+
Mounica Maddela and
|
| 2063 |
+
Khyati Mahajan and
|
| 2064 |
+
Saad Mahamood and
|
| 2065 |
+
Bodhisattwa Prasad Majumder and
|
| 2066 |
+
Pedro Henrique Martins and
|
| 2067 |
+
Angelina McMillan{-}Major and
|
| 2068 |
+
Simon Mille and
|
| 2069 |
+
Emiel van Miltenburg and
|
| 2070 |
+
Moin Nadeem and
|
| 2071 |
+
Shashi Narayan and
|
| 2072 |
+
Vitaly Nikolaev and
|
| 2073 |
+
Rubungo Andre Niyongabo and
|
| 2074 |
+
Salomey Osei and
|
| 2075 |
+
Ankur P. Parikh and
|
| 2076 |
+
Laura Perez{-}Beltrachini and
|
| 2077 |
+
Niranjan Ramesh Rao and
|
| 2078 |
+
Vikas Raunak and
|
| 2079 |
+
Juan Diego Rodriguez and
|
| 2080 |
+
Sashank Santhanam and
|
| 2081 |
+
Jo{\~{a}}o Sedoc and
|
| 2082 |
+
Thibault Sellam and
|
| 2083 |
+
Samira Shaikh and
|
| 2084 |
+
Anastasia Shimorina and
|
| 2085 |
+
Marco Antonio Sobrevilla Cabezudo and
|
| 2086 |
+
Hendrik Strobelt and
|
| 2087 |
+
Nishant Subramani and
|
| 2088 |
+
Wei Xu and
|
| 2089 |
+
Diyi Yang and
|
| 2090 |
+
Akhila Yerukola and
|
| 2091 |
+
Jiawei Zhou},
|
| 2092 |
+
title = {The {GEM} Benchmark: Natural Language Generation, its Evaluation and
|
| 2093 |
+
Metrics},
|
| 2094 |
+
journal = {CoRR},
|
| 2095 |
+
volume = {abs/2102.01672},
|
| 2096 |
+
year = {2021},
|
| 2097 |
+
url = {https://arxiv.org/abs/2102.01672},
|
| 2098 |
+
archivePrefix = {arXiv},
|
| 2099 |
+
eprint = {2102.01672}
|
| 2100 |
+
}
|
| 2101 |
+
```
|
| 2102 |
+
|
| 2103 |
+
### Contributions
|
| 2104 |
+
|
| 2105 |
+
Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset.
|
huggingface_dataset/Dataset_Card/hebrew_projectbenyehuda.md
ADDED
|
@@ -0,0 +1,233 @@
|
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|
|
|
|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
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|
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|
|
|
|
|
|
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|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language_creators:
|
| 5 |
+
- found
|
| 6 |
+
language:
|
| 7 |
+
- he
|
| 8 |
+
license:
|
| 9 |
+
- mit
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- 10K<n<100K
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
task_categories:
|
| 17 |
+
- text-generation
|
| 18 |
+
- fill-mask
|
| 19 |
+
task_ids:
|
| 20 |
+
- language-modeling
|
| 21 |
+
- masked-language-modeling
|
| 22 |
+
paperswithcode_id: null
|
| 23 |
+
pretty_name: Hebrew Projectbenyehuda
|
| 24 |
+
dataset_info:
|
| 25 |
+
features:
|
| 26 |
+
- name: id
|
| 27 |
+
dtype: int32
|
| 28 |
+
- name: url
|
| 29 |
+
dtype: string
|
| 30 |
+
- name: title
|
| 31 |
+
dtype: string
|
| 32 |
+
- name: authors
|
| 33 |
+
dtype: string
|
| 34 |
+
- name: translators
|
| 35 |
+
dtype: string
|
| 36 |
+
- name: original_language
|
| 37 |
+
dtype: string
|
| 38 |
+
- name: genre
|
| 39 |
+
dtype: string
|
| 40 |
+
- name: source_edition
|
| 41 |
+
dtype: string
|
| 42 |
+
- name: text
|
| 43 |
+
dtype: string
|
| 44 |
+
splits:
|
| 45 |
+
- name: train
|
| 46 |
+
num_bytes: 318732537
|
| 47 |
+
num_examples: 10078
|
| 48 |
+
download_size: 317749152
|
| 49 |
+
dataset_size: 318732537
|
| 50 |
+
---
|
| 51 |
+
|
| 52 |
+
# Dataset Card for Hebrew Projectbenyehuda
|
| 53 |
+
|
| 54 |
+
## Table of Contents
|
| 55 |
+
- [Dataset Description](#dataset-description)
|
| 56 |
+
- [Dataset Summary](#dataset-summary)
|
| 57 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 58 |
+
- [Languages](#languages)
|
| 59 |
+
- [Dataset Structure](#dataset-structure)
|
| 60 |
+
- [Data Instances](#data-instances)
|
| 61 |
+
- [Data Fields](#data-fields)
|
| 62 |
+
- [Data Splits](#data-splits)
|
| 63 |
+
- [Dataset Creation](#dataset-creation)
|
| 64 |
+
- [Curation Rationale](#curation-rationale)
|
| 65 |
+
- [Source Data](#source-data)
|
| 66 |
+
- [Annotations](#annotations)
|
| 67 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 68 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 69 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 70 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 71 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 72 |
+
- [Additional Information](#additional-information)
|
| 73 |
+
- [Dataset Curators](#dataset-curators)
|
| 74 |
+
- [Licensing Information](#licensing-information)
|
| 75 |
+
- [Citation Information](#citation-information)
|
| 76 |
+
- [Contributions](#contributions)
|
| 77 |
+
|
| 78 |
+
## Dataset Description
|
| 79 |
+
|
| 80 |
+
- **Homepage:** https://github.com/projectbenyehuda/public_domain_dump
|
| 81 |
+
- **Repository:** https://github.com/projectbenyehuda/public_domain_dump
|
| 82 |
+
- **Paper:**
|
| 83 |
+
- **Leaderboard:**
|
| 84 |
+
- **Point of Contact:**
|
| 85 |
+
|
| 86 |
+
### Dataset Summary
|
| 87 |
+
|
| 88 |
+
This repository contains a dump of thousands of public domain works in Hebrew, from Project Ben-Yehuda, in plaintext UTF-8 files, with and without diacritics (nikkud), and in HTML files. The pseudocatalogue.csv file is a list of titles, authors, genres, and file paths, to help you process the dump.
|
| 89 |
+
|
| 90 |
+
The Releases tab contains a downloadable ZIP archive of the full release. The git repo can be used to track individual file changes, or for incremenetal updates. In the ZIPs, each format (plaintext, plaintext stripped of diacritics, and HTML) has a ZIP file containing one directory per author, with all the author's works under that directory.
|
| 91 |
+
|
| 92 |
+
To request changes or improvements to this dump, file an issue against this repository.
|
| 93 |
+
|
| 94 |
+
All these works are in the public domain, so you are free to make any use of them, and do not need to ask for permission.
|
| 95 |
+
|
| 96 |
+
If you would like to give credit, please credit "Project Ben-Yehuda volunteers", and include a link to the site. We'd also love to hear about the uses you've made of this dump, as it encourages us to keep producing the dump. E-mail us with a brief description (and links, if/as appropriate) of your re-use, at editor@benyehuda.org.
|
| 97 |
+
|
| 98 |
+
There are 10078 files, 3181136 lines
|
| 99 |
+
|
| 100 |
+
Data Annotation:
|
| 101 |
+
|
| 102 |
+
### Supported Tasks and Leaderboards
|
| 103 |
+
|
| 104 |
+
[More Information Needed]
|
| 105 |
+
|
| 106 |
+
### Languages
|
| 107 |
+
|
| 108 |
+
Hebrew
|
| 109 |
+
|
| 110 |
+
## Dataset Structure
|
| 111 |
+
|
| 112 |
+
### Data Instances
|
| 113 |
+
|
| 114 |
+
Sample:
|
| 115 |
+
```
|
| 116 |
+
{
|
| 117 |
+
'id': 10,
|
| 118 |
+
'url': 'https://raw.githubusercontent.com/projectbenyehuda/public_domain_dump/master/txt/p23/m10.txt',
|
| 119 |
+
'title': 'חצי-נחמה',
|
| 120 |
+
'authors': 'אחד העם',
|
| 121 |
+
'translators': '',
|
| 122 |
+
'original_language': '',
|
| 123 |
+
'genre': 'מאמרים ומסות',
|
| 124 |
+
'source_edition': '',
|
| 125 |
+
'text': '\n\n\n\t\n\tחצי-נחמה\n\t\n\n\n\n1\n\nבין כל הצרות שנתחדשו עלינו בעת האחרונה תעשׂה ביחוד רושם מעציב בלב כל איש ישׂראל התחדשות ‘עלילת־הדם’. העלילה הנתעבה הזאת, בכל יָשנה, היתה ותהיה תמיד בעינינו כחדשה, ומימי הבינים ועד עתה תצטין בפעולתה החזקה על רוח עמנו, לא רק במקום המעשׂה, כי אם גם בארצות רחוקות שהגיעה אליהן השמועה.\n\nאמרתי: ‘על רוח עמנו’, כי אמנם רואה אני מקור החזיון הזה לא בסבּות חיצוניות, כי אם עמוק ברוח העם. בימי הבינים, שהיה כלל ישׂראל במקרים כאלה רגיל לחשוב עצמו כעומד במשפט ביחד עם אותם האומללים שעלה עליהם הגורל להיות כפּרותו, – יש מקום אמנם לראות בזה רק תוצאת הסכנה הגשמית הגדולה להכלל כולו, שהיתה כרוכה אז באמת בעקב כל עלילה כזו. גם לפני חמשים שנה, בימי מנוחה ושלוה, שעוררה עלילת דמשׂק רעש גדול כל־כך בארצות המערב, עדיין יש מקום לאמר, כי היתה בזה, להפך, יד הקנאה הגדולה לכבודם וזכויותיהם ששׂררה אז בלבות אחינו המערביים, אשר זה מעט יצאו מעבדות לחרות. אך בימינו אלה הרי מצד אחד אין הסכנה הגשמית גדולה עוד הרבה, ביחוד לקהלות רחוקות, ומצד אחר כבר הורגלנו לשמוע חרפתנו בקור רוח וקנאת כבודנו לא תאכלנו עוד, ואם בכל זאת גם עתה עודנו מתעוררים ומתנודדים בחזקה לשמע ‘עלילת־דם’, ורגש הכלל יתפרץ החוצה מכל עברים להשליך מעליו את החלאה הזאת, – אות הוא, כי לא הפחד ולא הכבוד החיצוני הם המניעים לזה, כי אם רוח העם הוא המרגיש פה את קלונו והוא זה המתעורר והמעורר; כי אעפ"י שבכל יתר הדברים כבר הביאונו צרותינו לאותו המצב שעליו אמר הנשׂיא החכם בימי קדם: ‘אין בשׂר המת מרגיש באיזמל’, – הנה פה אין ‘האיזמל’ חותך את ‘הבשׂר’ בלבד, כי אם עד הנפש יגע…\n\nאבל – ‘אין רע בלא טוב’, כלומר, בלא לקח טוב. גם הרע הגדול הזה שאנו עסוקים בו אינו ריק מלקח טוב, ואנחנו, אשר לא אדונים אנחנו לגורלנו וגם את הטוב גם את הרע נקבל מן החוץ שלא בטובתנו, ראוי לנו לבקש ברעותינו תמיד את התועלת הלמודית הצפונה בהן, והיתה לנו זאת, לפחות, חצי נחמה.\n\n\n\nאחד הכוחות היותר גדולים בחיי החברה הוא – ‘ההסכמה הכללית’. היו ימים שגם הפלוסופים ראו בהסכמה זו מופת נאמן על הדבר המוסכם ונתנו לה מקום בתוך שאר מופתיהם על מציאות האלהות. עתה אמנם יודעים הפלוסופים , שאין שקר ואין אולת אשר לא תוכל לבוא עליו ‘ההסכמה הכללית’, אם אך תנאי החיים נאותים לזה. אבל רק הפלוסופים יודעים זאת, ובעיני ההמון עוד גם עתה אין אַבטוֹריטט גדול מן ‘ההסכמה’: אם ‘כל העולם’ מאמינים שהדבר כן, בודאי כן הוא; ואם אני איני מבינו, אחרים מבינים; ואם אני רואה כעין סתירה לו, הרי ‘הכל’ רואים גם כן ואעפ"כ מאמינים, וכי חכם אני מכל העולם? – זה הוא בקירוב מהלך הרעיונות של האיש הפשוט, בדעת או בלי דעת ברורה, ומתוך כך הוא מסכים גם מצדו ונעשׂה בעצמו חלק מן ‘ההסכמה’.\n\nוכל־כך גדול כוח ‘ההסכמה’, עד שעל הרוב לא יוכל האדם למַלט נפשו מפעולתה גם כשהוא עצמו הוא ‘הדבר המוסכם’. אם ‘כל העולם’ אומרים על פלוני שגדול הוא בחכמה או ביראה, שיש בו מדה פלונית, טובה או רעה, – סופו להסכים לזה גם בעצמו, אע"פ שמתחלה לא מצא בנפשו אותו היתרון או החסרון שאחרים מיחסים לו. ולא זו בלבד אלא שההסכמה הזאת מצד ‘המוסכם’ עצמו פועלת מעט מעט על תכונת רוחו עד שמקרבתו באמת (או, לפחות, מולידה בו נטיה להתקרב) אל המצב ההוא שרואה בו ‘כל העולם’. על כן יזהירו הפדגוגים בצדק, לבלתי עורר את הילדים על מגרעותיהם המוסריות בראשית התפתחותן, וכל שכּן לבלתי יחס להם מגרעות שאין בהם, כי על ידי זה אפשר שנחזק בלבם את הראשונות ונוליד בם נטיה להאחרונות.\n\nואולם, הדבר מובן, כי ‘כל העולם’ אינו אחד לכל אחד. האדם רואה ‘עולמו’ רק באותה החברה שהוא חושב עצמו לחלק ממנה ורואה באישיה אנשים הקרובים לו מאיזה צד; אבל אין אדם חושב למאומה הסכמת אנשים שרוחם זרה לו לגמרי, שאינו מרגיש בנפשו שום יחס פנימי בינו ובינם. ככה אין האוֹרתוֹדוֹכּסים והמשׂכילים שלנו שׂמים לב כלל אלו להסכמתם של אלו, אף בדברים שאינם נוגעים לאמונה ודת, ושׂחקם ולעגם של אלו על אלו אינו עושׂה שום רושם בלבם של שניהם, לפי שכּל אחת משתי הכּתּות רואה את חברתה כאלו אינה. ואולם כשתנאי החיים מכריחים את בני הכתות השונות להמצא במשׂא ומתן תמידי זה עם זה והם מתרגלים לראות זה בזה קודם כל את האדם, – אז יתרחב ‘עולמם’ והשקפותיהם סובלות שנויים רבים על פי הסכמת ‘העולם’ במובנו החדש.\n\n\n\nלפיכך, בדורות שעברו, כשהיו אבותינו מאמינים בפשטו של ‘אתה בחרתנו’, לא היתה החרפּה שחרפום האומות פועלת כלל על טוהר נפשם פנימה. הם ידעו את ערכם ולא התפעלו עד מה מן ‘ההסכמה הכללית’ אשר מחוץ להם, בהיות כל חברת ‘המסכימים’ נחשבת בעיניהם למין מיוחד של בריות זרות להם ושונות מהם שנוי עצמי, בלי כל יחס וכל דמיון בינם ובינן. אז היה היהודי יכול לשמוע במנוחת לב כל המגרעות המוסריות והחטאים המעשׂיים שטפלה עליו הסכמת העמים, מבלי להרגיש בנפשו שום בושה או שפלוּת פנימית. כי מה לו ולמחשבות ‘הנכרים’ עליו ועל ערכּוֹ? לוּ רק יתנו לו לישב בשלוה! – אבל בדור הזה אין הדבר כן, עתה ‘עולמנו’ נתרחב הרבה, וההסכמה האירופּית פועלת עלינו בחזקה בכל ענפי החיים. ולפי שאין אנו מוציאים עוד את ‘הכל’ מן הכלל, לכן נתפעל בעל כרחנו ממה ש’הכל\' מוציאים אותנו מן הכלל, סופר אחד רוסי שאל באלו הימים בתמימוּת: אחר שכל העולם שׂונאים את היהודים, וכי אפשר לאמור, שכל העולם חייבים והיהודים זכאים? – ושאלה כזו מתגנבת עתה גם אל לב רבים מאחינו: וכי אפשר לאמור, שכל אותן התכונות הנשחתות והמעשׂים הרעים שכל העולם מיחס ליהודים אינם אלא ‘בדותא’?\n\nוהספק הזה, מכיון שנתעורר, מוצא לו מחיה בנקל באותם ההיקשים המוטעים ‘מן הפרט אל הכלל’ הרגילים מאד אצל המון בני האדם. הספור הידוע על דבר נוסע אחד, שבא לאחת הערים ונזדמן לאכסניא שהיה בה משרת כבד־פה, וכתב בפנקסו: בעיר פלונית משרתי האכסניות הם כבדי־פה, – הספור הזה מצייר בצורה של התוּל דרכי־ההגיון של ההמון ברוב משפטיו הכלליים. כל החזיונות הנראים באיזה דבר פרטי רגיל ההמון ליחס אל הכלל שהדבר ההוא מתחשב עליו לפי שמו התמידי, מבלי להתבונן, כי ‘פרט’ אחד יוכל להתחשב על ‘כללים’ רבים ביחד, כלומר, להיות שוּתף בתכוּנה אחת עם פרטיו של כלל אחד ובתכונה אחרת עם פרטיו של כלל אחר, בעוד שהשם הנקרא עליו מציין רק את התיחסותו לאחד הכללים באחד מצדדיו, לא בכולם. – על משפטים ממין זה תוכל להשען, וגם תשען באמת, ההסכמה הכללית ביחוסה אלינו: פלוני ופלוני הם יהודים לפי שמם ורמאים לפי תכוּנתם; שמע מינה, שהיהודים הם לפי תכונתם רמאים. ההגיון האמתי ישיב אמנם על זה, כי אף אם היו באמת כל היהודים בדורנו רמאים, אין מזה עוד ראיה, שהיהודים הם רמאים, כלומר, שתכוּנת הרמאוּת הנמצאת בכל יהודי נמצאת בו מצד התיחסותו אל הכלל ‘יהודים’ ולא מצד איזה כלל אחר (למשל, כלל ‘סוחרים’), שגם אליו מתיחס היהודי בתור פרט, ביחד עם אחרים אשר דבר אין להם עם הכלל ‘יהודים’. וכדי לברר הדבר, צריך לבדוֹק תחלה אותם ‘האחרים’ המשתתפים יחד עם היהודים בכללים אחרים. ורק אחר שנמצא על ידי בדיקה זו, שאין תכוּנת הרמאוּת מצויה בשום ‘כלל’ אחר המשותף ליהודים ולאחרים, – רק אז תהיה לנו צדקה לחרוץ משפט, כי היהדות היא אֵם הרמאוּת. – אבל, כאמור, אין דרכם של בני אדם להעמיק בהגיון, ואין אנו יכולים לדרוש כזאת גם מהמון בני עמנו. הם שומעים את המשפט החרוץ של ההסכמה הכללית ורואים עם זה, שרבים בקרבּנוּ כך הם באמת כמו שאומרת ההסכמה, ובזה די להם, והרי הם מתחילים להסכים גם בעצמם. וככה עוברות ‘תכוּנות היהודים’ כמטבע כשרה מיד ליד, מן ההסכמה החיצונית של העמים אל ההסכמה הפנימית בקרב עמנו, רק עם ההבדל הזה, שהעמים מונים את תכוּנותינו הרעות אחת לאחת בקול ענוֹת גבוּרה ולעג השאננים, ואנחנו עונים אחריהם מלה במלה בקול דממה דקה והצטדקות חלושה; הם ממשילים אותנו לכלי חרס, שאין לו תקנה אלא שבירה, ואנחנו ממשילים עצמנו לכלי מתכת, שאפשר לו בהגעלה ולבּוּן…\n\nהמצב הזה, אם יאריך ימים, יוכל לגרום לנו נזק מוסרי גדול. אין דבר מסוכּן לגוי ולאדם כהודאה על חטאים שאין בו. מי שחטא באמת, הרי שערי תשובה לא ננעלו, וברצונו הטוב יכול להסיר חלאתו מעליו. אבל מי שאחרים הביאוהו לחשוֹד עצמו במה שאין בו, איך יוכל להטהר בעיני עצמו? מצד אחד מאמין הוא לדברי האומרים לו: טול קורה מבין עיניך, ומצד אחר מרגיש הוא, שאינו יכול לטול את הקורה מבין עיניו, אחר שאינה באמת אלא בדמיון, והרי הוא במצב אותם המונומַנים הידועים, שמאיזו סבּה באו לידי אמונה, כי משׂא כבד תלוי להם בחוטמם מבלי שיוכלו להסירו. ולא עוד אלא שלפעמים תביא אמונה זו את האיש הפרטי להשתתף באותה המדה המגוּנה שלפי אמונתו היא קנין הכלל כולו, אעפ“י שהוא עצמו מצד פרטיותו אינו נוטה כלל לזה. אין ספק, למשל, כי בקרב העם שיצאו מתוכו אנשים כהרמב”ם נמצאים גם עתה בעלי דעה מיושבת ואוהבי סדר ושיטה בכל דבר, והם, בקחתם חלק בעבודת הצבּוּר, היו יכולים לתת בה את רוחם ולפעול גם על יתר העובדים. אבל מה נעשׂה, וכל גזרה ‘ההסכמה’, ששׂנאת הסדרים היא תכוּנה יהודית, וכבר הסכמנו גם אנחנו להסכמה זו (אעפ"י שעוד לא נתברר, אם התכוּנה הזאת, המצויה באמת בחלק גדול מעמנו, מתיחסת אל הכלל ‘יהודים’, או אולי – מה שיותר מתקבל על הלב – אל הכלל ‘חניכי־החדר’). ועל כן תרפינה ידי אוהבי הסדר, בהאמינם, כי אין עצה ואין תבונה נגד תכוּנת העם. ואם פטריוטים הם, יעקרו גם מלבם את האהבה לסדרים, המתנגדת לרוח עמם, ויעשׂו גם הם את מעשׂיהם כראוי ליהודים אמתיים…\n\n\n\nצריך איפוא לבקש איזה אמצעי, איך להוציא את עצמנו מתחת השפעת ‘ההסכמה הכללית’ בנוגע לתכוּנות ישׂראל וערכו המוסרי, כדי שלא נהיה בזויים בעיני עצמנו ולא נחשוב, שבאמת גרועים אנחנו מכל בני האדם תחת השמש, וכדי שלא נבוא עי"ז להיות ברבות הימים בפועל מה שאין אנו עתה אלא בדמיון.\n\nואת האמצעי הזה נותנת לנו ‘ההסכמה הכללית’ עצמה על ידי עלילת־הדם. העלילה הזאת היא היחידה בין כל רעותיה אשר בה לא תוכל ההסכמה להביא גם אותנו לידי ספק, אם באמת ‘כל העולם חייבים ואנחנו זכאים’, בהיותה מיוסדת כולה על שקר מוחלט ואין לה משען באיזה היקש מוטעה ‘מן הפרט על הכלל’. כל איש ישׂראל שנתחנך בתוך עמו יודע בבירור גמור, שאין בתוך כלל ישׂראל אף פרט אחד האוכל דם אדם לשם שמים. ואת הידיעה הברורה הזאת משגיאת ‘ההסכמה הכללית’, המתחדשת בלבנו מזמן לזמן על ידי התחדשות עלילת־הדם, צריכים אנו לשמור תמיד בזכרוננו, והיא תעזור לנו לעקור מלבנו את הנטיה להכּנע מפני האַבטוֹריטט של ‘כל העולם’ גם ביתר הדברים. יאמר כל העולם מה שיאמר על דבר פחיתוּת ערכּנוּ המוסרי, – אנחנו יודעים, כי ‘ההסכמה’ הזאת נשענת רק על הגיון המוני, בלי כל יסוד מדעי אמתּי. כי מי בא בסוד עמקי רוחנו וראה את ‘היהודי’ כמו שהוא מצד עצמו? מי שקל זה לעומת זה יהודים ושאינם יהודים הדומים אלו לאלו בכל יתר ‘הכללים’: סוחרים לעומת סוחרים, נרדפים לעומת נרדפים, רעבים לעומת ר��בים וכו\'. – מי שקל כל אלה במאזני החכמה האמתּית ומצא את הכף מַכרעת לאחד הצדדים?\n\n‘וכי אפשר שכּל העולם חייבים והיהודים זכאים?’\n\nאפשר ואפשר, ועלילת־הדם תוכיח. פה הרי היהודים זכאים וטהורים כמלאכי השרת: יהודי ודם! היש שני הפכים גדולים מאלו? – ואף על פי כן…\n\n\n\nה\' תשרי תרנ"ג\n\n\n\n\n\n\nנדפס ב‘המליץ’ י“ד תשרי תרנ”ג. \xa0↩\n\n\n\n\n\n\n\n\n\n\nאת הטקסט לעיל הפיקו מתנדבי פרויקט בן־יהודה באינטרנט. הוא זמין תמיד בכתובת הבאה:https://benyehuda.org/read/10'
|
| 126 |
+
}
|
| 127 |
+
```
|
| 128 |
+
|
| 129 |
+
### Data Fields
|
| 130 |
+
|
| 131 |
+
- `authors`
|
| 132 |
+
- `genre`
|
| 133 |
+
- `id`
|
| 134 |
+
- `original_language`
|
| 135 |
+
- `source_edition`
|
| 136 |
+
- `text`
|
| 137 |
+
- `title`
|
| 138 |
+
- `translators`
|
| 139 |
+
- `url`
|
| 140 |
+
|
| 141 |
+
### Data Splits
|
| 142 |
+
|
| 143 |
+
| | train |
|
| 144 |
+
|--------|------:|
|
| 145 |
+
| corpus | 10078 |
|
| 146 |
+
|
| 147 |
+
## Dataset Creation
|
| 148 |
+
|
| 149 |
+
### Curation Rationale
|
| 150 |
+
|
| 151 |
+
[More Information Needed]
|
| 152 |
+
|
| 153 |
+
### Source Data
|
| 154 |
+
|
| 155 |
+
#### Initial Data Collection and Normalization
|
| 156 |
+
|
| 157 |
+
[More Information Needed]
|
| 158 |
+
|
| 159 |
+
#### Who are the source language producers?
|
| 160 |
+
|
| 161 |
+
Researchers.
|
| 162 |
+
|
| 163 |
+
### Annotations
|
| 164 |
+
|
| 165 |
+
#### Annotation process
|
| 166 |
+
|
| 167 |
+
[More Information Needed]
|
| 168 |
+
|
| 169 |
+
#### Who are the annotators?
|
| 170 |
+
|
| 171 |
+
[More Information Needed]
|
| 172 |
+
|
| 173 |
+
### Personal and Sensitive Information
|
| 174 |
+
|
| 175 |
+
[More Information Needed]
|
| 176 |
+
|
| 177 |
+
## Considerations for Using the Data
|
| 178 |
+
|
| 179 |
+
### Social Impact of Dataset
|
| 180 |
+
|
| 181 |
+
[More Information Needed]
|
| 182 |
+
|
| 183 |
+
### Discussion of Biases
|
| 184 |
+
|
| 185 |
+
[More Information Needed]
|
| 186 |
+
|
| 187 |
+
### Other Known Limitations
|
| 188 |
+
|
| 189 |
+
[More Information Needed]
|
| 190 |
+
|
| 191 |
+
## Additional Information
|
| 192 |
+
|
| 193 |
+
### Dataset Curators
|
| 194 |
+
|
| 195 |
+
[More Information Needed]
|
| 196 |
+
|
| 197 |
+
### Licensing Information
|
| 198 |
+
|
| 199 |
+
MIT License
|
| 200 |
+
|
| 201 |
+
Permission is hereby granted, free of charge, to any person obtaining a copy
|
| 202 |
+
of this software and associated documentation files (the "Software"), to deal
|
| 203 |
+
in the Software without restriction, including without limitation the rights
|
| 204 |
+
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
| 205 |
+
copies of the Software, and to permit persons to whom the Software is
|
| 206 |
+
furnished to do so, subject to the following conditions:
|
| 207 |
+
|
| 208 |
+
The above copyright notice and this permission notice shall be included in all
|
| 209 |
+
copies or substantial portions of the Software.
|
| 210 |
+
|
| 211 |
+
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
| 212 |
+
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
| 213 |
+
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
| 214 |
+
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
| 215 |
+
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
| 216 |
+
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
| 217 |
+
SOFTWARE.
|
| 218 |
+
|
| 219 |
+
### Citation Information
|
| 220 |
+
|
| 221 |
+
```
|
| 222 |
+
@article{,
|
| 223 |
+
author = {},
|
| 224 |
+
title = {Public domain texts from Project Ben-Yehuda},
|
| 225 |
+
journal = {},
|
| 226 |
+
url = {https://github.com/projectbenyehuda/public_domain_dump},
|
| 227 |
+
year = {2020},
|
| 228 |
+
}
|
| 229 |
+
```
|
| 230 |
+
|
| 231 |
+
### Contributions
|
| 232 |
+
|
| 233 |
+
Thanks to [@imvladikon](https://github.com/imvladikon) for adding this dataset.
|
huggingface_dataset/Dataset_Card/huggingartists_veggietales.md
ADDED
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
tags:
|
| 5 |
+
- huggingartists
|
| 6 |
+
- lyrics
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Dataset Card for "huggingartists/veggietales"
|
| 10 |
+
|
| 11 |
+
## Table of Contents
|
| 12 |
+
- [Dataset Description](#dataset-description)
|
| 13 |
+
- [Dataset Summary](#dataset-summary)
|
| 14 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 15 |
+
- [Languages](#languages)
|
| 16 |
+
- [How to use](#how-to-use)
|
| 17 |
+
- [Dataset Structure](#dataset-structure)
|
| 18 |
+
- [Data Fields](#data-fields)
|
| 19 |
+
- [Data Splits](#data-splits)
|
| 20 |
+
- [Dataset Creation](#dataset-creation)
|
| 21 |
+
- [Curation Rationale](#curation-rationale)
|
| 22 |
+
- [Source Data](#source-data)
|
| 23 |
+
- [Annotations](#annotations)
|
| 24 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 25 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 26 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 27 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 28 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 29 |
+
- [Additional Information](#additional-information)
|
| 30 |
+
- [Dataset Curators](#dataset-curators)
|
| 31 |
+
- [Licensing Information](#licensing-information)
|
| 32 |
+
- [Citation Information](#citation-information)
|
| 33 |
+
- [About](#about)
|
| 34 |
+
|
| 35 |
+
## Dataset Description
|
| 36 |
+
|
| 37 |
+
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
|
| 38 |
+
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
|
| 39 |
+
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 40 |
+
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 41 |
+
- **Size of the generated dataset:** 0.220878 MB
|
| 42 |
+
|
| 43 |
+
|
| 44 |
+
<div class="inline-flex flex-col" style="line-height: 1.5;">
|
| 45 |
+
<div class="flex">
|
| 46 |
+
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/d14c9e27b39f0e250784a2dce037a03d.720x720x1.jpg')">
|
| 47 |
+
</div>
|
| 48 |
+
</div>
|
| 49 |
+
<a href="https://huggingface.co/huggingartists/veggietales">
|
| 50 |
+
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
|
| 51 |
+
</a>
|
| 52 |
+
<div style="text-align: center; font-size: 16px; font-weight: 800">VeggieTales</div>
|
| 53 |
+
<a href="https://genius.com/artists/veggietales">
|
| 54 |
+
<div style="text-align: center; font-size: 14px;">@veggietales</div>
|
| 55 |
+
</a>
|
| 56 |
+
</div>
|
| 57 |
+
|
| 58 |
+
### Dataset Summary
|
| 59 |
+
|
| 60 |
+
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
|
| 61 |
+
Model is available [here](https://huggingface.co/huggingartists/veggietales).
|
| 62 |
+
|
| 63 |
+
### Supported Tasks and Leaderboards
|
| 64 |
+
|
| 65 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 66 |
+
|
| 67 |
+
### Languages
|
| 68 |
+
|
| 69 |
+
en
|
| 70 |
+
|
| 71 |
+
## How to use
|
| 72 |
+
|
| 73 |
+
How to load this dataset directly with the datasets library:
|
| 74 |
+
|
| 75 |
+
```python
|
| 76 |
+
from datasets import load_dataset
|
| 77 |
+
|
| 78 |
+
dataset = load_dataset("huggingartists/veggietales")
|
| 79 |
+
```
|
| 80 |
+
|
| 81 |
+
## Dataset Structure
|
| 82 |
+
|
| 83 |
+
An example of 'train' looks as follows.
|
| 84 |
+
```
|
| 85 |
+
This example was too long and was cropped:
|
| 86 |
+
|
| 87 |
+
{
|
| 88 |
+
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
|
| 89 |
+
}
|
| 90 |
+
```
|
| 91 |
+
|
| 92 |
+
### Data Fields
|
| 93 |
+
|
| 94 |
+
The data fields are the same among all splits.
|
| 95 |
+
|
| 96 |
+
- `text`: a `string` feature.
|
| 97 |
+
|
| 98 |
+
|
| 99 |
+
### Data Splits
|
| 100 |
+
|
| 101 |
+
| train |validation|test|
|
| 102 |
+
|------:|---------:|---:|
|
| 103 |
+
|163| -| -|
|
| 104 |
+
|
| 105 |
+
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
|
| 106 |
+
|
| 107 |
+
```python
|
| 108 |
+
from datasets import load_dataset, Dataset, DatasetDict
|
| 109 |
+
import numpy as np
|
| 110 |
+
|
| 111 |
+
datasets = load_dataset("huggingartists/veggietales")
|
| 112 |
+
|
| 113 |
+
train_percentage = 0.9
|
| 114 |
+
validation_percentage = 0.07
|
| 115 |
+
test_percentage = 0.03
|
| 116 |
+
|
| 117 |
+
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
|
| 118 |
+
|
| 119 |
+
datasets = DatasetDict(
|
| 120 |
+
{
|
| 121 |
+
'train': Dataset.from_dict({'text': list(train)}),
|
| 122 |
+
'validation': Dataset.from_dict({'text': list(validation)}),
|
| 123 |
+
'test': Dataset.from_dict({'text': list(test)})
|
| 124 |
+
}
|
| 125 |
+
)
|
| 126 |
+
```
|
| 127 |
+
|
| 128 |
+
## Dataset Creation
|
| 129 |
+
|
| 130 |
+
### Curation Rationale
|
| 131 |
+
|
| 132 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 133 |
+
|
| 134 |
+
### Source Data
|
| 135 |
+
|
| 136 |
+
#### Initial Data Collection and Normalization
|
| 137 |
+
|
| 138 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 139 |
+
|
| 140 |
+
#### Who are the source language producers?
|
| 141 |
+
|
| 142 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 143 |
+
|
| 144 |
+
### Annotations
|
| 145 |
+
|
| 146 |
+
#### Annotation process
|
| 147 |
+
|
| 148 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 149 |
+
|
| 150 |
+
#### Who are the annotators?
|
| 151 |
+
|
| 152 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 153 |
+
|
| 154 |
+
### Personal and Sensitive Information
|
| 155 |
+
|
| 156 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 157 |
+
|
| 158 |
+
## Considerations for Using the Data
|
| 159 |
+
|
| 160 |
+
### Social Impact of Dataset
|
| 161 |
+
|
| 162 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 163 |
+
|
| 164 |
+
### Discussion of Biases
|
| 165 |
+
|
| 166 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 167 |
+
|
| 168 |
+
### Other Known Limitations
|
| 169 |
+
|
| 170 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 171 |
+
|
| 172 |
+
## Additional Information
|
| 173 |
+
|
| 174 |
+
### Dataset Curators
|
| 175 |
+
|
| 176 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 177 |
+
|
| 178 |
+
### Licensing Information
|
| 179 |
+
|
| 180 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 181 |
+
|
| 182 |
+
### Citation Information
|
| 183 |
+
|
| 184 |
+
```
|
| 185 |
+
@InProceedings{huggingartists,
|
| 186 |
+
author={Aleksey Korshuk}
|
| 187 |
+
year=2021
|
| 188 |
+
}
|
| 189 |
+
```
|
| 190 |
+
|
| 191 |
+
|
| 192 |
+
## About
|
| 193 |
+
|
| 194 |
+
*Built by Aleksey Korshuk*
|
| 195 |
+
|
| 196 |
+
[](https://github.com/AlekseyKorshuk)
|
| 197 |
+
|
| 198 |
+
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
|
| 199 |
+
|
| 200 |
+
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
|
| 201 |
+
|
| 202 |
+
For more details, visit the project repository.
|
| 203 |
+
|
| 204 |
+
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingface_dataset/Dataset_Card/irds_lotte_pooled_dev_search.md
ADDED
|
@@ -0,0 +1,50 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
pretty_name: '`lotte/pooled/dev/search`'
|
| 3 |
+
viewer: false
|
| 4 |
+
source_datasets: ['irds/lotte_pooled_dev']
|
| 5 |
+
task_categories:
|
| 6 |
+
- text-retrieval
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Dataset Card for `lotte/pooled/dev/search`
|
| 10 |
+
|
| 11 |
+
The `lotte/pooled/dev/search` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
|
| 12 |
+
For more information about the dataset, see the [documentation](https://ir-datasets.com/lotte#lotte/pooled/dev/search).
|
| 13 |
+
|
| 14 |
+
# Data
|
| 15 |
+
|
| 16 |
+
This dataset provides:
|
| 17 |
+
- `queries` (i.e., topics); count=2,931
|
| 18 |
+
- `qrels`: (relevance assessments); count=8,573
|
| 19 |
+
|
| 20 |
+
- For `docs`, use [`irds/lotte_pooled_dev`](https://huggingface.co/datasets/irds/lotte_pooled_dev)
|
| 21 |
+
|
| 22 |
+
## Usage
|
| 23 |
+
|
| 24 |
+
```python
|
| 25 |
+
from datasets import load_dataset
|
| 26 |
+
|
| 27 |
+
queries = load_dataset('irds/lotte_pooled_dev_search', 'queries')
|
| 28 |
+
for record in queries:
|
| 29 |
+
record # {'query_id': ..., 'text': ...}
|
| 30 |
+
|
| 31 |
+
qrels = load_dataset('irds/lotte_pooled_dev_search', 'qrels')
|
| 32 |
+
for record in qrels:
|
| 33 |
+
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
|
| 34 |
+
|
| 35 |
+
```
|
| 36 |
+
|
| 37 |
+
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
|
| 38 |
+
data in 🤗 Dataset format.
|
| 39 |
+
|
| 40 |
+
## Citation Information
|
| 41 |
+
|
| 42 |
+
```
|
| 43 |
+
@article{Santhanam2021ColBERTv2,
|
| 44 |
+
title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction",
|
| 45 |
+
author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia",
|
| 46 |
+
journal= "arXiv preprint arXiv:2112.01488",
|
| 47 |
+
year = "2021",
|
| 48 |
+
url = "https://arxiv.org/abs/2112.01488"
|
| 49 |
+
}
|
| 50 |
+
```
|
huggingface_dataset/Dataset_Card/keremberke_pokemon-classification.md
ADDED
|
@@ -0,0 +1,89 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
task_categories:
|
| 3 |
+
- image-classification
|
| 4 |
+
tags:
|
| 5 |
+
- roboflow
|
| 6 |
+
- roboflow2huggingface
|
| 7 |
+
- Gaming
|
| 8 |
+
---
|
| 9 |
+
|
| 10 |
+
<div align="center">
|
| 11 |
+
<img width="640" alt="keremberke/pokemon-classification" src="https://huggingface.co/datasets/keremberke/pokemon-classification/resolve/main/thumbnail.jpg">
|
| 12 |
+
</div>
|
| 13 |
+
|
| 14 |
+
### Dataset Labels
|
| 15 |
+
|
| 16 |
+
```
|
| 17 |
+
['Porygon', 'Goldeen', 'Hitmonlee', 'Hitmonchan', 'Gloom', 'Aerodactyl', 'Mankey', 'Seadra', 'Gengar', 'Venonat', 'Articuno', 'Seaking', 'Dugtrio', 'Machop', 'Jynx', 'Oddish', 'Dodrio', 'Dragonair', 'Weedle', 'Golduck', 'Flareon', 'Krabby', 'Parasect', 'Ninetales', 'Nidoqueen', 'Kabutops', 'Drowzee', 'Caterpie', 'Jigglypuff', 'Machamp', 'Clefairy', 'Kangaskhan', 'Dragonite', 'Weepinbell', 'Fearow', 'Bellsprout', 'Grimer', 'Nidorina', 'Staryu', 'Horsea', 'Electabuzz', 'Dratini', 'Machoke', 'Magnemite', 'Squirtle', 'Gyarados', 'Pidgeot', 'Bulbasaur', 'Nidoking', 'Golem', 'Dewgong', 'Moltres', 'Zapdos', 'Poliwrath', 'Vulpix', 'Beedrill', 'Charmander', 'Abra', 'Zubat', 'Golbat', 'Wigglytuff', 'Charizard', 'Slowpoke', 'Poliwag', 'Tentacruel', 'Rhyhorn', 'Onix', 'Butterfree', 'Exeggcute', 'Sandslash', 'Pinsir', 'Rattata', 'Growlithe', 'Haunter', 'Pidgey', 'Ditto', 'Farfetchd', 'Pikachu', 'Raticate', 'Wartortle', 'Vaporeon', 'Cloyster', 'Hypno', 'Arbok', 'Metapod', 'Tangela', 'Kingler', 'Exeggutor', 'Kadabra', 'Seel', 'Voltorb', 'Chansey', 'Venomoth', 'Ponyta', 'Vileplume', 'Koffing', 'Blastoise', 'Tentacool', 'Lickitung', 'Paras', 'Clefable', 'Cubone', 'Marowak', 'Nidorino', 'Jolteon', 'Muk', 'Magikarp', 'Slowbro', 'Tauros', 'Kabuto', 'Spearow', 'Sandshrew', 'Eevee', 'Kakuna', 'Omastar', 'Ekans', 'Geodude', 'Magmar', 'Snorlax', 'Meowth', 'Pidgeotto', 'Venusaur', 'Persian', 'Rhydon', 'Starmie', 'Charmeleon', 'Lapras', 'Alakazam', 'Graveler', 'Psyduck', 'Rapidash', 'Doduo', 'Magneton', 'Arcanine', 'Electrode', 'Omanyte', 'Poliwhirl', 'Mew', 'Alolan Sandslash', 'Mewtwo', 'Weezing', 'Gastly', 'Victreebel', 'Ivysaur', 'MrMime', 'Shellder', 'Scyther', 'Diglett', 'Primeape', 'Raichu']
|
| 18 |
+
```
|
| 19 |
+
|
| 20 |
+
|
| 21 |
+
### Number of Images
|
| 22 |
+
|
| 23 |
+
```json
|
| 24 |
+
{'train': 4869, 'valid': 1390, 'test': 732}
|
| 25 |
+
```
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
### How to Use
|
| 29 |
+
|
| 30 |
+
- Install [datasets](https://pypi.org/project/datasets/):
|
| 31 |
+
|
| 32 |
+
```bash
|
| 33 |
+
pip install datasets
|
| 34 |
+
```
|
| 35 |
+
|
| 36 |
+
- Load the dataset:
|
| 37 |
+
|
| 38 |
+
```python
|
| 39 |
+
from datasets import load_dataset
|
| 40 |
+
|
| 41 |
+
ds = load_dataset("keremberke/pokemon-classification", name="full")
|
| 42 |
+
example = ds['train'][0]
|
| 43 |
+
```
|
| 44 |
+
|
| 45 |
+
### Roboflow Dataset Page
|
| 46 |
+
[https://universe.roboflow.com/robert-demo-qvail/pokedex/dataset/14](https://universe.roboflow.com/robert-demo-qvail/pokedex/dataset/14?ref=roboflow2huggingface)
|
| 47 |
+
|
| 48 |
+
### Citation
|
| 49 |
+
|
| 50 |
+
```
|
| 51 |
+
@misc{ pokedex_dataset,
|
| 52 |
+
title = { Pokedex Dataset },
|
| 53 |
+
type = { Open Source Dataset },
|
| 54 |
+
author = { Lance Zhang },
|
| 55 |
+
howpublished = { \\url{ https://universe.roboflow.com/robert-demo-qvail/pokedex } },
|
| 56 |
+
url = { https://universe.roboflow.com/robert-demo-qvail/pokedex },
|
| 57 |
+
journal = { Roboflow Universe },
|
| 58 |
+
publisher = { Roboflow },
|
| 59 |
+
year = { 2022 },
|
| 60 |
+
month = { dec },
|
| 61 |
+
note = { visited on 2023-01-14 },
|
| 62 |
+
}
|
| 63 |
+
```
|
| 64 |
+
|
| 65 |
+
### License
|
| 66 |
+
Public Domain
|
| 67 |
+
|
| 68 |
+
### Dataset Summary
|
| 69 |
+
This dataset was exported via roboflow.com on December 20, 2022 at 5:34 PM GMT
|
| 70 |
+
|
| 71 |
+
Roboflow is an end-to-end computer vision platform that helps you
|
| 72 |
+
* collaborate with your team on computer vision projects
|
| 73 |
+
* collect & organize images
|
| 74 |
+
* understand unstructured image data
|
| 75 |
+
* annotate, and create datasets
|
| 76 |
+
* export, train, and deploy computer vision models
|
| 77 |
+
* use active learning to improve your dataset over time
|
| 78 |
+
|
| 79 |
+
It includes 6991 images.
|
| 80 |
+
Pokemon are annotated in folder format.
|
| 81 |
+
|
| 82 |
+
The following pre-processing was applied to each image:
|
| 83 |
+
* Auto-orientation of pixel data (with EXIF-orientation stripping)
|
| 84 |
+
* Resize to 224x224 (Fit (black edges))
|
| 85 |
+
|
| 86 |
+
No image augmentation techniques were applied.
|
| 87 |
+
|
| 88 |
+
|
| 89 |
+
|
huggingface_dataset/Dataset_Card/lbox_lbox_open.md
ADDED
|
@@ -0,0 +1,50 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: cc-by-nc-4.0
|
| 3 |
+
---
|
| 4 |
+
# Dataset Card for `lbox_open`
|
| 5 |
+
|
| 6 |
+
## Dataset Description
|
| 7 |
+
|
| 8 |
+
- **Homepage:** `https://lbox.kr`
|
| 9 |
+
- **Repository:** `https://github.com/lbox-kr/lbox_open`
|
| 10 |
+
- **Point of Contact:** [Wonseok Hwang](mailto:wonseok.hwang@lbox.kr)
|
| 11 |
+
|
| 12 |
+
### Dataset Summary
|
| 13 |
+
|
| 14 |
+
A Legal AI Benchmark Dataset from Korean Legal Cases.
|
| 15 |
+
|
| 16 |
+
### Languages
|
| 17 |
+
|
| 18 |
+
Korean
|
| 19 |
+
|
| 20 |
+
### How to use
|
| 21 |
+
```python
|
| 22 |
+
from datasets import load_dataset
|
| 23 |
+
|
| 24 |
+
# casename classficiation task
|
| 25 |
+
data_cn = load_dataset("lbox/lbox_open", "casename_classification")
|
| 26 |
+
data_cn_plus = load_dataset("lbox/lbox_open", "casename_classification_plus")
|
| 27 |
+
|
| 28 |
+
# statutes classification task
|
| 29 |
+
data_st = load_dataset("lbox/lbox_open", "statute_classification")
|
| 30 |
+
data_st_plus = load_dataset("lbox/lbox_open", "statute_classification_plus")
|
| 31 |
+
|
| 32 |
+
# Legal judgement prediction tasks
|
| 33 |
+
data_ljp_criminal = load_dataset("lbox/lbox_open", "ljp_criminal")
|
| 34 |
+
data_ljp_civil = load_dataset("lbox/lbox_open", "ljp_civil")
|
| 35 |
+
|
| 36 |
+
# case summarization task
|
| 37 |
+
data_summ = load_dataset("lbox/lbox_open", "summarization")
|
| 38 |
+
data_summ_plus = load_dataset("lbox/lbox_open", "summarization_plus")
|
| 39 |
+
|
| 40 |
+
# precedent corpus
|
| 41 |
+
data_corpus = load_dataset("lbox/lbox_open", "precedent_corpus")
|
| 42 |
+
|
| 43 |
+
```
|
| 44 |
+
|
| 45 |
+
For more information about the dataset, please visit <https://github.com/lbox-kr/lbox_open>.
|
| 46 |
+
|
| 47 |
+
## Licensing Information
|
| 48 |
+
Copyright 2022-present [LBox Co. Ltd.](https://lbox.kr/)
|
| 49 |
+
|
| 50 |
+
Licensed under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)
|
huggingface_dataset/Dataset_Card/molamin_Kinyarwanda_Engligh_Multilingual_ASR.md
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- rw
|
| 4 |
+
- en
|
| 5 |
+
license:
|
| 6 |
+
- cc-by-4.0
|
| 7 |
+
size_categories:
|
| 8 |
+
- 700K<n<800K
|
| 9 |
+
- ~3120 hours
|
| 10 |
+
---
|
| 11 |
+
|
| 12 |
+
This dataset was created from Mozilla's Common Voice dataset for the purposes of Multilingual ASR on Kinyarwanda and English.
|
| 13 |
+
|
| 14 |
+
The dataset contains 3000 hours of multilingual training samples, 300 hours of validation samples and 200 of testing samples.
|
huggingface_dataset/Dataset_Card/shibing624_source_code.md
ADDED
|
@@ -0,0 +1,179 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- no-annotation
|
| 4 |
+
language_creators:
|
| 5 |
+
- crowdsourced
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- cc-by-4.0
|
| 10 |
+
- gfdl
|
| 11 |
+
multilinguality:
|
| 12 |
+
- monolingual
|
| 13 |
+
size_categories:
|
| 14 |
+
- 100M<n<200M
|
| 15 |
+
source_datasets:
|
| 16 |
+
- https://github.com/shibing624/code-autocomplete
|
| 17 |
+
- https://github.com/bharathgs/Awesome-pytorch-list
|
| 18 |
+
- https://github.com/akullpp/awesome-java
|
| 19 |
+
- https://github.com/fffaraz/awesome-cpp
|
| 20 |
+
task_categories:
|
| 21 |
+
- text-generation
|
| 22 |
+
task_ids:
|
| 23 |
+
- language-modeling
|
| 24 |
+
---
|
| 25 |
+
# Dataset Card for "SourceCode"
|
| 26 |
+
## Table of Contents
|
| 27 |
+
- [Dataset Description](#dataset-description)
|
| 28 |
+
- [Dataset Summary](#dataset-summary)
|
| 29 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 30 |
+
- [Languages](#languages)
|
| 31 |
+
- [Dataset Structure](#dataset-structure)
|
| 32 |
+
- [Data Instances](#data-instances)
|
| 33 |
+
- [Data Fields](#data-fields)
|
| 34 |
+
- [Data Splits](#data-splits)
|
| 35 |
+
- [Dataset Creation](#dataset-creation)
|
| 36 |
+
- [Curation Rationale](#curation-rationale)
|
| 37 |
+
- [Source Data](#source-data)
|
| 38 |
+
- [Annotations](#annotations)
|
| 39 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 40 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 41 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 42 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 43 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 44 |
+
- [Additional Information](#additional-information)
|
| 45 |
+
- [Dataset Curators](#dataset-curators)
|
| 46 |
+
- [Licensing Information](#licensing-information)
|
| 47 |
+
- [Citation Information](#citation-information)
|
| 48 |
+
- [Contributions](#contributions)
|
| 49 |
+
|
| 50 |
+
## Dataset Description
|
| 51 |
+
- **Repository:** [code-autocomplete](https://github.com/shibing624/code-autocomplete)
|
| 52 |
+
- **Leaderboard:** [leaderboard](https://github.com/shibing624/code-autocomplete) (located on the homepage)
|
| 53 |
+
- **Size of downloaded dataset files:** 105 MB
|
| 54 |
+
- **Total amount of disk used:** 570 MB
|
| 55 |
+
|
| 56 |
+
### Dataset Summary
|
| 57 |
+
|
| 58 |
+
Source code dataset is a collection of Github awesome repos, it contains Python, Java, C++, and other programming languages.
|
| 59 |
+
This dataset can be used in different NLP tasks like language modeling and text generation tasks.
|
| 60 |
+
|
| 61 |
+
data source:
|
| 62 |
+
|
| 63 |
+
- PYTHON_CODE: https://github.com/bharathgs/Awesome-pytorch-list
|
| 64 |
+
- JAVA_CODE: https://github.com/akullpp/awesome-java
|
| 65 |
+
- CPP_CODE: https://github.com/fffaraz/awesome-cpp
|
| 66 |
+
|
| 67 |
+
|
| 68 |
+
### Supported Tasks and Leaderboards
|
| 69 |
+
- language modeling
|
| 70 |
+
- code generation tasks, **Leaderboard:** [code-autocomplete](https://github.com/shibing624/code-autocomplete)
|
| 71 |
+
|
| 72 |
+
### Languages
|
| 73 |
+
|
| 74 |
+
- programming languages: Python, Java, C++
|
| 75 |
+
- natural language: English
|
| 76 |
+
|
| 77 |
+
## Dataset Structure
|
| 78 |
+
### Data Instances
|
| 79 |
+
An example of 'train' looks as follows.
|
| 80 |
+
```
|
| 81 |
+
This example was too long and was cropped:
|
| 82 |
+
|
| 83 |
+
{
|
| 84 |
+
"text": """
|
| 85 |
+
import json
|
| 86 |
+
import argparse
|
| 87 |
+
|
| 88 |
+
|
| 89 |
+
def _parse_args():
|
| 90 |
+
parser = argparse.ArgumentParser(
|
| 91 |
+
description=__doc__,
|
| 92 |
+
formatter_class=argparse.RawTextHelpFormatter,
|
| 93 |
+
)
|
| 94 |
+
parser.add_argument(
|
| 95 |
+
'--model-file',
|
| 96 |
+
required=True,
|
| 97 |
+
help=(
|
| 98 |
+
'A pt file from '
|
| 99 |
+
'https://github.com/pytorch/fairseq/tree/main/examples/hubert'
|
| 100 |
+
)
|
| 101 |
+
)
|
| 102 |
+
return parser.parse_args()
|
| 103 |
+
"""
|
| 104 |
+
}
|
| 105 |
+
```
|
| 106 |
+
### Data Fields
|
| 107 |
+
The data fields are the same among all splits.
|
| 108 |
+
- `text`: a `string` feature.
|
| 109 |
+
### Data Splits
|
| 110 |
+
#### python
|
| 111 |
+
```shell
|
| 112 |
+
$ wc -l python/*
|
| 113 |
+
10000 python/test.txt
|
| 114 |
+
5215412 python/train.txt
|
| 115 |
+
10000 python/valid.txt
|
| 116 |
+
5235412 total
|
| 117 |
+
```
|
| 118 |
+
#### java
|
| 119 |
+
```shell
|
| 120 |
+
$ wc -l java/*
|
| 121 |
+
950083 java/test.txt
|
| 122 |
+
2802880 java/train.txt
|
| 123 |
+
940803 java/valid.txt
|
| 124 |
+
4693766 total
|
| 125 |
+
```
|
| 126 |
+
#### cpp
|
| 127 |
+
```shell
|
| 128 |
+
$ wc -l cpp/*
|
| 129 |
+
1060014 cpp/test.txt
|
| 130 |
+
3119241 cpp/train.txt
|
| 131 |
+
1099124 cpp/valid.txt
|
| 132 |
+
5278379 total
|
| 133 |
+
```
|
| 134 |
+
## Dataset Creation
|
| 135 |
+
### Curation Rationale
|
| 136 |
+
As code generation dataset, I upload it to huggingface datasets.
|
| 137 |
+
### Source Data
|
| 138 |
+
#### Initial Data Collection and Normalization
|
| 139 |
+
#### Who are the source language producers?
|
| 140 |
+
Citation:
|
| 141 |
+
|
| 142 |
+
APA:
|
| 143 |
+
```latex
|
| 144 |
+
Xu, M. code-autocomplete: Code AutoComplete with GPT2 model (Version 0.0.4) [Computer software]. https://github.com/shibing624/code-autocomplete
|
| 145 |
+
```
|
| 146 |
+
|
| 147 |
+
BibTeX:
|
| 148 |
+
```latex
|
| 149 |
+
@software{Xu_code-autocomplete_Code_AutoComplete,
|
| 150 |
+
author = {Xu, Ming},
|
| 151 |
+
title = {code-autocomplete: Code AutoComplete with GPT2 model},
|
| 152 |
+
url = {https://github.com/shibing624/code-autocomplete},
|
| 153 |
+
version = {0.0.4}
|
| 154 |
+
}
|
| 155 |
+
```
|
| 156 |
+
|
| 157 |
+
### Annotations
|
| 158 |
+
#### Annotation process
|
| 159 |
+
#### Who are the annotators?
|
| 160 |
+
nobody
|
| 161 |
+
### Personal and Sensitive Information
|
| 162 |
+
## Considerations for Using the Data
|
| 163 |
+
### Social Impact of Dataset
|
| 164 |
+
This dataset was developed as a benchmark for evaluating code generation model.
|
| 165 |
+
### Discussion of Biases
|
| 166 |
+
### Other Known Limitations
|
| 167 |
+
## Additional Information
|
| 168 |
+
### Dataset Curators
|
| 169 |
+
|
| 170 |
+
Github awesome programing code repos.
|
| 171 |
+
|
| 172 |
+
### Licensing Information
|
| 173 |
+
|
| 174 |
+
GNU Free Documentation License v1.3 or later.
|
| 175 |
+
|
| 176 |
+
For research use only.
|
| 177 |
+
|
| 178 |
+
### Contributions
|
| 179 |
+
Thanks to [@shibing624](https://github.com/shibing624) add this dataset.
|