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4a912e47900f448a596804a0d774d74f21dea5da
|
# Dataset Card for "ontonotes_zh_ner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
doushabao4766/ontonotes_zh_ner
|
[
"region:us"
] |
2023-05-26T07:28:18+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 7629700, "num_examples": 15724}, {"name": "test", "num_bytes": 3188216, "num_examples": 4346}, {"name": "validation", "num_bytes": 3074667, "num_examples": 4301}], "download_size": 1852625, "dataset_size": 13892583}}
|
2023-05-26T07:28:23+00:00
|
530adfbfd53ee5fea8aeccf31c46d8d3af41ab80
|
A dataset with docvqa format for special equipment.
|
lionking1988/docvqa_special_equip
|
[
"region:us"
] |
2023-05-26T07:34:32+00:00
|
{}
|
2023-05-26T07:35:14+00:00
|
95548e67c8cbd327f1fc0264955d28efbaffa12e
|
# Dataset Card for "b8331a6c"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/b8331a6c
|
[
"region:us"
] |
2023-05-26T07:46:29+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1330, "dataset_size": 182}}
|
2023-05-26T07:46:31+00:00
|
23541d204f801e8228449f48fab68318d1a0be78
|
# Dataset Card for Bel Conto and Chinese Folk Song Singing Tech
## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/ccmusic-database/bel_canto
```
## Dataset Description
- **Homepage:** <https://ccmusic-database.github.io>
- **Repository:** <https://huggingface.co/datasets/ccmusic-database/bel_canto>
- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
- **Point of Contact:** N/A
### Dataset Summary
This database contains hundreds of acapella singing clips that are sung in two styles, Bel Conto and Chinese national singing style by professional vocalists. All of them are sung by professional vocalists and were recorded in professional commercial recording studios.
### Supported Tasks and Leaderboards
Audio classification, Image classification, singing method classification, voice classification
### Languages
Chinese, English
## Dataset Structure
<style>
#belcanto td {
vertical-align: middle !important;
text-align: center;
}
#belcanto th {
text-align: center;
}
</style>
<table id="belcanto">
<tr>
<th>mel<br>(.jpg, 1.6s)</th>
<th>cqt<br>(.jpg, 1.6s)</th>
<th>chroma<br>(.jpg, 1.6s)</th>
<th>label<br>(4-class)</th>
<th>gender<br>(2-class)</th>
<th>singing_method<br>(2-class)</th>
</tr>
<tr>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/TSTXTg2s2j6gs3O8q_bpD.jpeg"></td>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/BiuWkk_rkYBfN2hqG60Iy.jpeg"></td>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/WmcP0UsMe_9lmLmNpAOzr.jpeg"></td>
<td>m_bel, f_bel, m_folk, f_folk</td>
<td>male, female</td>
<td>Folk_Singing, Bel_Canto</td>
</tr>
<tr>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
</table>
### Data Instances
.zip(.wav, .jpg)
### Data Fields
m_bel, f_bel, m_folk, f_folk
### Data Splits
| total | 9603 |
| :-------------: | :---: |
| train(80%) | 7682 |
| validation(10%) | 960 |
| test(10%) | 961 |
## Dataset Creation
### Curation Rationale
Lack of a dataset for Bel Conto and Chinese folk song singing tech
### Source Data
#### Initial Data Collection and Normalization
Zhaorui Liu, Monan Zhou
#### Who are the source language producers?
Students from CCMUSIC
### Annotations
#### Annotation process
All of them are sung by professional vocalists and were recorded in professional commercial recording studios.
#### Who are the annotators?
professional vocalists
### Personal and Sensitive Information
None
## Considerations for Using the Data
### Social Impact of Dataset
Promoting the development of AI in the music industry
### Discussion of Biases
Only for Chinese songs
### Other Known Limitations
Some singers may not have enough professional training in classical or ethnic vocal techniques.
## Additional Information
### Dataset Curators
Zijin Li
### Evaluation
<https://huggingface.co/ccmusic-database/bel_canto>
### Licensing Information
```
MIT License
Copyright (c) CCMUSIC
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
### Citation Information
```
@dataset{zhaorui_liu_2021_5676893,
author = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Yuan Wang, Zhaowen Wang, Wei Li and Zijin Li},
title = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research},
month = {nov},
year = {2021},
publisher = {Zenodo},
version = {1.1},
doi = {10.5281/zenodo.5676893},
url = {https://doi.org/10.5281/zenodo.5676893}
}
```
### Contributions
Provide a dataset for distinguishing Bel Conto and Chinese folk song singing tech
|
ccmusic-database/bel_canto
|
[
"task_categories:audio-classification",
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:zh",
"language:en",
"license:mit",
"music",
"art",
"region:us"
] |
2023-05-26T07:53:43+00:00
|
{"language": ["zh", "en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["audio-classification", "image-classification"], "pretty_name": "Bel Conto and Chinese Folk Song Singing Tech", "tags": ["music", "art"], "viewer": false}
|
2023-12-22T10:12:02+00:00
|
949cd10f2e82df9e8578e092e6cddb7b62c3d5ee
|
# Dataset Card for "d7c1dae5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/d7c1dae5
|
[
"region:us"
] |
2023-05-26T08:00:24+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 186, "num_examples": 10}], "download_size": 1344, "dataset_size": 186}}
|
2023-05-26T08:00:26+00:00
|
798d85d1cf06144e8447b86a82e5edd555dc55cb
|
# Medical image and text report dataset for exploring visual language model's capability
# data
* xray:
* MMIC-CXR
* openi
* CheXpert
# sample
```json
{
{
"image_id": "13faff2",
"caption": "Based on the x-ray image, the heart size is normal and the lungs appear clear. The presence of pneumonia, effusions, edema, pneumothorax, adenopathy, nodules or masses has been ruled out. The finding indicates everything is normal. In other words, the overall impression is that of a normal chest. Do you have any questions or concerns about this x-ray result?",
"lang": "en"
},
{
"image_id": "aff2525342",
"caption": "根据胸部X光图像，有散在的钙化肉芽肿存在，但没有发现活动性疾病，没有病灶浸润、胸腔积液或气胸。心脏的大小和纵隔的轮廓看起来正常。此外，脊柱也有退行性改变。",
"lang": "zh"
}
}
```
|
zirui3/med-image-reports
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-05-26T08:11:29+00:00
|
{"license": "cc-by-4.0"}
|
2023-05-27T13:25:21+00:00
|
fe2a90703d47136bd5b1a80d3d93e489f3fadc37
|
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zhangyue/test
|
[
"region:us"
] |
2023-05-26T08:21:45+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "package_name", "dtype": "string"}, {"name": "review", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "star", "dtype": "int64"}, {"name": "version_id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1508, "num_examples": 5}, {"name": "test", "num_bytes": 956, "num_examples": 5}], "download_size": 9453, "dataset_size": 2464}}
|
2023-05-26T08:21:53+00:00
|
57787b21ccdcd871f4f6907595cc1efbd8d04b73
|
# Open In Colab Chrome Extension
This is a simple chrome extension that, when clicked when viewing a Jupyter
notebook on github, will open that notebook in
[Google Colab](http://colab.research.google.com/).
The extension simply provides a URL redirect: it reads the current URL and opens
a new tab at http://colab.research.google.com/github/ with the user, repository,
and notebook path.
## Installing the Extension
The latest release of the extension can be installed from the
[Chrome Web Store](https://chrome.google.com/webstore/detail/open-in-colab/iogfkhleblhcpcekbiedikdehleodpjo).
## Development Install
To install the extension directly from source:
1. Clone this repository to your local disk.
2. Open the Chrome browser, and navigate to chrome://extensions.
3. Ensure that developer mode is enabled (see the switch in the upper-right).
4. Click "Load Unpacked" and choose the location of the `open_in_colab`
repository. You should see a little colab icon appear in your Chrome
extensions icons in your browser bar.
5. Navigate to a notebook on github (e.g.
https://github.com/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb),
and click the extension icon to open the notebook in Colab.
|
lemon967/open_in_colab-main
|
[
"region:us"
] |
2023-05-26T08:21:52+00:00
|
{}
|
2023-05-26T08:22:17+00:00
|
054ffd1dddf430a2f345c556b0e701849e96263e
|
# Dataset Card for "test-one"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zhangyue/test-one
|
[
"region:us"
] |
2023-05-26T08:23:47+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "package_name", "dtype": "string"}, {"name": "review", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "star", "dtype": "int64"}, {"name": "version_id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1508, "num_examples": 5}, {"name": "test", "num_bytes": 956, "num_examples": 5}], "download_size": 9453, "dataset_size": 2464}}
|
2023-05-26T08:23:54+00:00
|
76d21303cfc1716f2ef75d72a85ae238165b0a85
|
# Dataset Card for "test.one"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zhangyue/test.one
|
[
"region:us"
] |
2023-05-26T08:29:14+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "package_name", "dtype": "string"}, {"name": "review", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "star", "dtype": "int64"}, {"name": "version_id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1508, "num_examples": 5}, {"name": "test", "num_bytes": 956, "num_examples": 5}], "download_size": 9453, "dataset_size": 2464}}
|
2023-05-26T08:29:22+00:00
|
4ffb4046b194d16d174259212e72844b1078ca2c
|
# Dataset Card for "test_one"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zhangyue/test_one
|
[
"region:us"
] |
2023-05-26T08:30:16+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "package_name", "dtype": "string"}, {"name": "review", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "star", "dtype": "int64"}, {"name": "version_id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1508, "num_examples": 5}, {"name": "test", "num_bytes": 956, "num_examples": 5}], "download_size": 9453, "dataset_size": 2464}}
|
2023-05-26T08:30:23+00:00
|
b51cfa6a7a49689c2ddf3015b28cc4d488908635
|
Ela279/langchain_docs
|
[
"license:mit",
"region:us"
] |
2023-05-26T08:30:25+00:00
|
{"license": "mit"}
|
2023-05-26T08:42:36+00:00
|
|
33a0342aa50dde0d72815fc732f7a3c6c8b2534a
|
# Dataset Card for "diffusion.4.text_to_image.book"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lansinuote/diffusion.4.text_to_image.book
|
[
"region:us"
] |
2023-05-26T09:00:29+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7321196.654565875, "num_examples": 1000}], "download_size": 6528669, "dataset_size": 7321196.654565875}}
|
2023-05-26T09:00:42+00:00
|
30a2f544d0a44701f8df1e83310823a715867965
|
globalids/Pharma
|
[
"license:unknown",
"region:us"
] |
2023-05-26T09:10:47+00:00
|
{"license": "unknown"}
|
2023-06-02T12:11:20+00:00
|
|
b1385d25d29a2a98080101b65985f073ba9dca50
|
Paulo-hi/semEval22
|
[
"license:unknown",
"region:us"
] |
2023-05-26T09:14:05+00:00
|
{"license": "unknown"}
|
2023-06-27T12:55:27+00:00
|
|
fd5a6fa4b317a6e57be1e10beb766a64b654e767
|
mka0101/Workoutplannerdataset
|
[
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] |
2023-05-26T09:25:09+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"]}
|
2023-05-26T09:29:19+00:00
|
|
86af95baefaf76476c4584758e60e09396d0bf0e
|
NathanDrake/Education
|
[
"license:afl-3.0",
"region:us"
] |
2023-05-26T10:18:56+00:00
|
{"license": "afl-3.0"}
|
2023-05-26T10:18:56+00:00
|
|
3b96f5791582cf6f6f348ca46fd502a981918d85
|
# Dataset Card for "ATCO2-ASR"
This is audio data used for automatic speech recognition. The original source of the data is the [ATCO2 project](https://www.atco2.org), specifically the ASR part of the public speech corpus.
|
jlvdoorn/atco2-asr
|
[
"language:en",
"air traffic management",
"natural language processing",
"ATCO2",
"automatic speech recognition",
"atm",
"asr",
"doi:10.57967/hf/1377",
"region:us"
] |
2023-05-26T10:20:15+00:00
|
{"language": ["en"], "pretty_name": "ATCO2-ASR Data", "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "text", "dtype": "string"}, {"name": "info", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 99693121.0, "num_examples": 446}, {"name": "validation", "num_bytes": 27159767.0, "num_examples": 113}], "download_size": 125718188, "dataset_size": 126852888.0}, "tags": ["air traffic management", "natural language processing", "ATCO2", "automatic speech recognition", "atm", "asr"]}
|
2023-06-29T13:31:56+00:00
|
95ebf38e73c2f8ba7e8bbf9dc63baf3093e016b4
|
# Dataset Card for "vicuna_benchmark_pairwise"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
reciprocate/vicuna_benchmark_pairwise
|
[
"region:us"
] |
2023-05-26T10:27:02+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "selected", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 168709, "num_examples": 80}], "download_size": 100327, "dataset_size": 168709}}
|
2023-05-26T11:11:39+00:00
|
212942a6f4471e17dff8ff8a606c1a017688fca2
|
# Dataset Card for "weibo_ner_knowledge_V3_wc_bioes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
doushabao4766/weibo_ner_knowledge_V3_wc_bioes
|
[
"region:us"
] |
2023-05-26T10:34:56+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER.NOM", "2": "B-LOC.NAM", "3": "B-PER.NAM", "4": "B-GPE.NAM", "5": "B-ORG.NAM", "6": "B-ORG.NOM", "7": "B-LOC.NOM", "8": "B-GPE.NOM", "9": "I-PER.NAM", "10": "I-ORG.NAM", "11": "I-PER.NOM", "12": "I-ORG.NOM", "13": "I-LOC.NAM", "14": "I-LOC.NOM", "15": "I-GPE.NAM", "16": "E-PER.NOM", "17": "E-LOC.NAM", "18": "E-PER.NAM", "19": "E-GPE.NAM", "20": "E-ORG.NAM", "21": "E-ORG.NOM", "22": "E-LOC.NOM", "23": "E-GPE.NOM", "24": "S-PER.NOM", "25": "S-GPE.NAM", "26": "S-PER.NAM", "27": "S-LOC.NOM"}}}}, {"name": "knowledge", "dtype": "string"}, {"name": "token_words", "sequence": {"sequence": "string"}}, {"name": "knowledge_words", "sequence": {"sequence": "string"}}], "splits": [{"name": "train", "num_bytes": 7027512, "num_examples": 1350}, {"name": "test", "num_bytes": 1107689, "num_examples": 270}, {"name": "validation", "num_bytes": 1116528, "num_examples": 270}], "download_size": 2406555, "dataset_size": 9251729}}
|
2023-05-26T10:35:02+00:00
|
a1fe99ba32cd2c7049af5b7303a74b49eccab75a
|
# Dataset Card for "ccks_2019_ner_k_V3_wc_bioes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
doushabao4766/ccks_2019_ner_k_V3_wc_bioes
|
[
"region:us"
] |
2023-05-26T10:36:44+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-DISEASE", "2": "B-TESTIMAGE", "3": "B-TESTLAB", "4": "B-OPERATION", "5": "B-DRUG", "6": "B-ANATOMY", "7": "I-DISEASE", "8": "I-TESTIMAGE", "9": "I-TESTLAB", "10": "I-OPERATION", "11": "I-DRUG", "12": "I-ANATOMY", "13": "E-DISEASE", "14": "E-TESTIMAGE", "15": "E-TESTLAB", "16": "E-OPERATION", "17": "E-DRUG", "18": "E-ANATOMY", "19": "S-DISEASE", "20": "S-TESTIMAGE", "21": "S-TESTLAB", "22": "S-OPERATION", "23": "S-DRUG", "24": "S-ANATOMY"}}}}, {"name": "knowledge", "dtype": "string"}, {"name": "token_words", "sequence": {"sequence": "string"}}, {"name": "knowledge_words", "sequence": {"sequence": "string"}}], "splits": [{"name": "train", "num_bytes": 46556437, "num_examples": 7180}, {"name": "test", "num_bytes": 17770411, "num_examples": 2787}, {"name": "validation", "num_bytes": 11692351, "num_examples": 1864}], "download_size": 13451536, "dataset_size": 76019199}}
|
2023-05-26T10:36:52+00:00
|
b7f780e08b492dff0e902c662c04ffce6cffe4a2
|
# Dataset Card for "msra_ner_k_V3_wc_bioes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
doushabao4766/msra_ner_k_V3_wc_bioes
|
[
"region:us"
] |
2023-05-26T10:39:56+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "B-ORG", "3": "B-LOC", "4": "I-PER", "5": "I-ORG", "6": "I-LOC", "7": "E-PER", "8": "E-ORG", "9": "E-LOC", "10": "S-PER", "11": "S-ORG", "12": "S-LOC"}}}}, {"name": "knowledge", "dtype": "string"}, {"name": "token_words", "sequence": {"sequence": "string"}}, {"name": "knowledge_words", "sequence": {"sequence": "string"}}], "splits": [{"name": "train", "num_bytes": 334987989, "num_examples": 45000}, {"name": "test", "num_bytes": 25028455, "num_examples": 3442}], "download_size": 73312900, "dataset_size": 360016444}}
|
2023-05-26T10:40:06+00:00
|
c5b5e4b5b62e362705c767ea730f01bab17cb2c2
|
# REALSumm: Re-evaluating EvALuation in Summarization
Dataset assembled from https://github.com/neulab/REALSumm with the conversion script:
```python
idx = [1017, 10586, 11343, 1521, 2736, 3789, 5025, 5272, 5576, 6564, 7174, 7770, 8334, 9325, 9781, 10231, 10595, 11351, 1573, 2748, 3906, 5075, 5334, 5626, 6714, 7397, 7823, 8565, 9393, 9825, 10325, 10680, 11355, 1890, 307, 4043, 5099, 5357, 5635, 6731, 7535, 7910, 8613, 9502, 10368, 10721, 1153, 19, 3152, 4303, 5231, 5420, 5912, 6774, 7547, 8001, 8815, 9555, 10537, 10824, 1173, 1944, 3172, 4315, 5243, 5476, 6048, 6784, 7584, 8054, 8997, 9590, 10542, 11049, 1273, 2065, 3583, 4637, 5244, 5524, 6094, 6976, 7626, 8306, 9086, 9605, 10563, 11264, 1492, 2292, 3621, 4725, 5257, 5558, 6329, 7058, 7670, 8312, 9221, 9709]
link = "https://github.com/neulab/REALSumm/raw/master/scores_dicts/abs.pkl"
x = requests.get(link)
data = pickle.loads(x.content)
with open("/home/manuel/Downloads/summeval/src.txt", "r") as f:
src = f.readlines()
src_cleaned = [src[i] for i in idx]
del src
models = list(data[0]["system_summaries"].keys())
tot_df = pd.DataFrame()
ref_sums = [data[x]["ref_summ"] for x in range(100)]
for model in models:
df = pd.DataFrame([data[x]["system_summaries"][model]["scores"] for x in range(100)])
sums = [data[x]["system_summaries"][model]["system_summary"] for x in range(100)]²
df["model"] = model
df["model_summary"] = sums
df["ref_summary"] = ref_sums
df["source"] = src_cleaned
tot_df = pd.concat([tot_df, df])
tot_df = tot_df.reset_index()
tot_df = tot_df.rename(columns={"index": "doc_id"})
tot_df.index.name = "index"
```
## Dataset Structure
```
DatasetDict({
train: Dataset({
features: ['index', 'doc_id', 'rouge_1_f_score', 'rouge_2_recall', 'rouge_l_recall', 'rouge_2_precision', 'rouge_2_f_score', 'rouge_1_precision', 'rouge_1_recall', 'rouge_l_precision', 'rouge_l_f_score', 'js-2', 'mover_score', 'bert_recall_score', 'bert_precision_score', 'bert_f_score', 'litepyramid_recall', 'model', 'model_summary', 'ref_summary', 'source'],
num_rows: 1400
})
})
```
```
@inproceedings{Bhandari-2020-reevaluating,
title = "Re-evaluating Evaluation in Text Summarization",
author = "Bhandari, Manik and Narayan Gour, Pranav and Ashfaq, Atabak and Liu, Pengfei and Neubig, Graham ",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
year = "2020"
}
```
|
manu/REALSumm
|
[
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] |
2023-05-26T10:47:12+00:00
|
{"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["summarization"], "pretty_name": "Summarization with Human Feedback"}
|
2023-05-26T11:06:25+00:00
|
b5cd50612d65b6876e7aa45b5e230b99ebd68eb4
|
# Stack Overflow Python Q&A Dataset
## Description
Filtered Python Q&A with API_Usage subcategory without:
1. Images
2. Links
3. Blocks of code
Scores in Q1-Q3 scaled with MaxAbsScaler. Tanh function applyed to joint Scores.
|
Myashka/SO-Python_QA-API_Usage-tanh_score
|
[
"task_categories:text-generation",
"language:en",
"license:openrail",
"region:us"
] |
2023-05-26T10:48:58+00:00
|
{"language": ["en"], "license": "openrail", "task_categories": ["text-generation"]}
|
2023-07-10T15:51:28+00:00
|
973874d51ea3430c6d425a1243c150fe110a3a19
|
# Hugging Face Spaces Descriptions and Embeddings Dataset
I parsed all the available public 🤗 spaces as of May 22, 2023, generated concise descriptions of their functionality, and created embeddings for them.
The descriptions were generated using various LLMs from each space's app file (README.md -> app_file). The embeddings were created using the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) SentenceTransformer model.
The dataset comprises approximately 30,000 spaces that meet specific criteria: having more than 40 lines of code and over 1000 characters in the app file.
The descriptions provide an overview of the spaces and their features.
## Dataset Details
- **Name**: HF Spaces Descriptions and Embeddings
- **Creator**: [anzorq](https://huggingface.co/anzorq)
- **License**: MIT
## Dataset Usage
You can use this dataset for various natural language processing (NLP) tasks such as semantic search, clustering, etc.
## Loading the Dataset
You can load the dataset using the datasets library:
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("anzorq/hf-spaces-descriptions-embeddings")
# Access the different splits
train_split = dataset['train']
valid_split = dataset['valid']
test_split = dataset['test']
```
## Semantic Search Example
Performing a semantic search using the dataset's embeddings:
```python
import torch
from sentence_transformers import SentenceTransformer
from datasets import load_dataset
import numpy as np
# Load the dataset
dataset = load_dataset("anzorq/hf-spaces-descriptions-embeddings")
# Load the SentenceTransformer model
model = SentenceTransformer('all-MiniLM-L6-v2')
# Example query
query = "Removing background from images"
# Encode the query
query_embedding = model.encode([query], convert_to_tensor=True)
# Get the space descriptions and embeddings
descriptions = dataset['train']['description']
embeddings = np.array(dataset['train']['embedding'])
# Calculate cosine similarity
cosine_scores = torch.nn.functional.cosine_similarity(query_embedding, torch.tensor(embeddings))
# Sort the results
top_k = torch.topk(cosine_scores, k=5)
# Print the top-k results
print("Query:", query)
for idx in top_k.indices[0]:
print("Space ID:", dataset['train']['id'][idx])
print("Description:", descriptions[idx])
print("Score:", cosine_scores[idx].item())
```
## License
This dataset is distributed under the [MIT License](https://opensource.org/licenses/MIT).
|
anzorq/hf-spaces-descriptions-embeddings
|
[
"license:mit",
"region:us"
] |
2023-05-26T11:23:45+00:00
|
{"license": "mit", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "embedding", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 94758018, "num_examples": 29718}], "download_size": 78891306, "dataset_size": 94758018}}
|
2023-05-26T12:33:58+00:00
|
ea6019b92c502c2abf652defe54a84aea1db6da6
|
0xAnders/ama-bot
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-26T11:41:26+00:00
|
{"license": "apache-2.0"}
|
2023-05-26T11:42:29+00:00
|
|
7e4cac0abf48515eb57411d2bc0d9afb969dcd0c
|
This dataset consists of snapshots of limit order books and trades for BTC/USD (i.e. the Bitcoin / US dollars currency pair) from May 31, 2018 9:55 pm (UTC) through September 30, 2018 9:59 pm (UTC) from the Bitstamp exchange (https://www.bitstamp.net).
The data has been collected by Aisot Technologies AG, Zürich (www.aisot.com). Trade data is on a millisecond frequency. Limit order book snapshots are on minute frequency, with aggregated amounts for each price level with depth up to 5000 for each bid/ask side. For more information about the dataset, please refer to the citation below.
The data is provided “as is” without any warranties. A short approval process is required before accessing the data.
By accessing the dataset, you accept to not disseminate it elsewhere and to adhere to the
cc-by-nc-sa-4.0 license agreement.
Note, we approve requests with full name (first and last name) and email only.
How to cite the dataset: Antulov-Fantulin, N., Guo, T. & Lillo, F. (2021). “Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume.” In: Decisions Econ. Finan. 44, pp. 905–940. https://doi.org/10.1007/s10203-021-00344-9
|
AisotTechnologies/aisot_btc_lob_trades
|
[
"license:cc-by-nc-sa-4.0",
"finance",
"time-series",
"region:us"
] |
2023-05-26T11:43:50+00:00
|
{"license": "cc-by-nc-sa-4.0", "tags": ["finance", "time-series"]}
|
2023-09-14T14:21:03+00:00
|
0cc87304c5cf4b7503f94e7db00e44267ec84296
|
bilbil0903/uy_data
|
[
"task_categories:text-classification",
"size_categories:10M<n<100M",
"language:ug",
"license:openrail",
"not-for-all-audiences",
"region:us"
] |
2023-05-26T11:46:18+00:00
|
{"language": ["ug"], "license": "openrail", "size_categories": ["10M<n<100M"], "task_categories": ["text-classification"], "tags": ["not-for-all-audiences"]}
|
2023-05-26T11:47:40+00:00
|
|
a862643ee6ad09774c25ddd5ab5997b3ed15a29b
|
# Dataset Card for "fr-crawler-private-mlm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
factored/fr-crawler-private-mlm
|
[
"region:us"
] |
2023-05-26T11:48:57+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "Data Engineer", "1": "Data Analyst", "2": "Data Scientist", "3": "Machine Learning Engineer", "4": "Software Engineer", "5": "Analytics Engineer", "6": "Full-Stack Engineer", "7": "DevOps Engineer", "8": "Research Assistant", "9": "Data Architect", "10": "Business Intelligence Developer", "11": "Co-Founder", "12": "Intern", "13": "Risk Analyst", "14": "Technical Lead", "15": "Teaching Assistant", "16": "Database Administrator", "17": "Technical Leader", "18": "Developer", "19": "Consultant", "20": "Researcher", "21": "Senior Consultant", "22": "Engineer", "23": "BI Engineer", "24": "Software Architect", "25": "Professional Development", "26": "Data Consultant", "27": "Web Developer", "28": "Blockchain Developer", "29": "Operations Specialist", "30": "Quantitative Analyst", "31": "Statistician", "32": "Data Management Analyst", "33": "Statistical Consultant - United Nations Development Program - UNDP", "34": "Financial analyst", "35": "Data Mining Analyst", "36": "Senior Machine learning engineer.", "37": "Graduate Assistant", "38": "ArcGIS Insights Product Manager", "39": "Internship", "40": "Lead Frontend Architect", "41": "Marketplace Ops & Data Excellence Specialist", "42": "Financial Analysts", "43": "Lead Developer", "44": "Senior Big Data Architect", "45": "Postgres DBA", "46": "Lecturer", "47": "BI Intern", "48": "Full Stack Engineer", "49": "Professional Services Consultant", "50": "Business Intelligence Consultant", "51": "BI Developer Analyst, Makers Solutions SAS", "52": "C++ Developer", "53": "Functional Lead Sap BI", "54": "Senior Marketing Analyst", "55": "IT Analyst", "56": "Graduate Teaching Assistant", "57": "Digital Marketing", "58": "University Lecturer", "59": "Data Specialist", "60": "DevOps", "61": "Technology Assistant", "62": "Civil Engineer", "63": "Final Thesis Project", "64": "Technical Cofounder", "65": "Machine Learning Trainee", "66": "Data Manager / Enterprise Architect", "67": "BI Consultant", "68": "Solutions Specialist", "69": "Technical Leader Consultant", "70": "Senior Engineering Manager", "71": "BI & Data Specialist", "72": "Professor", "73": "Tech Lead", "74": "Business Intelligence Manager", "75": "Strategy Consultant", "76": "Junior Trader FICs", "77": "Front-End Developer", "78": "Senior Developer", "79": "Senior Integration Specialist", "80": "Freelance Consultant", "81": "Data Lead", "82": "Semi-Senior Engineer", "83": "Artificial Intelligence Researcher", "84": "Junior Consultant", "85": "Artificial Intelligence Specialist", "86": "Teacher", "87": "PS Consultant", "88": "Research Intern", "89": "Process Automation Team Leader", "90": "Modeling Analyst", "91": "Product Analyst", "92": "RAPPI", "93": "Operations Analyst I", "94": "Systems Analyst", "95": "PWC", "96": "Adjunct Professor", "97": "Six Sigma Project Consultant", "98": "Computational Designer & Engineer", "99": "Sr. Business Consultant", "100": "Information Security Analyst", "101": "Project Coordinator", "102": "Support Analyst II", "103": "Americas Retail Sales Analyst", "104": "Integrations Developer", "105": "CTO and Back-End Developer", "106": "Chief Technology Officer", "107": "Technology Consultant", "108": "Head of Machine Learning", "109": "Adjunct professor", "110": "Production Analyst", "111": "It Senior Analyst", "112": "Database Administrator - Systems Analyst", "113": "Bairesdev", "114": "Business Support Senior Specialist", "115": "Tech Lead for Real Time Time Data Systems", "116": "Project Analyst", "117": "Mobile Technology Consultant", "118": "DevSecOps/DevOps", "119": "Web Developer Intern", "120": "Senior Data Lead", "121": "Mobile And Web Developer", "122": "Technical Support Specialist", "123": "Financial Mathematics Lead", "124": "Teacher Assistant", "125": "Financial Analyst", "126": "BI Developer, Business Intelligence Team", "127": "Business Intelligence Intern", "128": "BI Lead", "129": "Senior Data Governance Specialist", "130": "Architecture & Devops Lead", "131": "Data Governance Analyst", "132": "Associate Consultant", "133": "Sr. Analyst", "134": "Sr. Systems Analyst", "135": "Sr DataLab Analyst", "136": "System Analyst", "137": "Big Data Architect", "138": "IT Functional Analyst", "139": "Young Researcher", "140": "Freelance Developer", "141": "Data Mining Analyst II", "142": "Operations Maintenance Engineer", "143": "Bi Tech Lead / Bi Dev", "144": "Server Administrator", "145": "Full Stack Engineer & Co-Founder", "146": "Game Developer", "147": "Devremote", "148": "Technical Project Manager", "149": "Mid Database Administrator", "150": "Senior Data Manager", "151": "OCC Analyst", "152": "BI Senior Specialist", "153": "Data Warehouse Architect", "154": "Information Governance Analyst", "155": "Sr. Data & Integrations Engineer", "156": "Trainee - Onsite QA/QC Analyst", "157": "Back-End Engineer", "158": "Java Software Architect", "159": "Lecturer and Research Engineer", "160": "Director Of Knowledge Management", "161": "Data Migration Analyst", "162": "Freelancer/Contractor", "163": "Undergraduate Researcher", "164": "Colombia Data Warehouse Administrator", "165": "Strategic Coordinator", "166": "Frontend Developer", "167": "Platform Engineer", "168": "Commercial Analyst Business Development", "169": "DBA Sql Server And Bi Analyst", "170": "BI Specialist", "171": "IT Infrastructure Engineer", "172": "Sr. Multicloud Python Engineer", "173": "Consultor De Sistemas", "174": "Co-Founder and CTO", "175": "Senior Devops Engineer", "176": "Mid-Level Engineer", "177": "Well Surveillance Engineer", "178": "Machine Learning Tech Lead", "179": "Pricing Analyst", "180": "Author", "181": "Consulting Analyst", "182": "Python Trainer", "183": "Machine Learning Research Intern", "184": "Industry and Commerce Analyst", "185": "Economics Advisor", "186": "Senior Business Intelligence", "187": "Product Development Analyst", "188": "SAP BW / BO Consultant", "189": "Junior Systems Engineer", "190": "Freelance Computer Vision Engineer", "191": "Research And Development Engineer", "192": "Data Sourcing Tech Lead", "193": "Freelancer", "194": "Commercial Intern", "195": "Co-Founder/ Back-End Engineer", "196": "Co-Founder & Cto", "197": "Project Engineer", "198": "RPA Analyst", "199": "Python Developer", "200": "Technical/Functional Leader", "201": "Head of IT", "202": "Intern FICs", "203": "Artificial Intelligence Intern", "204": "Site Reliability Engineer Intern", "205": "Graduate Teaching & Research Assistant", "206": "Geospatial Analyst", "207": "Digital Product Manager / Enterprise Architect", "208": "Temporary Professor Computer Engineer", "209": "Budget and Planning Leader", "210": "IT Project Intern", "211": "Logistics Analyst", "212": "AmericaS Retail Sales Analyst", "213": "Project Control And Budget Analyst", "214": "Credit Risk Analyst", "215": "TALENTU", "216": "Business Process Improvement Analyst", "217": "Product Owner", "218": "Senior DevOps Engineer", "219": "Big Data Senior Consultant", "220": "Energy Analyst", "221": "Software engineering consultant", "222": "Senior Data Specialist", "223": "Graduate Research Analyst Intern", "224": "Financial Mathematics Senior Analyst", "225": "Undergraduate Teaching Assistant (Multimedia Objects)", "226": "Senior Back-End Developer", "227": "Field Analyst", "228": "BITECHCO", "229": "Electronics Engineer", "230": "System Administrator", "231": "Seminar Assistant/Lecturer", "232": "Data Leader", "233": "Senior Platform Engineer", "234": "Retention & Fidelization Leader", "235": "Data Management Intern", "236": "Commercial Department Assistant", "237": "Research Advisor", "238": "Full Stack Ruby On Rails Developer", "239": "E-Commerce Operations Engineer", "240": "Head of Predictive Modeling", "241": "Cloud & Data Architect", "242": "Industrial Network Engineer", "243": "Production and Maintenance Intern", "244": "Python Developer/ETL Engineer", "245": "Project Leader", "246": "Java Developer", "247": "L2 Support Engineer, Customer Integration and Support", "248": "Freelance Web Developer", "249": "Undergraduate Research Assistant", "250": "Project Research Professional", "251": "IT Operations Specialist", "252": "It Consultant", "253": "AWS DevOps Engineer", "254": "Procurement Specialist", "255": "Geoscientist", "256": "Productivity Analyst And Coach", "257": "Product Designer", "258": "Electrical Engineer Intern", "259": "Financial Business Intelligence Intern", "260": "Civil Enginner", "261": "Revenue Management Analyst", "262": "IT Specialist", "263": "Data Governance Engineer", "264": "Instructor", "265": "Project Management Intern", "266": "Technical Product Owner", "267": "Cloud Developer", "268": "Cloud Python Developer", "269": "Bi Analyst", "270": "Infrastructure Intern", "271": "Analyst Specialist", "272": "Technology Intern", "273": "Machine Learning Researcher", "274": "Internal Researcher", "275": "Senior Integration Engineer", "276": "PHP/ Python Developer", "277": "Systems Architecture and Data Administration Assistant", "278": "JR Research Assistant at Apolo Scientific Computing Center", "279": "Financial & Statistics Advisor", "280": "Data Sciencist", "281": "Jr Actuary", "282": "Data Integrations Engineer", "283": "Innovations Intern", "284": "Regional Credit + Accounts Receivable Manager Assistant", "285": "Billing Analyst", "286": "Engineering Intern", "287": "Trainee Engineer", "288": "Security Specialist", "289": "Compliance Analyst", "290": "Student Researcher", "291": "Graduated Research Assistant", "292": "Analyst Intern", "293": "Sap Consultant", "294": "Administrative Financial Analyst", "295": "R&D Chemical Laboratory Analyst", "296": "Design Engineering Intern", "297": "Senior Business Intelligence Developer", "298": "Industry and Commerce Intern", "299": "Local Map Operations Analyst II", "300": "Project Assistant for the Audit Department", "301": "Software and Automation Engineer", "302": "Customer Intelligence Analyst", "303": "Pricing Lead", "304": "Technical Team Leader", "305": "Pre-Professional Paid Intern", "306": "Laboratory Assistant", "307": "Back-End Developer", "308": "Intern Researcher", "309": "Teacher and Researcher", "310": "Biomedical Computer Vision Research Asst.", "311": "CTO and Founder", "312": "Programming Analyst", "313": "Consultant on Customer Segmentation", "314": "Credit Analyst", "315": "Technical Analyst", "316": "BI Teach Lead", "317": "Assistant Professor", "318": "Asset Allocation Analyst", "319": "Research teacher\u2019s assistant", "320": "Mathematical Analyst", "321": "IT Consultant", "322": "Big Data Researcher", "323": "IT Operations AWS Engineer", "324": "Administrative Assistant", "325": "Machine Learning Research Co-op", "326": "It Intern", "327": "Functional Analyst", "328": "Graduate research assistant", "329": "Assistant Developer", "330": "Technical Maintenance Intern", "331": "Senior Implementation Consultant", "332": "System Development Manager", "333": "Frontend React Architect", "334": "Support Analyst", "335": "Submarine ROV Supervisor", "336": "Marketing & Sales Intern", "337": "Strategic Planning Leader", "338": "Business Intelligence Consultant and DBA", "339": "BI and Dev. Team Lead", "340": "Undergraduate Assistant", "341": "Assistant Professor of Probability and Statistic I & II", "342": "Technical Maintenance Clerk", "343": "Associate Professor Of Discrete Event Simulation", "344": "Supply Chain Analyst", "345": "Infrastructure Mgmt Senior Analyst", "346": "Data Analysis Researcher", "347": "Marketing Intelligence Specialist", "348": "Junior Programmer", "349": "Data Platform Engineer", "350": "CTO", "351": "Senior System Administrator", "352": "Data Administration and Security Control Analyst", "353": "Research assistant", "354": "Business Intelligence Leader", "355": "UNIVERSIDAD EAFIT", "356": "Risk Intern", "357": "Applied Mathematics - Research Assistant", "358": "IT Intern", "359": "Head Of Systems", "360": "Researcher and Developer", "361": "Senior Full Stack Engineer", "362": "Junior Project Manager", "363": "Engineer Manager", "364": "Technical Service Coordinator", "365": "Software Support Engineer", "366": "Research Analyst", "367": "Latin America Insights And Strategy Intern", "368": "Latin America Insights and Strategy Intern", "369": "Frontend Intern", "370": "Senior Infrastructure Administrator", "371": "Process Analyst", "372": "Product Manager", "373": "Bioinformatician", "374": "Qa Intern", "375": "Product and Data Manager", "376": "Finance Risk Intern", "377": "Maintenance Planner", "378": "Research Consultant", "379": "Graduate Research Assistant", "380": "It Assistant", "381": "Research Staff Member", "382": "Computational Biologist", "383": "Machine Learning Consultant", "384": "Engineering Tutor", "385": "Programming Fundamentals Instructor", "386": "Assistant Professor Industrial Engineering", "387": "Engineer Intern", "388": "Open Source Software Contributor", "389": "Junior Business Intelligence Consultant", "390": "Tutor of Statistics", "391": "INNOVA Project Analyst II", "392": "Analyst", "393": "Junior Project Analyst", "394": "Freelance developer", "395": "C++ Agent Developer", "396": "Data Research Analyst", "397": "Application Developer", "398": "IT Manager", "399": "Pre-professional Paid Intern", "400": "Senior Financial Risk Analyst", "401": "Freelance Java Developer", "402": "Junior Engineer", "403": "Senior Logistics Analyst", "404": "Factored", "405": "Technology Team Leader", "406": "Operations Analyst", "407": "Statistics Intern", "408": "Risk Management Supervisor/Analyst", "409": "Security Developer", "410": "Industrial Improvement Intern", "411": "Data Intelligence Lead", "412": "Jr Full Stack Scala Engineer", "413": "Product Lead", "414": "Academic Assistant", "415": "Growth Operations Senior Analyst", "416": "Mathematical Finance Researcher", "417": "Scientific Consultant and Founder", "418": "Solutions Engineer", "419": "Senior DevOps Engineer", "420": "Sales Promoter/Analyst/Marketing Intern", "421": "Junior Lean Manufacturing Consultant", "422": "Actuarial & Alternative Investments Specialist", "423": "Product Specialist", "424": "Engineering Assistant", "425": "Quantitative Portfolio Manager", "426": "Implementation Analyst", "427": "Statistical Analyst", "428": "Strategic Planning Analyst", "429": "Machine Learning Professor", "430": "Technology Strategy Intern", "431": "Quality Coordinator", "432": "Research Student", "433": "BI Architect Professional", "434": "Mid-Level Integration Engineer and Team Leader", "435": "ETL Leader", "436": "Database Analyst", "437": "Automation Tester", "438": "Master\u2019s Course Designer", "439": "Head Of Artificial Intelligence", "440": "Mathematics Instructor Teacher", "441": "Nanomagnetism Lab Graduate Research Assistant", "442": "Mathematics TA", "443": "Prodesp", "444": "Teaching Assistant: Choice Theory", "445": "Teacher & Assistant", "446": "Instructor Professor", "447": "Microeconometrics Professor", "448": "Algorithms Teaching Assistant", "449": "Competitive Programming Tutor", "450": "Innovation Analyst", "451": "Adjunct Teacher", "452": "Complementary Teacher", "453": "Computer Vision Engineer", "454": "Hadoop Developer", "455": "Instructor - Discrete Event Simulation", "456": "Online College Tutor", "457": "Statistics Teaching Assistant", "458": "University Subject Tutor", "459": "Nanomagnetism Lab Undergrad Research Assistant", "460": "Marketplace Sr Analyst LATAM", "461": "Regional Credit Accounts Receivable Manager Assistant", "462": "Graduate TA", "463": "Scala Back-End Developer", "464": "Operations Tech Lead", "465": "Research Assistant For Rise Group", "466": "Monitor", "467": "Mechanical Designer", "468": "<<Title02>>", "469": "SAP Business Objects Consultant", "470": "Bi Intern", "471": "Senior Database Administrator", "472": "Intelligent Automation Consultant", "473": "Trainee - Commercial Analyst", "474": "Trainee Commercial Analyst", "475": "Graduate Professor", "476": "Part-Time Lecturer", "477": "Information Technology Intern", "478": "Computer Architecture Tutor", "479": "Power BI Instructor", "480": "Code Reviewer", "481": "Quality Analyst", "482": "Golang Developer", "483": "Senior Tutor", "484": "Investments Analyst", "485": "Trader", "486": "Associate", "487": "Research Assistant at Apolo Scientific Computing Center", "488": "Business Development Operations Analyst", "489": "SR Tech Lead & Project Leader", "490": "Technical Consultant", "491": "Junior Front end Developer", "492": "Quality Analyst, Yuxi Global", "493": "SR Technical Leader", "494": "Economist", "495": "Corporate Information Specialist", "496": "IT Risk Consultant", "497": "SAP BI Consultant", "498": "Undergraduate Teaching Assistant", "499": "Artificial Intelligence Developer", "500": "Test Automation Engineer", "501": "Portfolio Manager", "502": "Credit Risk Senior Analyst", "503": "Data Migration Professional", "504": "Finance Intern", "505": "Adjunct Laboratory Assistant", "506": "Undergraduate Research Fellow", "507": "Online Tutor", "508": "Teacher Of Industrial Mechanics", "509": "Artificial Lift Intern", "510": "Master In Neurolinguistics Programming And Hypnotherapist", "511": "Statistics Trainee", "512": "Teacher / Lab Assistant", "513": "Civil Engineer Intern", "514": "Business Executive", "515": "Data Intern", "516": "PIBIC - CNPq", "517": "Junior Data Modeler", "518": "Solutions Architect", "519": "Science Intern", "520": "Information and Actuary Analyst", "521": "Teacher assistant for Programming Fundamentals Course", "522": "SR Technical & Project Leader", "523": "Logistics Innovation Intern", "524": "Oil Exploration Geologist", "525": "NET Developer", "526": "Processing Analyst", "527": "Associate Engineer", "528": "Electronics Engineer", "529": "IT Course Contributor", "530": "Embedded ARM developer", "531": "<<Title03>>", "532": "Marketing and Sales Assistant", "533": "Trainee", "534": "Jr Front-End Engineer", "535": "Jr Back End Developer", "536": "Programming Instructor", "537": "Teacher (ad Honorem)", "538": "HPE Solutions Architect", "539": "Software System Engineer", "540": "SQL Developer", "541": "Technical Office Assistant Engineer", "542": "Developer and Consultant", "543": "Netcom", "544": "Treasury Analyst", "545": "<<Title01>>", "546": "Geologo", "547": "Senior Technical Team Lead", "548": "Conversion Analyst", "549": "SAS Consultant", "550": "Computer Programmer", "551": "SAP Senior Consultant", "552": "Medea Interactiva", "553": "Mid-Level Integration Engineer", "554": "Apple Developer", "555": "Growth Marketing Analyst", "556": "Robotics And Control Systems Engineer", "557": "Financial Risk Analyst Intern"}}}}, {"name": "keywords", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1704497.3801787165, "num_examples": 2215}, {"name": "val", "num_bytes": 568678.8099106418, "num_examples": 739}, {"name": "test", "num_bytes": 568678.8099106418, "num_examples": 739}], "download_size": 877814, "dataset_size": 2841855.0}}
|
2023-08-18T20:00:58+00:00
|
3be265b2f4ce59ebea302796f967c090efeda6e6
|
Guangyuan/MEWL
|
[
"license:mit",
"region:us"
] |
2023-05-26T11:50:27+00:00
|
{"license": "mit"}
|
2023-05-26T11:50:27+00:00
|
|
2523eb7642c59820df41f003c38096c7277024d9
|
# Dataset Card for "strokelog_ap-hanjiang01_date-20230523_page-177.0.6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zhangyue/strokelog_ap-hanjiang01_date-20230523_page-177.0.6
|
[
"region:us"
] |
2023-05-26T11:58:51+00:00
|
{"dataset_info": {"features": [{"name": "k", "dtype": "string"}, {"name": "v", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 52765, "num_examples": 1}], "download_size": 25791, "dataset_size": 52765}}
|
2023-05-26T12:22:11+00:00
|
1b2b3b570283913937311be77e939175518b53c7
|
qq371/11111
|
[
"license:epl-2.0",
"region:us"
] |
2023-05-26T12:05:12+00:00
|
{"license": "epl-2.0"}
|
2023-06-14T07:07:25+00:00
|
|
b2256cc43580ee5d7007e8a8378861fe34fcd228
|
This repository contains train and test datasets of the preprocessed Fake/Real news and Disaster tweet datasets from Kaggle. The preprocessing notebook, along with the custom text-processing function, is available.
|
ilynmark/fake_real_data
|
[
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-05-26T12:05:34+00:00
|
{"language": ["en"], "license": "apache-2.0"}
|
2023-05-26T12:33:15+00:00
|
53c8474838e2af6d781cdfa7ca06ce370452682d
|
chenxwh/gen-winograd
|
[
"license:cc-by-nd-4.0",
"region:us"
] |
2023-05-26T12:14:18+00:00
|
{"license": "cc-by-nd-4.0"}
|
2023-05-26T13:49:00+00:00
|
|
65b5ee4b4900459c361f99717c92875133e28157
|
# Dataset Card for CaWikiTC
## Dataset Description
- **Point of Contact:** [Irene Baucells de la Peña]([email protected])
### Dataset Summary
CaWikiTC (Catalan Wikipedia Text Classification) is a text classification dataset authomatically created by scraping Catalan Wikipedia article summaries and their associated thematic category. It contains 21002 texts (19952 and 1050 in the train and dev partitions, respectively) classified under 67 exclusive categories.
For the dataset creation, we selected all the Catalan Wikipedia article summaries from a previously fixed variety of subcategories, most of which are professional disciplines and social sciences-related fields. The texts that were originally associated with more than one category were discarded to avoid class overlappings.
This dataset was created as part of the experiments from [Entailment-based Task Transfer for Catalan Text Classification in Small Data Regimes](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6551). Its original purpose was to serve as a task transfer source to train an entailment model, which was then used to perform a different text classification task.
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International</a>.
### Supported Tasks and Leaderboards
Text classification, Language Model
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
Two json files (train and development splits).
### Data Fields
Each example contains the following 3 fields:
* text: Catalan Wikipedia article summary (string)
* label: topic
#### Example:
<pre>
[
{
'text': "Novum Organum és el títol de l'obra més important de Francis Bacon, publicada el 1620. Rep el seu nom perquè pretén ser una superació del tractat sobre lògica d'Aristòtil, anomenat Organon. Es basa a trobar la causa de tot fenomen per inducció, observant quan passa i quan no i extrapolant aleshores les condicions que fan que es doni. Aquest raonament va influir decisivament en la formació del mètode científic, especialment en la fase d'elaboració d'hipòtesis. També indica que el prejudici és l'enemic de la ciència, perquè impideix generar noves idees. Els prejudicis més comuns s'expliquen amb la metàfora de l'ídol o allò que és falsament adorat. Existeixen ídols de la tribu (comuns a tots els éssers humans per la seva naturalesa), de la caverna (procedents de l'educació), del fòrum (causats per un ús incorrecte del llenguatge) i del teatre (basats en idees anteriors errònies, notablement en filosofia).",
'label': 'Filosofia',
},
...
]
</pre>
#### Labels
* 'Administració', 'Aeronàutica', 'Agricultura', 'Antropologia', 'Arqueologia', 'Arquitectura', 'Art', 'Astronomia', 'Astronàutica', 'Biblioteconomia', 'Biotecnologia', 'Catàstrofes', 'Circ', 'Ciència militar', 'Ciència-ficció', 'Ciències ambientals', 'Ciències de la salut', 'Ciències polítiques', 'Conflictes', 'Cronometria', 'Cultura popular', 'Dansa', 'Dret', 'Ecologia', 'Enginyeria', 'Epidèmies', 'Esoterisme', 'Estris', 'Festivals', 'Filologia', 'Filosofia', 'Fiscalitat', 'Física', 'Geografia', 'Geologia', 'Gestió', 'Heràldica', 'Història', 'Humor', 'Indumentària', 'Informàtica', 'Jaciments paleontològics', 'Jocs', 'Lingüística', 'Llengües', 'Llocs ficticis', 'Matemàtiques', 'Metodologia', 'Mitologia', 'Multimèdia', 'Museologia', 'Nàutica', 'Objectes astronòmics', 'Pedagogia', 'Periodisme', 'Protestes', 'Pseudociència', 'Psicologia', 'Química', 'Robòtica', 'Ràdio', 'Seguretat laboral', 'Sociologia', 'Telecomunicacions', 'Televisió', 'Teologia', 'Ètica'
### Data Splits
Train and development splits were created in a stratified fashion, following a 95% and 5% proportion, respectively. The sizes of each split are the following:
* train.json: 19952 examples
* dev.json: 1050 examples
### Annotations
#### Annotation process
The crawled data contained the categories' annotations, which were then used to create this dataset with the mentioned criteria.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this dataset contributes to the development of language modeCAls in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Irene Baucells ([email protected])
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International</a>.
### Citation Information
|
projecte-aina/CaWikiTC
|
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:automatically-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-sa-3.0",
"region:us"
] |
2023-05-26T12:22:43+00:00
|
{"annotations_creators": ["automatically-generated"], "language_creators": ["found"], "language": ["ca"], "license": ["cc-by-sa-3.0"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "pretty_name": "cawikitc"}
|
2023-11-25T05:32:19+00:00
|
ef43fd2adc007847dba74001094a8d68170b457f
|
This repository contains cleaned and filtered ShareGPT GPT-4 data used to train OpenChat. Details can be found in the [OpenChat repository](https://github.com/imoneoi/openchat).
|
openchat/openchat_sharegpt4_dataset
|
[
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] |
2023-05-26T12:45:36+00:00
|
{"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["conversational", "text-generation"], "pretty_name": "OpenChat"}
|
2023-07-01T12:20:31+00:00
|
8288c5aeedfb9736ded26134fdce8f58be8b17fb
|
# midjourney-v5-202304-clean
## 简介 Brief Introduction
非官方的，对Kaggle (Midjourney User Prompts & Generated Images (250k))[https://www.kaggle.com/datasets/succinctlyai/midjourney-texttoimage?select=general-01_2022_06_20.json] 上的数据集进行了清理，一共有 248,167对。
Unofficially, a cleanup of the dataset on Kaggle (Midjourney User Prompts & Generated Images (250k))[https://www.kaggle.com/datasets/succinctlyai/midjourney-texttoimage?select=general-01_2022_06_20.json] yielded 248,167 pairs.
## 数据集信息 Dataset Information
我做了一些清洗，清理出了两个文件：
- ori.parquet （145,918对，midjourney的四格图）
- upscaled.parquet （102,249对，使用了高清指令的图，这意味着这个图更受欢迎。）
I did some cleaning and cleaned out two files:
- ori_prompts_df.parquet (145,918 pairs, midjourney's four-frame diagrams)
- upscaled_prompts_df.parquet (102,249 pairs, graphs that use the Upscale command, which means this one is more popular.)
## 列信息 Column Information
1. `content` (内容): 这一列包含了消息的主要内容，可能包括文本、链接、或者其他元素。
This column contains the main content of the message, which may include text, links, or other elements.
2. `url` (网址): 这一列包含了附件的URL，通常是图片或者其他文件。
This column contains the URL of the attachment, usually an image or other file.
3. `proxy_url` (代理网址): 这一列包含了附件的代理URL，这个URL可以用来访问附件，即使在原始URL不可用的情况下。
This column contains the proxy URL of the attachment, which can be used to access the attachment even when the original URL is not available.
4. `width` (宽度): 这一列包含了附件的宽度，通常是图片的宽度。
This column contains the width of the attachment, usually the width of an image.
5. `height` (高度): 这一列包含了附件的高度，通常是图片的高度。
This column contains the height of the attachment, usually the height of an image.
6. `date` (日期): 这一列包含了消息的发送日期和时间。
This column contains the date and time the message was sent.
7. `message_type` (消息类型): 这一列包含了消息的类型，例如是否是初始消息、变体请求或者是放大请求。
This column contains the type of the message, such as whether it is an initial message, a variation request, or an upscale request.
8. `content_links` (内容链接): 这一列包含了消息内容中的所有链接。
This column contains all the links in the message content.
9. `prompt` (提示): 这一列包含了消息中的主要提示，通常是用户输入的文本。
This column contains the main prompt in the message, usually the text input by the user.
10. `prompt_additions` (提示补充): 这一列包含了消息中的提示补充，这些补充可能包括额外的信息或者指示。
This column contains the prompt additions in the message, these additions may include extra information or instructions.
11. `user_name` (用户名): 这一列包含了发送消息的用户的用户名。
This column contains the username of the user who sent the message.
12. `aspect` (宽高比): 这一列包含了附件的宽高比，通常是图片的宽高比。
This column contains the aspect ratio of the attachment, usually the aspect ratio of an image.
13. `clean_prompts` (清理后的提示): 这一列包含了清理后的提示，其中已经删除了所有的链接和奇怪的字符。
This column contains the cleaned prompts, where all links and weird characters have been removed.
|
wanng/midjourney-kaggle-clean
|
[
"task_categories:image-to-text",
"task_categories:text-to-image",
"language:en",
"license:cc0-1.0",
"midjourney",
"kaggle",
"region:us"
] |
2023-05-26T12:58:56+00:00
|
{"language": ["en"], "license": "cc0-1.0", "task_categories": ["image-to-text", "text-to-image"], "tags": ["midjourney", "kaggle"]}
|
2023-05-26T13:09:30+00:00
|
4039389442616f7314fe1c821b96c888b27295f2
|
# Dataset Card for "ontonotes_zh_ner_knowledge_V3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
doushabao4766/ontonotes_zh_ner_knowledge_V3
|
[
"region:us"
] |
2023-05-26T13:17:01+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "knowledge", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14260725, "num_examples": 15724}, {"name": "validation", "num_bytes": 4958037, "num_examples": 4301}, {"name": "test", "num_bytes": 5417233, "num_examples": 4346}], "download_size": 0, "dataset_size": 24635995}}
|
2023-05-26T13:29:08+00:00
|
369ce16fbe4467f46916efe3730d04fb607c0391
|
# Dataset Card for "robustLR"
https://github.com/INK-USC/RobustLR
```
@article{sanyal2022robustlr,
title={Robustlr: Evaluating robustness to logical perturbation in deductive reasoning},
author={Sanyal, Soumya and Liao, Zeyi and Ren, Xiang},
journal={arXiv preprint arXiv:2205.12598},
year={2022}
}
```
|
tasksource/robustLR
|
[
"region:us"
] |
2023-05-26T13:25:34+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "statement", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2436331, "num_examples": 2317}, {"name": "test", "num_bytes": 3066737, "num_examples": 3000}, {"name": "dev", "num_bytes": 2512690, "num_examples": 2317}], "download_size": 1330059, "dataset_size": 8015758}}
|
2023-06-22T13:12:37+00:00
|
64831105a5f2c6a2bcfaa0a7551604e1df5c35ea
|
chenxwh/gen-storycloze
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-05-26T13:46:16+00:00
|
{"license": "cc-by-nc-4.0"}
|
2023-05-26T13:51:11+00:00
|
|
0105714231696d346a67dba69ab6372f3f0598c7
|
# Dataset Card for "calvin_abc_d"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ShuaKang/calvin_abc_d
|
[
"region:us"
] |
2023-05-26T13:49:47+00:00
|
{"dataset_info": {"features": [{"name": "goal_image", "dtype": "image"}, {"name": "obs_image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1548380473.5, "num_examples": 17870}], "download_size": 1547702724, "dataset_size": 1548380473.5}}
|
2023-05-26T14:16:56+00:00
|
b9bf60e548dbec32017b11e3846bb20a3129c2c3
|
nicosalama/prueba
|
[
"language:es",
"license:mit",
"region:us"
] |
2023-05-26T14:15:07+00:00
|
{"language": ["es"], "license": "mit"}
|
2023-10-06T11:58:48+00:00
|
|
5892761ec6423565b03962ab7ee30ae403d8255f
|
# Dataset Card for "calvin_abc_d_val"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ShuaKang/calvin_abc_d_val
|
[
"region:us"
] |
2023-05-26T14:17:33+00:00
|
{"dataset_info": {"features": [{"name": "goal_image", "dtype": "image"}, {"name": "obs_image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 87812109.25, "num_examples": 1087}], "download_size": 85962699, "dataset_size": 87812109.25}}
|
2023-05-26T14:17:53+00:00
|
76e936066a0559a5853db39fac8608016dcfe083
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
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yubaiscat/SDRS
|
[
"task_categories:text-to-image",
"size_categories:10K<n<100K",
"language:en",
"language:zh",
"region:us"
] |
2023-05-26T14:17:35+00:00
|
{"language": ["en", "zh"], "size_categories": ["10K<n<100K"], "task_categories": ["text-to-image"], "pretty_name": "RS"}
|
2023-05-26T14:54:49+00:00
|
6275b67a4db3ac96622405024e1b462075e2d57a
|
Ryan-sjtu/celebahq-caption
|
[
"license:mit",
"region:us"
] |
2023-05-26T14:34:03+00:00
|
{"license": "mit", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2756863400.0, "num_examples": 30000}], "download_size": 2762815442, "dataset_size": 2756863400.0}}
|
2023-05-26T14:54:04+00:00
|
|
03cab92b9f37027eee7c7df8a2bbc679db484c91
|
# Dataset Card for "mcl-signal_processing_attacks_assembly"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TeamSODA/mcl-signal_processing_attacks_assembly_librispeech
|
[
"region:us"
] |
2023-05-26T14:46:56+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0-benign", "1": "1-kenan", "2": "2-yeehaw", "3": "3-imaginary_clipping"}}}}], "splits": [{"name": "train", "num_bytes": 100461849.0, "num_examples": 200}], "download_size": 87863935, "dataset_size": 100461849.0}}
|
2023-05-26T14:48:01+00:00
|
910cfd6591d761607853e065cf0b0e80c93431d2
| ERROR: type should be string, got "\nhttps://github.com/anthropics/hh-rlhf の内容のうち、helpful-base内のchosenに記載されている英文をfuguMTで翻訳、うまく翻訳できていないものを除外、修正したものです。 \n" |
nakayama/hh-rlhf-helpful-base-ja
|
[
"language:ja",
"license:mit",
"region:us"
] |
2023-05-26T14:48:51+00:00
|
{"language": ["ja"], "license": "mit"}
|
2023-05-26T14:57:12+00:00
|
be6a936f11d49c3464c3f8097405924d00601a44
|
# Dataset Card for "promptTTS_encodec"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kuanhuggingface/promptTTS_encodec_test
|
[
"region:us"
] |
2023-05-26T14:49:53+00:00
|
{"dataset_info": {"features": [{"name": "file_id", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "src_encodec_0", "sequence": "int64"}, {"name": "src_encodec_1", "sequence": "int64"}, {"name": "src_encodec_2", "sequence": "int64"}, {"name": "src_encodec_3", "sequence": "int64"}, {"name": "src_encodec_4", "sequence": "int64"}, {"name": "src_encodec_5", "sequence": "int64"}, {"name": "src_encodec_6", "sequence": "int64"}, {"name": "src_encodec_7", "sequence": "int64"}, {"name": "tgt_encodec_0", "sequence": "int64"}, {"name": "tgt_encodec_1", "sequence": "int64"}, {"name": "tgt_encodec_2", "sequence": "int64"}, {"name": "tgt_encodec_3", "sequence": "int64"}, {"name": "tgt_encodec_4", "sequence": "int64"}, {"name": "tgt_encodec_5", "sequence": "int64"}, {"name": "tgt_encodec_6", "sequence": "int64"}, {"name": "tgt_encodec_7", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 2866521473, "num_examples": 47300}, {"name": "valid", "num_bytes": 90284784, "num_examples": 1350}, {"name": "test", "num_bytes": 75361553, "num_examples": 1350}], "download_size": 420020432, "dataset_size": 3032167810}}
|
2023-05-26T15:06:48+00:00
|
7b322016036b7492e488888cc3bd392f2a01225c
|
Ryan-sjtu/ffhq512-caption
|
[
"license:mit",
"region:us"
] |
2023-05-26T14:59:17+00:00
|
{"license": "mit", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 26892697268.0, "num_examples": 70000}], "download_size": 27110037616, "dataset_size": 26892697268.0}}
|
2023-05-27T05:00:25+00:00
|
|
4507c0e02ce5ca16a152b70a6083469a407ebf3b
|
# Dataset Card for "diffusion_db_dedupe_from50k_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rbeauchamp/diffusion_db_dedupe_from50k_train
|
[
"region:us"
] |
2023-05-26T15:59:19+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "seed", "dtype": "uint32"}, {"name": "step", "dtype": "uint16"}, {"name": "cfg", "dtype": "float32"}, {"name": "sampler", "dtype": "string"}, {"name": "width", "dtype": "uint16"}, {"name": "height", "dtype": "uint16"}, {"name": "user_name", "dtype": "string"}, {"name": "timestamp", "dtype": "timestamp[ns, tz=UTC]"}, {"name": "image_nsfw", "dtype": "float32"}, {"name": "prompt_nsfw", "dtype": "float32"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "image_path", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 22139609531.30241, "num_examples": 34537}], "download_size": 21346107309, "dataset_size": 22139609531.30241}}
|
2023-05-26T16:22:18+00:00
|
3aa78400c488aee928b8d805e96f361339c4163c
|
# Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_A_CM_Q_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xxl_mode_A_CM_Q_rices_ns_1000
|
[
"region:us"
] |
2023-05-26T16:14:52+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_", "num_bytes": 142104, "num_examples": 1000}], "download_size": 53610, "dataset_size": 142104}}
|
2023-05-26T16:14:56+00:00
|
34d46cf846e7843b255788a99c8f53c3c25fed0f
|
# Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_T_CM_Q_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xxl_mode_T_CM_Q_rices_ns_1000
|
[
"region:us"
] |
2023-05-26T16:18:17+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_", "num_bytes": 141841, "num_examples": 1000}], "download_size": 53617, "dataset_size": 141841}}
|
2023-05-26T16:18:21+00:00
|
cf32d0246c35f9e06efdf1c7bc3f2538cf39a07f
|
# Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_CM_Q_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_CM_Q_rices_ns_1000
|
[
"region:us"
] |
2023-05-26T16:21:57+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_", "num_bytes": 142063, "num_examples": 1000}], "download_size": 53707, "dataset_size": 142063}}
|
2023-05-26T16:22:01+00:00
|
9eaca4b9393a1ea047552aa4687f5fc33b01536d
|
# Dataset Card for "diffusion_db_dedupe_from50k_val"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rbeauchamp/diffusion_db_dedupe_from50k_val
|
[
"region:us"
] |
2023-05-26T16:22:18+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "seed", "dtype": "uint32"}, {"name": "step", "dtype": "uint16"}, {"name": "cfg", "dtype": "float32"}, {"name": "sampler", "dtype": "string"}, {"name": "width", "dtype": "uint16"}, {"name": "height", "dtype": "uint16"}, {"name": "user_name", "dtype": "string"}, {"name": "timestamp", "dtype": "timestamp[ns, tz=UTC]"}, {"name": "image_nsfw", "dtype": "float32"}, {"name": "prompt_nsfw", "dtype": "float32"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "image_path", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 5343417657.740591, "num_examples": 8635}], "download_size": 5353656666, "dataset_size": 5343417657.740591}}
|
2023-05-26T16:27:37+00:00
|
cfe4eb96589e02d9e10e16513dbbd9567f480bf7
|
# Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_A_T_CM_Q_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xxl_mode_A_T_CM_Q_rices_ns_1000
|
[
"region:us"
] |
2023-05-26T16:25:35+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_", "num_bytes": 141914, "num_examples": 1000}], "download_size": 53553, "dataset_size": 141914}}
|
2023-05-26T16:25:39+00:00
|
27859178ae89298165190cfb8edce425c40f8ffa
|
# Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_D_PNP_GENERIC_CM_Q_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xxl_mode_D_PNP_GENERIC_CM_Q_rices_ns_1000
|
[
"region:us"
] |
2023-05-26T16:36:57+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_", "num_bytes": 12745403, "num_examples": 1000}], "download_size": 1889203, "dataset_size": 12745403}}
|
2023-06-08T22:30:50+00:00
|
f4443bd3eec903ac8d8776b6c1d236665dcb9153
|
# Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_CM_D_PNP_GENERIC_Q_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xxl_mode_CM_D_PNP_GENERIC_Q_rices_ns_1000
|
[
"region:us"
] |
2023-05-26T16:44:42+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_", "num_bytes": 12724191, "num_examples": 1000}], "download_size": 1882493, "dataset_size": 12724191}}
|
2023-06-08T20:42:05+00:00
|
75257d2729e6daa1adced0f4a90dfea86299c516
|
# Dataset Card for "EN_PARAGRAPH_HUMAN_JOINED"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bot-yaya/EN_PARAGRAPH_HUMAN_JOINED
|
[
"region:us"
] |
2023-05-26T17:04:03+00:00
|
{"dataset_info": {"features": [{"name": "record", "dtype": "string"}, {"name": "raw_text", "dtype": "string"}, {"name": "is_hard_linebreak", "sequence": "bool"}], "splits": [{"name": "train", "num_bytes": 393759, "num_examples": 17}], "download_size": 199223, "dataset_size": 393759}}
|
2023-05-26T17:04:10+00:00
|
faecd3e588e332d364c9a012d8fb1afb17bfc0d5
|
ColtonDevAcc/ProudctInformation
|
[
"license:openrail",
"region:us"
] |
2023-05-26T17:18:09+00:00
|
{"license": "openrail"}
|
2023-05-26T17:18:09+00:00
|
|
c1d8e6d997955635c89c9b67aa8459b3c6a20655
|
# Dataset Card for "diffusion_db_dedup_from50k_train_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
myradeng/diffusion_db_dedup_from50k_train_v2
|
[
"region:us"
] |
2023-05-26T17:19:17+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "seed", "dtype": "uint32"}, {"name": "step", "dtype": "uint16"}, {"name": "cfg", "dtype": "float32"}, {"name": "sampler", "dtype": "string"}, {"name": "width", "dtype": "uint16"}, {"name": "height", "dtype": "uint16"}, {"name": "user_name", "dtype": "string"}, {"name": "timestamp", "dtype": "timestamp[ns, tz=UTC]"}, {"name": "image_nsfw", "dtype": "float32"}, {"name": "prompt_nsfw", "dtype": "float32"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "image_path", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 20635424524.879997, "num_examples": 34716}], "download_size": 21150988303, "dataset_size": 20635424524.879997}}
|
2023-05-26T17:39:30+00:00
|
289d228c3dfef252bf40eecc532f79251c7bf2b4
|
# Dataset Card for "diffusion_db_dedup_from50k_val_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
myradeng/diffusion_db_dedup_from50k_val_v2
|
[
"region:us"
] |
2023-05-26T17:39:31+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "seed", "dtype": "uint32"}, {"name": "step", "dtype": "uint16"}, {"name": "cfg", "dtype": "float32"}, {"name": "sampler", "dtype": "string"}, {"name": "width", "dtype": "uint16"}, {"name": "height", "dtype": "uint16"}, {"name": "user_name", "dtype": "string"}, {"name": "timestamp", "dtype": "timestamp[ns, tz=UTC]"}, {"name": "image_nsfw", "dtype": "float32"}, {"name": "prompt_nsfw", "dtype": "float32"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "image_path", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 5204531732.444004, "num_examples": 8680}], "download_size": 5298548135, "dataset_size": 5204531732.444004}}
|
2023-05-26T17:44:38+00:00
|
97dc060e1f1ab1bbe72ca96c5611538f0f75700f
|
# Dataset Card for "SinQuAD"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
9wimu9/SinQuAD
|
[
"language:si",
"region:us"
] |
2023-05-26T18:16:25+00:00
|
{"language": "si", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 25556009.36743406, "num_examples": 11806}, {"name": "validation", "num_bytes": 2840037.63256594, "num_examples": 1312}], "download_size": 11791942, "dataset_size": 28396047.0}}
|
2024-01-22T14:10:30+00:00
|
2345730bf92fb99f50083133cf93f2ab0eb84af2
|
# Dataset Card for "RT_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
anwarshome/RT_data
|
[
"region:us"
] |
2023-05-26T18:30:40+00:00
|
{"dataset_info": {"features": [{"name": "uuid", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "audio", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 19603029093.342, "num_examples": 234414}, {"name": "test", "num_bytes": 85163007.0, "num_examples": 1000}], "download_size": 17509364468, "dataset_size": 19688192100.342}}
|
2023-05-26T18:37:01+00:00
|
9cdbc1f1f391f3989b0e44c5bbc192ae2e4b3178
|
This is a micro dataset used by the example [training script](https://github.com/NVIDIA/NeMo/blob/stable/examples/nlp/spellchecking_asr_customization/run_training.sh) for [SpellMapper](https://arxiv.org/abs/2306.02317) model.
A pretrained checkpoint is [available](https://huggingface.co/bene-ges/spellmapper_asr_customization_en).
|
bene-ges/spellmapper_en_train_micro
|
[
"language:en",
"license:cc-by-4.0",
"arxiv:2306.02317",
"region:us"
] |
2023-05-26T18:35:36+00:00
|
{"language": ["en"], "license": "cc-by-4.0"}
|
2023-06-06T13:38:35+00:00
|
beb92deb932d67a6319cc7ca71d8056ececc47d2
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository: https://github.com/minjechoi/SOCKET
- **Paper: Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark [link](https://arxiv.org/abs/2305.14938)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This Dataset contains the tasks used in the paper "Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark" [link](https://arxiv.org/abs/2305.14938).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
This benchmark is created by aggregating several existing NLP datasets that measure different aspects of social information.
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
@misc{choi2023llms,
title={Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark},
author={Minje Choi and Jiaxin Pei and Sagar Kumar and Chang Shu and David Jurgens},
year={2023},
eprint={2305.14938},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
### Contributions
[More Information Needed]
|
Blablablab/SOCKET
|
[
"license:cc-by-4.0",
"arxiv:2305.14938",
"region:us"
] |
2023-05-26T18:56:41+00:00
|
{"license": "cc-by-4.0"}
|
2023-11-12T23:28:36+00:00
|
8374d03e6dc5f8223336b9f01f99456b934d88c7
|
# Dataset Card for "sinhala_dataset_59m"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
9wimu9/sinhala_dataset_59m
|
[
"region:us"
] |
2023-05-26T19:07:15+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11841585825, "num_examples": 53579185}, {"name": "validation", "num_bytes": 1315811268, "num_examples": 5953243}], "download_size": 6207624011, "dataset_size": 13157397093}}
|
2023-05-26T19:21:54+00:00
|
fdad9cb3d58ee96edc3cd344e6d44b6f23d5be0e
|
VirtualRoyalty/toxic_comments
|
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] |
2023-05-26T19:23:54+00:00
|
{"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "toxic_comments"}
|
2023-05-26T19:41:18+00:00
|
|
8309e909dd9f2b5ff48fcbe5943413fd8697a9c3
|
abderafie/first
|
[
"license:openrail",
"region:us"
] |
2023-05-26T19:55:57+00:00
|
{"license": "openrail"}
|
2023-05-26T19:55:57+00:00
|
|
e52bd0f8ab1c25232f735239dedbe7b0d96c87ae
|
This dataset contains a selection of Q&A-related tasks gathered and cleaned from the webGPT_comparisons set and the databricks-dolly-15k set.
Unicode escapes were explicitly removed, and wikipedia citations in the "output" were stripped through regex to hopefully help any
end-product model ignore these artifacts within their input context.
This data is formatted for use in the alpaca instruction format, however the instruction, input, and output columns are kept separate in
the raw data to allow for other configurations. The data has been filtered so that every entry is less than our chosen truncation length of
1024 (LLaMA-style) tokens with the format:
```
"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Input:
{inputt}
### Response:
{output}"""
```
<h3>webGPT</h3>
This set was filtered from the webGPT_comparisons data by taking any Q&A option that was positively or neutrally-rated by humans (e.g. "score" >= 0).
This might not provide the ideal answer, but this dataset was assembled specifically for extractive Q&A with less regard for how humans
feel about the results.
This selection comprises 23826 of the total entries in the data.
<h3>Dolly</h3>
The dolly data was selected primarily to focus on closed-qa tasks. For this purpose, only entries in the "closed-qa", "information_extraction",
"summarization", "classification", and "creative_writing" were used. While not all of these include a context, they were judged to help
flesh out the training set.
This selection comprises 5362 of the total entries in the data.
|
starfishmedical/webGPT_x_dolly
|
[
"task_categories:question-answering",
"size_categories:10K<n<100K",
"license:cc-by-sa-3.0",
"region:us"
] |
2023-05-26T19:58:15+00:00
|
{"license": "cc-by-sa-3.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"]}
|
2023-05-30T18:47:30+00:00
|
65589545ac5c243b7a16e0f44f8c4cd617ea32ec
|
# Dataset Card for "c31e08a7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/c31e08a7
|
[
"region:us"
] |
2023-05-26T20:48:48+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 180, "num_examples": 10}], "download_size": 1332, "dataset_size": 180}}
|
2023-05-26T20:48:50+00:00
|
b8bcef8ddb0ce0b9213c2d250cd509584539ecbe
|
# Dataset Card for "natural_questions_helm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lighteval/natural_questions_helm
|
[
"region:us"
] |
2023-05-26T20:51:34+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "document", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "long_answers", "sequence": "string"}, {"name": "short_answers", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 12495666731, "num_examples": 307373}, {"name": "validation", "num_bytes": 319900546, "num_examples": 7830}], "download_size": 1733847123, "dataset_size": 12815567277}}
|
2023-05-27T04:33:12+00:00
|
f6f109ecf991a2c10baea837212a619765df5688
|
# Dataset Card for "cofrico"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sfblaauw/cofrico
|
[
"region:us"
] |
2023-05-26T21:17:35+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4907773.0, "num_examples": 6}], "download_size": 2743064, "dataset_size": 4907773.0}}
|
2023-05-26T21:17:41+00:00
|
14c3540ab89ec277065ba2636310ea97944b41b1
|
# Dataset Card for "Joe_Buck_the_GOAT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ks21/Joe_Buck_the_GOAT
|
[
"region:us"
] |
2023-05-26T21:17:51+00:00
|
{"dataset_info": {"features": [{"name": "caption", "dtype": "string"}, {"name": "image", "sequence": {"sequence": {"sequence": "uint8"}}}], "splits": [{"name": "train", "num_bytes": 258171320, "num_examples": 40}], "download_size": 64357844, "dataset_size": 258171320}}
|
2023-05-26T21:18:00+00:00
|
141dfda0dd75eaf9704db0ea4a300c89a79cd681
|
chatc/MACSum
|
[
"license:cc-by-nc-nd-4.0",
"region:us"
] |
2023-05-26T21:23:27+00:00
|
{"license": "cc-by-nc-nd-4.0"}
|
2023-05-26T21:26:25+00:00
|
|
d81cef7b6a306f38f81b60feb213458289e4fa77
|
yanmiamin/breastCancerEnhancedTrain
|
[
"license:openrail",
"region:us"
] |
2023-05-26T21:32:15+00:00
|
{"license": "openrail"}
|
2023-05-27T21:33:52+00:00
|
|
fbf504de5aeb8bda0acfc652c54acdf32f49a5b5
|
# Dataset Card for "OxfordPets_test_copy"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/OxfordPets_test_copy
|
[
"region:us"
] |
2023-05-26T21:47:17+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "abyssinian", "1": "american bulldog", "2": "american pit bull terrier", "3": "basset hound", "4": "beagle", "5": "bengal", "6": "birman", "7": "bombay", "8": "boxer", "9": "british shorthair", "10": "chihuahua", "11": "egyptian mau", "12": "english cocker spaniel", "13": "english setter", "14": "german shorthaired", "15": "great pyrenees", "16": "havanese", "17": "japanese chin", "18": "keeshond", "19": "leonberger", "20": "maine coon", "21": "miniature pinscher", "22": "newfoundland", "23": "persian", "24": "pomeranian", "25": "pug", "26": "ragdoll", "27": "russian blue", "28": "saint bernard", "29": "samoyed", "30": "scottish terrier", "31": "shiba inu", "32": "siamese", "33": "sphynx", "34": "staffordshire bull terrier", "35": "wheaten terrier", "36": "yorkshire terrier"}}}}, {"name": "species", "dtype": {"class_label": {"names": {"0": "Cat", "1": "Dog"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "clip_tag_ViT_L_14_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai_classes", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_oxfordpets", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_ensemble_specific", "dtype": "string"}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full_validate", "sequence": "string"}, {"name": "Attributes_ViT_B_16_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_ensemble_specific", "dtype": "string"}, {"name": "blip_caption_beam_5_Salesforce_blip2_opt_6.7b", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 7518510.0, "num_examples": 100}], "download_size": 7289872, "dataset_size": 7518510.0}}
|
2023-05-26T21:47:24+00:00
|
477f9daed200995ec42d0320c575a9fcb28df295
|
# Dataset Card for "OxfordPets_copy_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/OxfordPets_copy_test
|
[
"region:us"
] |
2023-05-26T21:48:06+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "abyssinian", "1": "american bulldog", "2": "american pit bull terrier", "3": "basset hound", "4": "beagle", "5": "bengal", "6": "birman", "7": "bombay", "8": "boxer", "9": "british shorthair", "10": "chihuahua", "11": "egyptian mau", "12": "english cocker spaniel", "13": "english setter", "14": "german shorthaired", "15": "great pyrenees", "16": "havanese", "17": "japanese chin", "18": "keeshond", "19": "leonberger", "20": "maine coon", "21": "miniature pinscher", "22": "newfoundland", "23": "persian", "24": "pomeranian", "25": "pug", "26": "ragdoll", "27": "russian blue", "28": "saint bernard", "29": "samoyed", "30": "scottish terrier", "31": "shiba inu", "32": "siamese", "33": "sphynx", "34": "staffordshire bull terrier", "35": "wheaten terrier", "36": "yorkshire terrier"}}}}, {"name": "species", "dtype": {"class_label": {"names": {"0": "Cat", "1": "Dog"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "clip_tag_ViT_L_14_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai_classes", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_oxfordpets", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_ensemble_specific", "dtype": "string"}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full_validate", "sequence": "string"}, {"name": "Attributes_ViT_B_16_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_ensemble_specific", "dtype": "string"}, {"name": "blip_caption_beam_5_Salesforce_blip2_opt_6.7b", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 7518510.0, "num_examples": 100}], "download_size": 7289872, "dataset_size": 7518510.0}}
|
2023-05-27T00:42:26+00:00
|
4df8d8deaed49c2d425d6d2cd9c5b5b8be559627
|
# Dataset Card for "viet_vivos"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quocanh34/viet_vivos
|
[
"region:us"
] |
2023-05-26T21:53:39+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1538496458.734306, "num_examples": 9964}, {"name": "test", "num_bytes": 80709780.0, "num_examples": 686}, {"name": "validation", "num_bytes": 107697815.0, "num_examples": 685}], "download_size": 1697050577, "dataset_size": 1726904053.734306}}
|
2023-05-29T11:41:57+00:00
|
6ca00476e44880c56ee04b4a2cfbd986dbf52c76
|
# Dataset Card for "scraped_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
umm-maybe/scraped_data
|
[
"region:us"
] |
2023-05-26T22:01:31+00:00
|
{"dataset_info": {"features": [{"name": "subreddit", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 101218786, "num_examples": 80430}], "download_size": 22713865, "dataset_size": 101218786}}
|
2023-05-26T22:01:34+00:00
|
8f86924c919449431d9714f0efd738191ad879e0
|
# Dataset Card for "bricks_ui_elements_v5_donut"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
donfour/bricks_ui_elements_v5_donut
|
[
"region:us"
] |
2023-05-26T22:05:28+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "button", "1": "others"}}}}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14255527.349, "num_examples": 1799}, {"name": "test", "num_bytes": 1265202.0, "num_examples": 200}], "download_size": 15196840, "dataset_size": 15520729.349}}
|
2023-05-26T22:32:25+00:00
|
e0aa6d223a303483c812e922a0ed002633fe92ee
|
# Dataset Card for "Joe_Buck_the_GOATv2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ks21/Joe_Buck_the_GOATv2
|
[
"region:us"
] |
2023-05-26T22:12:19+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "image", "sequence": {"sequence": {"sequence": "uint8"}}}], "splits": [{"name": "train", "num_bytes": 258171320, "num_examples": 40}], "download_size": 64357832, "dataset_size": 258171320}}
|
2023-05-26T22:12:27+00:00
|
41ee824a79dcd863c6d9c4ae1bf1709da7ad49f0
|
# Dataset Card for "Joe_Buck_the_GOATv3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ks21/Joe_Buck_the_GOATv3
|
[
"region:us"
] |
2023-05-26T22:21:51+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 7232335.0, "num_examples": 40}], "download_size": 3615356, "dataset_size": 7232335.0}}
|
2023-05-26T22:21:53+00:00
|
ddcc3f4faf0325877a8d5bef9e51f5ad5668adac
|
# Dataset Card for "viet_fleurs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quocanh34/viet_fleurs
|
[
"region:us"
] |
2023-05-26T23:06:00+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2091927028.3939998, "num_examples": 2994}, {"name": "validation", "num_bytes": 275255625.0, "num_examples": 361}, {"name": "test", "num_bytes": 692284636.0, "num_examples": 857}], "download_size": 3039859774, "dataset_size": 3059467289.394}}
|
2023-05-26T23:08:00+00:00
|
fab4da411a41e915cd69f16aefd00bd82039e25c
|
# Dataset Card for "Joe_Buck_the_GOATv4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ks21/Joe_Buck_the_GOATv4
|
[
"region:us"
] |
2023-05-27T00:04:49+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 821822226.35, "num_examples": 3533}], "download_size": 824864929, "dataset_size": 821822226.35}}
|
2023-05-27T00:06:20+00:00
|
11a083a81e7216a5fc701a10ef379ffa373fabd2
|
# Dataset Card for "multi_xsciene_train_512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
whu9/multi_xsciene_train_512
|
[
"region:us"
] |
2023-05-27T00:07:31+00:00
|
{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 35933602.37824755, "num_examples": 6623}], "download_size": 8012003, "dataset_size": 35933602.37824755}}
|
2023-05-27T00:07:33+00:00
|
8e9d613494ff431f49e1a75e3b6dc66c7cb5f63f
|
# Dataset Card for "arxiv_summarization_train_512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
whu9/arxiv_summarization_train_512
|
[
"region:us"
] |
2023-05-27T00:08:41+00:00
|
{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 59729625.99675921, "num_examples": 1684}], "download_size": 12106053, "dataset_size": 59729625.99675921}}
|
2023-05-27T00:08:43+00:00
|
4d2c506ecb5fcda9562647a8668ef10d0d2244ce
|
sungjun83/Ethics_DataSet_Ogn
|
[
"task_categories:text-classification",
"region:us"
] |
2023-05-27T00:09:07+00:00
|
{"task_categories": ["text-classification"]}
|
2023-05-27T00:11:20+00:00
|
|
676bd78181442e0f485c43a7800c63f0d9bd5766
|
# Dataset Card for "medsum_train_512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
whu9/medsum_train_512
|
[
"region:us"
] |
2023-05-27T00:09:08+00:00
|
{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 161451994.94889495, "num_examples": 17259}], "download_size": 16034976, "dataset_size": 161451994.94889495}}
|
2023-05-27T00:09:10+00:00
|
bedca42cbdfb610f34c4bcc3603a31221fe1a59f
|
# Dataset Card for "xsum_train_512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
whu9/xsum_train_512
|
[
"region:us"
] |
2023-05-27T00:09:28+00:00
|
{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 294533971.11464626, "num_examples": 126056}], "download_size": 109783064, "dataset_size": 294533971.11464626}}
|
2023-05-27T00:09:34+00:00
|
d8f8f775557aa79ba7668cc3ae44d83d14cff818
|
# Dataset Card for "reddit_train_512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
whu9/reddit_train_512
|
[
"region:us"
] |
2023-05-27T00:11:08+00:00
|
{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4892617977.498785, "num_examples": 3026925}], "download_size": 1993294804, "dataset_size": 4892617977.498785}}
|
2023-05-27T00:12:58+00:00
|
c959a91971e6aa2f24ad9a26cb2a62306b214a11
|
# Dataset Card for "billsum_train_512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
whu9/billsum_train_512
|
[
"region:us"
] |
2023-05-27T00:15:16+00:00
|
{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 68797.2209615283, "num_examples": 6}], "download_size": 12243, "dataset_size": 68797.2209615283}}
|
2023-05-27T00:15:17+00:00
|
0cb8c7ca61ecdff9303bc0ef2a2c0f5a71c9470e
|
sungjun83/Ethics_DataSet_small
|
[
"task_categories:text-classification",
"region:us"
] |
2023-05-27T00:25:19+00:00
|
{"task_categories": ["text-classification"]}
|
2023-05-27T00:29:09+00:00
|
|
077b8f4008446ed0f67f537b42c6d6d92ce3949e
|
yanmiamin/breastCancerEnhancedTest
|
[
"license:openrail",
"region:us"
] |
2023-05-27T00:41:01+00:00
|
{"license": "openrail"}
|
2023-05-27T21:35:19+00:00
|
|
fa704f5493c523e89974f5af5c3b18da6252a418
|
jackylive/sdconfig
|
[
"license:openrail",
"region:us"
] |
2023-05-27T00:41:42+00:00
|
{"license": "openrail"}
|
2023-05-27T00:41:42+00:00
|
|
5b20d331dd4b62bf54c969f4d31ccf524bca157b
|
### Dataset Summary
- 45505 Scraped News Article From Harakah Daily From 2017 to 21st May 2023
- Nearly all malay , small portion in english
### Dataset Format
```
{"url": "...", "headline": "...", "content": [...,...]}
```
|
aisyahhrazak/ms-news-harakahdaily
|
[
"language:ms",
"region:us"
] |
2023-05-27T00:47:23+00:00
|
{"language": ["ms"]}
|
2023-06-23T23:24:27+00:00
|
2f45451adca8f9362d63d045bd0417b29e78fa8e
|
# Distribution Shifts for KBQA
- Official data repo for the paper "Distribution Shifts Are Bottlenecks: Extensive Evaluation for Grounding Language Models to Knowledge Bases".
- Data for TIARA + GAIN method.
- [[arXiv](https://arxiv.org/pdf/2309.08345.pdf)] [[GitHub](https://github.com/yhshu/Distribution-Shifts-for-KBQA)]
|
yhshu/TIARA-GAIN
|
[
"language:en",
"arxiv:2309.08345",
"region:us"
] |
2023-05-27T00:48:33+00:00
|
{"language": ["en"]}
|
2024-01-23T02:23:57+00:00
|
ae90cc2a7be58bbf0de379622d7fa79470315346
|
# Dataset Card for "ontonotes_zh_ner_knowledge_V3_wc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
doushabao4766/ontonotes_zh_ner_knowledge_V3_wc
|
[
"region:us"
] |
2023-05-27T00:56:42+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "knowledge", "dtype": "string"}, {"name": "token_words", "sequence": {"sequence": "string"}}, {"name": "knowledge_words", "sequence": {"sequence": "string"}}], "splits": [{"name": "train", "num_bytes": 87045598, "num_examples": 15724}, {"name": "validation", "num_bytes": 28512103, "num_examples": 4301}, {"name": "test", "num_bytes": 32267375, "num_examples": 4346}], "download_size": 29522634, "dataset_size": 147825076}}
|
2023-05-27T00:56:57+00:00
|
2109663e00c0110f8afa069b685e8bbd015c56fb
|
# Dataset Card for "product_ads_c"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
coeuslearning/product_ads_c
|
[
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:openrail",
"art",
"region:us"
] |
2023-05-27T02:29:21+00:00
|
{"language": ["en"], "license": "openrail", "size_categories": ["1K<n<10K"], "task_categories": ["text-generation"], "pretty_name": "Product Ads Current", "dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5006, "num_examples": 25}], "download_size": 6203, "dataset_size": 5006}, "tags": ["art"]}
|
2023-05-27T02:30:26+00:00
|
38002f314fb64a305e142028796c23e22f74e79a
|
Polaculi/fer
|
[
"license:unknown",
"region:us"
] |
2023-05-27T02:52:29+00:00
|
{"license": "unknown"}
|
2023-05-27T02:54:12+00:00
|
|
96b2b88745f646d0c863859db2cb2786607bde16
|
ykorlk/The_Analects_of_Confucius.In_Chinese
|
[
"license:wtfpl",
"region:us"
] |
2023-05-27T03:04:44+00:00
|
{"license": "wtfpl"}
|
2023-05-28T00:55:04+00:00
|
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