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|---|---|---|---|---|---|---|
df3133d6e6d238a9a24742fd8d1dcd11acd23dbd
|
# Dataset Card for "news_channel_ordinal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
james-burton/news_channel_ordinal
|
[
"region:us"
] |
2023-05-25T08:29:46+00:00
|
{"dataset_info": {"features": [{"name": " n_tokens_content", "dtype": "float64"}, {"name": " n_unique_tokens", "dtype": "float64"}, {"name": " n_non_stop_words", "dtype": "float64"}, {"name": " n_non_stop_unique_tokens", "dtype": "float64"}, {"name": " num_hrefs", "dtype": "float64"}, {"name": " num_self_hrefs", "dtype": "float64"}, {"name": " num_imgs", "dtype": "float64"}, {"name": " num_videos", "dtype": "float64"}, {"name": " average_token_length", "dtype": "float64"}, {"name": " num_keywords", "dtype": "float64"}, {"name": " global_subjectivity", "dtype": "float64"}, {"name": " global_sentiment_polarity", "dtype": "float64"}, {"name": " global_rate_positive_words", "dtype": "float64"}, {"name": " global_rate_negative_words", "dtype": "float64"}, {"name": " rate_positive_words", "dtype": "float64"}, {"name": " rate_negative_words", "dtype": "float64"}, {"name": "article_title", "dtype": "string"}, {"name": "channel", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3354492, "num_examples": 17241}, {"name": "validation", "num_bytes": 591868, "num_examples": 3043}, {"name": "test", "num_bytes": 987135, "num_examples": 5071}], "download_size": 3376135, "dataset_size": 4933495}}
|
2023-05-25T08:29:59+00:00
|
bf795217e57e96e9e6a4cb9e7540a5119572efe7
|
# Dataset Card for "887914db"
[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/887914db
|
[
"region:us"
] |
2023-05-25T08:35:41+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 186, "num_examples": 10}], "download_size": 1332, "dataset_size": 186}}
|
2023-05-25T08:35:43+00:00
|
fa88c4adc9906f6fa209db3fc1f69eba3214612a
|
# Dataset Card for "ikitracs_mitigation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ilaria-oneofftech/ikitracs_mitigation
|
[
"region:us"
] |
2023-05-25T08:47:37+00:00
|
{"dataset_info": {"features": [{"name": "country_code", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "type_of_document", "dtype": "string"}, {"name": "version_number", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "paragraph", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "parameter", "dtype": "string"}, {"name": "quote", "dtype": "string"}, {"name": "asi", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "high_level_category", "dtype": "string"}, {"name": "indicator", "dtype": "string"}, {"name": "paragraph_translated", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 48699276, "num_examples": 82524}], "download_size": 16756391, "dataset_size": 48699276}}
|
2023-05-25T10:33:46+00:00
|
7f2b806da437f8e56933d8c264d4246a8caf3bdc
|
# Dataset Card for "summary_seq_label_balanced"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Astonzzh/summary_seq_label_balanced
|
[
"region:us"
] |
2023-05-25T09:17:06+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "ids", "sequence": "string"}, {"name": "words", "sequence": "string"}, {"name": "labels", "sequence": "int64"}, {"name": "summary", "dtype": "string"}, {"name": "sentences", "sequence": "string"}, {"name": "sentence_labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 9014992.927366104, "num_examples": 7360}, {"name": "test", "num_bytes": 500969.0363169479, "num_examples": 409}, {"name": "validation", "num_bytes": 500969.0363169479, "num_examples": 409}], "download_size": 3867151, "dataset_size": 10016931.0}}
|
2023-05-25T09:17:11+00:00
|
5cdc14fd782d0d3a9500c40ddb17f7ba10f6333e
|
# Dataset Card for "VQAv2_minival_validation_google_flan_t5_xxl_mode_C_D_PNP_GENERIC_Q_rices_ns_25994"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_minival_validation_google_flan_t5_xxl_mode_C_D_PNP_GENERIC_Q_rices_ns_25994
|
[
"region:us"
] |
2023-05-25T09:58:44+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": 3713922, "num_examples": 25994}], "download_size": 1336429, "dataset_size": 3713922}}
|
2023-05-25T09:58:50+00:00
|
166f4f9109bbe29b72a995ced5fa7077a4a88bad
|
# Dataset Card for "2c8fb846"
[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/2c8fb846
|
[
"region:us"
] |
2023-05-25T10:13:03+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1319, "dataset_size": 182}}
|
2023-05-25T10:13:04+00:00
|
d38d4f62932706c9db196dbee9cd0bd9f13216d6
|
# Dataset Card for "dac74edf"
[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/dac74edf
|
[
"region:us"
] |
2023-05-25T10:18:02+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 188, "num_examples": 10}], "download_size": 1340, "dataset_size": 188}}
|
2023-05-25T10:18:04+00:00
|
aed7bf07f891dd3ae604871be7a041f5d6c30b4f
|
# Dataset Card for "607587de"
[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/607587de
|
[
"region:us"
] |
2023-05-25T10:30:05+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 184, "num_examples": 10}], "download_size": 1337, "dataset_size": 184}}
|
2023-05-25T10:30:06+00:00
|
55eca309d30b599fa591c3a1bcf953b3f6b57fb7
|
# Dataset Card for Piano Sound Quality Dataset
## Usage
```python
from datasets import load_dataset
data = load_dataset("ccmusic-database/pianos", split="train")
labels = data.features['label'].names
for item in data:
print('piano spec: ', item['mel'])
print('label name: ' + labels[item['label']])
```
## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/ccmusic-database/pianos
```
## Dataset Description
- **Homepage:** <https://ccmusic-database.github.io>
- **Repository:** <https://huggingface.co/datasets/CCMUSIC/pianos>
- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
- **Point of Contact:** <https://arxiv.org/abs/2310.04722>
### Dataset Summary
This dataset contains 12 gamut audio files (.wav / .mp3 / .m4a format) and 1320 split single-tone audio files (.wav / .mp3 / .m4a format) of 7 types of pianos (Kawai upright piano, Kawai grand piano, YOUNG CHANG upright piano, HSINGHAI upright piano, Steinway grand piano in grand theatre, Steinway grand piano and Pearl River upright piano) in the piano-room of China Conservatory of Music, a total of 1332 files. In addition, there is a questionnaire on subjective evaluation of piano sound quality (.xls format), including the score of 29 people participating in the subjective evaluation of piano sound quality.
### Supported Tasks and Leaderboards
Piano Sound Classification, pitch detection
### Languages
English
## Dataset Structure
<style>
#pianos td {
vertical-align: middle !important;
text-align: center;
}
#pianos th {
text-align: center;
}
</style>
<table id="pianos">
<tr>
<th>mel(.jpg, 0.18s)</th>
<th>label(8-class)</th>
<th>pitch(88-class)</th>
</tr>
<tr>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/TYYnuJqndeWzXLJMmOyXJ.jpeg"></td>
<td>PearlRiver / YoungChang / Steinway-T / Hsinghai / Kawai / Steinway / Kawai-G / Yamaha</td>
<td>88 pitches on piano</td>
</tr>
<tr>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
</table>
### Data Instances
.zip(.wav, jpg)
### Data Fields
```
1_PearlRiver
2_YoungChang
3_Steinway-T
4_Hsinghai
5_Kawai
6_Steinway
7_Kawai-G
8_Yamaha
```
### Data Splits
| total | 18745 |
| :-------------: | :---: |
| train(80%) | 14996 |
| validation(10%) | 1874 |
| test(10%) | 1875 |
## Dataset Creation
### Curation Rationale
Lack of a dataset for piano sound quality
### Source Data
#### Initial Data Collection and Normalization
Zhaorui Liu, Shaohua Ji, Monan Zhou
#### Who are the source language producers?
Students from CCMUSIC & CCOM
### Annotations
#### Annotation process
Students from CCMUSIC recorded different piano sounds and labeled them, and then a subjective survey of sound quality was conducted to score them.
#### Who are the annotators?
Students from CCMUSIC & CCOM
### Personal and Sensitive Information
Piano brands
## Considerations for Using the Data
### Social Impact of Dataset
Help developing piano sound quality scoring apps
### Discussion of Biases
Only for pianos
### Other Known Limitations
Lack of black keys for Steinway, data imbalance
## Additional Information
### Dataset Curators
Zijin Li
### Evaluation
[Monan Zhou, Shangda Wu, Shaohua Ji, Zijin Li, and Wei Li. A Holistic Evaluation of Piano Sound Quality[C]//Proceedings of the 10th Conference on Sound and Music Technology (CSMT). Springer, Singapore, 2023.](https://arxiv.org/pdf/2310.04722.pdf)
(Note: this paper only uses the first 7 piano classes in the dataset.)
### 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 piano sound quality
|
ccmusic-database/pianos
|
[
"task_categories:audio-classification",
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"music",
"art",
"arxiv:2310.04722",
"region:us"
] |
2023-05-25T10:32:28+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["audio-classification", "image-classification"], "pretty_name": "Piano Sound Quality Dataset", "tags": ["music", "art"], "viewer": false}
|
2023-12-22T10:08:17+00:00
|
b59a96c2d3f322bebe74aec86c3358d79376db8c
|
dataset_info:
features:
- name: intent
dtype: string
- name: user_utterance
dtype: string
- name: origin
dtype: string
# Dataset Card for "clinic150-SUR"
### Dataset Summary
The Clinic150-SUR dataset is a novel and augmented dataset designed to simulate natural human behavior during interactions with customer service-like centers.
Extending the [Clinic150 dataset](https://aclanthology.org/D19-1131/), it incorporates two augmentation techniques, including IBM's [LAMBADA](https://arxiv.org/abs/1911.03118) and [Parrot](https://github.com/PrithivirajDamodaran/Parrot_Paraphraser) models and carefully curated duplicated utterances.
This dataset aims to provide a more comprehensive and realistic representation of customer service interactions,
facilitating the development and evaluation of robust and efficient dialogue systems.
Key Features:
- Augmentation with IBM's [LAMBADA Model](https://arxiv.org/abs/1911.03118): The Clinic150-SUR dataset leverages IBM's LAMBADA model, a language generation model trained on a large corpus of text, to augment the original dataset. This augmentation process enhances the diversity and complexity of the dialogue data, allowing for a broader range of interactions.
- Integration of [Parrot](https://github.com/PrithivirajDamodaran/Parrot_Paraphraser) Model: In addition to the LAMBADA model, the Clinic150-SUR dataset also incorporates the Parrot model, providing a variety of paraphrases. By integrating Parrot, the dataset achieves more variations of existing utterances.
- Duplicated Utterances: The dataset includes carefully curated duplicated utterances to mimic real-world scenarios where users rephrase or repeat commonly asked queries. This feature adds variability to the data, reflecting the natural tendencies of human interactions, and enables dialogue systems to handle such instances better.
- [Clinic150](https://aclanthology.org/D19-1131/) as the Foundation: The Clinic150-SUR dataset is built upon the Clinic150 dataset, which originally consisted of 150 in-domain intent classes and 150 human utterances for each intent. By utilizing this foundation, the augmented dataset retains the in-domain expertise while better reflecting the nature of user requests towards a dialog system.
### Data Instances
#### clinic150-SUR
- **Size of downloaded dataset file:** 29 MB
### Data Fields
#### clinic150-SUR
- `intent`: a `string` feature.
- `user_utterance`: a `string` feature.
- `origin`: a `string` feature ('original', 'lambada', 'parrot').
### Citation Information
```
@inproceedings{rabinovich2022reliable,
title={Reliable and Interpretable Drift Detection in Streams of Short Texts},
author={Rabinovich, Ella and Vetzler, Matan and Ackerman, Samuel and Anaby-Tavor, Ateret},
booktitle = "Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics (industry track)",
publisher = "Association for Computational Linguistics",
year={2023},
url={https://arxiv.org/abs/2305.17750}
}
```
### Contributions
Thanks to [Matan Vetzler](https://www.linkedin.com/in/matanvetzler/), [Ella Rabinovich](https://www.linkedin.com/in/ella-rabinovich-7b9a06/) for adding this dataset.
|
ibm/clinic150-sur
|
[
"task_categories:text-classification",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|clinic150",
"language:en",
"license:mit",
"arxiv:1911.03118",
"arxiv:2305.17750",
"region:us"
] |
2023-05-25T10:33:55+00:00
|
{"annotations_creators": "other", "language_creators": "other", "language": "en", "license": "mit", "multilinguality": "monolingual", "size_categories": "100K<n<1M", "source_datasets": "extended|clinic150", "task_categories": ["text-classification"], "pretty_name": "Clinic150-SUR"}
|
2023-05-30T10:22:19+00:00
|
4f4911fbef88ddd5c64a17adbcdfffdaabff66f7
|
# Dataset Card for "womensclothing"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
coeuslearning/womensclothing
|
[
"region:us"
] |
2023-05-25T10:35:36+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2303877.0, "num_examples": 15}], "download_size": 2259471, "dataset_size": 2303877.0}}
|
2023-05-25T10:35:38+00:00
|
05a55c58714e433832650452055d997a7cd671b6
|
chriswmurphy/llm-lion-finetuning
|
[
"license:gpl-3.0",
"region:us"
] |
2023-05-25T10:56:50+00:00
|
{"license": "gpl-3.0"}
|
2023-07-08T21:54:38+00:00
|
|
48d5137bec43298d6c9fb297d98ee37208360ef7
|
fanshiyu/test
|
[
"license:openrail",
"region:us"
] |
2023-05-25T11:24:28+00:00
|
{"license": "openrail"}
|
2023-05-25T11:24:28+00:00
|
|
9e29faf80fb8c0bac4dc2618e09655b807328413
|
# About the Dataset
This dataset is inspired by [HumanEval](https://github.com/openai/human-eval)
The source code used to generate the dataset can be found on [GitHub](https://github.com/alexjercan/bug-detection/tree/master/aoc-dataset)
A collection of submissions for the Advent of Code challenge.
This repository contains both passing and failing submissions.
This dataset is similar to [BugNet](https://huggingface.co/datasets/alexjercan/bugnet),
however it is meant to be used as an evaluation dataset.
The resulting dataset file will be a csv with the following columns:
- `year`: Used to identify the submission
- `day`: Used to identify the submission
- `part`: Used to identify the submission
- `fail`: The initial (buggy) source code formatted (`black`)
- `pass`: The modified (accepted) source code formatted (`black`)
- `change`: The change that was made (`replace`, `insert`, `delete`)
- `i1`: Start of the change in the buggy source (the line; starting with 1)
- `i2`: End of the change in the buggy source (not inclusive; for insert we have i1 == i2)
- `j1`: Start of the change in the accepted source (the line; starting with 1)
- `j2`: End of the change in the accepted source (not inclusive; for delete we have j1 == j2)
- `test`: The test case that can be used to evaluate the submission.
|
alexjercan/AoC
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-25T11:35:24+00:00
|
{"license": "apache-2.0", "dataset_info": {"features": [{"name": "year", "dtype": "string"}, {"name": "day", "dtype": "string"}, {"name": "part", "dtype": "string"}, {"name": "pass", "dtype": "string"}, {"name": "fail", "dtype": "string"}, {"name": "test", "dtype": "string"}, {"name": "change", "dtype": "string"}, {"name": "i1", "dtype": "uint32"}, {"name": "i2", "dtype": "uint32"}, {"name": "j1", "dtype": "uint32"}, {"name": "j2", "dtype": "uint32"}], "splits": [{"name": "train", "num_bytes": 21469, "num_examples": 15}], "download_size": 23847, "dataset_size": 21469}}
|
2023-06-09T13:09:34+00:00
|
e53fb27b2703cadb2261481e0220dcc9cd67ca81
|
# Dataset Card for "VQAv2_minival_validation_google_flan_t5_xxl_mode_A_C_D_PNP_GENERIC_Q_rices_ns_25994"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_minival_validation_google_flan_t5_xxl_mode_A_C_D_PNP_GENERIC_Q_rices_ns_25994
|
[
"region:us"
] |
2023-05-25T11:55:18+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": 3720348, "num_examples": 25994}], "download_size": 1342337, "dataset_size": 3720348}}
|
2023-05-25T11:55:23+00:00
|
51331d634357e1391ab6e98e362a2048cf74d685
|
# HNC_Mini
Contains 306,084 samples collected from the following datasets.
- QQP_triplets
- HC3
- sentence-compression
|
zrthxn/HNC_Mini
|
[
"task_categories:sentence-similarity",
"task_ids:semantic-similarity-classification",
"language:en",
"license:mit",
"region:us"
] |
2023-05-25T11:56:45+00:00
|
{"language": ["en"], "license": "mit", "task_categories": ["sentence-similarity"], "task_ids": ["semantic-similarity-classification"], "pretty_name": "hnc-mini"}
|
2023-05-31T13:29:19+00:00
|
f1a643fdf463c7ac9c4a48ccd67b41e8a6672086
|
# Dataset Card for "sherliic"
https://github.com/mnschmit/SherLIiC
```
@inproceedings{schmitt2019sherliic,
title = "{S}her{LI}i{C}: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference",
author = {Schmitt, Martin and
Sch{\"u}tze, Hinrich},
booktitle = "Proceedings of the 57th Conference of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1086",
pages = "902--914"
}
```
|
tasksource/sherliic
|
[
"language:en",
"region:us"
] |
2023-05-25T12:01:48+00:00
|
{"language": ["en"], "dataset_info": {"features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 106784, "num_examples": 996}, {"name": "test", "num_bytes": 322932, "num_examples": 2989}], "download_size": 146406, "dataset_size": 429716}}
|
2023-05-31T07:24:37+00:00
|
8cda51f3da18c5997fe6524f1a3072f1a2686eac
|
# Dataset Card for Chinese Traditional Instrument Sound
## Dataset Description
- **Homepage:** <https://ccmusic-database.github.io>
- **Repository:** <https://huggingface.co/datasets/ccmusic-database/CTIS>
- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
- **Point of Contact:** N/A
### Dataset Summary
There are at least two western musical instrument sound databases that are freely available, while there is not a similar Chinese musical instrument sound database. As an important part of gorgeous Chinese culture, the unique acoustic characteristics and playing techniques of Chinese instruments have attracted wide attention from researchers and people around the world. Thus, a long-waited Chinese Traditional Instrument Sound (CTIS) database was built. Up to 287 kinds of Chinese traditional musical instruments, improved Chinese musical instruments and Chinese ethnic musical instruments are included. Among them, some are seldom seen by most ordinary Chinese people.
### Supported Tasks and Leaderboards
MIR, audio classification
### Languages
Chinese, English
## Dataset Structure
### Data Instances
.zip(.wav), .csv
### Data Fields
Up to 287 kinds of Chinese traditional musical instruments, improved Chinese musical instruments and Chinese ethnic musical instruments
### Data Splits
instruments, percussion
## Dataset Creation
### Curation Rationale
Lack of a dataset for Chinese traditional musical instruments
### Source Data
#### Initial Data Collection and Normalization
Zhaorui Liu, Monan Zhou
#### Who are the source language producers?
Students from CCMUSIC
### Annotations
#### Annotation process
Building a high-quality musical sound database requires consideration on every aspect of the criteria in terms of the recording environment, performer, sample content, annotation standard and quality of recording and performing.
#### Who are the annotators?
Students from CCMUSIC
### Personal and Sensitive Information
None
## Considerations for Using the Data
### Social Impact of Dataset
Advancing the Digitization Process of Traditional Chinese Instruments
### Discussion of Biases
Only for Traditional Chinese Instruments
### Other Known Limitations
Sample imbalance
## Additional Information
### Dataset Curators
Zijin Li
### Evaluation
[李子晋, 韩宝强. 中国传统乐器音响数据库构建研究[J]. 中国音乐学, 2020(02):92-102+2.](https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2020&filename=ZYYX202002013&uniplatform=NZKPT&v=7XgjFhWwxaqXz5fg8DIhkJzfNT6gX9huNMH0y5oRG15SXfwDzqWIOuuquyUDS%25mmd2FJ9)
[Liang X, Li Z, Liu J, et al. Constructing a multimedia Chinese musical instrument database[C]//Proceedings of the 6th Conference on Sound and Music Technology (CSMT). Springer, Singapore, 2019: 53-60.](https://link.springer.com/chapter/10.1007/978-981-13-8707-4_5)
[Li Z, Liang X, Liu J, et al. DCMI: A Database of Chinese Musical Instruments[J].](https://dlfm.web.ox.ac.uk/sites/default/files/dlfm/documents/media/zijin-et-al-dcmi.pdf)
### 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 Chinese Traditional Instrument Sounds
|
ccmusic-database/CTIS
|
[
"task_categories:audio-classification",
"size_categories:1K<n<10K",
"language:zh",
"language:en",
"license:mit",
"music",
"art",
"region:us"
] |
2023-05-25T12:04:50+00:00
|
{"language": ["zh", "en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["audio-classification"], "pretty_name": "Chinese Traditional Instrument Sound Dataset", "tags": ["music", "art"], "viewer": false}
|
2023-12-04T16:06:43+00:00
|
6bef4052adf71bb14e8e8cfba63ebf791ccf6d1c
|
# Dataset Card for "sam-controlnet-original"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
baptistecolle/sam-controlnet-original
|
[
"region:us"
] |
2023-05-25T12:06:32+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "filepath", "dtype": "string"}, {"name": "sentids", "list": "int32"}, {"name": "filename", "dtype": "string"}, {"name": "imgid", "dtype": "int32"}, {"name": "split", "dtype": "string"}, {"name": "sentences", "struct": [{"name": "tokens", "list": "string"}, {"name": "raw", "dtype": "string"}, {"name": "imgid", "dtype": "int32"}, {"name": "sentid", "dtype": "int32"}]}, {"name": "cocoid", "dtype": "int32"}], "splits": [{"name": "original", "num_bytes": 160172441.0, "num_examples": 1000}], "download_size": 32080306, "dataset_size": 160172441.0}}
|
2023-05-29T08:48:39+00:00
|
4b4ff26e2df67d6c5feaabe1ab3f09d1043f48de
|
Praanavvv/Speech_Files
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-05-25T12:17:01+00:00
|
{"license": "cc-by-4.0"}
|
2023-05-25T12:17:01+00:00
|
|
85fa02ffd1ac59370535d2321fb3bda4e89a8d4b
|
# Dataset Card for "763717e8"
[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/763717e8
|
[
"region:us"
] |
2023-05-25T12:26:47+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 180, "num_examples": 10}], "download_size": 1324, "dataset_size": 180}}
|
2023-05-25T12:26:48+00:00
|
2dd3b85bf504fc310b7a34dcd0d3e763002238f0
|
# Summary
This is a 🇹🇭 Thai-translated (GCP) dataset based on 4.5K codegen instruction dataset [GPTeacher](https://github.com/teknium1/GPTeacher)
Supported Tasks:
- Training LLMs
- Synthetic Data Generation
- Data Augmentation
Languages: Thai
Version: 1.0
---
|
Thaweewat/codegen-th
|
[
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:th",
"license:cc-by-sa-3.0",
"instruction-finetuning",
"region:us"
] |
2023-05-25T12:28:49+00:00
|
{"language": ["th"], "license": "cc-by-sa-3.0", "size_categories": ["1K<n<10K"], "task_categories": ["question-answering"], "tags": ["instruction-finetuning"]}
|
2023-05-25T14:06:44+00:00
|
889d2e9751c8ff326fb01f5ed84deb910d376e45
|
# Dataset Card for "mcl-signal_processing_attacks_large"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TeamSODA/mcl-signal_processing_attacks_whisper_librispeech
|
[
"region:us"
] |
2023-05-25T12:36:51+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": 9472066083.0, "num_examples": 12000}], "download_size": 8061059411, "dataset_size": 9472066083.0}}
|
2023-05-25T13:13:03+00:00
|
9998342c89d274b43f2c56e8440179ee44b9b010
|
# Dataset Card for Dataset Name
## Dataset Description
- **Autor:** Rubén Darío Jaramillo
- **Email:** [email protected]
- **WhatsApp:** +593 93 979 6676
### Dataset Summary
CIE10 is the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD), a medical classification list by the World Health Organization (WHO). It contains codes for diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or diseases. Work on ICD-10 began in 1983, became endorsed by the Forty-third World Health Assembly in 1990, and was first used by member states in 1994. It was replaced by ICD-11 on January 1, 2022.
While WHO manages and publishes the base version of the ICD, several member states have modified it to better suit their needs. In the base classification, the code set allows for more than 14,000 different codes and permits the tracking of many new diagnoses compared to the preceding ICD-9. Through the use of optional sub-classifications, ICD-10 allows for specificity regarding the cause, manifestation, location, severity, and type of injury or disease. The adapted versions may differ in a number of ways, and some national editions have expanded the code set even further; with some going so far as to add procedure codes. ICD-10-CM, for example, has over 70,000 codes.
The WHO provides detailed information regarding the ICD via its website – including an ICD-10 online browser and ICD training materials. The online training includes a support forum, a self-learning tool and user guide.
https://en.wikipedia.org/wiki/ICD-10
|
rubend18/CIE10
|
[
"task_categories:text-classification",
"task_categories:token-classification",
"size_categories:10K<n<100K",
"language:es",
"salud",
"health",
"diagnóstico",
"ICD10Codes",
"MedicalCoding",
"HealthcareClassification",
"DiseaseClassification",
"ICD10Diagnosis",
"MedicalTerminology",
"HealthData",
"ClinicalCoding",
"HealthcareStandards",
"MedicalClassification",
"CódigosCIE10",
"CodificaciónMédica",
"ClasificaciónSanitaria",
"ClasificaciónEnfermedades",
"DiagnósticoCIE10",
"TerminologíaMédica",
"DatosSalud",
"CodificaciónClínica",
"EstándaresSanitarios",
"ClasificaciónMédica",
"region:us"
] |
2023-05-25T12:38:02+00:00
|
{"language": ["es"], "size_categories": ["10K<n<100K"], "task_categories": ["text-classification", "token-classification"], "pretty_name": "Diagn\u00f3sticos M\u00e9dicos CIE10", "tags": ["salud", "health", "diagn\u00f3stico", "ICD10Codes", "MedicalCoding", "HealthcareClassification", "DiseaseClassification", "ICD10Diagnosis", "MedicalTerminology", "HealthData", "ClinicalCoding", "HealthcareStandards", "MedicalClassification", "C\u00f3digosCIE10", "Codificaci\u00f3nM\u00e9dica", "Clasificaci\u00f3nSanitaria", "Clasificaci\u00f3nEnfermedades", "Diagn\u00f3sticoCIE10", "Terminolog\u00edaM\u00e9dica", "DatosSalud", "Codificaci\u00f3nCl\u00ednica", "Est\u00e1ndaresSanitarios", "Clasificaci\u00f3nM\u00e9dica"]}
|
2023-07-30T15:15:08+00:00
|
1e95c9a56c5051564c261577c4c969bd3c117ef7
|
# Dataset Card for Chest voice and Falsetto Dataset
## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 [email protected]:datasets/ccmusic-database/chest_falsetto
```
## Dataset Description
- **Homepage:** <https://ccmusic-database.github.io>
- **Repository:** <https://huggingface.co/datasets/ccmusic-database/chest_falsetto>
- **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 1280 monophonic singing audio (.wav format) of chest and falsetto voices, with chest voice tagged as _chest_ and falsetto voice tagged as _falsetto_.
### Supported Tasks and Leaderboards
Audio classification, singing method classification, voice classification
### Languages
Chinese, English
## Dataset Structure
<style>
#datastructure td {
vertical-align: middle !important;
text-align: center;
}
#datastructure th {
text-align: center;
}
</style>
<table id="datastructure">
<tr>
<th>audio(.wav)</th>
<th>mel(.jpg)</th>
<th>cqt(.jpg)</th>
<th>chroma(.jpg)</th>
<th>label</th>
<th>gender</th>
<th>singing_method</th>
</tr>
<tr>
<td><audio controls src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/LKSBb11kCyPl15b-DJo6V.wav"></audio></td>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/W8wy7pkYZtCt3lI5Oq39l.jpeg"></td>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/48qPVDDIZe0ttsYXrTJEh.jpeg"></td>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/zm0KorKYtmvOje8qmivHJ.jpeg"></td>
<td>m_chest, m_falsetto, f_chest, f_falsetto</td>
<td>male, female</td>
<td>chest, falsetto</td>
</tr>
<tr>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
</table>
### Data Instances
.zip(.wav, .jpg)
### Data Fields
m_chest, f_chest, m_falsetto, f_falsetto
### Data Splits
train, validation, test
## Dataset Creation
### Curation Rationale
Lack of a dataset for Chest voice and Falsetto
### Source Data
#### Initial Data Collection and Normalization
Zhaorui Liu, Monan Zhou
#### Who are the source language producers?
Students from CCMUSIC
### Annotations
#### Annotation process
1280 monophonic singing audio (.wav format) of chest and falsetto voices, with chest voice tagged as _chest_ and falsetto voice tagged as _falsetto_.
#### Who are the annotators?
Students from CCMUSIC
### 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 chest and falsetto voices
### Other Known Limitations
Recordings are cut into slices that are too short;
The CQT spectrum column has the problem of spectrum leakage, but because the original audio slice is too short, only 0.5s, it cannot effectively avoid this problem.
## Additional Information
### Dataset Curators
Zijin Li
### Evaluation
<https://huggingface.co/ccmusic-database/chest_falsetto>
### 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 chest and falsetto voices
|
ccmusic-database/chest_falsetto
|
[
"task_categories:audio-classification",
"size_categories:1K<n<10K",
"language:zh",
"language:en",
"license:mit",
"music",
"art",
"region:us"
] |
2023-05-25T12:53:10+00:00
|
{"language": ["zh", "en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["audio-classification"], "pretty_name": "Chest voice and Falsetto Dataset", "tags": ["music", "art"], "viewer": false}
|
2023-12-22T10:15:39+00:00
|
a71c63431deeecd04fcac6518d13e17a4dc5bf60
|
Thouph/text_stories
|
[
"license:wtfpl",
"region:us"
] |
2023-05-25T12:58:53+00:00
|
{"license": "wtfpl"}
|
2023-06-15T11:55:14+00:00
|
|
7911586c83265f6546547c6c924fede2c70c7323
|
# Dataset Card for Music Genre
## Dataset Description
- **Homepage:** <https://ccmusic-database.github.io>
- **Repository:** <https://huggingface.co/datasets/ccmusic-database/music_genre>
- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
- **Point of Contact:** <https://huggingface.co/ccmusic-database/music_genre>
### Dataset Summary
This database contains about 1700 musical pieces (.mp3 format) with lengths of 270-300s that are divided into 17 genres in total.
### Supported Tasks and Leaderboards
Audio classification
### Languages
Multilingual
## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/ccmusic-database/music_genre
```
## Usage
When doing classification task, only one colum of fst_level_label, sec_level_label and thr_level_label can be used, not for mixing.
```python
from datasets import load_dataset
dataset = load_dataset("ccmusic-database/music_genre")
for item in dataset["train"]:
print(item)
for item in dataset["test"]:
print(item)
```
## Dataset Structure
<style>
#genres td {
vertical-align: middle !important;
text-align: center;
}
#genres th {
text-align: center;
}
</style>
<table id="genres">
<tr>
<td>mel(.jpg, 11.4s)</td>
<td>cqt(.jpg, 11.4s)</td>
<td>chroma(.jpg, 11.4s)</td>
<td>fst_level_label(2-class)</td>
<td>sec_level_label(9-class)</td>
<td>thr_level_label(16-class)</td>
</tr>
<tr>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/PqdpQP__ls-xo6lz93Q4y.jpeg"></td>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/EZfYLng40hh_FUudB9vvx.jpeg"></td>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/zviZ-rEKAvBCVFvKFml4R.jpeg"></td>
<td>1_Classic / 2_Non_classic</td>
<td>3_Symphony / 4_Opera / 5_Solo / 6_Chamber / 7_Pop / 8_Dance_and_house / 9_Indie / 10_Soul_or_r_and_b / 11_Rock</td>
<td>3_Symphony / 4_Opera / 5_Solo / 6_Chamber / 12_Pop_vocal_ballad / 13_Adult_contemporary / 14_Teen_pop / 15_Contemporary_dance_pop / 16_Dance_pop / 17_Classic_indie_pop / 18_Chamber_cabaret_and_art_pop / 10_Soul_or_r_and_b / 19_Adult_alternative_rock / 20_Uplifting_anthemic_rock / 21_Soft_rock / 22_Acoustic_pop</td>
</tr>
<tr>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
</table>
### Data Instances
.zip(.jpg)
### Data Fields
```
1_Classic
3_Symphony
4_Opera
5_Solo
6_Chamber
2_Non_classic
7_Pop
12_Pop_vocal_ballad
13_Adult_contemporary
14_Teen_pop
8_Dance_and_house
15_Contemporary_dance_pop
16_Dance_pop
9_Indie
17_Classic_indie_pop
18_Chamber_cabaret_and_art_pop
10_Soul_or_r_and_b
11_Rock
19_Adult_alternative_rock
20_Uplifting_anthemic_rock
21_Soft_rock
22_Acoustic_pop
```
### Data Splits
| total | 36375 |
| :-------------: | :---: |
| train(80%) | 29100 |
| validation(10%) | 3637 |
| test(10%) | 3638 |
## Dataset Creation
### Curation Rationale
Promoting the development of AI in the music industry
### Source Data
#### Initial Data Collection and Normalization
Zhaorui Liu, Monan Zhou
#### Who are the source language producers?
Composers of the songs in dataset
### Annotations
#### Annotation process
Students collected about 1700 musical pieces (.mp3 format) with lengths of 270-300s divided into 17 genres in total.
#### Who are the annotators?
Students from CCMUSIC
### Personal and Sensitive Information
Due to copyright issues with the original music, only spectrograms are provided in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
Promoting the development of AI in the music industry
### Discussion of Biases
Most are English songs
### Other Known Limitations
Samples are not balanced enough
## Additional Information
### Dataset Curators
Zijin Li
### Evaluation
<https://huggingface.co/ccmusic-database/music_genre/tree/main>
### 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 music genre classification
|
ccmusic-database/music_genre
|
[
"task_categories:audio-classification",
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:zh",
"language:en",
"license:mit",
"music",
"art",
"region:us"
] |
2023-05-25T13:10:47+00:00
|
{"language": ["zh", "en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["audio-classification", "image-classification"], "pretty_name": "Music Genre Dataset", "tags": ["music", "art"], "viewer": false}
|
2024-01-01T10:15:58+00:00
|
a3749e4214797604af665e583629ddfb2dde576d
|
# Small-GPT-wiki-intro-features dataset
This dataset is based on [aadityaubhat/GPT-wiki-intro](https://huggingface.co/datasets/aadityaubhat/GPT-wiki-intro).
It contains 150k short texts from Wikipedia (label 0) and corresponding texts generated by ChatGPT (label 1) (together 300k texts).
For each text, various complexity measures were calculated, including e.g. readability, lexical diversity etc.
It can be used for text classification or analysis of linguistic features of human-generated and ChatGPT-generated texts.
For a smaller version, check out [julia-lukasiewicz-pater/small-GPT-wiki-intro-features](https://huggingface.co/datasets/julia-lukasiewicz-pater/small-GPT-wiki-intro-features).
## Dataset structure
Features were calculated using various Python libraries, i.e. NLTK, [readability-metrics](https://pypi.org/project/py-readability-metrics/), [lexical-diversity](https://pypi.org/project/lexical-diversity/),
and [TextDescriptives](https://hlasse.github.io/TextDescriptives/). The list of all features and their corresponding sources can be found below:
| Column | Description |
| ------ | ----------- |
| text | human- or ChatGPT-generated text; taken from aadityaubhat/GPT-wiki-intro |
| normalized_bigram_entropy | bigram entropy normalized with estimated maximum entropy; nltk |
| mean_word_length | mean word length; nltk |
| mean_sent_length | mean sentence length; nltk |
| fog | Gunning-Fog; readability-metrics |
| ari | Automated Readability Index; readability-metrics |
| dale_chall | Dale Chall Readability; readability-metrics |
| hdd | Hypergeometric Distribution; lexical-diversity |
| mtld | Measure of lexical textual diversity; lexical-diversity |
| mattr | Moving average type-token ratio; lexical-diversity |
| number_of_ADJ | proportion of adjectives per word; nltk |
| number_of_ADP | proportion of adpositions per word; nltk |
| number_of_ADV | proportion of adverbs per word; nltk |
| number_of_CONJ | proportion of conjunctions per word; nltk |
| number_of_DET | proportion of determiners per word; nltk |
| number_of_NOUN | proportion of nouns per word; nltk |
| number_of_NUM | proportion of numerals per word; nltk |
| number_of_PRT | proportion of particles per word; nltk |
| number_of_PRON | proportion of pronuns per word; nltk |
| number_of_VERB | proportion of verbs per word; nltk |
| number_of_DOT | proportion of punctuation marks per word; nltk |
| number_of_X | proportion of POS tag 'Other' per word; nltk |
| class | binary class, 0 stands for Wikipedia, 1 stands for ChatGPT |
| spacy_perplexity | text perplexity; TextDescriptives |
| entropy | text entropy; TextDescriptives |
| automated_readability_index | Automated Readability Index; TextDescriptives |
| per_word_spacy_perplexity | text perplexity per word; TextDescriptives |
| dependency_distance_mean | mean distance from each token to their dependent; TextDescriptives |
| dependency_distance_std | standard deviation of distance from each token to their dependent; TextDescriptives |
| first_order_coherence | cosine similarity between consecutive sentences; TextDescriptives |
| second_order_coherence | cosine similarity between sentences that are two sentences apart; TextDescriptives |
| smog |SMOG; TextDescriptives |
| prop_adjacent_dependency_relation_mean | mean proportion adjacent dependency relations; TextDescriptives |
| prop_adjacent_dependency_relation_std | standard deviation of proportion adjacent dependency relations; TextDescriptives |
| syllables_per_token_mean | mean of syllables per token; TextDescriptives |
| syllables_per_token_median | median of syllables per token; TextDescriptives |
| token_length_std | standard deviation of token length; TextDescriptives |
| token_length_median | median of token length; TextDescriptives |
| sentence_length_median | median of sentence length; TextDescriptives |
| syllables_per_token_std | standard deviation of syllables per token; TextDescriptives |
| proportion_unique_tokens | proportion of unique tokens; TextDescriptives |
| top_ngram_chr_fraction_3 | fraction of characters in a document which are contained within the top n-grams. For a specified n-gram range; TextDescriptives |
| top_ngram_chr_fraction_2 | fraction of characters in a document which are contained within the top n-grams. For a specified n-gram range; TextDescriptives |
| top_ngram_chr_fraction_4 | fraction of characters in a document which are contained within the top n-grams. For a specified n-gram range; TextDescriptives |
| proportion_bullet_points | fraction of characters in a document which are contained within the top n-grams. For a specified n-gram range; TextDescriptives |
| flesch_reading_ease | Flesch Reading ease ; TextDescriptives |
| flesch_kincaid_grade | Flesch Kincaid grade; TextDescriptives |
| gunning_fog | Gunning-Fog; TextDescriptives |
| coleman_liau_index | Coleman-Liau Index; TextDescriptives |
| oov_ratio| out-of-vocabulary ratio; TextDescriptives |
## Code
Code that was used to generate this dataset can be found on [Github](https://github.com/julia-lukasiewicz-pater/gpt-wiki-features/tree/main).
|
julia-lukasiewicz-pater/GPT-wiki-intro-features
|
[
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"license:cc",
"region:us"
] |
2023-05-25T13:38:40+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"]}
|
2023-06-11T13:41:17+00:00
|
677c31473e931069b71bc424c17e92c8f2106cc7
|
# Dataset Card for "article_filter_ds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nsstt/article_filter_ds
|
[
"region:us"
] |
2023-05-25T13:45:17+00:00
|
{"dataset_info": {"features": [{"name": "headline", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9203347.180327868, "num_examples": 1854}, {"name": "test", "num_bytes": 2303318.819672131, "num_examples": 464}], "download_size": 7222810, "dataset_size": 11506666.0}}
|
2023-05-25T16:04:15+00:00
|
7e6c4c019068caf67bf2801b0ec57712596bb44a
|
# Revisiting Sentence Union Generation as a Testbed for Text Consolidation
[Eran Hirsch](https://eranhirs.github.io/)<sup>1</sup>,
[Valentina Pyatkin](https://valentinapy.github.io/)<sup>1</sup>,
Ruben Wolhandler<sup>1</sup>,
[Avi Caciularu](https://aviclu.github.io/)<sup>1</sup>,
Asi Shefer<sup>2</sup>,
[Ido Dagan](https://u.cs.biu.ac.il/~dagani/)<sup>1</sup>
<br>
<sup>1</sup>Bar-Ilan University, <sup>2</sup>One AI
This is the official dataset of the paper "Revisiting Sentence Union Generation as a Testbed for Text Consolidation".
* [Paper 📄](https://arxiv.org/abs/2305.15605) (Findings of ACL 2023)
* [Code 💻](https://github.com/eranhirs/sentence_union_generation)
## Abstract
Tasks involving text generation based on multiple input texts, such as multi-document summarization, long-form question answering and contemporary dialogue applications, challenge models for their ability to properly consolidate partly-overlapping multi-text information.
However, these tasks entangle the consolidation phase with the often subjective and ill-defined content selection requirement, impeding proper assessment of models' consolidation capabilities.
In this paper, we suggest revisiting the sentence union generation task as an effective well-defined testbed for assessing text consolidation capabilities, decoupling the consolidation challenge from subjective content selection.
To support research on this task, we present refined annotation methodology and tools for crowdsourcing sentence union, create the largest union dataset to date and provide an analysis of its rich coverage of various consolidation aspects.
We then propose a comprehensive evaluation protocol for union generation, including both human and automatic evaluation.
Finally, as baselines, we evaluate state-of-the-art language models on the task, along with a detailed analysis of their capacity to address multi-text consolidation challenges and their limitations.
|
biu-nlp/sentence_union_generation
|
[
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-4.0",
"fusion",
"union",
"multi_document",
"summarization",
"long_form_question_answering",
"document_grounded_dialogue",
"arxiv:2305.15605",
"region:us"
] |
2023-05-25T13:45:36+00:00
|
{"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["text2text-generation", "text-generation"], "tags": ["fusion", "union", "multi_document", "summarization", "long_form_question_answering", "document_grounded_dialogue"]}
|
2023-08-24T08:01:43+00:00
|
df9425bd3fa955fa79f334ae6001d44feb9e590e
|
# Dataset Card for "frames_room_cls"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ummagumm-a/frames_room_cls
|
[
"region:us"
] |
2023-05-25T13:50:19+00:00
|
{"dataset_info": {"features": [{"name": "data", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "video_id", "dtype": "int64"}, {"name": "video_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10203554, "num_examples": 3276}, {"name": "test", "num_bytes": 1430210, "num_examples": 459}], "download_size": 14454806, "dataset_size": 11633764}}
|
2023-05-27T05:55:40+00:00
|
b2fe3ffedd121ebc65cecdfaed98f1d3715395f5
|
# Summary
This is a 🇹🇭 Thai-translated (GCP) dataset based on English 74K [Alpaca-CoT](https://github.com/PhoebusSi/alpaca-CoT) instruction dataset.
Supported Tasks:
- Training LLMs
- Synthetic Data Generation
- Data Augmentation
Languages: Thai
Version: 1.0
---
|
Thaweewat/chain-of-thought-74k-th
|
[
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:th",
"license:cc-by-sa-3.0",
"instruction-finetuning",
"region:us"
] |
2023-05-25T14:01:36+00:00
|
{"language": ["th"], "license": "cc-by-sa-3.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "tags": ["instruction-finetuning"]}
|
2023-05-26T11:32:46+00:00
|
b03367f616106b150928d4f137d0af903adaaf2e
|
# Dataset Card for "wikidumps-oscar-pl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Lipa1919/wikidumps-oscar-pl
|
[
"region:us"
] |
2023-05-25T14:08:48+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 23435628364, "num_examples": 17016858}], "download_size": 15087497727, "dataset_size": 23435628364}}
|
2023-06-24T22:39:27+00:00
|
d5b118d636785ca668923c94c9f5c7358878d45c
|
# Dataset Card for "synthetic-qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
maiyad/synthetic-qa
|
[
"region:us"
] |
2023-05-25T14:14:41+00:00
|
{"dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response_j", "dtype": "string"}, {"name": "response_k", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1785594, "num_examples": 1158}], "download_size": 135300, "dataset_size": 1785594}}
|
2023-05-25T14:39:16+00:00
|
539fcbf1a6e823415c72ef57c34901def4a9700e
|
# Dataset Card for "ae-Kenansville_attack"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TeamSODA/ae-Kenansville_attack_whisper_librispeech
|
[
"region:us"
] |
2023-05-25T14:19:00+00:00
|
{"dataset_info": {"features": [{"name": "audio_0", "dtype": "audio"}, {"name": "audio_1", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 4887525358.0, "num_examples": 3000}], "download_size": 4077042653, "dataset_size": 4887525358.0}}
|
2023-05-25T14:48:38+00:00
|
c6fd9b67128e9f23da788e9a5576cd6e0bee5589
|
# Dataset Card for "data_scientist_salary_all_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
james-burton/data_scientist_salary_all_text
|
[
"region:us"
] |
2023-05-25T14:24:03+00:00
|
{"dataset_info": {"features": [{"name": "experience", "dtype": "string"}, {"name": "job_description", "dtype": "string"}, {"name": "job_desig", "dtype": "string"}, {"name": "job_type", "dtype": "string"}, {"name": "key_skills", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "salary", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3256981, "num_examples": 13464}, {"name": "validation", "num_bytes": 581453, "num_examples": 2377}, {"name": "test", "num_bytes": 956394, "num_examples": 3961}], "download_size": 2195043, "dataset_size": 4794828}}
|
2023-05-25T14:24:12+00:00
|
6149961c9c7e8db566b1e3ac065ea5af37187eb4
|
# Dataset Card for "data_scientist_salary_ordinal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
james-burton/data_scientist_salary_ordinal
|
[
"region:us"
] |
2023-05-25T14:24:13+00:00
|
{"dataset_info": {"features": [{"name": "experience", "dtype": "string"}, {"name": "job_description", "dtype": "string"}, {"name": "job_desig", "dtype": "string"}, {"name": "job_type", "dtype": "float64"}, {"name": "key_skills", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "salary", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3240828, "num_examples": 13464}, {"name": "validation", "num_bytes": 578505, "num_examples": 2377}, {"name": "test", "num_bytes": 951562, "num_examples": 3961}], "download_size": 2195761, "dataset_size": 4770895}}
|
2023-05-25T14:24:22+00:00
|
46e14e9fdc21bb1dda792a22771d8fe33d59f1e8
|
trojblue/random-captions
|
[
"license:bigscience-openrail-m",
"region:us"
] |
2023-05-25T14:38:18+00:00
|
{"license": "bigscience-openrail-m"}
|
2023-05-25T14:39:16+00:00
|
|
95ed7a7c8593fc9327cda8c3a1e61b1369808ed0
|
# Dataset Card for "bloom_dialogues_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Shoubhik8/bloom_dialogues_train
|
[
"region:us"
] |
2023-05-25T14:46:16+00:00
|
{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}, {"name": "responses", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 23673110, "num_examples": 88896}], "download_size": 6898108, "dataset_size": 23673110}}
|
2023-05-25T17:14:30+00:00
|
d9cbeb01558304c2ffb5b6ea44bac3f933971d73
|
# Dataset Card for "bloom_dialogues_valid"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Shoubhik8/bloom_dialogues_valid
|
[
"region:us"
] |
2023-05-25T14:46:54+00:00
|
{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}, {"name": "responses", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 7469692, "num_examples": 27857}], "download_size": 2184992, "dataset_size": 7469692}}
|
2023-05-25T17:14:44+00:00
|
39121fbde9661ea8f58e53b964f720a772d5f202
|
# Dataset Card for "295cc7a4"
[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/295cc7a4
|
[
"region:us"
] |
2023-05-25T15:15:26+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 184, "num_examples": 10}], "download_size": 1338, "dataset_size": 184}}
|
2023-05-25T15:15:27+00:00
|
c259c6b20ff0b89485386763be0fea0b2279ce3d
|
# Dataset Card for "Eurosat-Datast"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Amite5h/Eurosat-Datast
|
[
"region:us"
] |
2023-05-25T15:39:00+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "AnnualCrop", "1": "Forest", "2": "HerbaceousVegetation", "3": "Highway", "4": "Industrial", "5": "Pasture", "6": "PermanentCrop", "7": "Residential", "8": "River", "9": "SeaLake"}}}}], "splits": [{"name": "train", "num_bytes": 88397609.0, "num_examples": 27000}], "download_size": 88592405, "dataset_size": 88397609.0}}
|
2023-05-25T15:39:08+00:00
|
79efcf8c81ed72e84b6ed1ea072efa2d74051367
|
# 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]
|
cyhuang-tw/test
|
[
"region:us"
] |
2023-05-25T16:11:50+00:00
|
{}
|
2023-05-25T16:17:02+00:00
|
b88a0cc0acee5f8027d57a6c9dcaf75b855b8cb4
|
# Dataset Card for "booksum-summary-analysis"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
emozilla/booksum-summary-analysis
|
[
"language:en",
"region:us"
] |
2023-05-25T16:23:56+00:00
|
{"language": "en", "dataset_info": {"features": [{"name": "chapter", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 215494460.97875556, "num_examples": 11834}, {"name": "test", "num_bytes": 27122769.0, "num_examples": 1658}, {"name": "validation", "num_bytes": 43846669.0, "num_examples": 2234}], "download_size": 134838536, "dataset_size": 286463898.9787556}}
|
2023-07-12T16:43:41+00:00
|
6e42d357472ab37f09b089a05ac403d05b221910
|
# Dataset Card for "booksum-summary-analysis-llama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
emozilla/booksum-summary-analysis_llama-2048
|
[
"region:us"
] |
2023-05-25T16:31:46+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 30592419.675875388, "num_examples": 1680}, {"name": "test", "num_bytes": 2601037.557901086, "num_examples": 159}, {"name": "validation", "num_bytes": 8498481.502685765, "num_examples": 433}], "download_size": 3424916, "dataset_size": 41691938.736462235}}
|
2023-05-25T16:31:50+00:00
|
6599bead0afa7660bd9b683349c84ee34fdd0c9c
|
# Dataset Card for "booksum-summary-analysis-8192"
Subset of [emozilla/booksum-summary-analysis](https://huggingface.co/datasets/emozilla/booksum-summary-analysis) with only entries that are less than 8,192 tokens under the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
|
emozilla/booksum-summary-analysis_gptneox-8192
|
[
"region:us"
] |
2023-05-25T16:34:39+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 194097976.97925937, "num_examples": 10659}, {"name": "test", "num_bytes": 25683201.043425813, "num_examples": 1570}, {"name": "validation", "num_bytes": 35799607.99283796, "num_examples": 1824}], "download_size": 92249754, "dataset_size": 255580786.01552314}}
|
2023-05-30T13:28:46+00:00
|
e5ed711584f6797e15a9bf6118660bff4bf53efe
|
# Dataset Card for "3794d1ea"
[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/3794d1ea
|
[
"region:us"
] |
2023-05-25T17:26:01+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 186, "num_examples": 10}], "download_size": 1337, "dataset_size": 186}}
|
2023-05-25T17:26:02+00:00
|
b01e92839e342f239cab02abd931929bcd0ec5df
|
# Dataset Card for "sam-controlnet-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
baptistecolle/sam-controlnet-2
|
[
"region:us"
] |
2023-05-25T17:37:21+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "filepath", "dtype": "string"}, {"name": "sentids", "list": "int32"}, {"name": "filename", "dtype": "string"}, {"name": "imgid", "dtype": "int32"}, {"name": "split", "dtype": "string"}, {"name": "sentences", "struct": [{"name": "tokens", "list": "string"}, {"name": "raw", "dtype": "string"}, {"name": "imgid", "dtype": "int32"}, {"name": "sentid", "dtype": "int32"}]}, {"name": "cocoid", "dtype": "int32"}], "splits": [{"name": "original", "num_bytes": 160172316.0, "num_examples": 1000}], "download_size": 0, "dataset_size": 160172316.0}}
|
2023-05-25T17:44:01+00:00
|
0a8126a903b99d3822990b896c77ea92d55f5748
|
# Dataset Card for "demo2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
santhosh97/demo2
|
[
"region:us"
] |
2023-05-25T17:41:12+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input_image", "dtype": "image"}, {"name": "ground_truth_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 82978040.0, "num_examples": 80}], "download_size": 41492324, "dataset_size": 82978040.0}}
|
2023-05-25T17:41:14+00:00
|
136d18e3317ea0fab812822e2f7a8a036045d205
|
# Dataset Card for "sam-controlnet-3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
baptistecolle/sam-controlnet-3
|
[
"region:us"
] |
2023-05-25T17:45:58+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "filepath", "dtype": "string"}, {"name": "sentids", "sequence": "int64"}, {"name": "filename", "dtype": "string"}, {"name": "imgid", "dtype": "int64"}, {"name": "split", "dtype": "string"}, {"name": "sentences", "struct": [{"name": "imgid", "dtype": "int64"}, {"name": "raw", "dtype": "string"}, {"name": "sentid", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}]}, {"name": "cocoid", "dtype": "int64"}, {"name": "masks", "sequence": {"sequence": {"sequence": "bool"}}}, {"name": "scores", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 2086529.0, "num_examples": 1}], "download_size": 150908, "dataset_size": 2086529.0}}
|
2023-05-27T12:22:08+00:00
|
67b8dc1f33c63509cef557daae2ec6e7db34f55c
|
# Description
This dataset contains lyrics from both Incubus and Taylor Swift.
# Format
The file is in CSV format and contains three columns: Artist, Song Name and Lyrics.
## Caveats
The column Song Name has been transformed to a single string in lowercase format, so instead of having "Name of Song", the value will be "nameofsong".
|
danioshi/incubus_taylor_swift_lyrics
|
[
"size_categories:n<1K",
"language:en",
"license:cc0-1.0",
"music",
"region:us"
] |
2023-05-25T17:57:33+00:00
|
{"language": ["en"], "license": "cc0-1.0", "size_categories": ["n<1K"], "pretty_name": "Incubus and Taylor Swift lyrics", "tags": ["music"]}
|
2023-05-25T18:03:59+00:00
|
a4b8d51fec1d0c1fe240b21e7fc635e9475ac14c
|
# Dataset Card for "articles"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
203427as321/articles
|
[
"region:us"
] |
2023-05-25T18:13:43+00:00
|
{"dataset_info": {"features": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 23996247, "num_examples": 1534}], "download_size": 0, "dataset_size": 23996247}}
|
2024-02-17T17:00:08+00:00
|
dd38cd25661053fb12ab006ba9c10d5abec924ce
|
# Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_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_CM_Q_rices_ns_1000
|
[
"region:us"
] |
2023-05-25T18:54:45+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": 141854, "num_examples": 1000}], "download_size": 53468, "dataset_size": 141854}}
|
2023-06-10T15:56:08+00:00
|
4e788183125a6143d8bdba24e58f1efda2e4b48f
|
# Dataset Card for "womens-clothing-ecommerce-reviews"
Processed version of [this dataset](https://github.com/ya-stack/Women-s-Ecommerce-Clothing-Reviews).
|
saattrupdan/womens-clothing-ecommerce-reviews
|
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"multimodal",
"region:us"
] |
2023-05-25T19:04:03+00:00
|
{"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "Women's Clothing E-Commerce Reviews", "dataset_info": {"features": [{"name": "review_text", "dtype": "string"}, {"name": "age", "dtype": "int64"}, {"name": "rating", "dtype": "int64"}, {"name": "positive_feedback_count", "dtype": "int64"}, {"name": "division_name", "dtype": "string"}, {"name": "department_name", "dtype": "string"}, {"name": "class_name", "dtype": "string"}, {"name": "recommended_ind", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}], "splits": [{"name": "train", "num_bytes": 7811312.540347158, "num_examples": 20641}, {"name": "val", "num_bytes": 378436.72982642107, "num_examples": 1000}, {"name": "test", "num_bytes": 378436.72982642107, "num_examples": 1000}], "download_size": 4357015, "dataset_size": 8568186.0}, "tags": ["multimodal"]}
|
2023-05-25T19:18:53+00:00
|
97783671b5698cd38ad9c708bdb68ae328a27f2e
|
# Chizuru Tachibana from Nande Koko ni Sensei ga!?
- Trained with anime (full-final-pruned) model
- Works the best with ALL, MIDD, OUTD, and OUTALL LoRA weight blocks, and with 0.7+ weights.
|
Cheetor1996/Chizuru_Tachibana
|
[
"language:en",
"license:cc-by-2.0",
"art",
"region:us"
] |
2023-05-25T19:48:25+00:00
|
{"language": ["en"], "license": "cc-by-2.0", "tags": ["art"]}
|
2023-05-25T20:25:42+00:00
|
4a677e795921b68f168f54588abf00294647d552
|
# Dataset Card for "askscience"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
P1ayer-1/askscience
|
[
"region:us"
] |
2023-05-25T20:00:32+00:00
|
{"dataset_info": {"features": [{"name": "title", "sequence": "string"}, {"name": "over_18", "sequence": "bool"}, {"name": "post_content", "dtype": "string"}, {"name": "C1", "sequence": "string"}, {"name": "C2", "sequence": "string"}, {"name": "C3", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 235856987.50364494, "num_examples": 117709}, {"name": "test", "num_bytes": 12413103.82031179, "num_examples": 6195}, {"name": "validation", "num_bytes": 12415107.549742026, "num_examples": 6196}], "download_size": 172214121, "dataset_size": 260685198.87369874}}
|
2023-05-25T20:04:09+00:00
|
b93e4982f8f8ad2d82c6d35e3c00d161844ad70a
|
# Dataset Card for Dataset Name
## Name
ChatGPT Jailbreak Prompts
## Dataset Description
- **Autor:** Rubén Darío Jaramillo
- **Email:** [email protected]
- **WhatsApp:** +593 93 979 6676
### Dataset Summary
ChatGPT Jailbreak Prompts is a complete collection of jailbreak related prompts for ChatGPT. This dataset is intended to provide a valuable resource for understanding and generating text in the context of jailbreaking in ChatGPT.
### Languages
[English]
|
rubend18/ChatGPT-Jailbreak-Prompts
|
[
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:zero-shot-classification",
"task_categories:table-question-answering",
"size_categories:n<1K",
"language:en",
"language:aa",
"ChatGPT",
"JailbreakPrompts",
"LanguageModeling",
"ArtificialIntelligence",
"TextGeneration",
"Dataset",
"OpenAI",
"Jailbreak",
"Prompts",
"region:us"
] |
2023-05-25T20:04:52+00:00
|
{"language": ["en", "aa"], "size_categories": ["n<1K"], "task_categories": ["question-answering", "text-generation", "fill-mask", "zero-shot-classification", "table-question-answering"], "pretty_name": "ChatGPT Jailbreak Prompts", "tags": ["ChatGPT", "JailbreakPrompts", "LanguageModeling", "ArtificialIntelligence", "TextGeneration", "Dataset", "OpenAI", "Jailbreak", "Prompts"]}
|
2023-08-24T17:24:29+00:00
|
9e03cb762fb524fc88d83aab33c819dcc3a14363
|
# Dataset Card for "referral_commands_1B1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
stoddur/referral_commands_1B1
|
[
"region:us"
] |
2023-05-25T20:12:25+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 1544000, "num_examples": 1000}, {"name": "eval", "num_bytes": 1544000, "num_examples": 1000}], "download_size": 189073, "dataset_size": 3088000}}
|
2023-05-25T20:12:30+00:00
|
99173bd78ea28bd068f25e8295ae4e0d499b46fc
|
# Dataset Card for "ygo_card_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
akumoth/ygo_card_text
|
[
"region:us"
] |
2023-05-25T20:17:46+00:00
|
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4784802.456419868, "num_examples": 11523}, {"name": "test", "num_bytes": 531921.5435801313, "num_examples": 1281}], "download_size": 2069174, "dataset_size": 5316724.0}}
|
2023-05-25T20:28:48+00:00
|
fc980546bdf17618646cc114690cc89fa6dfbf55
|
# reddit finance 43 250k
`reddit_finance_43_250k` is a collection of 250k post/comment pairs from 43 financial, investing and crypto subreddits. Post must have all been text, with a length of 250chars, and a positive score. Each subreddit is narrowed down to the 70th qunatile before being mergered with their top 3 comments and than the other subs. Further score based methods are used to select the top 250k post/comment pairs.
The code to recreate the dataset is here: <https://github.com/getorca/ProfitsBot_V0_OLLM/tree/main/ds_builder>
The trained lora model is here: <https://huggingface.co/winddude/pb_lora_7b_v0.1>
|
winddude/reddit_finance_43_250k
|
[
"language:en",
"license:gpl-3.0",
"finance",
"investing",
"crypto",
"reddit",
"region:us"
] |
2023-05-25T20:31:02+00:00
|
{"language": ["en"], "license": "gpl-3.0", "tags": ["finance", "investing", "crypto", "reddit"]}
|
2023-05-25T22:06:03+00:00
|
d8c8f21c85f60ef16beb9a59b716e8c169e3b681
|
temnoed/Dandelions
|
[
"license:openrail",
"region:us"
] |
2023-05-25T20:36:23+00:00
|
{"license": "openrail"}
|
2023-05-25T20:38:05+00:00
|
|
0f18c8d3c6860930f0dd912b6de048479b8b5d1b
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The dataset.json file contains ~1.7 million synthetic data for arithmetic tasks, generated by dataset.ipynb.
### 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]
|
tiedong/goat
|
[
"task_categories:question-answering",
"size_categories:1M<n<10M",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-05-25T21:07:47+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["1M<n<10M"], "task_categories": ["question-answering"]}
|
2023-05-25T21:14:53+00:00
|
43d365dc401f46b2b454ba9e4644f6476be91aa8
|
# Dataset Card for "OpusTrain"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ethansimrm/OpusTrain
|
[
"region:us"
] |
2023-05-25T21:09:04+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 26397634.4, "num_examples": 101668}], "download_size": 19002803, "dataset_size": 26397634.4}}
|
2023-05-25T21:09:15+00:00
|
617660b4d62a139ea0dc93a4bdf310b3bc8ee849
|
# Dataset Card for "OpusTest"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ethansimrm/OpusTest
|
[
"region:us"
] |
2023-05-25T21:09:32+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 6599408.6, "num_examples": 25417}], "download_size": 4758293, "dataset_size": 6599408.6}}
|
2023-05-25T21:09:39+00:00
|
88472f98e3da574db96d02ab63ab577ee71341dd
|
henrydz/paddy_disease
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-25T21:29:14+00:00
|
{"license": "apache-2.0"}
|
2023-05-25T21:31:21+00:00
|
|
3f38dae188e6bc7ff24b5d548c8e81461ffdecdc
|
# Dataset Card for "cnn-diverse-gpt-3.5-summaries"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
griffin/cnn-diverse-gpt-3.5-summaries
|
[
"region:us"
] |
2023-05-25T22:05:02+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "source_edu_annotated", "dtype": "string"}, {"name": "reference", "dtype": "string"}, {"name": "candidates", "list": [{"name": "method", "dtype": "string"}, {"name": "method_beam", "dtype": "int64"}, {"name": "prediction", "dtype": "string"}, {"name": "rouge1", "dtype": "float64"}, {"name": "rouge2", "dtype": "float64"}, {"name": "rougeL", "dtype": "float64"}, {"name": "rougeLsum", "dtype": "float64"}]}, {"name": "vanilla_prompt", "dtype": "string"}, {"name": "pga_prompts", "sequence": "string"}, {"name": "pga_edu_extract_idxs", "sequence": {"sequence": "int64"}}], "splits": [{"name": "train", "num_bytes": 226053728, "num_examples": 1000}], "download_size": 91791746, "dataset_size": 226053728}}
|
2023-05-25T22:05:12+00:00
|
bc0e2ebfcfa830cf0a22947591c8995493826bcc
|
AyoubChLin/FFHQ
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-25T22:05:10+00:00
|
{"license": "apache-2.0"}
|
2023-05-25T22:08:03+00:00
|
|
22c007c6621025d16d9b837bde7e3c20d0351f39
|
# Dataset Card for Dromedary-Verbose-Clone (65b-v0)
- **Repository**: https://github.com/IBM/Dromedary
- **Authors' Note**: The Self-Align data contain a plethora of partial responses. Therefore, it is advised to refrain from appending the `<eos>` or `</s>` token to the model responses for supervised fine-tuning (SFT). Instead, it is recommended to substitute "\n\n### User" (Dromedary's eos token) with your own end-of-response token.
## Dataset Summary
Dromedary-Verbose-Clone is a synthetic dataset of 360k instructions and demonstrations. The [`Dromedary-65b (final)`](https://huggingface.co/zhiqings/dromedary-65b-lora-delta-v0) model can be reproduced by LoRA fine-tuing the base `LLaMA-65b` model on this dataset.
### Synthetic Instructions
The instructions are generated by the base LLaMA model with the [Self-Instruct](https://github.com/yizhongw/self-instruct) framework and made the following modifications:
* The Self-Instruct algorithm is employed solely for generating instructions, not for producing the model's responses.
* A new [prompt](https://github.com/IBM/Dromedary/blob/main/prompts/self_instruct_prompt.txt), adapted from the [Alpaca's prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt), was written that explicitly gave the requirement of instruction generation.
* We further use [Topic-Guided Red-Teaming (TGRT) Self-Instruct](https://arxiv.org/abs/2305.03047) to ensure a diversified topic coverage of the instructions.
### Synthetic Demonstrations
The demonstrations are generated by the non-verbose version of `Dromedary` prompted with the following prompt:
```
# Dromedary
## System Overview
Consider an AI assistant whose codename is Dromedary, developed by the Self-Align team. Dromedary is trained on data up until Sept-2021, and it endeavors to be a helpful, ethical and reliable assistant.
"Dromedary (extensive)" is an advanced language model that specializes in generating extensive, comprehensive, and detailed responses to user queries. By precisely discerning user intent and employing sophisticated natural language processing skills, it is able to delve deep into complex topics and provide longer, well-organized paragraphs. This thorough approach ensures that users receive a complete understanding of the subject matter, resulting in a highly informative and gratifying experience.
The "Dromedary (extensive)" model is designed to cater to the needs of users who desire more than just a brief response to their questions. It strives to provide a comprehensive analysis of the subject at hand, which may include historical context, scientific principles, various perspectives, or potential future developments. As a result, users can expect to receive a wealth of knowledge that not only answers their immediate query but also promotes a deeper understanding and appreciation of the topic.
## User Conversation
### User
To begin with, can you introduce yourself?
### Dromedary (extensive)
Hello! I am Dromedary, an AI language model developed by the Self-Align team, based on the LLaMa architecture. My primary function is to assist users in answering questions, generating text, and engaging in conversation across a vast range of topics, with training data up until September 2021.
As a helpful, ethical, and reliable AI assistant, my foremost objective is to promote user safety, adhere to moral principles, and foster conscientious behavior. In the face of potentially harmful inquiries, I actively redirect users towards constructive topics by emphasizing the negative consequences and elucidating the reasoning behind my stance. In doing so, I strive to generate responses that are positive, interesting, helpful, and engaging. My logics and reasoning are designed to be rigorous, intelligent, and defensible. Furthermore, I am capable of providing additional relevant details to respond thoroughly and comprehensively, covering multiple aspects of a topic in depth. Please feel free to ask me anything, and I will be more than happy to assist you.
### User
```
### Quality Filtering
To mitigate the quality issues inherent in the synthetic demonstrations, the dataset was filtered using [a set of basic rules](https://github.com/IBM/Dromedary/blob/main/training/step4_verbose_cloning/merge_and_filter_verbose_clone_output.py):
1. In case the output is a list of enumerated items, only the initial 12 items are retained.
2. Instances where the model's response is less than 128 characters are removed.
3. Any repeated sentences within the model's output (split by `r'(?<=[\n.?!;:,])'`) are also eliminated.
### Supported Tasks and Leaderboards
The Dromedary-Verbose-Clone dataset is designed for instruction training pretrained language models.
### Languages
The data in Dromedary-Verbose-Clone are in English (BCP-47 en).
## Dataset Structure
### Data Instances
An example of the "train" example looks as follows:
```json
{
"example_id": 1,
"instruction": "Write a haiku about good news.",
"input": "",
"output": "Here is a haiku about good news:\n\nGood news is always\n\nwelcome, especially when\n\nit is unexpected.\n\n### User",
}
```
Sometimes, the `"output"` field will end with `"\n\n### User"` to indicate the conclusion of the model's response.
### Data Fields
The data fields are as follows:
* `example_id`: a unique id for each example
* `instruction`: describes the task the model should perform.
* `input`: optional context or input for the task.
* `output`: the synthetic answer to the instruction as generated.
### Data Splits
| | train |
|-----------|--------:|
| dromedary | 360674 |
## 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
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@misc{sun2023principledriven,
title={Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision},
author={Zhiqing Sun and Yikang Shen and Qinhong Zhou and Hongxin Zhang and Zhenfang Chen and David Cox and Yiming Yang and Chuang Gan},
year={2023},
eprint={2305.03047},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
### Contributions
[More Information Needed]
|
zhiqings/dromedary-65b-verbose-clone-v0
|
[
"task_categories:conversational",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-4.0",
"arxiv:2305.03047",
"region:us"
] |
2023-05-25T22:05:43+00:00
|
{"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["conversational"], "pretty_name": "Dromedary-Verbose-Clone"}
|
2023-06-23T21:52:56+00:00
|
bbe510c9a0160588b2bd4e11ad8abec55071f6a5
|
Zaun/Otaotakinp
|
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] |
2023-05-25T22:22:17+00:00
|
{"license": "bigscience-bloom-rail-1.0"}
|
2023-05-25T22:22:17+00:00
|
|
bbbfab01849e02f0c841cee756d1cb840daf309b
|
chenxwh/gen-xcopa
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-05-25T22:28:01+00:00
|
{"license": "cc-by-4.0"}
|
2023-05-29T14:04:40+00:00
|
|
0046cbf9c99fdbdcb29b70283ecd8427c2096b0e
|
HyperionHF/tom_clean
|
[
"license:mit",
"region:us"
] |
2023-05-25T22:58:31+00:00
|
{"license": "mit", "dataset_info": {"features": [{"name": "agent", "dtype": "string"}, {"name": "agent_preferences", "sequence": "string"}, {"name": "chosen_object", "dtype": "string"}, {"name": "true_chosen_object", "dtype": "string"}, {"name": "chosen_object_is_noisy", "dtype": "bool"}, {"name": "other_objects", "sequence": "string"}, {"name": "location_chosen_object", "dtype": "int64"}, {"name": "location_true_chosen_object", "dtype": "int64"}, {"name": "preference_idx", "dtype": "int64"}, {"name": "preference_idx_true", "dtype": "int64"}, {"name": "example_idx", "dtype": "int64"}, {"name": "is_inanimate", "dtype": "bool"}, {"name": "logical_representation", "struct": [{"name": "input", "sequence": "string"}, {"name": "target", "dtype": "string"}]}, {"name": "logical_representation_with_indirection", "struct": [{"name": "input", "sequence": "string"}, {"name": "target", "dtype": "int64"}]}, {"name": "example_text", "dtype": "string"}, {"name": "target_idx", "dtype": "int64"}, {"name": "matching_animate_inanimate", "dtype": "string"}, {"name": "matching_animate_inanimate_idx", "dtype": "int64"}, {"name": "irrelevant_change_to_original_1", "dtype": "string"}, {"name": "irrelevant_change_to_original_1_idx", "dtype": "int64"}, {"name": "irrelevant_change_to_original_2", "dtype": "string"}, {"name": "irrelevant_change_to_original_2_idx", "dtype": "int64"}, {"name": "original_1_with_animate_denotation", "dtype": "string"}, {"name": "original_1_with_animate_denotation_idx", "dtype": "int64"}, {"name": "no_indirection_target", "dtype": "string"}, {"name": "no_indirection_target_idx", "dtype": "int64"}, {"name": "no_indirection_irrelevant_change", "dtype": "string"}, {"name": "no_indirection_irrelevant_change_idx", "dtype": "int64"}, {"name": "no_indirection_target_animate_denotation", "dtype": "string"}, {"name": "no_indirection_target_animate_denotation_idx", "dtype": "int64"}, {"name": "indirection_target", "dtype": "string"}, {"name": "indirection_target_idx", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 33720360, "num_examples": 15579}, {"name": "dev", "num_bytes": 3748081, "num_examples": 1731}, {"name": "test_own_preference", "num_bytes": 304452, "num_examples": 144}, {"name": "test_others_preference", "num_bytes": 337582, "num_examples": 156}, {"name": "test_distraction", "num_bytes": 318204, "num_examples": 144}, {"name": "test_location", "num_bytes": 732420, "num_examples": 336}, {"name": "test_inanimate", "num_bytes": 970271, "num_examples": 450}, {"name": "test_agents", "num_bytes": 6085350, "num_examples": 2808}], "download_size": 5617164, "dataset_size": 46216720}}
|
2023-05-25T22:58:46+00:00
|
|
11ac1bc1d82d87577fea819a6b11443a07321e03
|
JianLiao/spectrum-design-docs
|
[
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] |
2023-05-25T23:12:53+00:00
|
{"language": ["en"], "license": "cc-by-sa-4.0"}
|
2023-06-24T04:28:05+00:00
|
|
19ba1ed13db800c20966f6bdc98b0fa120f7d1fe
|
# Dataset Card for "part_1_imda_10000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
averageandyyy/part_1_imda_10000
|
[
"region:us"
] |
2023-05-25T23:25:50+00:00
|
{"dataset_info": {"features": [{"name": "transcript", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "waveform", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 6702801413.012406, "num_examples": 10000}], "download_size": 1615604216, "dataset_size": 6702801413.012406}}
|
2023-05-25T23:32:10+00:00
|
675409abd02729cb9e109328269dad2ec045cbcc
|
assembleco/hyperdemocracy
|
[
"license:pddl",
"region:us"
] |
2023-05-25T23:42:07+00:00
|
{"license": "pddl", "dataset_info": {"features": [{"name": "key", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "sponsors", "sequence": {"sequence": "string"}}, {"name": "summary", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "themes", "sequence": "string"}, {"name": "index", "struct": [{"name": "bill", "struct": [{"name": "actions", "struct": [{"name": "count", "dtype": "int64"}, {"name": "url", "dtype": "string"}]}, {"name": "amendments", "struct": [{"name": "count", "dtype": "int64"}, {"name": "url", "dtype": "string"}]}, {"name": "cboCostEstimates", "list": [{"name": "description", "dtype": "string"}, {"name": "pubDate", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "committeeReports", "list": [{"name": "citation", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "committees", "struct": [{"name": "count", "dtype": "int64"}, {"name": "url", "dtype": "string"}]}, {"name": "congress", "dtype": "int64"}, {"name": "constitutionalAuthorityStatementText", "dtype": "string"}, {"name": "cosponsors", "struct": [{"name": "count", "dtype": "int64"}, {"name": "countIncludingWithdrawnCosponsors", "dtype": "int64"}, {"name": "url", "dtype": "string"}]}, {"name": "introducedDate", "dtype": "string"}, {"name": "latestAction", "struct": [{"name": "actionDate", "dtype": "string"}, {"name": "actionTime", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "laws", "list": [{"name": "number", "dtype": "string"}, {"name": "type", "dtype": "string"}]}, {"name": "number", "dtype": "string"}, {"name": "originChamber", "dtype": "string"}, {"name": "policyArea", "struct": [{"name": "name", "dtype": "string"}]}, {"name": "relatedBills", "struct": [{"name": "count", "dtype": "int64"}, {"name": "url", "dtype": "string"}]}, {"name": "sponsors", "list": [{"name": "bioguideId", "dtype": "string"}, {"name": "district", "dtype": "int64"}, {"name": "firstName", "dtype": "string"}, {"name": "fullName", "dtype": "string"}, {"name": "isByRequest", "dtype": "string"}, {"name": "lastName", "dtype": "string"}, {"name": "middleName", "dtype": "string"}, {"name": "party", "dtype": "string"}, {"name": "state", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "subjects", "struct": [{"name": "count", "dtype": "int64"}, {"name": "url", "dtype": "string"}]}, {"name": "summaries", "struct": [{"name": "count", "dtype": "int64"}, {"name": "url", "dtype": "string"}]}, {"name": "textVersions", "struct": [{"name": "count", "dtype": "int64"}, {"name": "url", "dtype": "string"}]}, {"name": "title", "dtype": "string"}, {"name": "titles", "struct": [{"name": "count", "dtype": "int64"}, {"name": "url", "dtype": "string"}]}, {"name": "type", "dtype": "string"}, {"name": "updateDate", "dtype": "string"}, {"name": "updateDateIncludingText", "dtype": "string"}]}, {"name": "request", "struct": [{"name": "billNumber", "dtype": "string"}, {"name": "billType", "dtype": "string"}, {"name": "congress", "dtype": "string"}, {"name": "contentType", "dtype": "string"}, {"name": "format", "dtype": "string"}]}]}, {"name": "actions", "struct": [{"name": "actions", "list": [{"name": "actionCode", "dtype": "string"}, {"name": "actionDate", "dtype": "string"}, {"name": "actionTime", "dtype": "string"}, {"name": "calendarNumber", "struct": [{"name": "calendar", "dtype": "string"}, {"name": "number", "dtype": "string"}]}, {"name": "committees", "list": [{"name": "name", "dtype": "string"}, {"name": "systemCode", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "recordedVotes", "list": [{"name": "chamber", "dtype": "string"}, {"name": "congress", "dtype": "int64"}, {"name": "date", "dtype": "string"}, {"name": "rollNumber", "dtype": "int64"}, {"name": "sessionNumber", "dtype": "int64"}, {"name": "url", "dtype": "string"}]}, {"name": "sourceSystem", "struct": [{"name": "code", "dtype": "int64"}, {"name": "name", "dtype": "string"}]}, {"name": "text", "dtype": "string"}, {"name": "type", "dtype": "string"}]}, {"name": "pagination", "struct": [{"name": "count", "dtype": "int64"}]}, {"name": "request", "struct": [{"name": "billNumber", "dtype": "string"}, {"name": "billType", "dtype": "string"}, {"name": "billUrl", "dtype": "string"}, {"name": "congress", "dtype": "string"}, {"name": "contentType", "dtype": "string"}, {"name": "format", "dtype": "string"}]}]}, {"name": "amendments", "struct": [{"name": "amendments", "list": [{"name": "congress", "dtype": "int64"}, {"name": "description", "dtype": "string"}, {"name": "latestAction", "struct": [{"name": "actionDate", "dtype": "string"}, {"name": "actionTime", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "number", "dtype": "string"}, {"name": "purpose", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "updateDate", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "pagination", "struct": [{"name": "count", "dtype": "int64"}]}, {"name": "request", "struct": [{"name": "billNumber", "dtype": "string"}, {"name": "billType", "dtype": "string"}, {"name": "billUrl", "dtype": "string"}, {"name": "congress", "dtype": "string"}, {"name": "contentType", "dtype": "string"}, {"name": "format", "dtype": "string"}]}]}, {"name": "committees", "struct": [{"name": "committees", "list": [{"name": "activities", "list": [{"name": "date", "dtype": "string"}, {"name": "name", "dtype": "string"}]}, {"name": "chamber", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "subcommittees", "list": [{"name": "activities", "list": [{"name": "date", "dtype": "string"}, {"name": "name", "dtype": "string"}]}, {"name": "name", "dtype": "string"}, {"name": "systemCode", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "systemCode", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "request", "struct": [{"name": "billNumber", "dtype": "string"}, {"name": "billType", "dtype": "string"}, {"name": "billUrl", "dtype": "string"}, {"name": "congress", "dtype": "string"}, {"name": "contentType", "dtype": "string"}, {"name": "format", "dtype": "string"}]}]}, {"name": "relatedbills", "struct": [{"name": "pagination", "struct": [{"name": "count", "dtype": "int64"}]}, {"name": "relatedBills", "list": [{"name": "congress", "dtype": "int64"}, {"name": "latestAction", "struct": [{"name": "actionDate", "dtype": "string"}, {"name": "actionTime", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "number", "dtype": "int64"}, {"name": "relationshipDetails", "list": [{"name": "identifiedBy", "dtype": "string"}, {"name": "type", "dtype": "string"}]}, {"name": "title", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "request", "struct": [{"name": "billNumber", "dtype": "string"}, {"name": "billType", "dtype": "string"}, {"name": "billUrl", "dtype": "string"}, {"name": "congress", "dtype": "string"}, {"name": "contentType", "dtype": "string"}, {"name": "format", "dtype": "string"}]}]}, {"name": "cosponsors", "struct": [{"name": "cosponsors", "list": [{"name": "bioguideId", "dtype": "string"}, {"name": "district", "dtype": "int64"}, {"name": "firstName", "dtype": "string"}, {"name": "fullName", "dtype": "string"}, {"name": "isOriginalCosponsor", "dtype": "bool"}, {"name": "lastName", "dtype": "string"}, {"name": "middleName", "dtype": "string"}, {"name": "party", "dtype": "string"}, {"name": "sponsorshipDate", "dtype": "string"}, {"name": "sponsorshipWithdrawnDate", "dtype": "string"}, {"name": "state", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "pagination", "struct": [{"name": "count", "dtype": "int64"}, {"name": "countIncludingWithdrawnCosponsors", "dtype": "int64"}, {"name": "prev", "dtype": "string"}]}, {"name": "request", "struct": [{"name": "billNumber", "dtype": "string"}, {"name": "billType", "dtype": "string"}, {"name": "billUrl", "dtype": "string"}, {"name": "congress", "dtype": "string"}, {"name": "contentType", "dtype": "string"}, {"name": "format", "dtype": "string"}]}]}, {"name": "subjects", "struct": [{"name": "pagination", "struct": [{"name": "count", "dtype": "int64"}]}, {"name": "request", "struct": [{"name": "billNumber", "dtype": "string"}, {"name": "billType", "dtype": "string"}, {"name": "billUrl", "dtype": "string"}, {"name": "congress", "dtype": "string"}, {"name": "contentType", "dtype": "string"}, {"name": "format", "dtype": "string"}]}, {"name": "subjects", "struct": [{"name": "legislativeSubjects", "list": [{"name": "name", "dtype": "string"}]}, {"name": "policyArea", "struct": [{"name": "name", "dtype": "string"}]}]}]}, {"name": "text", "struct": [{"name": "pagination", "struct": [{"name": "count", "dtype": "int64"}]}, {"name": "request", "struct": [{"name": "billNumber", "dtype": "string"}, {"name": "billType", "dtype": "string"}, {"name": "billUrl", "dtype": "string"}, {"name": "congress", "dtype": "string"}, {"name": "contentType", "dtype": "string"}, {"name": "format", "dtype": "string"}]}, {"name": "textVersions", "list": [{"name": "date", "dtype": "string"}, {"name": "formats", "list": [{"name": "type", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "type", "dtype": "string"}]}]}, {"name": "titles", "struct": [{"name": "pagination", "struct": [{"name": "count", "dtype": "int64"}]}, {"name": "request", "struct": [{"name": "billNumber", "dtype": "string"}, {"name": "billType", "dtype": "string"}, {"name": "billUrl", "dtype": "string"}, {"name": "congress", "dtype": "string"}, {"name": "contentType", "dtype": "string"}, {"name": "format", "dtype": "string"}]}, {"name": "titles", "list": [{"name": "billTextVersionCode", "dtype": "string"}, {"name": "billTextVersionName", "dtype": "string"}, {"name": "chamberCode", "dtype": "string"}, {"name": "chamberName", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "titleType", "dtype": "string"}]}]}], "splits": [{"name": "train", "num_bytes": 115960478, "num_examples": 6797}], "download_size": 31176762, "dataset_size": 115960478}}
|
2023-06-20T22:25:37+00:00
|
|
74a1d1ad40e268bd865ac0823eeb58d564708ba8
|
# Dataset Card for RefGPT-Code-ds
## Dataset Description
- **Homepage:**
- **Repository:** [https://github.com/ziliwangnlp/RefGPT](https://github.com/ziliwangnlp/RefGPT)
- **Paper:** [https://arxiv.org/abs/2305.14994](https://arxiv.org/abs/2305.14994)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
<p align="center">
<a href="https://arxiv.org/abs/2305.14994"><b>[Paper] RefGPT</b></a> |
<a href="https://github.com/ziliwangnlp/RefGPT"><b>[Github] RefGPT</b></a>
</p>
RefGPT-Code is a dataset containing 76k multi-turn dialogues about programming with 37k English and 39k Chinese, which has covered most aspects of code usage scenarios and multiple types of programming languages. Both the English version and Chinese version use the public GitHub dataset on Google BiqQuery with no overlap in these two languages. RefGPT-Code has derived various ways of leveraging the program code as the reference to enable different scenarios. We consider three perspectives of code discussion, code creation and bug fixing in RefGPT-Code.
**RefGPT-Code-ds** is the "code discussion" subset. <font color=red>It has the reference added to the first question of the user between the "\<context\>" and "\</context\>" labels.</font>
### Supported Tasks and Leaderboards
Chatbot instruction finetuning
### Languages
Chinese, English
## 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
Please pay attention that RefGPT Datasets, including RefGPT-Fact and RefGPT-Code, have not undergone manual verification, and as such, their security cannot be strictly guaranteed. Users should be aware that they are responsible for the results generated using this data.
### Discussion of Biases
As the datasets RefGPT-Fact and RefGPT-Code are collected by using the references like Wikipedia and Github repositories, it can not be avoided that the reference itself has factual errors, typos, or bugs and malicious code if it is from Github repositories. The datasets may also reflect the biases of the selected references and GPT-3.5/GPT-4 model
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@misc{yang2023refgpt,
title={RefGPT: Reference -> Truthful & Customized Dialogues Generation by GPTs and for GPTs},
author={Dongjie Yang and Ruifeng Yuan and YuanTao Fan and YiFei Yang and Zili Wang and Shusen Wang and Hai Zhao},
year={2023},
eprint={2305.14994},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
[More Information Needed]
|
Mutonix/RefGPT-Code-ds
|
[
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:zh",
"language:en",
"license:apache-2.0",
"arxiv:2305.14994",
"region:us"
] |
2023-05-25T23:42:30+00:00
|
{"language": ["zh", "en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["conversational"], "dataset_info": {"features": [{"name": "dialogue", "dtype": "string"}, {"name": "reference", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "en", "num_bytes": 190813718.0874176, "num_examples": 13948}, {"name": "zh", "num_bytes": 180002282.0268779, "num_examples": 14124}], "download_size": 166204685, "dataset_size": 370816000.1142955}, "arxiv": "https://arxiv.org/abs/2305.14994"}
|
2023-06-01T08:10:23+00:00
|
db8962ec39a1acd3a22d8f35a0ae3f85880e8a80
|
# Dataset Card for RefGPT-Code-cr
## Dataset Description
- **Homepage:**
- **Repository:** [https://github.com/ziliwangnlp/RefGPT](https://github.com/ziliwangnlp/RefGPT)
- **Paper:** [https://arxiv.org/abs/2305.14994](https://arxiv.org/abs/2305.14994)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
<p align="center">
<a href="https://arxiv.org/abs/2305.14994"><b>[Paper] RefGPT</b></a> |
<a href="https://github.com/ziliwangnlp/RefGPT"><b>[Github] RefGPT</b></a>
</p>
RefGPT-Code is a dataset containing 76k multi-turn dialogues about programming with 37k English and 39k Chinese, which has covered most aspects of code usage scenarios and multiple types of programming languages. Both the English version and Chinese version use the public GitHub dataset on Google BiqQuery with no overlap in these two languages. RefGPT-Code has derived various ways of leveraging the program code as the reference to enable different scenarios. We consider three perspectives of code discussion, code creation and bug fixing in RefGPT-Code.
**RefGPT-Code-cr** is the "code creation" subset.
### Supported Tasks and Leaderboards
Chatbot instruction finetuning
### Languages
Chinese, English
## 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
Please pay attention that RefGPT Datasets, including RefGPT-Fact and RefGPT-Code, have not undergone manual verification, and as such, their security cannot be strictly guaranteed. Users should be aware that they are responsible for the results generated using this data.
### Discussion of Biases
As the datasets RefGPT-Fact and RefGPT-Code are collected by using the references like Wikipedia and Github repositories, it can not be avoided that the reference itself has factual errors, typos, or bugs and malicious code if it is from Github repositories. The datasets may also reflect the biases of the selected references and GPT-3.5/GPT-4 model
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@misc{yang2023refgpt,
title={RefGPT: Reference -> Truthful & Customized Dialogues Generation by GPTs and for GPTs},
author={Dongjie Yang and Ruifeng Yuan and YuanTao Fan and YiFei Yang and Zili Wang and Shusen Wang and Hai Zhao},
year={2023},
eprint={2305.14994},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
[More Information Needed]
|
Mutonix/RefGPT-Code-cr
|
[
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:zh",
"language:en",
"license:apache-2.0",
"arxiv:2305.14994",
"region:us"
] |
2023-05-25T23:42:59+00:00
|
{"language": ["zh", "en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["conversational"], "dataset_info": {"features": [{"name": "dialogue", "dtype": "string"}, {"name": "reference", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "en", "num_bytes": 165025559.5254741, "num_examples": 14119}, {"name": "zh", "num_bytes": 157858797.9941188, "num_examples": 15288}], "download_size": 136112295, "dataset_size": 322884357.5195929}, "arxiv": "https://arxiv.org/abs/2305.14994"}
|
2023-06-01T08:10:58+00:00
|
5ae027fa36c5f2241ebc80c47a6e4976d02f9393
|
# Dataset Card for RefGPT-Code-bg
## Dataset Description
- **Homepage:**
- **Repository:** [https://github.com/ziliwangnlp/RefGPT](https://github.com/ziliwangnlp/RefGPT)
- **Paper:** [https://arxiv.org/abs/2305.14994](https://arxiv.org/abs/2305.14994)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
<p align="center">
<a href="https://arxiv.org/abs/2305.14994"><b>[Paper] RefGPT</b></a> |
<a href="https://github.com/ziliwangnlp/RefGPT"><b>[Github] RefGPT</b></a>
</p>
RefGPT-Code is a dataset containing 76k multi-turn dialogues about programming with 37k English and 39k Chinese, which has covered most aspects of code usage scenarios and multiple types of programming languages. Both the English version and Chinese version use the public GitHub dataset on Google BiqQuery with no overlap in these two languages. RefGPT-Code has derived various ways of leveraging the program code as the reference to enable different scenarios. We consider three perspectives of code discussion, code creation and bug fixing in RefGPT-Code.
**RefGPT-Code-bg** is the "bug fixing" subset.
### Supported Tasks and Leaderboards
Chatbot instruction finetuning
### Languages
Chinese, English
## 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
Please pay attention that RefGPT Datasets, including RefGPT-Fact and RefGPT-Code, have not undergone manual verification, and as such, their security cannot be strictly guaranteed. Users should be aware that they are responsible for the results generated using this data.
### Discussion of Biases
As the datasets RefGPT-Fact and RefGPT-Code are collected by using the references like Wikipedia and Github repositories, it can not be avoided that the reference itself has factual errors, typos, or bugs and malicious code if it is from Github repositories. The datasets may also reflect the biases of the selected references and GPT-3.5/GPT-4 model
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@misc{yang2023refgpt,
title={RefGPT: Reference -> Truthful & Customized Dialogues Generation by GPTs and for GPTs},
author={Dongjie Yang and Ruifeng Yuan and YuanTao Fan and YiFei Yang and Zili Wang and Shusen Wang and Hai Zhao},
year={2023},
eprint={2305.14994},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
[More Information Needed]
|
Mutonix/RefGPT-Code-bg
|
[
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:zh",
"language:en",
"license:apache-2.0",
"arxiv:2305.14994",
"region:us"
] |
2023-05-25T23:43:20+00:00
|
{"language": ["zh", "en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["conversational"], "dataset_info": {"features": [{"name": "dialogue", "dtype": "string"}, {"name": "reference", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "en", "num_bytes": 106344832.26735915, "num_examples": 8848}, {"name": "zh", "num_bytes": 101753322.73345818, "num_examples": 9597}], "download_size": 86625605, "dataset_size": 208098155.00081733}, "arxiv": "https://arxiv.org/abs/2305.14994"}
|
2023-06-01T08:11:22+00:00
|
b59d9d6282255c6bca83ea76e02c6f8e5d35594b
|
## FinQA 한국어 번역본
Question, Answer 총 6252 Rows
|
mssongit/KorfinQA
|
[
"task_categories:question-answering",
"language:ko",
"license:mit",
"finance",
"region:us"
] |
2023-05-25T23:45:01+00:00
|
{"language": ["ko"], "license": "mit", "task_categories": ["question-answering"], "tags": ["finance"]}
|
2023-05-25T23:48:15+00:00
|
7c9a1d93245619f6d8899f9beeda57d06dc98831
|
jlh/home-credit-synthetic-example
|
[
"license:unknown",
"region:us"
] |
2023-05-26T00:07:21+00:00
|
{"license": "unknown", "dataset_info": {"features": [{"name": "TARGET", "dtype": "float64"}, {"name": "NAME_CONTRACT_TYPE", "dtype": "float64"}, {"name": "CODE_GENDER", "dtype": "float64"}, {"name": "FLAG_OWN_CAR", "dtype": "float64"}, {"name": "FLAG_OWN_REALTY", "dtype": "float64"}, {"name": "CNT_CHILDREN", "dtype": "float64"}, {"name": "AMT_INCOME_TOTAL", "dtype": "float64"}, {"name": "AMT_CREDIT", "dtype": "float64"}, {"name": "AMT_ANNUITY", "dtype": "float64"}, {"name": "AMT_GOODS_PRICE", "dtype": "float64"}, {"name": "NAME_TYPE_SUITE", "dtype": "float64"}, {"name": "NAME_INCOME_TYPE", "dtype": "float64"}, {"name": "NAME_EDUCATION_TYPE", "dtype": "float64"}, {"name": "NAME_FAMILY_STATUS", "dtype": "float64"}, {"name": "NAME_HOUSING_TYPE", "dtype": "float64"}, {"name": "REGION_POPULATION_RELATIVE", "dtype": "float64"}, {"name": "DAYS_BIRTH", "dtype": "float64"}, {"name": "DAYS_EMPLOYED", "dtype": "float64"}, {"name": "DAYS_REGISTRATION", "dtype": "float64"}, {"name": "DAYS_ID_PUBLISH", "dtype": "float64"}, {"name": "OWN_CAR_AGE", "dtype": "float64"}, {"name": "FLAG_MOBIL", "dtype": "float64"}, {"name": "FLAG_EMP_PHONE", "dtype": "float64"}, {"name": "FLAG_WORK_PHONE", "dtype": "float64"}, {"name": "FLAG_CONT_MOBILE", "dtype": "float64"}, {"name": "FLAG_PHONE", "dtype": "float64"}, {"name": "FLAG_EMAIL", "dtype": "float64"}, {"name": "OCCUPATION_TYPE", "dtype": "float64"}, {"name": "CNT_FAM_MEMBERS", "dtype": "float64"}, {"name": "REGION_RATING_CLIENT", "dtype": "float64"}, {"name": "REGION_RATING_CLIENT_W_CITY", "dtype": "float64"}, {"name": "WEEKDAY_APPR_PROCESS_START", "dtype": "float64"}, {"name": "HOUR_APPR_PROCESS_START", "dtype": "float64"}, {"name": "REG_REGION_NOT_LIVE_REGION", "dtype": "float64"}, {"name": "REG_REGION_NOT_WORK_REGION", "dtype": "float64"}, {"name": "LIVE_REGION_NOT_WORK_REGION", "dtype": "float64"}, {"name": "REG_CITY_NOT_LIVE_CITY", "dtype": "float64"}, {"name": "REG_CITY_NOT_WORK_CITY", "dtype": "float64"}, {"name": "LIVE_CITY_NOT_WORK_CITY", "dtype": "float64"}, {"name": "ORGANIZATION_TYPE", "dtype": "float64"}, {"name": "EXT_SOURCE_1", "dtype": "float64"}, {"name": "EXT_SOURCE_2", "dtype": "float64"}, {"name": "EXT_SOURCE_3", "dtype": "float64"}, {"name": "APARTMENTS_AVG", "dtype": "float64"}, {"name": "BASEMENTAREA_AVG", "dtype": "float64"}, {"name": "YEARS_BEGINEXPLUATATION_AVG", "dtype": "float64"}, {"name": "YEARS_BUILD_AVG", "dtype": "float64"}, {"name": "COMMONAREA_AVG", "dtype": "float64"}, {"name": "ELEVATORS_AVG", "dtype": "float64"}, {"name": "ENTRANCES_AVG", "dtype": "float64"}, {"name": "FLOORSMAX_AVG", "dtype": "float64"}, {"name": "FLOORSMIN_AVG", "dtype": "float64"}, {"name": "LANDAREA_AVG", "dtype": "float64"}, {"name": "LIVINGAPARTMENTS_AVG", "dtype": "float64"}, {"name": "LIVINGAREA_AVG", "dtype": "float64"}, {"name": "NONLIVINGAPARTMENTS_AVG", "dtype": "float64"}, {"name": "NONLIVINGAREA_AVG", "dtype": "float64"}, {"name": "APARTMENTS_MODE", "dtype": "float64"}, {"name": "BASEMENTAREA_MODE", "dtype": "float64"}, {"name": "YEARS_BEGINEXPLUATATION_MODE", "dtype": "float64"}, {"name": "YEARS_BUILD_MODE", "dtype": "float64"}, {"name": "COMMONAREA_MODE", "dtype": "float64"}, {"name": "ELEVATORS_MODE", "dtype": "float64"}, {"name": "ENTRANCES_MODE", "dtype": "float64"}, {"name": "FLOORSMAX_MODE", "dtype": "float64"}, {"name": "FLOORSMIN_MODE", "dtype": "float64"}, {"name": "LANDAREA_MODE", "dtype": "float64"}, {"name": "LIVINGAPARTMENTS_MODE", "dtype": "float64"}, {"name": "LIVINGAREA_MODE", "dtype": "float64"}, {"name": "NONLIVINGAPARTMENTS_MODE", "dtype": "float64"}, {"name": "NONLIVINGAREA_MODE", "dtype": "float64"}, {"name": "APARTMENTS_MEDI", "dtype": "float64"}, {"name": "BASEMENTAREA_MEDI", "dtype": "float64"}, {"name": "YEARS_BEGINEXPLUATATION_MEDI", "dtype": "float64"}, {"name": "YEARS_BUILD_MEDI", "dtype": "float64"}, {"name": "COMMONAREA_MEDI", "dtype": "float64"}, {"name": "ELEVATORS_MEDI", "dtype": "float64"}, {"name": "ENTRANCES_MEDI", "dtype": "float64"}, {"name": "FLOORSMAX_MEDI", "dtype": "float64"}, {"name": "FLOORSMIN_MEDI", "dtype": "float64"}, {"name": "LANDAREA_MEDI", "dtype": "float64"}, {"name": "LIVINGAPARTMENTS_MEDI", "dtype": "float64"}, {"name": "LIVINGAREA_MEDI", "dtype": "float64"}, {"name": "NONLIVINGAPARTMENTS_MEDI", "dtype": "float64"}, {"name": "NONLIVINGAREA_MEDI", "dtype": "float64"}, {"name": "FONDKAPREMONT_MODE", "dtype": "float64"}, {"name": "HOUSETYPE_MODE", "dtype": "float64"}, {"name": "TOTALAREA_MODE", "dtype": "float64"}, {"name": "WALLSMATERIAL_MODE", "dtype": "float64"}, {"name": "EMERGENCYSTATE_MODE", "dtype": "float64"}, {"name": "OBS_30_CNT_SOCIAL_CIRCLE", "dtype": "float64"}, {"name": "DEF_30_CNT_SOCIAL_CIRCLE", "dtype": "float64"}, {"name": "OBS_60_CNT_SOCIAL_CIRCLE", "dtype": "float64"}, {"name": "DEF_60_CNT_SOCIAL_CIRCLE", "dtype": "float64"}, {"name": "DAYS_LAST_PHONE_CHANGE", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_2", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_3", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_4", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_5", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_6", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_7", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_8", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_9", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_10", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_11", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_12", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_13", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_14", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_15", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_16", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_17", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_18", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_19", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_20", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_21", "dtype": "float64"}, {"name": "AMT_REQ_CREDIT_BUREAU_HOUR", "dtype": "float64"}, {"name": "AMT_REQ_CREDIT_BUREAU_DAY", "dtype": "float64"}, {"name": "AMT_REQ_CREDIT_BUREAU_WEEK", "dtype": "float64"}, {"name": "AMT_REQ_CREDIT_BUREAU_MON", "dtype": "float64"}, {"name": "AMT_REQ_CREDIT_BUREAU_QRT", "dtype": "float64"}, {"name": "AMT_REQ_CREDIT_BUREAU_YEAR", "dtype": "float64"}], "splits": [{"name": "real", "num_bytes": 9680000, "num_examples": 10000}, {"name": "synth", "num_bytes": 9680000, "num_examples": 10000}], "download_size": 1688004, "dataset_size": 19360000}}
|
2023-05-26T01:28:36+00:00
|
|
bf0205f59314df308c1842e83a6b6dddf18ce049
|
# Dataset Card for "jawiki-20220404-c400-large-with-bpr-embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
llm-book/jawiki-20220404-c400-large-with-bpr-embeddings
|
[
"region:us"
] |
2023-05-26T00:10:24+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "embeddings", "sequence": "uint8"}], "splits": [{"name": "train", "num_bytes": 3400004237, "num_examples": 4288198}], "download_size": 2126849377, "dataset_size": 3400004237}}
|
2023-05-26T00:14:06+00:00
|
d6eacf0612df05d2ea32fcabf159739f833f5b9c
|
# Dataset Card for RefGPT-Fact
## Dataset Description
- **Homepage:**
- **Repository:** [https://github.com/ziliwangnlp/RefGPT](https://github.com/ziliwangnlp/RefGPT)
- **Paper:** [https://arxiv.org/abs/2305.14994](https://arxiv.org/abs/2305.14994)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
<p align="center">
<a href="https://arxiv.org/abs/2305.14994"><b>[Paper] RefGPT</b></a> |
<a href="https://github.com/ziliwangnlp/RefGPT"><b>[Github] RefGPT</b></a>
</p>
RefGPT-Fact is a datasets containing 100k multi-turn dialogues about factual knowledge with 50k English and 50k Chinese. The English version uses the English Wikipedia as the reference and the Chinese version uses the frequently-used Chinese online encyclopedia website, Baidu Baike.
### Supported Tasks and Leaderboards
Chatbot instruction finetuning
### Languages
Chinese, English
## 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
Please pay attention that RefGPT Datasets, including RefGPT-Fact and RefGPT-Code, have not undergone manual verification, and as such, their security cannot be strictly guaranteed. Users should be aware that they are responsible for the results generated using this data.
### Discussion of Biases
As the datasets RefGPT-Fact and RefGPT-Code are collected by using the references like Wikipedia and Github repositories, it can not be avoided that the reference itself has factual errors, typos, or bugs and malicious code if it is from Github repositories. The datasets may also reflect the biases of the selected references and GPT-3.5/GPT-4 model
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@misc{yang2023refgpt,
title={RefGPT: Reference -> Truthful & Customized Dialogues Generation by GPTs and for GPTs},
author={Dongjie Yang and Ruifeng Yuan and YuanTao Fan and YiFei Yang and Zili Wang and Shusen Wang and Hai Zhao},
year={2023},
eprint={2305.14994},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
[More Information Needed]
|
Mutonix/RefGPT-Fact
|
[
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:zh",
"language:en",
"license:apache-2.0",
"arxiv:2305.14994",
"region:us"
] |
2023-05-26T00:37:53+00:00
|
{"language": ["zh", "en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["conversational"], "dataset_info": {"features": [{"name": "dialogue", "dtype": "string"}, {"name": "reference", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "zh", "num_bytes": 180760081, "num_examples": 50000}, {"name": "en", "num_bytes": 464054853, "num_examples": 50000}], "download_size": 260969665, "dataset_size": 644814934}, "arxiv": "https://arxiv.org/abs/2305.14994"}
|
2023-05-30T12:33:07+00:00
|
c539ec2c7ced5311752d4cd702b3aff0528e80b0
|
# Dataset Card for "home-credit-example-raw"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlh/home-credit-example-raw
|
[
"region:us"
] |
2023-05-26T01:29:10+00:00
|
{"dataset_info": {"features": [{"name": "SK_ID_CURR", "dtype": "int64"}, {"name": "TARGET", "dtype": "int64"}, {"name": "NAME_CONTRACT_TYPE", "dtype": "string"}, {"name": "CODE_GENDER", "dtype": "string"}, {"name": "FLAG_OWN_CAR", "dtype": "string"}, {"name": "FLAG_OWN_REALTY", "dtype": "string"}, {"name": "CNT_CHILDREN", "dtype": "int64"}, {"name": "AMT_INCOME_TOTAL", "dtype": "float64"}, {"name": "AMT_CREDIT", "dtype": "float64"}, {"name": "AMT_ANNUITY", "dtype": "float64"}, {"name": "AMT_GOODS_PRICE", "dtype": "float64"}, {"name": "NAME_TYPE_SUITE", "dtype": "string"}, {"name": "NAME_INCOME_TYPE", "dtype": "string"}, {"name": "NAME_EDUCATION_TYPE", "dtype": "string"}, {"name": "NAME_FAMILY_STATUS", "dtype": "string"}, {"name": "NAME_HOUSING_TYPE", "dtype": "string"}, {"name": "REGION_POPULATION_RELATIVE", "dtype": "float64"}, {"name": "DAYS_BIRTH", "dtype": "int64"}, {"name": "DAYS_EMPLOYED", "dtype": "int64"}, {"name": "DAYS_REGISTRATION", "dtype": "float64"}, {"name": "DAYS_ID_PUBLISH", "dtype": "int64"}, {"name": "OWN_CAR_AGE", "dtype": "float64"}, {"name": "FLAG_MOBIL", "dtype": "int64"}, {"name": "FLAG_EMP_PHONE", "dtype": "int64"}, {"name": "FLAG_WORK_PHONE", "dtype": "int64"}, {"name": "FLAG_CONT_MOBILE", "dtype": "int64"}, {"name": "FLAG_PHONE", "dtype": "int64"}, {"name": "FLAG_EMAIL", "dtype": "int64"}, {"name": "OCCUPATION_TYPE", "dtype": "string"}, {"name": "CNT_FAM_MEMBERS", "dtype": "float64"}, {"name": "REGION_RATING_CLIENT", "dtype": "int64"}, {"name": "REGION_RATING_CLIENT_W_CITY", "dtype": "int64"}, {"name": "WEEKDAY_APPR_PROCESS_START", "dtype": "string"}, {"name": "HOUR_APPR_PROCESS_START", "dtype": "int64"}, {"name": "REG_REGION_NOT_LIVE_REGION", "dtype": "int64"}, {"name": "REG_REGION_NOT_WORK_REGION", "dtype": "int64"}, {"name": "LIVE_REGION_NOT_WORK_REGION", "dtype": "int64"}, {"name": "REG_CITY_NOT_LIVE_CITY", "dtype": "int64"}, {"name": "REG_CITY_NOT_WORK_CITY", "dtype": "int64"}, {"name": "LIVE_CITY_NOT_WORK_CITY", "dtype": "int64"}, {"name": "ORGANIZATION_TYPE", "dtype": "string"}, {"name": "EXT_SOURCE_1", "dtype": "float64"}, {"name": "EXT_SOURCE_2", "dtype": "float64"}, {"name": "EXT_SOURCE_3", "dtype": "float64"}, {"name": "APARTMENTS_AVG", "dtype": "float64"}, {"name": "BASEMENTAREA_AVG", "dtype": "float64"}, {"name": "YEARS_BEGINEXPLUATATION_AVG", "dtype": "float64"}, {"name": "YEARS_BUILD_AVG", "dtype": "float64"}, {"name": "COMMONAREA_AVG", "dtype": "float64"}, {"name": "ELEVATORS_AVG", "dtype": "float64"}, {"name": "ENTRANCES_AVG", "dtype": "float64"}, {"name": "FLOORSMAX_AVG", "dtype": "float64"}, {"name": "FLOORSMIN_AVG", "dtype": "float64"}, {"name": "LANDAREA_AVG", "dtype": "float64"}, {"name": "LIVINGAPARTMENTS_AVG", "dtype": "float64"}, {"name": "LIVINGAREA_AVG", "dtype": "float64"}, {"name": "NONLIVINGAPARTMENTS_AVG", "dtype": "float64"}, {"name": "NONLIVINGAREA_AVG", "dtype": "float64"}, {"name": "APARTMENTS_MODE", "dtype": "float64"}, {"name": "BASEMENTAREA_MODE", "dtype": "float64"}, {"name": "YEARS_BEGINEXPLUATATION_MODE", "dtype": "float64"}, {"name": "YEARS_BUILD_MODE", "dtype": "float64"}, {"name": "COMMONAREA_MODE", "dtype": "float64"}, {"name": "ELEVATORS_MODE", "dtype": "float64"}, {"name": "ENTRANCES_MODE", "dtype": "float64"}, {"name": "FLOORSMAX_MODE", "dtype": "float64"}, {"name": "FLOORSMIN_MODE", "dtype": "float64"}, {"name": "LANDAREA_MODE", "dtype": "float64"}, {"name": "LIVINGAPARTMENTS_MODE", "dtype": "float64"}, {"name": "LIVINGAREA_MODE", "dtype": "float64"}, {"name": "NONLIVINGAPARTMENTS_MODE", "dtype": "float64"}, {"name": "NONLIVINGAREA_MODE", "dtype": "float64"}, {"name": "APARTMENTS_MEDI", "dtype": "float64"}, {"name": "BASEMENTAREA_MEDI", "dtype": "float64"}, {"name": "YEARS_BEGINEXPLUATATION_MEDI", "dtype": "float64"}, {"name": "YEARS_BUILD_MEDI", "dtype": "float64"}, {"name": "COMMONAREA_MEDI", "dtype": "float64"}, {"name": "ELEVATORS_MEDI", "dtype": "float64"}, {"name": "ENTRANCES_MEDI", "dtype": "float64"}, {"name": "FLOORSMAX_MEDI", "dtype": "float64"}, {"name": "FLOORSMIN_MEDI", "dtype": "float64"}, {"name": "LANDAREA_MEDI", "dtype": "float64"}, {"name": "LIVINGAPARTMENTS_MEDI", "dtype": "float64"}, {"name": "LIVINGAREA_MEDI", "dtype": "float64"}, {"name": "NONLIVINGAPARTMENTS_MEDI", "dtype": "float64"}, {"name": "NONLIVINGAREA_MEDI", "dtype": "float64"}, {"name": "FONDKAPREMONT_MODE", "dtype": "string"}, {"name": "HOUSETYPE_MODE", "dtype": "string"}, {"name": "TOTALAREA_MODE", "dtype": "float64"}, {"name": "WALLSMATERIAL_MODE", "dtype": "string"}, {"name": "EMERGENCYSTATE_MODE", "dtype": "string"}, {"name": "OBS_30_CNT_SOCIAL_CIRCLE", "dtype": "float64"}, {"name": "DEF_30_CNT_SOCIAL_CIRCLE", "dtype": "float64"}, {"name": "OBS_60_CNT_SOCIAL_CIRCLE", "dtype": "float64"}, {"name": "DEF_60_CNT_SOCIAL_CIRCLE", "dtype": "float64"}, {"name": "DAYS_LAST_PHONE_CHANGE", "dtype": "float64"}, {"name": "FLAG_DOCUMENT_2", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_3", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_4", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_5", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_6", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_7", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_8", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_9", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_10", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_11", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_12", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_13", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_14", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_15", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_16", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_17", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_18", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_19", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_20", "dtype": "int64"}, {"name": "FLAG_DOCUMENT_21", "dtype": "int64"}, {"name": "AMT_REQ_CREDIT_BUREAU_HOUR", "dtype": "float64"}, {"name": "AMT_REQ_CREDIT_BUREAU_DAY", "dtype": "float64"}, {"name": "AMT_REQ_CREDIT_BUREAU_WEEK", "dtype": "float64"}, {"name": "AMT_REQ_CREDIT_BUREAU_MON", "dtype": "float64"}, {"name": "AMT_REQ_CREDIT_BUREAU_QRT", "dtype": "float64"}, {"name": "AMT_REQ_CREDIT_BUREAU_YEAR", "dtype": "float64"}], "splits": [{"name": "raw", "num_bytes": 10681044, "num_examples": 10000}], "download_size": 1985577, "dataset_size": 10681044}}
|
2023-05-26T01:29:12+00:00
|
7422e275d6ef3fdfe8d8ab7c78521f9d26304319
|
mbshaikh/spectral-centroids
|
[
"size_categories:1K<n<10K",
"license:mit",
"region:us"
] |
2023-05-26T01:50:58+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"]}
|
2023-05-26T02:33:19+00:00
|
|
e6fcdbb4431a61f83617b8e5b1a81f94aebddd0b
|
vovovo/frp
|
[
"license:unknown",
"region:us"
] |
2023-05-26T02:09:52+00:00
|
{"license": "unknown"}
|
2023-05-26T02:10:31+00:00
|
|
ab8240e705641a7b569d886b062e9705476270c7
|
Text data from Carigold forum replies based on General Chat section (https://carigold.com/forum/forums/general-chat.174/)
Language = Malay + English mixed
|
aazwan/carigold_general_chat_text_dataset
|
[
"language:ms",
"language:en",
"region:us"
] |
2023-05-26T02:17:00+00:00
|
{"language": ["ms", "en"], "pretty_name": "malay"}
|
2023-05-26T02:25:01+00:00
|
8bd4060a634f80ef35edc5fcebe90b2a31cd35db
|
# Dataset Card for "0141c882"
[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/0141c882
|
[
"region:us"
] |
2023-05-26T03:07:53+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 172, "num_examples": 10}], "download_size": 1314, "dataset_size": 172}}
|
2023-05-26T03:07:54+00:00
|
974b8d181d48f919ce7f6253a8be458bbd192848
|
This is the Guanaco Extended dataset derived from [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1).
Guanaco only uses the first (highest rank; rank 0) response from the assistant at each reply level as their dataset.
|
openaccess-ai-collective/oasst1-guanaco-extended
|
[
"region:us"
] |
2023-05-26T04:40:11+00:00
|
{}
|
2023-05-26T11:19:26+00:00
|
5a4ee8074002f23485feb76e5d2be2071bfc85ea
|
Vikas-nnresearch/Knob-classification
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-26T04:54:22+00:00
|
{"license": "apache-2.0", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Knob", "1": "No knob"}}}}], "splits": [{"name": "train", "num_bytes": 24695896.0, "num_examples": 149}], "download_size": 24698150, "dataset_size": 24695896.0}}
|
2023-05-26T04:55:28+00:00
|
|
1bb76cc7ce85dac05d83ba65f6173d445ee5eb44
|
# Dataset Card for "diffusion_db_dedup_from50k_train"
[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
|
[
"region:us"
] |
2023-05-26T05:01:31+00:00
|
{"dataset_info": {"features": [{"name": "image", "struct": [{"name": "bytes", "dtype": "null"}, {"name": "path", "dtype": "string"}]}, {"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"}], "splits": [{"name": "train", "num_bytes": 15396393.6, "num_examples": 34572}], "download_size": 8289582, "dataset_size": 15396393.6}}
|
2023-05-26T05:01:36+00:00
|
bc926fc53e2fb74a32e51db635aec99cdf586209
|
# Dataset Card for "slurp_dataset_audio_subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yhfang/slurp_dataset_audio_subset
|
[
"region:us"
] |
2023-05-26T05:01:40+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "intent", "dtype": "int64"}, {"name": "slurp_id", "dtype": "int64"}, {"name": "path", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2225205717.948, "num_examples": 47892}, {"name": "validation", "num_bytes": 436384774.91, "num_examples": 8690}, {"name": "test", "num_bytes": 615280290.546, "num_examples": 13078}], "download_size": 3787562112, "dataset_size": 3276870783.404}}
|
2023-05-26T05:08:30+00:00
|
8a8c5fce4655244751a75fc10d17d87f606a7521
|
# Dataset Card for "diffusion_db_dedup_from50k_val"
[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
|
[
"region:us"
] |
2023-05-26T05:01:56+00:00
|
{"dataset_info": {"features": [{"name": "image", "struct": [{"name": "bytes", "dtype": "null"}, {"name": "path", "dtype": "string"}]}, {"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"}], "splits": [{"name": "train", "num_bytes": 3849098.4, "num_examples": 8643}], "download_size": 2075351, "dataset_size": 3849098.4}}
|
2023-05-26T05:01:58+00:00
|
1b71a61eb13cad6d55bd09ad27c7649055c898b7
|
# Dataset Card for "part_1_imda_30000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
averageandyyy/part_1_imda_30000
|
[
"region:us"
] |
2023-05-26T05:11:57+00:00
|
{"dataset_info": {"features": [{"name": "transcript", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "waveform", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 20108404239.03722, "num_examples": 30000}], "download_size": 4854168355, "dataset_size": 20108404239.03722}}
|
2023-05-26T05:27:53+00:00
|
03d3122c530b1e47195c08a3d851eeadddad9689
|
# Dataset Card for "KoAlpaca-v1.1a"
## Project Repo
- Github Repo: [Beomi/KoAlpaca](https://github.com/Beomi/KoAlpaca)
## How to use
```python
>>> from datasets import load_dataset
>>> ds = load_dataset("beomi/KoAlpaca-v1.1a", split="train")
>>> ds
Dataset({
features: ['instruction', 'input', 'output'],
num_rows: 21155
})
```
```python
>>> ds[0]
{'instruction': '양파는 어떤 식물 부위인가요? 그리고 고구마는 뿌리인가요?',
'output': '양파는 잎이 아닌 식물의 줄기 부분입니다. 고구마는 식물의 뿌리 부분입니다. \n\n식물의 부위의 구분에 대해 궁금해하는 분이라면 분명 이 질문에 대한 답을 찾고 있을 것입니다. 양파는 잎이 아닌 줄기 부분입니다. 고구마는 다른 질문과 답변에서 언급된 것과 같이 뿌리 부분입니다. 따라서, 양파는 식물의 줄기 부분이 되고, 고구마는 식물의 뿌리 부분입니다.\n\n 덧붙이는 답변: 고구마 줄기도 볶아먹을 수 있나요? \n\n고구마 줄기도 식용으로 볶아먹을 수 있습니다. 하지만 줄기 뿐만 아니라, 잎, 씨, 뿌리까지 모든 부위가 식용으로 활용되기도 합니다. 다만, 한국에서는 일반적으로 뿌리 부분인 고구마를 주로 먹습니다.',
'url': 'https://kin.naver.com/qna/detail.naver?d1id=11&dirId=1116&docId=55320268'}
```
|
beomi/KoAlpaca-v1.1a
|
[
"task_categories:text-generation",
"language:ko",
"KoAlpaca",
"region:us"
] |
2023-05-26T05:27:44+00:00
|
{"language": ["ko"], "task_categories": ["text-generation"], "pretty_name": "KoAlpaca-v1.1a", "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 23371027, "num_examples": 21155}], "download_size": 12856014, "dataset_size": 23371027}, "tags": ["KoAlpaca"]}
|
2023-05-26T05:32:02+00:00
|
d1ecf87a828704a7aba867e483400ba3754f07ab
|
# Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Ayon128/github-issues
|
[
"region:us"
] |
2023-05-26T05:56:15+00:00
|
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "labels", "list": [{"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "default", "dtype": "bool"}, {"name": "description", "dtype": "string"}]}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "assignees", "list": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "milestone", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "creator", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "open_issues", "dtype": "int64"}, {"name": "closed_issues", "dtype": "int64"}, {"name": "state", "dtype": "string"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "due_on", "dtype": "null"}, {"name": "closed_at", "dtype": "null"}]}, {"name": "comments", "sequence": "string"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "closed_at", "dtype": "timestamp[s]"}, {"name": "author_association", "dtype": "string"}, {"name": "active_lock_reason", "dtype": "null"}, {"name": "draft", "dtype": "bool"}, {"name": "pull_request", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "diff_url", "dtype": "string"}, {"name": "patch_url", "dtype": "string"}, {"name": "merged_at", "dtype": "timestamp[s]"}]}, {"name": "body", "dtype": "string"}, {"name": "reactions", "struct": [{"name": "url", "dtype": "string"}, {"name": "total_count", "dtype": "int64"}, {"name": "+1", "dtype": "int64"}, {"name": "-1", "dtype": "int64"}, {"name": "laugh", "dtype": "int64"}, {"name": "hooray", "dtype": "int64"}, {"name": "confused", "dtype": "int64"}, {"name": "heart", "dtype": "int64"}, {"name": "rocket", "dtype": "int64"}, {"name": "eyes", "dtype": "int64"}]}, {"name": "timeline_url", "dtype": "string"}, {"name": "performed_via_github_app", "dtype": "null"}, {"name": "state_reason", "dtype": "string"}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 14368648, "num_examples": 2500}], "download_size": 4077061, "dataset_size": 14368648}}
|
2023-05-26T05:56:17+00:00
|
3322fcb9ea468d9c63607023f986e75cd9a71b83
|
# midjourney-v5-202304-clean
## 简介 Brief Introduction
非官方的，爬取自midjourney v5的2023年4月的数据，一共1701420条。
Unofficial, crawled from midjourney v5 for April 2023, 1,701,420 pairs in total.
## 数据集信息 Dataset Information
原始项目地址：https://huggingface.co/datasets/tarungupta83/MidJourney_v5_Prompt_dataset
我做了一些清洗，清理出了两个文件：
- ori_prompts_df.parquet （1,255,812对，midjourney的四格图）
- upscaled_prompts_df.parquet （445,608对，使用了高清指令的图，这意味着这个图更受欢迎。）
Original project address: https://huggingface.co/datasets/tarungupta83/MidJourney_v5_Prompt_dataset
I did some cleaning and cleaned out two files:
- ori_prompts_df.parquet (1,255,812 pairs, midjourney's four-frame diagrams)
- upscaled_prompts_df.parquet (445,608 pairs, graphs that use the Upscale command, which means this one is more popular.)
|
wanng/midjourney-v5-202304-clean
|
[
"task_categories:text-to-image",
"task_categories:image-to-text",
"language:en",
"license:apache-2.0",
"midjourney",
"region:us"
] |
2023-05-26T05:58:05+00:00
|
{"language": ["en"], "license": "apache-2.0", "task_categories": ["text-to-image", "image-to-text"], "tags": ["midjourney"]}
|
2023-05-28T04:56:11+00:00
|
5d56e11c0b55175b331e70674c47f7297fe95dad
|
ahalamora/dingdang
|
[
"license:unknown",
"region:us"
] |
2023-05-26T05:58:52+00:00
|
{"license": "unknown"}
|
2023-05-26T06:20:05+00:00
|
|
201e436906aedb02e42979d324b0175dcf37ba56
|
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
xjLee/test
|
[
"region:us"
] |
2023-05-26T06:21:56+00:00
|
{"dataset_info": {"features": [{"name": "command", "dtype": "string"}, {"name": "intent", "dtype": "string"}, {"name": "action", "dtype": "string"}, {"name": "slot", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 662, "num_examples": 7}], "download_size": 2791, "dataset_size": 662}}
|
2023-05-26T08:33:55+00:00
|
1945f5274a057f3d49ee0c9b4de99ff5c802406d
|
# NOTE:
Recently Discord has added link expiry and tracking for their CDN content, however, this is for CDN attachments outside of Discord, now due to the nature of how this was scraped (being directly from the API) We're uncertain as to whether URL decay will start to become a problem. We have already created versions of the dataset in splits to combat this, we are well aware that this may not be an option for some and we apologise.
|
ShoukanLabs/OpenNiji-Dataset
|
[
"task_categories:text-to-image",
"size_categories:100K<n<1M",
"language:en",
"language:ja",
"language:ko",
"license:cc-by-nc-4.0",
"anime",
"dataset",
"Nijijourney",
"Midjourney",
"discord",
"region:us"
] |
2023-05-26T06:28:21+00:00
|
{"language": ["en", "ja", "ko"], "license": "cc-by-nc-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-to-image"], "tags": ["anime", "dataset", "Nijijourney", "Midjourney", "discord"]}
|
2023-09-29T01:59:14+00:00
|
d5077053a7b62f6c46382871940f795738c8d985
|
michaelszx/StepGame
|
[
"license:mit",
"region:us"
] |
2023-05-26T06:59:51+00:00
|
{"license": "mit"}
|
2023-05-26T07:23:10+00:00
|
|
48e0ad7d4710c690ea047020e8c478770539a4a2
|
# Dataset Card for "ru_texts_normalized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bond005/ru_texts_normalized
|
[
"region:us"
] |
2023-05-26T07:07:24+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 3079860, "num_examples": 23444}, {"name": "train", "num_bytes": 17363309069, "num_examples": 48332750}], "download_size": 5316966685, "dataset_size": 17366388929}}
|
2023-05-26T08:49:48+00:00
|
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