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Qika/xray_stage3 | Qika | 2025-05-05T07:03:50Z | 0 | 0 | [
"size_categories:1K<n<10K",
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"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-05T06:58:05Z | null | ---
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path: data/train-*
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---
|
mlfoundations-dev/openthoughts2_math_1k | mlfoundations-dev | 2025-05-05T04:00:31Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T04:00:29Z | null | ---
dataset_info:
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mih12345/italian_clean_5th_may_v1 | mih12345 | 2025-05-05T02:42:42Z | 0 | 0 | [
"license:mit",
"region:us"
] | [] | 2025-05-05T02:15:22Z | null | ---
license: mit
configs:
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path: data/train-*
dataset_info:
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|
javasoup/koch_test_2025_1 | javasoup | 2025-05-05T01:45:06Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"region:us",
"LeRobot",
"tutorial"
] | [
"robotics"
] | 2025-05-05T01:44:56Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
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```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
dongseon/so100_0505 | dongseon | 2025-05-05T01:34:47Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"region:us",
"LeRobot",
"so100",
"tutorial"
] | [
"robotics"
] | 2025-05-05T01:34:32Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 10,
"total_frames": 5632,
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"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
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}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
dushj98/waikato_aerial_imagery_2017_7cls | dushj98 | 2025-05-05T00:42:47Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T00:42:11Z | null | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': deciduous_hardwood
'1': harvested_forest
'2': high_producing_grassland
'3': indigenous_forest
'4': lake_pond
'5': shortrotation_cropland
'6': urban_build_up
splits:
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num_bytes: 385213214.82
num_examples: 4662
- name: validation
num_bytes: 192453335.532
num_examples: 2338
download_size: 577723078
dataset_size: 577666550.352
configs:
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data_files:
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path: data/train-*
- split: validation
path: data/validation-*
---
|
rainbowbridge/x_dataset_15977 | rainbowbridge | 2025-05-05T00:39:48Z | 1,093 | 0 | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_ids:sentiment-analysis",
"task_ids:topic-classification",
"task_ids:named-entity-recognition",
"task_ids:language-modeling",
"task_ids:text-scoring",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"task_ids:extractive-qa",
"task_ids:news-articles-summarization",
"multilinguality:multilingual",
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"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-01-29T02:44:14Z | null | ---
license: mit
multilinguality:
- multilingual
source_datasets:
- original
task_categories:
- text-classification
- token-classification
- question-answering
- summarization
- text-generation
task_ids:
- sentiment-analysis
- topic-classification
- named-entity-recognition
- language-modeling
- text-scoring
- multi-class-classification
- multi-label-classification
- extractive-qa
- news-articles-summarization
---
# Bittensor Subnet 13 X (Twitter) Dataset
<center>
<img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer">
</center>
<center>
<img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer">
</center>
## Dataset Description
- **Repository:** rainbowbridge/x_dataset_15977
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5DfHeJeLJRLeMNMaatPDfKYJDzXGCN7tDcxPrGRzeNgfCucD
### Dataset Summary
This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks.
For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe).
### Supported Tasks
The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs.
For example:
- Sentiment Analysis
- Trend Detection
- Content Analysis
- User Behavior Modeling
### Languages
Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation.
## Dataset Structure
### Data Instances
Each instance represents a single tweet with the following fields:
### Data Fields
- `text` (string): The main content of the tweet.
- `label` (string): Sentiment or topic category of the tweet.
- `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present.
- `datetime` (string): The date when the tweet was posted.
- `username_encoded` (string): An encoded version of the username to maintain user privacy.
- `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present.
### Data Splits
This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp.
## Dataset Creation
### Source Data
Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines.
### Personal and Sensitive Information
All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information.
## Considerations for Using the Data
### Social Impact and Biases
Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population.
### Limitations
- Data quality may vary due to the decentralized nature of collection and preprocessing.
- The dataset may contain noise, spam, or irrelevant content typical of social media platforms.
- Temporal biases may exist due to real-time collection methods.
- The dataset is limited to public tweets and does not include private accounts or direct messages.
- Not all tweets contain hashtags or URLs.
## Additional Information
### Licensing Information
The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use.
### Citation Information
If you use this dataset in your research, please cite it as follows:
```
@misc{rainbowbridge2025datauniversex_dataset_15977,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={rainbowbridge},
year={2025},
url={https://huggingface.co/datasets/rainbowbridge/x_dataset_15977},
}
```
### Contributions
To report issues or contribute to the dataset, please contact the miner or use the Bittensor Subnet 13 governance mechanisms.
## Dataset Statistics
[This section is automatically updated]
- **Total Instances:** 40599220
- **Date Range:** 2025-01-23T00:00:00Z to 2025-02-13T00:00:00Z
- **Last Updated:** 2025-02-18T20:52:28Z
### Data Distribution
- Tweets with hashtags: 48.22%
- Tweets without hashtags: 51.78%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 21021404 | 51.78% |
| 2 | #riyadh | 328678 | 0.81% |
| 3 | #zelena | 271129 | 0.67% |
| 4 | #tiktok | 184793 | 0.46% |
| 5 | #jhope_at_galadespiècesjaunes | 155793 | 0.38% |
| 6 | #bbb25 | 121789 | 0.30% |
| 7 | #ad | 108287 | 0.27% |
| 8 | #bbmzansi | 62585 | 0.15% |
| 9 | #grandefratello | 58608 | 0.14% |
| 10 | #pr | 56638 | 0.14% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-01-29T02:45:06Z | 2152001 | 2152001 |
| 2025-02-01T14:47:40Z | 8070361 | 10222362 |
| 2025-02-05T02:50:45Z | 9239941 | 19462303 |
| 2025-02-08T14:54:26Z | 10767494 | 30229797 |
| 2025-02-12T03:00:46Z | 8737385 | 38967182 |
| 2025-02-18T05:51:19Z | 808942 | 39776124 |
| 2025-02-18T20:52:28Z | 823096 | 40599220 |
|
HungVu2003/opt-350m_beta_1.0_alpha_0.8_num-company_3_dataset_2_for_gen_4 | HungVu2003 | 2025-05-05T00:23:15Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T00:23:13Z | null | ---
dataset_info:
features:
- name: question
dtype: string
splits:
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num_bytes: 4241647
num_examples: 12498
download_size: 1773113
dataset_size: 4241647
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
gokulp01/ae598_main | gokulp01 | 2025-05-04T23:14:06Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"region:us",
"LeRobot",
"so100",
"tutorial"
] | [
"robotics"
] | 2025-05-04T23:02:40Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
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"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
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"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
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```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
HungVu2003/opt-350m_beta_1.0_alpha_0.4_num-company_2_dataset_1_for_gen_1_v2 | HungVu2003 | 2025-05-04T22:36:10Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T22:36:08Z | null | ---
dataset_info:
features:
- name: question
dtype: string
splits:
- name: train
num_bytes: 6163003
num_examples: 15000
download_size: 3170274
dataset_size: 6163003
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Odog16/eval_act_lekiwi_test_4.2 | Odog16 | 2025-05-04T22:28:14Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"region:us",
"LeRobot",
"tutorial"
] | [
"robotics"
] | 2025-05-04T22:27:17Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
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"shoulder_pan",
"shoulder_lift",
"elbow_flex",
"wrist_flex",
"wrist_roll",
"gripper",
"x_mm",
"y_mm",
"theta"
]
},
"observation.images.front": {
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480,
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"height",
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}
},
"observation.images.wrist": {
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"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"timestamp": {
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],
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},
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1
],
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}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
osama24sy/llama3.1-8b-it-10k-qwen-singleturn-onesolution-r64-MODEL-countdown-results | osama24sy | 2025-05-04T22:02:39Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T22:02:38Z | null | ---
dataset_info:
features:
- name: index
dtype: int64
- name: numbers
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sequence: string
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splits:
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num_examples: 150
download_size: 82984
dataset_size: 204506
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
SayantanJoker/Shrutilipi_Hindi_resampled_44100_merged_13_quality_metadata_description | SayantanJoker | 2025-05-04T20:45:51Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T20:13:26Z | null | ---
dataset_info:
features:
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configs:
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data_files:
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path: data/train-*
---
|
SeaLLMs/TrueFalse-Statements-multilingual | SeaLLMs | 2025-05-04T20:23:41Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T20:23:39Z | null | ---
dataset_info:
features:
- name: statements
dtype: string
- name: true/false
dtype: bool
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dtype: string
- name: language
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splits:
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download_size: 1369810
dataset_size: 4727153
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
|
BasedLukas/so101_test_3 | BasedLukas | 2025-05-04T19:11:08Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"region:us",
"LeRobot",
"so101",
"tutorial"
] | [
"robotics"
] | 2025-05-04T19:10:48Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so101
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so101",
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"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
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],
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"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
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],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.laptop": {
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480,
640,
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],
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"height",
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],
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"video.channels": 3,
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}
},
"observation.images.phone": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
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}
},
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}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
jumava/adv-ele | jumava | 2025-05-04T18:02:14Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T18:02:12Z | null | ---
dataset_info:
features:
- name: ADV
dtype: string
- name: ELE
dtype: string
splits:
- name: train
num_bytes: 430918.56140350876
num_examples: 1732
- name: test
num_bytes: 107978.43859649122
num_examples: 434
download_size: 296569
dataset_size: 538897.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
rricc22/so100_record50 | rricc22 | 2025-05-04T17:55:16Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"region:us",
"LeRobot",
"so100",
"tutorial"
] | [
"robotics"
] | 2025-05-04T17:54:43Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 50,
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]
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],
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"main_shoulder_pan",
"main_shoulder_lift",
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"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.phone": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 30.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"timestamp": {
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},
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],
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}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
mteb/wit | mteb | 2025-05-04T16:12:14Z | 31 | 0 | [
"task_categories:visual-document-retrieval",
"task_categories:image-to-text",
"task_categories:text-to-image",
"annotations_creators:derived",
"multilinguality:multilingual",
"language:ara",
"language:bul",
"language:dan",
"language:ell",
"language:eng",
"language:est",
"language:ind",
"language:jpn",
"language:kor",
"language:tur",
"language:vie",
"license:cc-by-sa-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text",
"image"
] | [
"visual-document-retrieval",
"image-to-text",
"text-to-image"
] | 2024-08-19T18:37:23Z | null | ---
annotations_creators:
- derived
language:
- ara
- bul
- dan
- ell
- eng
- est
- ind
- jpn
- kor
- tur
- vie
license: cc-by-sa-4.0
multilinguality: multilingual
task_categories:
- visual-document-retrieval
- image-to-text
- text-to-image
task_ids: []
dataset_info:
features:
- name: image_id
dtype: string
- name: image
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num_examples: 869
- name: en
num_bytes: 25444712.0
num_examples: 685
download_size: 320979492
dataset_size: 321174654.0
configs:
- config_name: default
data_files:
- split: ar
path: data/ar-*
- split: bg
path: data/bg-*
- split: da
path: data/da-*
- split: el
path: data/el-*
- split: et
path: data/et-*
- split: id
path: data/id-*
- split: ja
path: data/ja-*
- split: ko
path: data/ko-*
- split: tr
path: data/tr-*
- split: vi
path: data/vi-*
- split: en
path: data/en-*
tags:
- mteb
- text
- image
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">WITT2IRetrieval</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>
Retrieve images based on multilingual descriptions.
| | |
|---------------|---------------------------------------------|
| Task category | t2i |
| Domains | Encyclopaedic, Written |
| Reference | https://proceedings.mlr.press/v162/bugliarello22a/bugliarello22a.pdf |
## How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
```python
import mteb
task = mteb.get_tasks(["WITT2IRetrieval"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```
<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
## Citation
If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).
```bibtex
@inproceedings{bugliarello2022iglue,
author = {Bugliarello, Emanuele and Liu, Fangyu and Pfeiffer, Jonas and Reddy, Siva and Elliott, Desmond and Ponti, Edoardo Maria and Vuli{\'c}, Ivan},
booktitle = {International Conference on Machine Learning},
organization = {PMLR},
pages = {2370--2392},
title = {IGLUE: A benchmark for transfer learning across modalities, tasks, and languages},
year = {2022},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
```
# Dataset Statistics
<details>
<summary> Dataset Statistics</summary>
The following code contains the descriptive statistics from the task. These can also be obtained using:
```python
import mteb
task = mteb.get_task("WITT2IRetrieval")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"number_of_characters": 506601,
"num_samples": 18137,
"num_queries": 9584,
"num_documents": 8553,
"min_document_length": 0,
"average_document_length": 0,
"max_document_length": 0,
"unique_documents": 0,
"num_document_images": 8553,
"min_query_length": 9,
"average_query_length": 52.85903589315526,
"max_query_length": 779,
"unique_queries": 9076,
"num_query_images": 0,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 8553
}
}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)* |
mteb/Touche2020-PL | mteb | 2025-05-04T16:11:31Z | 18 | 0 | [
"task_categories:text-retrieval",
"task_ids:multiple-choice-qa",
"annotations_creators:derived",
"multilinguality:translated",
"source_datasets:mteb/touche2020",
"language:pol",
"license:unknown",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2305.19840",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text"
] | [
"text-retrieval"
] | 2025-02-07T16:35:00Z | null | ---
annotations_creators:
- derived
language:
- pol
license: unknown
multilinguality: translated
source_datasets:
- mteb/touche2020
task_categories:
- text-retrieval
task_ids:
- multiple-choice-qa
dataset_info:
- config_name: corpus
features:
- name: _id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 744260994
num_examples: 382545
download_size: 417361577
dataset_size: 744260994
- config_name: default
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 125677
num_examples: 2214
download_size: 41295
dataset_size: 125677
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 3260
num_examples: 49
download_size: 3805
dataset_size: 3260
configs:
- config_name: corpus
data_files:
- split: test
path: corpus/test-*
- config_name: default
data_files:
- split: test
path: data/test-*
- config_name: queries
data_files:
- split: test
path: queries/test-*
tags:
- mteb
- text
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">Touche2020-PL</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>
Touché Task 1: Argument Retrieval for Controversial Questions
| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Academic |
| Reference | https://webis.de/events/touche-20/shared-task-1.html |
## How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
```python
import mteb
task = mteb.get_tasks(["Touche2020-PL"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```
<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
## Citation
If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).
```bibtex
@misc{wojtasik2024beirpl,
archiveprefix = {arXiv},
author = {Konrad Wojtasik and Vadim Shishkin and Kacper Wołowiec and Arkadiusz Janz and Maciej Piasecki},
eprint = {2305.19840},
primaryclass = {cs.IR},
title = {BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language},
year = {2024},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
```
# Dataset Statistics
<details>
<summary> Dataset Statistics</summary>
The following code contains the descriptive statistics from the task. These can also be obtained using:
```python
import mteb
task = mteb.get_task("Touche2020-PL")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"num_samples": 382594,
"number_of_characters": 680238138,
"num_documents": 382545,
"min_document_length": 3,
"average_document_length": 1778.1842371485707,
"max_document_length": 106110,
"unique_documents": 382545,
"num_queries": 49,
"min_query_length": 18,
"average_query_length": 54.06122448979592,
"max_query_length": 96,
"unique_queries": 49,
"none_queries": 0,
"num_relevant_docs": 2214,
"min_relevant_docs_per_query": 40,
"average_relevant_docs_per_query": 19.020408163265305,
"max_relevant_docs_per_query": 52,
"unique_relevant_docs": 2099,
"num_instructions": null,
"min_instruction_length": null,
"average_instruction_length": null,
"max_instruction_length": null,
"unique_instructions": null,
"num_top_ranked": null,
"min_top_ranked_per_query": null,
"average_top_ranked_per_query": null,
"max_top_ranked_per_query": null
}
}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)* |
mteb/NQ_test_top_250_only_w_correct-v2 | mteb | 2025-05-04T16:10:29Z | 174 | 0 | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"source_datasets:mteb/nq",
"language:eng",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text"
] | [
"text-retrieval"
] | 2024-09-28T05:31:19Z | null | ---
language:
- eng
multilinguality: monolingual
source_datasets:
- mteb/nq
task_categories:
- text-retrieval
task_ids: []
dataset_info:
- config_name: corpus
features:
- name: _id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 102405421.72767939
num_examples: 198779
download_size: 76290132
dataset_size: 102405421.72767939
- config_name: default
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 38495.78647940967
num_examples: 1213
download_size: 16497
dataset_size: 38495.78647940967
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 63867.90266512167
num_examples: 1000
download_size: 40924
dataset_size: 63867.90266512167
configs:
- config_name: corpus
data_files:
- split: test
path: corpus/test-*
- config_name: default
data_files:
- split: test
path: data/test-*
- config_name: queries
data_files:
- split: test
path: queries/test-*
tags:
- mteb
- text
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">NQHardNegatives</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>
NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval. The hard negative version has been created by pooling the 250 top documents per query from BM25, e5-multilingual-large and e5-mistral-instruct.
| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | None |
| Reference | https://ai.google.com/research/NaturalQuestions/ |
## How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
```python
import mteb
task = mteb.get_tasks(["NQHardNegatives"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```
<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
## Citation
If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).
```bibtex
@article{47761,
author = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh
and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee
and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le
and Slav Petrov},
journal = {Transactions of the Association of Computational
Linguistics},
title = {Natural Questions: a Benchmark for Question Answering Research},
year = {2019},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
```
# Dataset Statistics
<details>
<summary> Dataset Statistics</summary>
The following code contains the descriptive statistics from the task. These can also be obtained using:
```python
import mteb
task = mteb.get_task("NQHardNegatives")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"num_samples": 199779,
"number_of_characters": 120068721,
"num_documents": 198779,
"min_document_length": 5,
"average_document_length": 603.7903551179953,
"max_document_length": 17008,
"unique_documents": 198779,
"num_queries": 1000,
"min_query_length": 29,
"average_query_length": 47.878,
"max_query_length": 94,
"unique_queries": 1000,
"none_queries": 0,
"num_relevant_docs": 1213,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.213,
"max_relevant_docs_per_query": 4,
"unique_relevant_docs": 1213,
"num_instructions": null,
"min_instruction_length": null,
"average_instruction_length": null,
"max_instruction_length": null,
"unique_instructions": null,
"num_top_ranked": null,
"min_top_ranked_per_query": null,
"average_top_ranked_per_query": null,
"max_top_ranked_per_query": null
}
}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)* |
mteb/nfcorpus | mteb | 2025-05-04T16:10:27Z | 3,686 | 2 | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:eng",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text"
] | [
"text-retrieval"
] | 2024-03-02T21:17:27Z | null | ---
language:
- eng
multilinguality: monolingual
task_categories:
- text-retrieval
task_ids: []
config_names:
- corpus
tags:
- mteb
- text
dataset_info:
- config_name: default
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 3720942
num_examples: 110575
- name: dev
num_bytes: 383427
num_examples: 11385
- name: test
num_bytes: 415220
num_examples: 12334
- config_name: corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 5856698
num_examples: 3633
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 128355
num_examples: 3237
configs:
- config_name: default
data_files:
- split: train
path: qrels/train.jsonl
- split: dev
path: qrels/dev.jsonl
- split: test
path: qrels/test.jsonl
- config_name: corpus
data_files:
- split: corpus
path: corpus.jsonl
- config_name: queries
data_files:
- split: queries
path: queries.jsonl
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">NFCorpus</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>
NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval
| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Medical, Academic, Written |
| Reference | https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/ |
## How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
```python
import mteb
task = mteb.get_tasks(["NFCorpus"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```
<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
## Citation
If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).
```bibtex
@inproceedings{boteva2016,
author = {Boteva, Vera and Gholipour, Demian and Sokolov, Artem and Riezler, Stefan},
city = {Padova},
country = {Italy},
journal = {Proceedings of the 38th European Conference on Information Retrieval},
journal-abbrev = {ECIR},
title = {A Full-Text Learning to Rank Dataset for Medical Information Retrieval},
url = {http://www.cl.uni-heidelberg.de/~riezler/publications/papers/ECIR2016.pdf},
year = {2016},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
```
# Dataset Statistics
<details>
<summary> Dataset Statistics</summary>
The following code contains the descriptive statistics from the task. These can also be obtained using:
```python
import mteb
task = mteb.get_task("NFCorpus")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"num_samples": 3956,
"number_of_characters": 5786348,
"num_documents": 3633,
"min_document_length": 123,
"average_document_length": 1590.783925130746,
"max_document_length": 10090,
"unique_documents": 3633,
"num_queries": 323,
"min_query_length": 3,
"average_query_length": 21.764705882352942,
"max_query_length": 72,
"unique_queries": 323,
"none_queries": 0,
"num_relevant_docs": 12334,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 38.18575851393189,
"max_relevant_docs_per_query": 475,
"unique_relevant_docs": 3128,
"num_instructions": null,
"min_instruction_length": null,
"average_instruction_length": null,
"max_instruction_length": null,
"unique_instructions": null,
"num_top_ranked": null,
"min_top_ranked_per_query": null,
"average_top_ranked_per_query": null,
"max_top_ranked_per_query": null
}
}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)* |
mteb/IN22-Conv | mteb | 2025-05-04T16:08:53Z | 14 | 0 | [
"task_categories:translation",
"annotations_creators:expert-annotated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:asm",
"language:ben",
"language:brx",
"language:doi",
"language:eng",
"language:gom",
"language:guj",
"language:hin",
"language:kan",
"language:kas",
"language:mai",
"language:mal",
"language:mar",
"language:mni",
"language:npi",
"language:ory",
"language:pan",
"language:san",
"language:sat",
"language:snd",
"language:tam",
"language:tel",
"language:urd",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text"
] | [
"translation"
] | 2024-05-14T21:57:25Z | null | ---
annotations_creators:
- expert-annotated
language_creators:
- expert-generated
language:
- asm
- ben
- brx
- doi
- eng
- gom
- guj
- hin
- kan
- kas
- mai
- mal
- mar
- mni
- npi
- ory
- pan
- san
- sat
- snd
- tam
- tel
- urd
license: cc-by-4.0
multilinguality: multilingual
size_categories:
- 1K<n<10K
task_categories:
- translation
task_ids: []
pretty_name: in22-conv
language_details: asm_Beng, ben_Beng, brx_Deva, doi_Deva, eng_Latn, gom_Deva, guj_Gujr,
hin_Deva, kan_Knda, kas_Arab, mai_Deva, mal_Mlym, mar_Deva, mni_Mtei, npi_Deva,
ory_Orya, pan_Guru, san_Deva, sat_Olck, snd_Deva, tam_Taml, tel_Telu, urd_Arab
configs:
- config_name: default
data_files:
- split: test
path: test.parquet
tags:
- mteb
- text
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">IN22ConvBitextMining</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>
IN22-Conv is a n-way parallel conversation domain benchmark dataset for machine translation spanning English and 22 Indic languages.
| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Social, Spoken, Fiction, Spoken |
| Reference | https://huggingface.co/datasets/ai4bharat/IN22-Conv |
## How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
```python
import mteb
task = mteb.get_tasks(["IN22ConvBitextMining"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```
<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
## Citation
If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).
```bibtex
@article{gala2023indictrans,
author = {Jay Gala and Pranjal A Chitale and A K Raghavan and Varun Gumma and Sumanth Doddapaneni and Aswanth Kumar M and Janki Atul Nawale and Anupama Sujatha and Ratish Puduppully and Vivek Raghavan and Pratyush Kumar and Mitesh M Khapra and Raj Dabre and Anoop Kunchukuttan},
issn = {2835-8856},
journal = {Transactions on Machine Learning Research},
note = {},
title = {IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages},
url = {https://openreview.net/forum?id=vfT4YuzAYA},
year = {2023},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
```
# Dataset Statistics
<details>
<summary> Dataset Statistics</summary>
The following code contains the descriptive statistics from the task. These can also be obtained using:
```python
import mteb
task = mteb.get_task("IN22ConvBitextMining")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"num_samples": 760518,
"number_of_characters": 82637104,
"unique_pairs": 759283,
"min_sentence1_length": 3,
"average_sentence1_length": 54.32948595562498,
"max_sentence1_length": 239,
"unique_sentence1": 34430,
"min_sentence2_length": 3,
"average_sentence2_length": 54.32948595562498,
"max_sentence2_length": 239,
"unique_sentence2": 34430
}
}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)* |
mteb/mtop_domain | mteb | 2025-05-04T16:08:07Z | 3,789 | 2 | [
"task_categories:text-classification",
"annotations_creators:human-annotated",
"multilinguality:multilingual",
"language:deu",
"language:eng",
"language:fra",
"language:hin",
"language:spa",
"language:tha",
"license:unknown",
"modality:text",
"arxiv:2008.09335",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text"
] | [
"text-classification"
] | 2022-05-19T15:04:17Z | null | ---
annotations_creators:
- human-annotated
language:
- deu
- eng
- fra
- hin
- spa
- tha
license: unknown
multilinguality: multilingual
task_categories:
- text-classification
task_ids: []
dataset_info:
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features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
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- name: validation
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- name: text
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- name: text
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configs:
- config_name: de
data_files:
- split: train
path: de/train-*
- split: validation
path: de/validation-*
- split: test
path: de/test-*
- config_name: en
data_files:
- split: train
path: en/train-*
- split: validation
path: en/validation-*
- split: test
path: en/test-*
- config_name: es
data_files:
- split: train
path: es/train-*
- split: validation
path: es/validation-*
- split: test
path: es/test-*
- config_name: fr
data_files:
- split: train
path: fr/train-*
- split: validation
path: fr/validation-*
- split: test
path: fr/test-*
- config_name: hi
data_files:
- split: train
path: hi/train-*
- split: validation
path: hi/validation-*
- split: test
path: hi/test-*
- config_name: th
data_files:
- split: train
path: th/train-*
- split: validation
path: th/validation-*
- split: test
path: th/test-*
tags:
- mteb
- text
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">MTOPDomainClassification</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>
MTOP: Multilingual Task-Oriented Semantic Parsing
| | |
|---------------|---------------------------------------------|
| Task category | t2c |
| Domains | Spoken, Spoken |
| Reference | https://arxiv.org/pdf/2008.09335.pdf |
## How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
```python
import mteb
task = mteb.get_tasks(["MTOPDomainClassification"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```
<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
## Citation
If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).
```bibtex
@inproceedings{li-etal-2021-mtop,
abstract = {Scaling semantic parsing models for task-oriented dialog systems to new languages is often expensive and time-consuming due to the lack of available datasets. Available datasets suffer from several shortcomings: a) they contain few languages b) they contain small amounts of labeled examples per language c) they are based on the simple intent and slot detection paradigm for non-compositional queries. In this paper, we present a new multilingual dataset, called MTOP, comprising of 100k annotated utterances in 6 languages across 11 domains. We use this dataset and other publicly available datasets to conduct a comprehensive benchmarking study on using various state-of-the-art multilingual pre-trained models for task-oriented semantic parsing. We achieve an average improvement of +6.3 points on Slot F1 for the two existing multilingual datasets, over best results reported in their experiments. Furthermore, we demonstrate strong zero-shot performance using pre-trained models combined with automatic translation and alignment, and a proposed distant supervision method to reduce the noise in slot label projection.},
address = {Online},
author = {Li, Haoran and
Arora, Abhinav and
Chen, Shuohui and
Gupta, Anchit and
Gupta, Sonal and
Mehdad, Yashar},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
doi = {10.18653/v1/2021.eacl-main.257},
editor = {Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut},
month = apr,
pages = {2950--2962},
publisher = {Association for Computational Linguistics},
title = {{MTOP}: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark},
url = {https://aclanthology.org/2021.eacl-main.257},
year = {2021},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
```
# Dataset Statistics
<details>
<summary> Dataset Statistics</summary>
The following code contains the descriptive statistics from the task. These can also be obtained using:
```python
import mteb
task = mteb.get_task("MTOPDomainClassification")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"validation": {
"num_samples": 10837,
"number_of_characters": 431895,
"number_texts_intersect_with_train": 127,
"min_text_length": 5,
"average_text_length": 39.85374181046415,
"max_text_length": 154,
"unique_text": 10830,
"unique_labels": 11,
"labels": {
"1": {
"count": 1688
},
"10": {
"count": 754
},
"7": {
"count": 849
},
"3": {
"count": 681
},
"6": {
"count": 985
},
"2": {
"count": 647
},
"9": {
"count": 872
},
"0": {
"count": 833
},
"5": {
"count": 1182
},
"4": {
"count": 982
},
"8": {
"count": 1364
}
},
"hf_subset_descriptive_stats": {
"en": {
"num_samples": 2235,
"number_of_characters": 81663,
"number_texts_intersect_with_train": 7,
"min_text_length": 8,
"average_text_length": 36.53825503355705,
"max_text_length": 125,
"unique_text": 2235,
"unique_labels": 11,
"labels": {
"1": {
"count": 329
},
"10": {
"count": 185
},
"7": {
"count": 183
},
"3": {
"count": 134
},
"6": {
"count": 186
},
"2": {
"count": 123
},
"9": {
"count": 196
},
"0": {
"count": 176
},
"5": {
"count": 228
},
"4": {
"count": 207
},
"8": {
"count": 288
}
}
},
"de": {
"num_samples": 1815,
"number_of_characters": 77727,
"number_texts_intersect_with_train": 23,
"min_text_length": 10,
"average_text_length": 42.824793388429754,
"max_text_length": 154,
"unique_text": 1814,
"unique_labels": 11,
"labels": {
"0": {
"count": 99
},
"1": {
"count": 303
},
"2": {
"count": 104
},
"3": {
"count": 122
},
"6": {
"count": 165
},
"4": {
"count": 157
},
"7": {
"count": 141
},
"5": {
"count": 203
},
"8": {
"count": 220
},
"10": {
"count": 133
},
"9": {
"count": 168
}
}
},
"es": {
"num_samples": 1527,
"number_of_characters": 67720,
"number_texts_intersect_with_train": 41,
"min_text_length": 11,
"average_text_length": 44.34839554682384,
"max_text_length": 134,
"unique_text": 1525,
"unique_labels": 11,
"labels": {
"1": {
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},
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"0": {
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}
}
},
"fr": {
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"unique_text": 1575,
"unique_labels": 11,
"labels": {
"0": {
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"10": {
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}
}
},
"hi": {
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"unique_text": 2011,
"unique_labels": 11,
"labels": {
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},
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}
}
},
"th": {
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"unique_text": 1670,
"unique_labels": 11,
"labels": {
"0": {
"count": 128
},
"1": {
"count": 277
},
"2": {
"count": 104
},
"3": {
"count": 106
},
"4": {
"count": 150
},
"5": {
"count": 185
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"count": 163
},
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"count": 126
},
"8": {
"count": 206
},
"9": {
"count": 125
},
"10": {
"count": 101
}
}
}
}
},
"test": {
"num_samples": 19680,
"number_of_characters": 781580,
"number_texts_intersect_with_train": 332,
"min_text_length": 3,
"average_text_length": 39.71443089430894,
"max_text_length": 168,
"unique_text": 19627,
"unique_labels": 11,
"labels": {
"2": {
"count": 977
},
"5": {
"count": 2372
},
"6": {
"count": 2014
},
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"count": 2572
},
"9": {
"count": 1317
},
"1": {
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},
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"count": 1330
},
"3": {
"count": 1351
},
"0": {
"count": 1459
},
"7": {
"count": 1535
},
"4": {
"count": 1688
}
},
"hf_subset_descriptive_stats": {
"en": {
"num_samples": 4386,
"number_of_characters": 161376,
"number_texts_intersect_with_train": 15,
"min_text_length": 3,
"average_text_length": 36.79343365253078,
"max_text_length": 132,
"unique_text": 4384,
"unique_labels": 11,
"labels": {
"2": {
"count": 197
},
"5": {
"count": 487
},
"6": {
"count": 418
},
"8": {
"count": 613
},
"9": {
"count": 346
},
"1": {
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},
"10": {
"count": 358
},
"3": {
"count": 290
},
"0": {
"count": 341
},
"7": {
"count": 354
},
"4": {
"count": 369
}
}
},
"de": {
"num_samples": 3549,
"number_of_characters": 151445,
"number_texts_intersect_with_train": 69,
"min_text_length": 7,
"average_text_length": 42.67258382642998,
"max_text_length": 162,
"unique_text": 3536,
"unique_labels": 11,
"labels": {
"0": {
"count": 193
},
"10": {
"count": 264
},
"1": {
"count": 553
},
"2": {
"count": 163
},
"3": {
"count": 256
},
"5": {
"count": 439
},
"4": {
"count": 306
},
"6": {
"count": 353
},
"7": {
"count": 279
},
"8": {
"count": 452
},
"9": {
"count": 291
}
}
},
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```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)* |
nhagar/moscar_urls | nhagar | 2025-05-04T16:01:05Z | 5 | 0 | [
"task_categories:text-generation",
"license:cc-by-4.0",
"size_categories:100M<n<1B",
"region:us"
] | [
"text-generation"
] | 2025-04-26T18:14:46Z | null | ---
license: cc-by-4.0
task_categories:
- text-generation
size_categories:
- 100M<n<1B
---
# Dataset Card for moscar_urls
This dataset provides the URLs and top-level domains associated with training records in [oscar-corpus/mOSCAR](https://huggingface.co/datasets/oscar-corpus/mOSCAR). It is part of a [collection of datasets](https://huggingface.co/collections/nhagar/llm-urls-neurips-681698adac0862be6c65c72b) curated to make exploring LLM training datasets more straightforward and accessible.
## Dataset Details
### Dataset Description
This dataset was created by downloading the source data, extracting URLs and top-level domains, and retaining only those record identifiers. In doing so, it allows researchers and practitioners to explore the contents of these training datasets without having to manage terabytes of raw text. You can explore the pipeline used to construct this dataset on [GitHub](https://github.com/NHagar/cc-genealogy).
- **Curated by:** [Nick Hagar](https://huggingface.co/nhagar) and [Jack Bandy](https://huggingface.co/jackbandy)
- **License:** Same as source dataset
### Dataset Sources
- **Repository:** [oscar-corpus/mOSCAR](https://huggingface.co/datasets/oscar-corpus/mOSCAR)
## Uses
This dataset is intended to allow researchers and practitioners to analyze the contents of large LLM training datasets without having to wade through terabytes of unwieldy text data.
### Direct Use
The main use case for these data is to explore the contents of LLM training datasets at scale. This might involve:
- Identifying the most-used websites
- Categorizing URLs to understand domain- or topic-level dataset composition
- Comparing URLs across datasets
- Digging into inclusion/exclusion patterns for a particular website
### Out-of-Scope Use
This dataset is not intend to replicate or replace the source data, nor is it intended to enable large-scale scraping of the URLs listed. For source text, refer to the original dataset.
## Dataset Structure
This dataset contains every record with a URL from the source dataset. It contains two columns:
- `url`: The raw URL associated with each record
- `domain`: The top-level domain for each URL, extracted with `tldextract`
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed] |
GitBag/a_star_final_a_star_dapo_7_actor_aime-25_eval | GitBag | 2025-05-04T15:51:12Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T15:51:11Z | null | ---
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---
|
Oriolshhh/parlabe-errors-ortografia-45k | Oriolshhh | 2025-05-04T15:45:45Z | 0 | 0 | [
"language:ca",
"license:apache-2.0",
"size_categories:10K<n<100K",
"region:us",
"català",
"grammar-correction",
"ortografia",
"text-to-text",
"synthetic"
] | [] | 2025-05-04T15:43:55Z | null | ---
language: ca
license: apache-2.0
tags:
- català
- grammar-correction
- ortografia
- text-to-text
- synthetic
size_categories:
- 10K<n<100K
---
# Dataset d’errors ortogràfics en català (45.000 parelles)
Aquest dataset conté **45.000 parelles de frases** en format:
```text_erroni,text_correcte```
Està dissenyat per entrenar models de **correcció ortogràfica general** en català, abastant una gran varietat d’errors comuns en l’escriptura manual, digitació ràpida, ASR o OCR.
---
## Què inclou?
Aquestes parelles cobreixen errors com:
- Lletres intercanviades o repetides: *Axo és una prova* → *Això és una prova*
- Omissions o afegits de caràcters: *probablament* → *probablement*
- Substitucions de sons semblants: *conexió* → *connexió*
- Errors de segmentació: *dela feina* → *de la feina*
- Confusions típiques en digitació: *hiverncle* → *hivernacle*
---
## Procés de generació
Les frases han estat generades a partir de:
- **API de GPT**, que va crear errors ortogràfics realistes
- **Automatització amb scripts en Python**
- **Filtratge i validació** per assegurar qualitat lingüística i diversitat
---
## Format
- Llengua: Català (`ca`)
- Format: `.csv` amb dues columnes:
- `text_erroni`
- `text_correcte`
- Nombre de parelles: 45.000
|
HungVu2003/opt-350m_beta_0.0_alpha_0.2_num-company_2_dataset_0_for_gen_10_v2 | HungVu2003 | 2025-05-04T15:43:36Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T15:43:34Z | null | ---
dataset_info:
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- name: question
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splits:
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num_bytes: 2722918
num_examples: 13750
download_size: 960160
dataset_size: 2722918
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
HungVu2003/opt-350m_beta_1.0_alpha_0.2_num-company_2_dataset_0_for_gen_15_v2 | HungVu2003 | 2025-05-04T14:40:44Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T14:40:43Z | null | ---
dataset_info:
features:
- name: question
dtype: string
splits:
- name: train
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num_examples: 13750
download_size: 1099680
dataset_size: 2001777
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
UNISG-MCS/NLP | UNISG-MCS | 2025-05-04T14:05:52Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T13:58:02Z | null | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 8630400
num_examples: 2400
- name: validation
num_bytes: 2168388
num_examples: 603
download_size: 0
dataset_size: 10798788
---
# Dataset Card for "NLP"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hf-doc-build/doc-build | hf-doc-build | 2025-05-04T13:19:01Z | 319,590 | 9 | [
"license:mit",
"region:us"
] | [] | 2022-10-24T15:39:05Z | null | ---
license: mit
pretty_name: Generated Docs for HF
viewer: false
---
This repo contains all the docs published on https://huggingface.co/docs.
The docs are generated with https://github.com/huggingface/doc-builder.
<!-- comment to trigger webhook.= --> |
mteb/flores | mteb | 2025-05-04T13:09:38Z | 30 | 0 | [
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"language:zsm",
"language:zul",
"license:cc-by-sa-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text"
] | [
"translation"
] | 2024-05-14T20:39:18Z | null | ---
annotations_creators:
- human-annotated
language:
- ace
- acm
- acq
- aeb
- afr
- ajp
- aka
- als
- amh
- apc
- arb
- ars
- ary
- arz
- asm
- ast
- awa
- ayr
- azb
- azj
- bak
- bam
- ban
- bel
- bem
- ben
- bho
- bjn
- bod
- bos
- bug
- bul
- cat
- ceb
- ces
- cjk
- ckb
- crh
- cym
- dan
- deu
- dik
- dyu
- dzo
- ell
- eng
- epo
- est
- eus
- ewe
- fao
- fij
- fin
- fon
- fra
- fur
- fuv
- gaz
- gla
- gle
- glg
- grn
- guj
- hat
- hau
- heb
- hin
- hne
- hrv
- hun
- hye
- ibo
- ilo
- ind
- isl
- ita
- jav
- jpn
- kab
- kac
- kam
- kan
- kas
- kat
- kaz
- kbp
- kea
- khk
- khm
- kik
- kin
- kir
- kmb
- kmr
- knc
- kon
- kor
- lao
- lij
- lim
- lin
- lit
- lmo
- ltg
- ltz
- lua
- lug
- luo
- lus
- lvs
- mag
- mai
- mal
- mar
- min
- mkd
- mlt
- mni
- mos
- mri
- mya
- nld
- nno
- nob
- npi
- nso
- nus
- nya
- oci
- ory
- pag
- pan
- pap
- pbt
- pes
- plt
- pol
- por
- prs
- quy
- ron
- run
- rus
- sag
- san
- sat
- scn
- shn
- sin
- slk
- slv
- smo
- sna
- snd
- som
- sot
- spa
- srd
- srp
- ssw
- sun
- swe
- swh
- szl
- tam
- taq
- tat
- tel
- tgk
- tgl
- tha
- tir
- tpi
- tsn
- tso
- tuk
- tum
- tur
- twi
- tzm
- uig
- ukr
- umb
- urd
- uzn
- vec
- vie
- war
- wol
- xho
- ydd
- yor
- yue
- zho
- zsm
- zul
license: cc-by-sa-4.0
multilinguality: multilingual
task_categories:
- translation
task_ids: []
configs:
- config_name: default
data_files:
- split: dev
path: dev.parquet
- split: devtest
path: devtest.parquet
tags:
- mteb
- text
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">FloresBitextMining</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>
FLORES is a benchmark dataset for machine translation between English and low-resource languages.
| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Non-fiction, Encyclopaedic, Written |
| Reference | https://huggingface.co/datasets/facebook/flores |
## How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
```python
import mteb
task = mteb.get_tasks(["FloresBitextMining"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```
<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
## Citation
If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).
```bibtex
@inproceedings{goyal2022flores,
author = {Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and Ju, Da and Krishnan, Sanjana and Ranzato, Marc'Aurelio and Guzm{\'a}n, Francisco},
booktitle = {Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
pages = {19--35},
title = {The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation},
year = {2022},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
```
# Dataset Statistics
<details>
<summary> Dataset Statistics</summary>
The following code contains the descriptive statistics from the task. These can also be obtained using:
```python
import mteb
task = mteb.get_task("FloresBitextMining")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"devtest": {
"num_samples": 41908944,
"number_of_characters": 11221665014,
"unique_pairs": 41545149,
"min_sentence1_length": 10,
"average_sentence1_length": 133.88150527009222,
"max_sentence1_length": 597,
"unique_sentence1": 205519,
"min_sentence2_length": 10,
"average_sentence2_length": 133.88150527009222,
"max_sentence2_length": 597,
"unique_sentence2": 205519
}
}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)* |
HungVu2003/opt-350m_beta_1.0_alpha_0.2_num-company_2_dataset_1_for_gen_13_v2 | HungVu2003 | 2025-05-04T12:51:41Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T12:51:39Z | null | ---
dataset_info:
features:
- name: question
dtype: string
splits:
- name: train
num_bytes: 6681057
num_examples: 13750
download_size: 3326270
dataset_size: 6681057
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
MBZUAI-IFM/AM_clean_final_90perc | MBZUAI-IFM | 2025-05-04T12:03:06Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T11:55:18Z | null | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: dataset_source
dtype: string
- name: metadata
dtype: string
splits:
- name: train
num_bytes: 28212498400
num_examples: 1260000
download_size: 13048260960
dataset_size: 28212498400
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
beyoru/FC_bench_800 | beyoru | 2025-05-04T11:59:29Z | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-04T09:31:36Z | null | ---
dataset_info:
features:
- name: question
dtype: string
- name: functions
dtype: string
- name: model_gen
dtype: string
- name: model_gen_base
dtype: string
splits:
- name: train
num_bytes: 733098
num_examples: 812
download_size: 269026
dataset_size: 733098
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
wyyyz139/character | wyyyz139 | 2025-05-04T10:22:48Z | 2 | 0 | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"region:us"
] | [] | 2025-05-04T02:57:50Z | null | ---
license: apache-2.0
---
|
Yuyeong/rw_pubmed_mdlr_2_mask_public | Yuyeong | 2025-05-04T10:06:57Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T10:06:25Z | null | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
- name: group_idx
dtype: int64
- name: node_idx
dtype: int64
splits:
- name: train
num_bytes: 9996190.376832176
num_examples: 6000
- name: validation
num_bytes: 83301586.47360146
num_examples: 50000
- name: test
num_bytes: 166603172.94720292
num_examples: 100000
download_size: 131239520
dataset_size: 259900949.79763657
---
# Dataset Card for "rw_pubmed_mdlr_2_mask_public"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BramVanroy/CommonCrawl-CreativeCommons | BramVanroy | 2025-05-04T10:00:59Z | 98 | 7 | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language:afr",
"language:deu",
"language:eng",
"language:fra",
"language:fry",
"language:ita",
"language:nld",
"language:spa",
"language:af",
"language:de",
"language:en",
"language:fr",
"language:fy",
"language:it",
"language:nl",
"language:es",
"license:cc",
"size_categories:100M<n<1B",
"modality:text",
"doi:10.57967/hf/5340",
"region:us"
] | [
"text-generation"
] | 2025-01-28T13:12:13Z | 6 | ---
license: cc
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: Common Crawl Creative Commons Corpus (C5)
language:
- afr
- deu
- eng
- fra
- fry
- ita
- nld
- spa
- af
- de
- en
- fr
- fy
- it
- nl
- es
configs:
- config_name: v1
data_files:
- data/CC-MAIN-2019-30/**/*.parquet
- data/CC-MAIN-2020-05/**/*.parquet
- data/CC-MAIN-2022-05/**/*.parquet
- data/CC-MAIN-2023-06/**/*.parquet
- data/CC-MAIN-2024-46/**/*.parquet
- data/CC-MAIN-2024-51/**/*.parquet
- data/CC-MAIN-2025-05/**/*.parquet
- config_name: default
data_files: data/**/*.parquet
# Languages
- config_name: afr
data_files: data/**/afr/*.parquet
- config_name: deu
data_files: data/**/deu/*.parquet
- config_name: eng
data_files: data/**/eng/*.parquet
- config_name: spa
data_files: data/**/spa/*.parquet
- config_name: fra
data_files: data/**/fra/*.parquet
- config_name: fry
data_files: data/**/fry/*.parquet
- config_name: ita
data_files: data/**/ita/*.parquet
- config_name: nld
data_files: data/**/nld/*.parquet
# Per-crawl
# CC-MAIN-2019-30
- config_name: CC-MAIN-2019-30
data_files: data/CC-MAIN-2019-30/**/*.parquet
- config_name: CC-MAIN-2019-30-afr
data_files: data/CC-MAIN-2019-30/afr/*.parquet
- config_name: CC-MAIN-2019-30-deu
data_files: data/CC-MAIN-2019-30/deu/*.parquet
- config_name: CC-MAIN-2019-30-eng
data_files: data/CC-MAIN-2019-30/eng/*.parquet
- config_name: CC-MAIN-2019-30-spa
data_files: data/CC-MAIN-2019-30/spa/*.parquet
- config_name: CC-MAIN-2019-30-fra
data_files: data/CC-MAIN-2019-30/fra/*.parquet
- config_name: CC-MAIN-2019-30-fry
data_files: data/CC-MAIN-2019-30/fry/*.parquet
- config_name: CC-MAIN-2019-30-ita
data_files: data/CC-MAIN-2019-30/ita/*.parquet
- config_name: CC-MAIN-2019-30-nld
data_files: data/CC-MAIN-2019-30/nld/*.parquet
# CC-MAIN-2020-05
- config_name: CC-MAIN-2020-05
data_files: data/CC-MAIN-2020-05/**/*.parquet
- config_name: CC-MAIN-2020-05-afr
data_files: data/CC-MAIN-2020-05/afr/*.parquet
- config_name: CC-MAIN-2020-05-deu
data_files: data/CC-MAIN-2020-05/deu/*.parquet
- config_name: CC-MAIN-2020-05-eng
data_files: data/CC-MAIN-2020-05/eng/*.parquet
- config_name: CC-MAIN-2020-05-spa
data_files: data/CC-MAIN-2020-05/spa/*.parquet
- config_name: CC-MAIN-2020-05-fra
data_files: data/CC-MAIN-2020-05/fra/*.parquet
- config_name: CC-MAIN-2020-05-fry
data_files: data/CC-MAIN-2020-05/fry/*.parquet
- config_name: CC-MAIN-2020-05-ita
data_files: data/CC-MAIN-2020-05/ita/*.parquet
- config_name: CC-MAIN-2020-05-nld
data_files: data/CC-MAIN-2020-05/nld/*.parquet
# CC-MAIN-2022-05
- config_name: CC-MAIN-2022-05
data_files: data/CC-MAIN-2022-05/**/*.parquet
- config_name: CC-MAIN-2022-05-afr
data_files: data/CC-MAIN-2022-05/afr/*.parquet
- config_name: CC-MAIN-2022-05-deu
data_files: data/CC-MAIN-2022-05/deu/*.parquet
- config_name: CC-MAIN-2022-05-eng
data_files: data/CC-MAIN-2022-05/eng/*.parquet
- config_name: CC-MAIN-2022-05-spa
data_files: data/CC-MAIN-2022-05/spa/*.parquet
- config_name: CC-MAIN-2022-05-fra
data_files: data/CC-MAIN-2022-05/fra/*.parquet
- config_name: CC-MAIN-2022-05-fry
data_files: data/CC-MAIN-2022-05/fry/*.parquet
- config_name: CC-MAIN-2022-05-ita
data_files: data/CC-MAIN-2022-05/ita/*.parquet
- config_name: CC-MAIN-2022-05-nld
data_files: data/CC-MAIN-2022-05/nld/*.parquet
# CC-MAIN-2023-06
- config_name: CC-MAIN-2023-06
data_files: data/CC-MAIN-2023-06/**/*.parquet
- config_name: CC-MAIN-2023-06-afr
data_files: data/CC-MAIN-2023-06/afr/*.parquet
- config_name: CC-MAIN-2023-06-deu
data_files: data/CC-MAIN-2023-06/deu/*.parquet
- config_name: CC-MAIN-2023-06-eng
data_files: data/CC-MAIN-2023-06/eng/*.parquet
- config_name: CC-MAIN-2023-06-spa
data_files: data/CC-MAIN-2023-06/spa/*.parquet
- config_name: CC-MAIN-2023-06-fra
data_files: data/CC-MAIN-2023-06/fra/*.parquet
- config_name: CC-MAIN-2023-06-fry
data_files: data/CC-MAIN-2023-06/fry/*.parquet
- config_name: CC-MAIN-2023-06-ita
data_files: data/CC-MAIN-2023-06/ita/*.parquet
- config_name: CC-MAIN-2023-06-nld
data_files: data/CC-MAIN-2023-06/nld/*.parquet
# CC-MAIN-2024-46
- config_name: CC-MAIN-2024-46
data_files: data/CC-MAIN-2024-46/**/*.parquet
- config_name: CC-MAIN-2024-46-afr
data_files: data/CC-MAIN-2024-46/afr/*.parquet
- config_name: CC-MAIN-2024-46-deu
data_files: data/CC-MAIN-2024-46/deu/*.parquet
- config_name: CC-MAIN-2024-46-eng
data_files: data/CC-MAIN-2024-46/eng/*.parquet
- config_name: CC-MAIN-2024-46-spa
data_files: data/CC-MAIN-2024-46/spa/*.parquet
- config_name: CC-MAIN-2024-46-fra
data_files: data/CC-MAIN-2024-46/fra/*.parquet
- config_name: CC-MAIN-2024-46-fry
data_files: data/CC-MAIN-2024-46/fry/*.parquet
- config_name: CC-MAIN-2024-46-ita
data_files: data/CC-MAIN-2024-46/ita/*.parquet
- config_name: CC-MAIN-2024-46-nld
data_files: data/CC-MAIN-2024-46/nld/*.parquet
# CC-MAIN-2024-51
- config_name: CC-MAIN-2024-51
data_files: data/CC-MAIN-2024-51/**/*.parquet
- config_name: CC-MAIN-2024-51-afr
data_files: data/CC-MAIN-2024-51/afr/*.parquet
- config_name: CC-MAIN-2024-51-deu
data_files: data/CC-MAIN-2024-51/deu/*.parquet
- config_name: CC-MAIN-2024-51-eng
data_files: data/CC-MAIN-2024-51/eng/*.parquet
- config_name: CC-MAIN-2024-51-spa
data_files: data/CC-MAIN-2024-51/spa/*.parquet
- config_name: CC-MAIN-2024-51-fra
data_files: data/CC-MAIN-2024-51/fra/*.parquet
- config_name: CC-MAIN-2024-51-fry
data_files: data/CC-MAIN-2024-51/fry/*.parquet
- config_name: CC-MAIN-2024-51-ita
data_files: data/CC-MAIN-2024-51/ita/*.parquet
- config_name: CC-MAIN-2024-51-nld
data_files: data/CC-MAIN-2024-51/nld/*.parquet
# CC-MAIN-2025-05
- config_name: CC-MAIN-2025-05
data_files: data/CC-MAIN-2025-05/**/*.parquet
- config_name: CC-MAIN-2025-05-afr
data_files: data/CC-MAIN-2025-05/afr/*.parquet
- config_name: CC-MAIN-2025-05-deu
data_files: data/CC-MAIN-2025-05/deu/*.parquet
- config_name: CC-MAIN-2025-05-eng
data_files: data/CC-MAIN-2025-05/eng/*.parquet
- config_name: CC-MAIN-2025-05-spa
data_files: data/CC-MAIN-2025-05/spa/*.parquet
- config_name: CC-MAIN-2025-05-fra
data_files: data/CC-MAIN-2025-05/fra/*.parquet
- config_name: CC-MAIN-2025-05-fry
data_files: data/CC-MAIN-2025-05/fry/*.parquet
- config_name: CC-MAIN-2025-05-ita
data_files: data/CC-MAIN-2025-05/ita/*.parquet
- config_name: CC-MAIN-2025-05-nld
data_files: data/CC-MAIN-2025-05/nld/*.parquet
---
# The Common Crawl Creative Commons Corpus (C5)
> **Raw CommonCrawl crawls, annotated with Creative Commons license information**
C5 is an effort to collect Creative Commons-licensed web data in one place.
The licensing information is extracted from the web pages based on whether they link to Creative Commons licenses either overtly in `a` tags (like in the footer of Wikipedia) or in metadata fields indicating deliberate Creative Commons publication. **However, false positives may occur! See Recommendations and Caveats below!** Also see [Personal and Sensitive Information](#personal-and-sensitive-information).
## Code
I am very grateful to the Flemish Supercomputer to provide compute necessary to create this dataset, but as you can tell there is still a lot of data left to be processed. Therefore, I am happy to collaborate to process as many Common Crawl crawls as possible. [Shoot me a message](mailto:[email protected]) if you want to sponsor this project with compute! You can also simply run the code yourself if you'd like. You can find the whole code base, based on `datatrove`, on [Github](https://github.com/BramVanroy/CommonCrawl-CreativeCommons). If you use the code, please [reference my work](https://github.com/BramVanroy/CommonCrawl-CreativeCommons?tab=readme-ov-file#citation) accordingly and share your processed crawls with the rest of the world (or get in touch with me so I can add them to this repo).
## Usage
```python
from datasets import load_dataset
# Everything, most recent -- massive, you will need streaming
ds = load_dataset("BramVanroy/CommonCrawl-CreativeCommons", streaming=True)
# v1 (2019-30, 2020-05, 2022-05, 2023-06, 2024-51, 2025-05, 2024-46)
ds = load_dataset("BramVanroy/CommonCrawl-CreativeCommons", "v1", streaming=True)
# Single dump, all languages -- large, you may need streaming on non-server hardware
ds = load_dataset("BramVanroy/CommonCrawl-CreativeCommons", "CC-MAIN-2019-30")
# Single language, all dumps -- very large, you will likely need streaming
ds = load_dataset("BramVanroy/CommonCrawl-CreativeCommons", "nld", streaming=True)
# Single language, single dump
ds = load_dataset("BramVanroy/CommonCrawl-CreativeCommons", "CC-MAIN-2019-30-nld")
```
## Progress
In the `v1` release, the following crawls are included
- CC-MAIN-2019-30
- CC-MAIN-2020-05
- CC-MAIN-2023-06
- CC-MAIN-2024-51
- CC-MAIN-2024-46
- CC-MAIN-2025-05
- CC-MAIN-2022-05
Other crawls are continuously being added.
## Languages
The following languages are included. This is a limited set due to computational and storage limitations.
- Afrikaans: afr
- German: deu
- English: eng
- French: fra
- Frysian: fry
- Italian: ita
- Dutch: nld
- Spanish: spa
## Quantity
Detailed number of tokens (Llama 3.3 tokenizer) and number of documents are given in the [counts.json](https://huggingface.co/datasets/BramVanroy/CommonCrawl-CreativeCommons/blob/main/counts.json) file.
| Language | Number of Documents | Number of Tokens |
| --------- | ------------------- | ------------------- |
| afr | 312,262 | 358,873,448 |
| deu | 9,530,746 | 11,362,859,534 |
| eng | 92,635,372 | 87,537,859,958 |
| fra | 9,234,900 | 12,366,480,025 |
| fry | 230,910 | 197,430,774 |
| ita | 10,734,597 | 11,913,669,333 |
| nld | 2,827,636 | 2,757,074,705 |
| spa | 22,226,944 | 22,515,709,432 |
| **Total** | **147,733,367** | **149,009,957,209** |
## Fields
In some cases, multiple licenses are found on a single page. All licenses are collected in `potential_licenses`. From these, the "best guess" is selected
based on three criteria:
1. location_preference_order: meta_tag, json-ld, link_tag, a_tag
2. head_preference_order: True, False
3. footer_preference_order: True, False
Based on these criteria, the "best guessed" license is picked as the one in the `license_*` columns. Potential disagreement between multiple licenses is given in `license_disagreement`.
- text: the extracted text (unmodified)
- id: WARC-Record-ID
- dump: Common Crawl crawl
- url: original url for document
- date: crawl date
- file_path: file path on the S3 bucket
- license_abbr: the license type. Possible values: "cc-unknown" (recommended to filter this one out), "by", "by-sa", "by-nd", "by-nc", "by-nc-sa", "by-nc-nd", "zero", "certification", "mark". If multiple licenses were found (`potential_licenses`)
- license_version: the license version, e.g. "4.0"
- license_location: the location where the license was found. Possible values: "meta_tag", "json-ld", "link_tag", "a_tag"
- license_in_head: whether the license was found inside a `head` HTML element
- license_in_footer: whether the license was found inside a `footer` HTML element, or an HTML element that had `footer` in the ID or class name
- potential_licenses:
- abbr: list of all found license abbreviations
- version: list of all found license versions
- location: list of all found license locations
- in_head: list of whether licenses were found in the head
- in_footer: list of whether licenses were found in a footer
- license_parse_error: whether there was a problem when trying to extract the license, e.g. an unparseable HTML document
- license_disagreement: whether the `potential_licenses["abbr"]` disagree, i.e., different types of licenses were found. License *versions* are not included in the comparison!
- language: the language, as detected by glotlid
- language_score: the language identification confidence score
- found_in_fw: whether this sample was found in FineWeb(-2). For non-English, crawls that are more recent than FW2 (everything after 2024-18) is marked as None. For English, crawls that are more recent than FW v1.3 is marked as None (after 2024-51).
## Recommendations and Caveats
- Raw CommonCrawl data is processed in an attempt to extract licensing information. No quality filtering is done!! It is **highly** recommended to filter this data further on quality, fluency, toxicity, etc.
- Similarly, the data has **not been deduplicated**.
- The licenses include all possible Creative Commons licenses, including non-commercial ones. Take care about what kind of data you wish to use, and filter out non-commercial licenses when needed.
- The column `license_disagreement` indicates whether multiple licenses were found that have not the same abbreviation, e.g. `cc-by` and `cc-by-nc`. It is recommended to filter these out.
- The column `license_parse_error` indicates whether an error occurred when parsing the license. You probably want to filter out documents where this was the case, though this should be extremely rare.
- Unsurpisingly, the data contains a lot of Wikipedia/Wikimedia content. Depending on what you need, you may wish to filter those out. For Wikipedia specifically, you may opt to use the more thoroughly parsed (but potentially more outdated) [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) set.
- In exceptional cases, a link to creativecommons.org is found but the exact license could not be found. These are under `license_abbr="cc-unknown"` which you may wish to filter out.
Recommendation:
```python
from datasets import load_dataset
ds = load_dataset("BramVanroy/CommonCrawl-CreativeCommons", "CC-MAIN-2019-30", split="train")
ds = ds.filter(
lambda x: (
(not x["license_disagreement"]) and # Only use pages with a consistent license
x["found_in_fw"] and # Only use pages that are in FineWeb(-2)
"nc" not in x["license_abbr"] and # Exclude non-commercial licenses
x["license_abbr"] != "cc-unknown" and # Exclude unknown licenses
"wiki" not in x["url"] # Exclude Wiki-like pages (best to get those from a more reliable parser)
),
num_proc=16
)
```
## Personal and Sensitive Information
C5 is a heavily filtered version of the Common Crawl dataset. CommonCrawl respects robots.txt and will not include websites if their robots.txt say so. Even so, if you find that your website was included you can submit a [removal request](https://docs.google.com/forms/d/e/1FAIpQLSddAIuUui5xnAzBqft6MnzPYihr-AaS-Nj8x01Y6AM8NQ0YLQ/viewform?usp=sharing) indicating that you are the owner of the website.
Take-down notices on other Common Crawl-based datasets such as FineWeb are considered. Domains specified and verified in those take-down notices are not included in this dataset.
In this dataset, measures are taken to anonymise email addresses and public IP addresses following the [FineWeb-2 approach](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2#personal-and-sensitive-information-and-opt-out). Email addresses matching a regular expression are replaced with `[email protected]`. Similarly, IP addresses allocated for [public networks](https://www.iana.org/assignments/iana-ipv4-special-registry/iana-ipv4-special-registry.xhtml) are replaced by unused IP addresses. Despite these best efforts on such large volumes of text, you may still encounter that your personal information is present in the dataset. In that case you can submit a [removal request](https://docs.google.com/forms/d/e/1FAIpQLSddAIuUui5xnAzBqft6MnzPYihr-AaS-Nj8x01Y6AM8NQ0YLQ/viewform?usp=sharing).
## Citation
In the current absence of a publication, please cite [the dataset](https://huggingface.co/datasets/BramVanroy/CommonCrawl-CreativeCommons) as follows. Including a footnote url to this page is also appreciated!
```bibtex
@misc{vanroy2025C5,
author = { Bram Vanroy },
title = { CommonCrawl CreativeCommons Corpus (C5) },
year = 2025,
url = { https://huggingface.co/datasets/BramVanroy/CommonCrawl-CreativeCommons },
doi = { 10.57967/hf/5340 },
publisher = { Hugging Face }
}
```
If you use or modify [the software](https://github.com/BramVanroy/CommonCrawl-CreativeCommons), please cite:
```bibtex
@software{Vanroy_CommonCrawl-CreativeCommons_2025,
author = {Vanroy, Bram},
license = {GPL-3.0},
month = feb,
title = {{CommonCrawl-CreativeCommons}},
url = {https://github.com/BramVanroy/CommonCrawl-CreativeCommons},
version = {1.3.0},
year = {2025}
}
```
## Acknowledgments
- The [Common Crawl](https://commoncrawl.org/) non-profit organization.
- [TNO](https://www.tno.nl/nl/), who funded the work hours to accomplish this code. They intend to use (parts of) [the generated material](https://huggingface.co/datasets/BramVanroy/CommonCrawl-CreativeCommons) for the [GPT-NL project](https://gpt-nl.nl/).
- [Flemish Supercomputer Center](https://www.vscentrum.be/) for part of the compute under grant 2024-107
- Guilherme Penedo ([@guipenedo](https://huggingface.co/guipenedo)) and the rest of the [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb) and [datatrove](https://github.com/huggingface/datatrove) team for the help and insights
- ML6 and specifically Robin Van Craenenbroek for their [Fondant Creative Commons](https://github.com/ml6team/fondant-usecase-filter-creative-commons/tree/add-fondant-usecase-cc-image-extraction) filter for image datasets. While my approach is different, their code did serve as inspiration.
|
LuftmenschPose/sub-news-sapo | LuftmenschPose | 2025-05-04T08:58:16Z | 0 | 0 | [
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-04T08:53:54Z | null | ---
license: apache-2.0
---
|
jlpang888/ultrafeedback_identical_pairs_7387_revised | jlpang888 | 2025-05-04T08:52:42Z | 0 | 0 | [
"size_categories:1K<n<10K",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-04T08:52:38Z | null | ---
dataset_info:
features:
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dtype: string
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dtype: string
- name: chosen
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dtype: string
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dtype: string
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dtype: string
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path: data/train-*
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path: data/test-*
---
|
Yuyeong/rw_cora_mdlr_1_mask_public | Yuyeong | 2025-05-04T08:29:16Z | 0 | 0 | [
"size_categories:100K<n<1M",
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"modality:tabular",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-04T08:28:51Z | null | ---
dataset_info:
features:
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dtype: string
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dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
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'5': '5'
'6': '6'
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num_examples: 100000
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---
# Dataset Card for "rw_cora_mdlr_1_mask_public"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
seungheondoh/socialfx-cls-eval | seungheondoh | 2025-05-04T08:09:00Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T08:08:56Z | null | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
sequence: string
- name: binary
sequence: int64
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sequence: string
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- name: eq
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- name: reverb
num_bytes: 1429379
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download_size: 50308
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configs:
- config_name: default
data_files:
- split: eq
path: data/eq-*
- split: reverb
path: data/reverb-*
---
|
alexchilton/nanobody-contact-maps | alexchilton | 2025-05-04T08:00:01Z | 0 | 0 | [
"task_categories:text-generation",
"license:mit",
"region:us",
"protein",
"contact-map",
"structure",
"nanobody"
] | [
"text-generation",
"image-generation"
] | 2025-05-04T07:59:34Z | null | ---
license: mit
task_categories:
- text-generation
- image-generation
tags:
- protein
- contact-map
- structure
- nanobody
---
# Protein Contact Map Dataset
## Dataset Description
This dataset contains protein structures with contact maps and related information from nanobody sequences.
### Dataset Summary
- **Number of proteins:** 2992
- **Source:** Nanobody protein structures (nanos_networkx_small)
- **Created by:** alexchilton
- **Date:** 2025-05-04
### Dataset Structure
Each protein entry contains:
- `amino_acid_sequence`: List of amino acid names
- `length`: Number of residues
- `c_alpha_coordinates`: List of [x,y,z] coordinates for C-alpha atoms
- `distance_matrix`: Pairwise distance matrix between C-alpha atoms
- `contact_maps`: List of binary contact maps with different distance thresholds
- `contact_map_configs`: Configuration for each contact map (lower/upper bounds)
### Usage
```python
from datasets import load_dataset
dataset = load_dataset("alexchilton/nanobody-contact-maps")
# Access a protein
protein = dataset['train'][0]
print(f"Length: {protein['length']}")
print(f"First 10 residues: {protein['amino_acid_sequence'][:10]}")
```
### Citation
If you use this dataset, please cite:
```
@dataset{protein_contact_maps,
title={Nanobody Protein Contact Map Dataset},
author={Alex Chilton},
year={2025},
url={https://huggingface.co/datasets/alexchilton/nanobody-contact-maps}
}
```
|
Kgshop/Aikas | Kgshop | 2025-05-04T07:33:30Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | [] | 2025-05-04T06:37:57Z | null | ---
license: apache-2.0
---
|
rlawltjd/korean-nl2bash | rlawltjd | 2025-05-04T07:04:23Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T07:04:15Z | null | ---
dataset_info:
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
arielcerdap/tts-disfluencies-DA | arielcerdap | 2025-05-04T06:55:17Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T06:54:53Z | null | ---
dataset_info:
features:
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- config_name: default
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- split: train
path: data/train-*
---
|
f1rdavs/tajik_lemmas | f1rdavs | 2025-05-04T06:50:14Z | 0 | 0 | [
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"modality:text",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-04T06:46:55Z | null | ---
license: apache-2.0
---
|
Hkang/summarize_sft-test_lm-pythia1b-oai-summary-PPO-0KL-newrm_12K_seed-42_numex-250 | Hkang | 2025-05-04T06:48:34Z | 0 | 0 | [
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"region:us"
] | [] | 2025-05-04T06:48:33Z | null | ---
dataset_info:
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path: data/test-*
---
|
upvantage/deberta-1m-v2humanized | upvantage | 2025-05-04T06:39:20Z | 0 | 0 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-04T06:30:44Z | null | ---
dataset_info:
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path: data/train-*
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path: data/validation-*
---
|
gabrielbo/mmlu-pro-baseline-scored | gabrielbo | 2025-05-04T06:22:38Z | 0 | 0 | [
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-03T23:40:15Z | null | ---
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---
|
flyingbugs/OpenR1-Math-220k-pruned-keep-0.75-end-start-0.0 | flyingbugs | 2025-05-04T05:15:27Z | 0 | 0 | [
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"library:dask",
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"library:polars",
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] | [] | 2025-05-04T05:14:21Z | null | ---
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---
|
HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_0_for_gen_16 | HungVu2003 | 2025-05-04T03:38:07Z | 0 | 0 | [
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"library:mlcroissant",
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"region:us"
] | [] | 2025-05-04T03:38:06Z | null | ---
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---
|
ma921/oasst1-filtered | ma921 | 2025-05-04T03:04:05Z | 0 | 0 | [
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] | [] | 2025-05-04T03:04:03Z | null | ---
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path: data/test-*
---
|
ma921/golden-hh-filtered | ma921 | 2025-05-04T02:34:45Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T02:34:39Z | null | ---
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---
|
mlfoundations-dev/mix_avg_domain | mlfoundations-dev | 2025-05-04T01:58:57Z | 0 | 0 | [
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] | [] | 2025-05-04T01:54:40Z | null | ---
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---
|
ParkSY/data_nerf_oorg_style_anything_depthmap_normalmap | ParkSY | 2025-05-04T00:33:09Z | 0 | 0 | [
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] | [] | 2025-05-04T00:33:04Z | null | ---
dataset_info:
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---
|
HungVu2003/opt-350m_beta_0.0_alpha_0.2_num-company_2_dataset_1_for_gen_3_v2 | HungVu2003 | 2025-05-04T00:23:27Z | 0 | 0 | [
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---
|
HungVu2003/opt-350m_beta_1.0_alpha_0.2_num-company_2_dataset_1_for_gen_3_v2 | HungVu2003 | 2025-05-04T00:13:39Z | 0 | 0 | [
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|
edwindn/voice_cloning_finetune_0.1 | edwindn | 2025-05-04T00:06:08Z | 0 | 0 | [
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] | [] | 2025-05-04T00:06:03Z | null | ---
dataset_info:
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---
|
HungVu2003/opt-350m_beta_1.0_alpha_0.6_num-company_3_dataset_2_for_gen_16 | HungVu2003 | 2025-05-03T23:58:56Z | 0 | 0 | [
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dataset_info:
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---
|
Triangle104/jondurbin_gutenberg-dpo-v0.1 | Triangle104 | 2025-05-03T22:42:58Z | 0 | 0 | [
"language:en",
"license:cc-by-4.0",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"dpo"
] | [] | 2025-05-03T22:42:58Z | null | ---
license: cc-by-4.0
language:
- en
tags:
- dpo
pretty_name: Gutenberg DPO
size_categories:
- n<1K
---
# Gutenberg DPO
## Overview
This is a dataset meant to enhance novel writing capabilities of LLMs, by using public domain books from [Project Gutenberg](https://gutenberg.org/)
## Process
First, the each book is parsed, split into chapters, cleaned up from the original format (remove superfluous newlines, illustration tags, etc.).
Once we have chapters, an LLM is prompted with each chapter to create a synthetic prompt that would result in that chapter being written.
Each chapter has a summary created as well, so that the prompts for each chapter after the also include a summary of the previous chapter to provide additional context.
We then use the synthetic prompt with previous chapter summary to write the chapter with an LLM (llama-2-13b-chat, bagel-7b-v0.1, dolphin-2.2-34b).
The human written text, that is, the original chapter, is used as the "chosen" value, and the LLM written chapter is used as the rejected value.
## Books used
These books were chosen main because they appeared in the popular section on project gutenberg, and they function correctly with the chapterize library.
- Huckleberry Finn
- Treasure Island
- Anna Karenina
- Uncle Tom’s Cabin
- Wuthering Heights
- Madame Bovary
- The Turn of the Screw
- The War of the Worlds
- A Study in Scarlet
- Middlemarch
- Pride and Prejudice
- The Brothers Karamazov
- Through the Looking Glass
- Moby Dick
- Frankenstein
- A Tale of Two Cities |
AJ97/dd | AJ97 | 2025-05-03T22:31:37Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | [] | 2025-05-03T22:31:37Z | null | ---
license: apache-2.0
---
|
mlfoundations-dev/e1_science_ms_qwq | mlfoundations-dev | 2025-05-03T21:54:45Z | 0 | 0 | [
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] | [] | 2025-05-03T21:54:23Z | null | ---
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---
|
kothasuhas/llp-gold-37m-1.5m_clip0.004_T2048.0_I2048 | kothasuhas | 2025-05-03T21:38:44Z | 0 | 0 | [
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] | [] | 2025-05-03T21:37:17Z | null | ---
dataset_info:
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---
|
nikhilchandak/MATH_mc | nikhilchandak | 2025-05-03T21:25:49Z | 0 | 0 | [
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dataset_info:
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---
|
dgambettaphd/D_llm2_gen9_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST | dgambettaphd | 2025-05-03T20:53:46Z | 0 | 0 | [
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] | [] | 2025-05-03T20:53:43Z | null | ---
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---
|
anonymousEcaiHateLLM/lgb_data_2_label | anonymousEcaiHateLLM | 2025-05-03T20:43:27Z | 0 | 0 | [
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dataset_info:
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path: data/lgb_data_2_label-*
---
|
HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_2_dataset_1_for_gen_9_v2 | HungVu2003 | 2025-05-03T20:15:56Z | 0 | 0 | [
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] | [] | 2025-05-03T20:15:55Z | null | ---
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---
|
KBayoud/Darija-VLM-Dataset-VQA-V1.0 | KBayoud | 2025-05-03T20:15:26Z | 0 | 0 | [
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] | [] | 2025-05-03T19:34:49Z | null | ---
dataset_info:
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---
|
Eluza133/Z1e1u | Eluza133 | 2025-05-03T20:01:52Z | 530 | 0 | [
"license:apache-2.0",
"region:us"
] | [] | 2025-03-08T07:48:07Z | null | ---
license: apache-2.0
---
|
mteb/banking77 | mteb | 2025-05-03T20:01:44Z | 6,306 | 3 | [
"task_categories:text-classification",
"annotations_creators:human-annotated",
"multilinguality:monolingual",
"language:eng",
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"arxiv:2003.04807",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text"
] | [
"text-classification"
] | 2022-05-17T12:14:06Z | null | ---
annotations_creators:
- human-annotated
language:
- eng
license: mit
multilinguality: monolingual
task_categories:
- text-classification
task_ids: []
tags:
- mteb
- text
configs:
- config_name: default
data_files:
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path: data/train-*
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path: data/test-*
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dtype: string
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num_bytes: 715028
num_examples: 10003
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num_bytes: 204010
num_examples: 3080
download_size: 379134
dataset_size: 919038
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">Banking77Classification</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>
Dataset composed of online banking queries annotated with their corresponding intents.
| | |
|---------------|---------------------------------------------|
| Task category | t2c |
| Domains | Written |
| Reference | https://arxiv.org/abs/2003.04807 |
## How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
```python
import mteb
task = mteb.get_tasks(["Banking77Classification"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```
<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
## Citation
If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).
```bibtex
@inproceedings{casanueva-etal-2020-efficient,
address = {Online},
author = {Casanueva, I{\~n}igo and
Tem{\v{c}}inas, Tadas and
Gerz, Daniela and
Henderson, Matthew and
Vuli{\'c}, Ivan},
booktitle = {Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI},
doi = {10.18653/v1/2020.nlp4convai-1.5},
editor = {Wen, Tsung-Hsien and
Celikyilmaz, Asli and
Yu, Zhou and
Papangelis, Alexandros and
Eric, Mihail and
Kumar, Anuj and
Casanueva, I{\~n}igo and
Shah, Rushin},
month = jul,
pages = {38--45},
publisher = {Association for Computational Linguistics},
title = {Efficient Intent Detection with Dual Sentence Encoders},
url = {https://aclanthology.org/2020.nlp4convai-1.5},
year = {2020},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
```
# Dataset Statistics
<details>
<summary> Dataset Statistics</summary>
The following code contains the descriptive statistics from the task. These can also be obtained using:
```python
import mteb
task = mteb.get_task("Banking77Classification")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"num_samples": 3080,
"number_of_characters": 167036,
"number_texts_intersect_with_train": 0,
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```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)* |
Svngoku/CheickAntaDiopOriginOfCivilization | Svngoku | 2025-05-03T19:57:11Z | 0 | 0 | [
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] | [] | 2025-05-03T19:57:09Z | null | ---
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---
|
cchoi1/kodcode-complete_1000_qwen7b_sol_iter0_att10_sol5_debug | cchoi1 | 2025-05-03T19:55:36Z | 18 | 0 | [
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] | [] | 2025-04-28T02:31:14Z | null | ---
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---
|
Bretagne/wikiann_br | Bretagne | 2025-05-03T19:40:12Z | 21 | 0 | [
"task_categories:token-classification",
"language:br",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"token-classification"
] | 2024-10-16T21:21:02Z | null | ---
dataset_info:
features:
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sequence: string
- name: ner_tags
sequence: int64
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num_examples: 952
download_size: 120493
dataset_size: 379384
configs:
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data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
language: br
task_categories:
- token-classification
---
### Description
Version nettoyée de [WikiAnn](https://huggingface.co/datasets/tner/wikiann).
En effet, la version originale contenait des leaks et des duplications.
De 1000 effectifs par split, la nouvelle répartition devient alors la suivante :
```
DatasetDict({
train: Dataset({
features: ['tokens', 'ner_tags'],
num_rows: 915
})
validation: Dataset({
features: ['tokens', 'ner_tags'],
num_rows: 946
})
test: Dataset({
features: ['tokens', 'ner_tags'],
num_rows: 952
})
})
```
### Label ID
Le dictionnaire label2id est disponible [ici](https://huggingface.co/datasets/tner/wikiann/raw/main/dataset/label.json).
```python
{
"B-LOC": 0,
"B-ORG": 1,
"B-PER": 2,
"I-LOC": 3,
"I-ORG": 4,
"I-PER": 5,
"O": 6
}
``` |
Samarth0710/neurips-2024-peer-reviews | Samarth0710 | 2025-05-03T18:22:43Z | 0 | 0 | [
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] | [] | 2025-05-03T18:14:28Z | null | ---
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path: data/train-*
---
|
HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_2_dataset_1_for_gen_16_v2 | HungVu2003 | 2025-05-03T18:10:38Z | 0 | 0 | [
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] | [] | 2025-05-03T18:10:36Z | null | ---
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path: data/train-*
---
|
HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_2_dataset_1_for_gen_9_v2 | HungVu2003 | 2025-05-03T17:33:25Z | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-03T17:33:23Z | null | ---
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---
|
HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_2_dataset_0_for_gen_8_v2 | HungVu2003 | 2025-05-03T17:28:03Z | 0 | 0 | [
"size_categories:10K<n<100K",
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"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-03T17:28:01Z | null | ---
dataset_info:
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path: data/train-*
---
|
dopaul/simple_pawn_move | dopaul | 2025-05-03T17:05:46Z | 0 | 0 | [
"task_categories:robotics",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"phosphobot",
"so100",
"phospho-dk"
] | [
"robotics"
] | 2025-05-03T16:58:26Z | null |
---
tags:
- phosphobot
- so100
- phospho-dk
task_categories:
- robotics
---
# simple_pawn_move
**This dataset was generated using a [phospho starter pack](https://robots.phospho.ai).**
This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot and RLDS.
|
labofsahil/aws-pricing-dataset | labofsahil | 2025-05-03T17:00:00Z | 231 | 0 | [
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"region:us",
"finance",
"aws",
"pricing"
] | [] | 2024-10-22T17:54:07Z | null | ---
license: mit
language:
- en
tags:
- finance
- aws
- pricing
pretty_name: AWS Pricing Dataset
size_categories:
- 1M<n<10M
configs:
- config_name: EC2
data_files:
- split: EC2
path: AmazonEC2.csv
---
The following data is pulled from AWS official pricing API.
Contains all pricing data across AWS services
Source: https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/using-price-list-query-api.html
Update Frequency: Gets auto updated weekly |
gunnybd01/Consumer_smr | gunnybd01 | 2025-05-03T16:58:57Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-02T15:21:02Z | null | ---
dataset_info:
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
xbilek25/hall_train_36000 | xbilek25 | 2025-05-03T16:41:35Z | 0 | 0 | [
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] | [] | 2025-05-03T16:38:01Z | null | ---
dataset_info:
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path: data/train-*
---
|
amekerishvili/ATCO2_full_files | amekerishvili | 2025-05-03T16:14:01Z | 0 | 0 | [
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] | [] | 2025-05-03T12:01:28Z | null | ---
dataset_info:
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path: data/validation-*
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path: data/test-*
---
|
Neelectric/OpenR1-Math-220k_CN-K12_OLMo-2_4096toks | Neelectric | 2025-05-03T16:01:00Z | 0 | 0 | [
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] | [] | 2025-05-03T16:00:07Z | null | ---
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---
|
TIMBER-Lab/Qwen2.5-7B-Instruct-Turbo_labeled_numina_difficulty_162K_10_selected | TIMBER-Lab | 2025-05-03T15:55:01Z | 0 | 0 | [
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"region:us"
] | [] | 2025-05-03T07:39:42Z | null | ---
dataset_info:
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---
|
FrancophonIA/Glossaire_pilotes_et_personne_services_circulation_aerienne | FrancophonIA | 2025-05-03T15:41:36Z | 0 | 0 | [
"task_categories:translation",
"language:fra",
"language:eng",
"region:us"
] | [
"translation"
] | 2025-05-03T15:40:52Z | null | ---
language:
- fra
- eng
viewer: false
task_categories:
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---
> [!NOTE]
> Dataset origin: https://publications.gc.ca/site/eng/9.693563/publication.html |
FrancophonIA/Glossaire_procedure_parlementaire | FrancophonIA | 2025-05-03T15:37:24Z | 0 | 0 | [
"task_categories:translation",
"language:fra",
"language:eng",
"region:us"
] | [
"translation"
] | 2025-05-03T15:36:48Z | null | ---
language:
- fra
- eng
viewer: false
task_categories:
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---
> [!NOTE]
> Dataset origin: https://publications.gc.ca/site/eng/9.693563/publication.html |
yalhessi/lemexp-task1-v2 | yalhessi | 2025-05-03T15:17:12Z | 105 | 0 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
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] | [] | 2025-04-28T22:30:08Z | null | ---
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---
|
FrancophonIA/Le-vocabulaire-s-acclimate | FrancophonIA | 2025-05-03T15:12:40Z | 6 | 0 | [
"task_categories:translation",
"language:fra",
"language:eng",
"region:us"
] | [
"translation"
] | 2025-04-28T20:17:05Z | null | ---
language:
- fra
- eng
viewer: false
task_categories:
- translation
---
> [!NOTE]
> Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Le-vocabulaire-s-acclimate |
FrancophonIA/Vocabulaire-de-la-biologie-2017 | FrancophonIA | 2025-05-03T15:11:38Z | 6 | 0 | [
"task_categories:translation",
"language:fra",
"language:eng",
"region:us"
] | [
"translation"
] | 2025-04-28T20:15:12Z | null | ---
language:
- fra
- eng
viewer: false
task_categories:
- translation
---
> [!NOTE]
> Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vocabulaire-de-la-biologie-2017
## Description
La Délégation générale à la langue française et aux langues de France publie pour la première fois un Vocabulaire de la biologie : 611 termes et définitions concernant des notions nouvelles dont beaucoup n’avaient pas de désignation en français. |
Maxscha/json-instruct-generation-large | Maxscha | 2025-05-03T15:03:37Z | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-03T15:03:31Z | null | ---
dataset_info:
features:
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dtype: string
- name: input
dtype: string
- name: output
dtype: string
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dtype: string
splits:
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num_examples: 50000
download_size: 31084332
dataset_size: 99642713
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
FrancophonIA/Vous-pouvez-le-dire-en-francais-Si-tu-veux-la-Paix | FrancophonIA | 2025-05-03T15:03:16Z | 6 | 0 | [
"task_categories:translation",
"language:fra",
"language:eng",
"region:us"
] | [
"translation"
] | 2025-04-28T21:38:49Z | null | ---
language:
- fra
- eng
viewer: false
task_categories:
- translation
---
> [!NOTE]
> Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vous-pouvez-le-dire-en-francais-Si-tu-veux-la-Paix |
FrancophonIA/Vocabulaire-de-l-education-et-de-la-recherche-2013 | FrancophonIA | 2025-05-03T14:58:25Z | 2 | 0 | [
"task_categories:translation",
"language:fra",
"language:eng",
"region:us"
] | [
"translation"
] | 2025-04-29T20:38:01Z | null | ---
language:
- fra
- eng
viewer: false
task_categories:
- translation
---
> [!NOTE]
> Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vocabulaire-de-l-education-et-de-la-recherche-2013 |
FrancophonIA/Vocabulaire-des-sports-2011 | FrancophonIA | 2025-05-03T14:47:04Z | 3 | 0 | [
"task_categories:translation",
"language:fra",
"language:eng",
"region:us"
] | [
"translation"
] | 2025-04-29T20:47:59Z | null | ---
language:
- fra
- eng
viewer: false
task_categories:
- translation
---
> [!NOTE]
> Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vocabulaire-des-sports-2011 |
jaeyong2/Math-Qwen3-06B-Ko | jaeyong2 | 2025-05-03T14:32:35Z | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-02T16:11:26Z | null | ---
dataset_info:
features:
- name: content
dtype: string
- name: response
sequence: string
splits:
- name: train
num_bytes: 384553697
num_examples: 2000
download_size: 124040666
dataset_size: 384553697
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Kamyar-zeinalipour/llama1b_kg | Kamyar-zeinalipour | 2025-05-03T14:23:34Z | 17 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-07T11:29:56Z | null | ---
dataset_info:
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- name: assistant_output
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- name: role
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num_examples: 50
download_size: 5134114
dataset_size: 13468156
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
kothasuhas/llp-gold-37m-1.5m_T32768.0_I32768 | kothasuhas | 2025-05-03T14:18:00Z | 0 | 0 | [
"size_categories:1M<n<10M",
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"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-03T14:17:12Z | null | ---
dataset_info:
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dtype: string
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configs:
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path: data/train-*
---
|
SayantanJoker/Shrutilipi_Hindi_resampled_44100_merged_5 | SayantanJoker | 2025-05-03T14:08:28Z | 38 | 0 | [
"size_categories:10K<n<100K",
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"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-01T17:01:51Z | null | ---
dataset_info:
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dtype: audio
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configs:
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path: data/train-*
---
|
anthonyav/so100-lego-v2 | anthonyav | 2025-05-03T14:08:02Z | 126 | 0 | [
"task_categories:robotics",
"size_categories:n<1K",
"modality:video",
"library:datasets",
"library:mlcroissant",
"region:us",
"phosphobot",
"so100",
"phospho-dk"
] | [
"robotics"
] | 2025-04-27T10:25:55Z | null |
---
tags:
- phosphobot
- so100
- phospho-dk
task_categories:
- robotics
---
# so100-lego-v2
**This dataset was generated using a [phospho starter pack](https://robots.phospho.ai).**
This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot and RLDS.
|
gaia-benchmark/results_public | gaia-benchmark | 2025-05-03T14:03:54Z | 2,615 | 14 | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2023-10-31T16:03:44Z | null | ---
dataset_info:
config_name: '2023'
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dtype: string
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dtype: string
splits:
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num_examples: 75
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num_bytes: 21842
num_examples: 102
download_size: 30216
dataset_size: 46087
configs:
- config_name: '2023'
data_files:
- split: validation
path: 2023/validation-*
- split: test
path: 2023/test-*
---
# Dataset Card for "resultspublic"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
alfonsusrr/DISC-Law-SFT-Alpaca | alfonsusrr | 2025-05-03T13:29:36Z | 79 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2309.11325",
"region:us"
] | [] | 2025-04-09T16:14:03Z | null | ---
dataset_info:
features:
- name: id
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 513113825
num_examples: 257201
- name: test
num_bytes: 56839924
num_examples: 28580
download_size: 285914010
dataset_size: 569953749
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Processed DISC-Law-SFT Dataset (Alpaca Format)
This repository provides a processed version of the [DISC-Law-SFT dataset](https://huggingface.co/datasets/ShengbinYue/DISC-Law-SFT) for easier usage in instruction tuning and aligned language model training. The dataset has been converted into the **Alpaca format**, which is commonly used for supervised fine-tuning of language models on instruction-following tasks.
## Dataset Description
The original DISC-Law-SFT dataset was proposed for developing intelligent legal service systems with large language models. This processed version reorganizes the data into the Alpaca format:
```json
{
"instruction": "Instruction/question to the model",
"input": "Optional context or additional input",
"output": "Expected model response"
}
```
The conversion makes it easier to fine-tune models like LLaMA, Mistral, or other instruction-following LLMs.
## Source Files
The processed dataset is derived from the following files in the original DISC-Law-SFT dataset:
- DISC-Law-SFT-Pair.jsonl
- DISC-Law-SFT-Pair-QA-released.jsonl
- DISC-Law-SFT-Triplet-released.jsonl
- DISC-Law-SFT-Triplet-QA-released.jsonl
These files contain pairs and triplets of legal questions and answers, manually annotated or curated for fine-tuning.
## Citation
If you use this dataset or any derivative of DISC-Law-SFT, please cite the original authors:
```bibtex
@misc{yue2023disclawllm,
title={DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services},
author={Shengbin Yue and Wei Chen and Siyuan Wang and Bingxuan Li and Chenchen Shen and Shujun Liu and Yuxuan Zhou and Yao Xiao and Song Yun and Xuanjing Huang and Zhongyu Wei},
year={2023},
eprint={2309.11325},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@inproceedings{yue2024lawllm,
title={LawLLM: Intelligent Legal System with Legal Reasoning and Verifiable Retrieval},
author={Yue, Shengbin and Liu, Shujun and Zhou, Yuxuan and Shen, Chenchen and Wang, Siyuan and Xiao, Yao and Li, Bingxuan and Song, Yun and Shen, Xiaoyu and Chen, Wei and others},
booktitle={International Conference on Database Systems for Advanced Applications},
pages={304--321},
year={2024},
organization={Springer}
}
```
## License
Refer to the original dataset license for usage restrictions. |
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