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kaiwenw/distill-r1-qwen-1.5b-hmmt-feb-24-4096-with-old-prm-indices_30720_38400 | kaiwenw | 2025-05-05T18:56:21Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T18:56:08Z | null | ---
dataset_info:
features:
- name: message_id
dtype: string
- name: problem
dtype: string
- name: answer
dtype: string
- name: processed_answer
dtype: string
- name: responses
dtype: string
- name: reward
dtype: bool
- name: prompt_len
dtype: int64
- name: response_len
dtype: int64
- name: classifier_scores
sequence: float64
splits:
- name: train
num_bytes: 1120119801
num_examples: 7680
download_size: 265485450
dataset_size: 1120119801
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Aman6u5/bucket | Aman6u5 | 2025-05-05T12:54:48Z | 0 | 0 | [
"license:apache-2.0",
"size_categories:n<1K",
"modality:video",
"library:datasets",
"library:mlcroissant",
"region:us"
] | [] | 2025-05-05T11:12:57Z | null | ---
license: apache-2.0
---
|
herwoww/MultiDiac | herwoww | 2025-05-05T09:45:55Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T09:41:16Z | null | ---
configs:
- config_name: yor
data_files:
- split: train
path: yor_train.csv
# - split: test
# path: yor_test.csv
- split: dev
path: yor_dev.csv
# - config_name: ara
# data_files:
# - split: test
# path: ara_test.csv
--- |
dgambettaphd/D_llm2_gen10_WXS_doc1000_synt64_lr1e-04_acm_MPP | dgambettaphd | 2025-05-05T08:58:53Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T08:58:25Z | null | ---
dataset_info:
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- name: text
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splits:
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download_size: 8995691
dataset_size: 14923305
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Denhotech/denes_data | Denhotech | 2025-05-05T07:07:32Z | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-05T06:56:15Z | null | ---
dataset_info:
features:
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dtype: string
- name: Age
dtype: int64
- name: City
dtype: string
- name: Score
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num_bytes: 734
num_examples: 20
download_size: 2104
dataset_size: 734
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Yuyeong/rw_roman-empire_node2vec_1_mask_public | Yuyeong | 2025-05-05T06:59:25Z | 0 | 0 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-05T06:50:27Z | null | ---
dataset_info:
features:
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dtype: string
- name: label
dtype:
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'1': '1'
'2': '2'
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'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
'9': '9'
'10': '10'
'11': '11'
'12': '12'
'13': '13'
'14': '14'
'15': '15'
'16': '16'
'17': '17'
- name: group_idx
dtype: int64
- name: node_idx
dtype: int64
splits:
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num_bytes: 3825069706.0214458
num_examples: 2264100
- name: validation
num_bytes: 3606793567.150384
num_examples: 2134900
- name: test
num_bytes: 3602232068.8922424
num_examples: 2132200
download_size: 3312737632
dataset_size: 11034095342.064072
---
# Dataset Card for "rw_roman-empire_node2vec_1_mask_public"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Rakuto/DailyTalkContiguous-ja | Rakuto | 2025-05-05T04:51:27Z | 0 | 0 | [
"task_categories:automatic-speech-recognition",
"language:ja",
"license:cc-by-sa-4.0",
"arxiv:2207.01063",
"region:us"
] | [
"automatic-speech-recognition"
] | 2025-05-04T16:37:39Z | null | ---
license: cc-by-sa-4.0
task_categories:
- automatic-speech-recognition
language:
- ja
---
# DailyTalkContiguous-ja: Spoken Dialogue Dataset in Japanese
DailyTalkContiguous-ja is a synthetic multi-turn Japanese conversational speech dataset in which [DailyTalk](https://arxiv.org/abs/2207.01063) [Keon Lee etal., 2022]
translated by [Gemma-3-27B](https://huggingface.co/google/gemma-3-27b-it) and speech data is synthesized by TTS engine [Zyphra/Zonos-v0.1-transformer](https://github.com/Zyphra/Zonos).
For each speaker in covnersation, different voice is randomly asssigned from voice dataset with five voices in total.
As like with [kyutai/DailyTalkContiguous](https://huggingface.co/datasets/kyutai/DailyTalkContiguous), rather than having separate files for each speaker's turn, this uses a stereo file for each conversation.
The two speakers in a conversation are put separately on the left and right channels.
**Dataset size**: 25 hours speech with 2.5k conversation |
Eluza133/A12d12s12 | Eluza133 | 2025-05-05T04:02:26Z | 2,965 | 0 | [
"license:apache-2.0",
"size_categories:n<1K",
"modality:image",
"modality:video",
"library:datasets",
"library:mlcroissant",
"region:us"
] | [] | 2025-02-27T15:03:01Z | null | ---
license: apache-2.0
---
|
aiwithvarun7/theekkathir-text-dataset | aiwithvarun7 | 2025-05-05T02:23:26Z | 3,388 | 1 | [
"task_categories:text-generation",
"language:ta",
"license:cc-by-nc-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-generation"
] | 2024-11-02T17:48:43Z | null | ---
license: cc-by-nc-4.0
dataset_info:
features:
- name: தேதி
dtype: string
- name: தலைப்பு
dtype: string
- name: செய்தி-வகை
dtype: string
- name: எழுத்தாளர்
dtype: string
- name: இணைப்பு
dtype: string
- name: மொழி
dtype: string
- name: குறிமுறை தரநிலை
dtype: string
- name: உள்ளடக்கம்
dtype: string
- name: சேகரிக்கப்பட்ட தேதி
dtype: string
configs:
- config_name: sample parquets
data_files: TheekkathirDataset/parquets/*.parquet
language:
- ta
task_categories:
- text-generation
size_categories:
- 100K<n<1M
---
<h1 align="center"><b>theekkathir-text-dataset <-> தீக்கதிர் தரவுத்தொகுப்பு</b></h1>
<p align="center">
<img src="https://github.com/user-attachments/assets/3731edf1-70b9-4e0a-98c1-6b89c4e03395" />
</p>
---
<a href="https://github.com/vishnumur777/theekkathir-text-dataset/tree/main">
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/64d848ce620c17bfa092e051/4ySVV0-jiAT_P3iIde0ei.png" alt="hugging face group" width="500px" height="700px"/>
</p>
</a>
<h2 align="center">Click above button to view GitHub Repository</h2>
<h3>இலக்கு:</h3>
இந்த திட்டத்தின் இலக்கு தீக்கதிர் இதழின் செய்தி கட்டுரைகளை தரவுத்தொகுப்பாக மாற்றுவதாகும், இது இயற்கை மொழி பதிவு (NLP) மற்றும் LLM ஆராய்ச்சி நோக்கங்களுக்கு பயன்படுத்தப்படலாம்.
<h3>Goal:</h3>
The goal of the project is to convert news articles from theekkathir magazine into dataset, which can be used for Natural Language Processing (NLP) and LLM research purposes
# Columns in .parquet
- வெளியிட்ட தேதி (Released Date)
- தலைப்பு (Title)
- செய்தி வகை (Categories)
- எழுத்தாளர் (Author)
- மொழி (Language)
- குறிமுறைத் தரநிலை (Character Encoding)
- உள்ளடக்கம் (Content)
- சேகரிக்கப்பட்ட தேதி (Scraped Date)
### You can also get [texts](https://huggingface.co/datasets/aiwithvarun7/theekkathir-text-dataset/tree/main/TheekkathirDataset/texts) apart from parquet files.
# How to Contribute
If you want to contribute to this project, Contact me via [LinkedIn](https://linkedin.com/in/varun-muralidhar)
- If possible, write CONTRIBUTING.md and make Pull Request here.
- Able to Read and Write Tamil.
- Follow [Medium](https://medium.com/@VARUNMURALIDHAR), For detailed documentation and I will update on any contribution.
- Raise issues and PR, if possible.
# எவ்வாறு பங்களிக்கலாம்
இந்த திட்டத்திற்கு பங்களிக்க விரும்பினால், [LinkedIn](https://linkedin.com/in/varun-muralidhar) மூலம் என்னை தொடர்பு கொள்ளவும்.
- தமிழ் மொழியை படிக்க, எழுத தெரிய வேண்டும்.
- சாத்தியமானால், CONTRIBUTING.md எழுதி இங்கு Pull Request செய்யவும்.
- விரிவான ஆவணங்களுக்காக [Medium](https://medium.com/@VARUNMURALIDHAR) பின்தொடரவும். நான் எந்தவொரு பங்களிப்பையும் புதுப்பிக்கிறேன்.
- சாத்தியமானால், பிரச்சினைகளையும் PR (Pull Request) யையும் உயர்த்தவும். |
THU-ATOM/DrugCLIP_data | THU-ATOM | 2025-05-05T02:12:28Z | 100 | 0 | [
"license:cc-by-4.0",
"region:us"
] | [] | 2024-08-29T06:36:29Z | null | ---
license: cc-by-4.0
---
---
license: cc-by-4.0
---
# 🧬 DrugCLIP data repository
This repository hosts benchmark datasets, pre-computed molecular embeddings, pretrained model weights, and supporting files used in the **DrugCLIP** project. It also includes data and models used for **wet lab validation experiments**.
---
## 📁 Repository Contents
### 1. `DUD-E.zip`
- Full dataset for the **DUD-E benchmark**.
- Includes ligand and target files for all targets.
---
### 2. `LIT-PCBA.zip`
- Full dataset for the **LIT-PCBA benchmark**.
- Includes ligand and target files for all targets.
---
### 3. `encoded_mol_embs.zip`
- Pre-encoded molecular embeddings from the **ChemDiv** compound library.
- Each `.pkl` file contains:
- `name_list`: `[hitid, SMILES]`
- `embedding_list`: list of **128-dimensional** vectors
- Versions included:
- **8-fold** version of the full ChemDiv library
- **6-fold** version of the full ChemDiv library
- **6-fold** version of a filtered ChemDiv library
---
### 4. `benchmark_weights.zip`
Contains **pretrained model weights** for **benchmark experiments** on the DUD-E and LIT-PCBA datasets using various ligand and target filtering strategies.
#### 🔬 DUD-E: Ligand Filtering Strategies
| Filename | Description |
|----------------------|-------------|
| `dude_ecfp_90.pt` | Trained by removing ligands with **ECFP4 similarity > 0.9**. |
| `dude_ecfp_60.pt` | Trained by removing ligands with **ECFP4 similarity > 0.6**. |
| `dude_ecfp_30.pt` | Trained by removing ligands with **ECFP4 similarity > 0.3**. |
| `dude_scaffold.pt` | Trained by removing ligands sharing **scaffolds** with test set. |
#### 🧬 DUD-E: Target Filtering Strategies
| Filename | Description |
|------------------------|-------------|
| `dude_identity_90.pt` | Removed targets with **MMseqs2 identity > 0.9**. |
| `dude_identity_60.pt` | Removed targets with **MMseqs2 identity > 0.6**. |
| `dude_identity_30.pt` | Removed targets with **MMseqs2 identity > 0.3**. |
| `dude_identity_0.pt` | Removed targets based on **HMMER sequence identity**. |
#### 🧪 LIT-PCBA: Target Filtering Strategy
| Filename | Description |
|-------------------------|-------------|
| `litpcba_identity_90.pt`| Removed targets with **MMseqs2 identity > 0.9**. |
---
### 5. `model_weights.zip`
Contains model weights trained specifically for **wet lab experiments**. These models were trained using:
- **6-fold** data splits
- **8-fold** data splits
Used to predict compounds validated in real-world assays for the following targets:
- `5HT2a`
- `NET`
- `Trip12`
---
### 6. `WetLab_PDBs_and_LMDBs`
Target data used for wet lab validation experiments:
- **LMDB files**: For DrugCLIP screening
Includes data for:
- `5HT2a`
- `NET`
- `Trip12`
---
### 7. `benchmark_throughput`
Files for reproducing throughput benchmark results.
|
john-1111/x_dataset_060792 | john-1111 | 2025-05-05T01:29:48Z | 273 | 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",
"source_datasets:original",
"license:mit",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-01-25T07:15:47Z | 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:** john-1111/x_dataset_060792
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5GKGMoTKZRasLaJevQBAEDksBj7RGDgvVb9zqkY3ygXtx3bo
### Miner Data Compliance Agreement
In uploading this dataset, I am agreeing to the [Macrocosmos Miner Data Compliance Policy](https://github.com/macrocosm-os/data-universe/blob/add-miner-policy/docs/miner_policy.md).
### 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{john-11112025datauniversex_dataset_060792,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={john-1111},
year={2025},
url={https://huggingface.co/datasets/john-1111/x_dataset_060792},
}
```
### 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:** 3197762
- **Date Range:** 2025-01-02T00:00:00Z to 2025-04-25T00:00:00Z
- **Last Updated:** 2025-05-05T01:29:48Z
### Data Distribution
- Tweets with hashtags: 5.04%
- Tweets without hashtags: 94.96%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 1261593 | 88.67% |
| 2 | #granhermano | 10301 | 0.72% |
| 3 | #riyadh | 9374 | 0.66% |
| 4 | #箱根駅伝 | 8147 | 0.57% |
| 5 | #thameposeriesep9 | 7605 | 0.53% |
| 6 | #tiktok | 6765 | 0.48% |
| 7 | #ad | 5367 | 0.38% |
| 8 | #zelena | 4878 | 0.34% |
| 9 | #smackdown | 4844 | 0.34% |
| 10 | #कबीर_परमेश्वर_निर्वाण_दिवस | 4843 | 0.34% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-01-25T07:14:13Z | 414446 | 414446 |
| 2025-01-25T07:14:44Z | 453526 | 867972 |
| 2025-01-25T07:15:15Z | 453526 | 1321498 |
| 2025-01-25T07:15:45Z | 453526 | 1775024 |
| 2025-01-25T07:16:15Z | 453526 | 2228550 |
| 2025-02-18T03:38:58Z | 471834 | 2700384 |
| 2025-05-05T01:29:48Z | 497378 | 3197762 |
|
rainbowbridge/x_dataset_57071 | rainbowbridge | 2025-05-05T01:11:58Z | 1,300 | 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",
"source_datasets:original",
"license:mit",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-01-27T00:25:54Z | 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_57071
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5F2S4Xnn1UqWXhWmdu1kgfeu1ZpFoQEYbxF8oCNpRHnMZNar
### 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_57071,
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_57071},
}
```
### 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:** 46728701
- **Date Range:** 2025-01-21T00:00:00Z to 2025-02-09T00:00:00Z
- **Last Updated:** 2025-02-18T18:45:20Z
### Data Distribution
- Tweets with hashtags: 43.62%
- Tweets without hashtags: 56.38%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 26346933 | 56.38% |
| 2 | #riyadh | 326612 | 0.70% |
| 3 | #zelena | 248547 | 0.53% |
| 4 | #tiktok | 199186 | 0.43% |
| 5 | #bbb25 | 123042 | 0.26% |
| 6 | #ad | 115507 | 0.25% |
| 7 | #granhermano | 68204 | 0.15% |
| 8 | #jhope_at_galadespiècesjaunes | 67706 | 0.14% |
| 9 | #bbmzansi | 63947 | 0.14% |
| 10 | #pr | 61448 | 0.13% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-01-27T00:26:55Z | 3588990 | 3588990 |
| 2025-01-30T12:29:25Z | 8527338 | 12116328 |
| 2025-02-03T00:32:41Z | 9724909 | 21841237 |
| 2025-02-06T12:35:39Z | 7123646 | 28964883 |
| 2025-02-10T00:39:12Z | 9349448 | 38314331 |
| 2025-02-13T12:43:01Z | 6970444 | 45284775 |
| 2025-02-18T03:44:05Z | 636505 | 45921280 |
| 2025-02-18T18:45:20Z | 807421 | 46728701 |
|
marry-1111/x_dataset_0502178 | marry-1111 | 2025-05-05T01:07:56Z | 316 | 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",
"source_datasets:original",
"license:mit",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-01-25T07:14:45Z | 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:** marry-1111/x_dataset_0502178
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5HmfhkSP5knw1QcrE8udotNj1C9JD2rriUKtWT7DmigBdr8A
### Miner Data Compliance Agreement
In uploading this dataset, I am agreeing to the [Macrocosmos Miner Data Compliance Policy](https://github.com/macrocosm-os/data-universe/blob/add-miner-policy/docs/miner_policy.md).
### 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{marry-11112025datauniversex_dataset_0502178,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={marry-1111},
year={2025},
url={https://huggingface.co/datasets/marry-1111/x_dataset_0502178},
}
```
### 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:** 3234371
- **Date Range:** 2025-01-02T00:00:00Z to 2025-04-25T00:00:00Z
- **Last Updated:** 2025-05-05T01:07:55Z
### Data Distribution
- Tweets with hashtags: 4.32%
- Tweets without hashtags: 95.68%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 1261593 | 90.03% |
| 2 | #granhermano | 10301 | 0.74% |
| 3 | #箱根駅伝 | 8147 | 0.58% |
| 4 | #thameposeriesep9 | 7605 | 0.54% |
| 5 | #tiktok | 5032 | 0.36% |
| 6 | #zelena | 4878 | 0.35% |
| 7 | #smackdown | 4844 | 0.35% |
| 8 | #कबीर_परमेश्वर_निर्वाण_दिवस | 4843 | 0.35% |
| 9 | #ad | 3533 | 0.25% |
| 10 | #delhielectionresults | 3476 | 0.25% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-01-25T07:13:13Z | 454010 | 454010 |
| 2025-01-25T07:13:46Z | 471976 | 925986 |
| 2025-01-25T07:14:15Z | 453526 | 1379512 |
| 2025-01-25T07:14:44Z | 453526 | 1833038 |
| 2025-01-25T07:15:13Z | 453526 | 2286564 |
| 2025-02-18T03:39:05Z | 471834 | 2758398 |
| 2025-05-05T01:07:55Z | 475973 | 3234371 |
|
robert-1111/x_dataset_040752 | robert-1111 | 2025-05-05T00:55:52Z | 165 | 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",
"source_datasets:original",
"license:mit",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-01-25T07:11:28Z | 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:** robert-1111/x_dataset_040752
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5Gq8xaWKd8cNHFkD8Mt38BjL1dzBGi8ZhdfMskmv3v2H5hLC
### Miner Data Compliance Agreement
In uploading this dataset, I am agreeing to the [Macrocosmos Miner Data Compliance Policy](https://github.com/macrocosm-os/data-universe/blob/add-miner-policy/docs/miner_policy.md).
### 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{robert-11112025datauniversex_dataset_040752,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={robert-1111},
year={2025},
url={https://huggingface.co/datasets/robert-1111/x_dataset_040752},
}
```
### 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:** 2636450
- **Date Range:** 2025-01-02T00:00:00Z to 2025-04-25T00:00:00Z
- **Last Updated:** 2025-05-05T00:55:52Z
### Data Distribution
- Tweets with hashtags: 5.36%
- Tweets without hashtags: 94.64%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 1251882 | 89.86% |
| 2 | #箱根駅伝 | 8147 | 0.58% |
| 3 | #thameposeriesep9 | 7605 | 0.55% |
| 4 | #riyadh | 7255 | 0.52% |
| 5 | #tiktok | 6802 | 0.49% |
| 6 | #nfldraft2025 | 6802 | 0.49% |
| 7 | #ad | 5266 | 0.38% |
| 8 | #zelena | 4878 | 0.35% |
| 9 | #smackdown | 4844 | 0.35% |
| 10 | #कबीर_परमेश्वर_निर्वाण_दिवस | 4843 | 0.35% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-01-25T07:10:27Z | 414446 | 414446 |
| 2025-01-25T07:10:56Z | 414446 | 828892 |
| 2025-01-25T07:11:27Z | 414446 | 1243338 |
| 2025-01-25T07:11:56Z | 453526 | 1696864 |
| 2025-02-18T03:38:23Z | 471834 | 2168698 |
| 2025-05-05T00:55:52Z | 467752 | 2636450 |
|
semran1/calibration_test | semran1 | 2025-05-05T00:48:33Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T00:48:21Z | null | ---
dataset_info:
features:
- name: text
dtype: string
- name: cc-path
dtype: string
- name: domain
dtype: string
- name: lang
dtype: string
- name: lang_score
dtype: float64
- name: timestamp
dtype: string
- name: url
dtype: string
- name: math_score
dtype: float64
- name: type
dtype: string
splits:
- name: train
num_bytes: 205426140.0
num_examples: 50000
download_size: 106854338
dataset_size: 205426140.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ieuniversity/group_7_submission | ieuniversity | 2025-05-05T00:11:00Z | 403 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-02-19T13:43:26Z | null | ---
dataset_info:
features:
- name: ID
dtype: int64
- name: CLASE
dtype: string
splits:
- name: train
num_bytes: 580197
num_examples: 25808
download_size: 176229
dataset_size: 580197
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sophiayk20/repetition-one-speaker | sophiayk20 | 2025-05-04T23:31:47Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T23:27:58Z | null | ---
dataset_info:
features:
- name: id
dtype: string
- name: dialogue
dtype: string
- name: disfluent_dialogue
dtype: string
- name: summary
dtype: string
splits:
- name: ATAS
num_bytes: 2373554
num_examples: 1500
- name: ATOS
num_bytes: 2373554
num_examples: 1500
- name: OTAS
num_bytes: 2666795
num_examples: 1500
- name: OTOS
num_bytes: 2666795
num_examples: 1500
download_size: 2126064
dataset_size: 10080698
configs:
- config_name: default
data_files:
- split: ATAS
path: data/ATAS-*
- split: ATOS
path: data/ATOS-*
- split: OTAS
path: data/OTAS-*
- split: OTOS
path: data/OTOS-*
---
|
sortl005/Superconductor | sortl005 | 2025-05-04T23:30:39Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T23:30:29Z | null | ---
dataset_info:
features:
- name: x
sequence: float64
- name: y
dtype: float64
splits:
- name: train
num_bytes: 10716300
num_examples: 15309
download_size: 290587
dataset_size: 10716300
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
marcuscedricridia/OpenMathInstruct-2-sampled-balanced | marcuscedricridia | 2025-05-04T23:28:57Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T23:28:02Z | null | ---
dataset_info:
features:
- name: problem
dtype: string
- name: generated_solution
dtype: string
- name: problem_source
dtype: string
splits:
- name: train
num_bytes: 1103869.0114952696
num_examples: 1000
download_size: 450651
dataset_size: 1103869.0114952696
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
momo1942/x_dataset_19124 | momo1942 | 2025-05-04T23:21:24Z | 1,239 | 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",
"source_datasets:original",
"license:mit",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-01-27T06:34:57Z | 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:** momo1942/x_dataset_19124
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5FvdP3P4KfWBM4YSPM5SaL5eGThA6g2NUwvPzMeCq6WRY9TD
### 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{momo19422025datauniversex_dataset_19124,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={momo1942},
year={2025},
url={https://huggingface.co/datasets/momo1942/x_dataset_19124},
}
```
### 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:** 58904296
- **Date Range:** 2025-01-21T00:00:00Z to 2025-02-12T00:00:00Z
- **Last Updated:** 2025-02-18T19:00:41Z
### Data Distribution
- Tweets with hashtags: 42.09%
- Tweets without hashtags: 57.91%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 34109059 | 57.91% |
| 2 | #riyadh | 360800 | 0.61% |
| 3 | #zelena | 305824 | 0.52% |
| 4 | #tiktok | 239491 | 0.41% |
| 5 | #bbb25 | 173195 | 0.29% |
| 6 | #ad | 137871 | 0.23% |
| 7 | #granhermano | 94348 | 0.16% |
| 8 | #bbmzansi | 80293 | 0.14% |
| 9 | #jhope_at_galadespiècesjaunes | 74039 | 0.13% |
| 10 | #pr | 72837 | 0.12% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-01-27T06:36:14Z | 4698495 | 4698495 |
| 2025-01-30T18:39:09Z | 9621596 | 14320091 |
| 2025-02-03T06:43:27Z | 12152156 | 26472247 |
| 2025-02-06T18:48:17Z | 12216961 | 38689208 |
| 2025-02-10T06:51:17Z | 6273827 | 44963035 |
| 2025-02-13T18:56:05Z | 12477634 | 57440669 |
| 2025-02-18T03:59:19Z | 829865 | 58270534 |
| 2025-02-18T19:00:41Z | 633762 | 58904296 |
|
asafxrev/so100_jenga_box_simple | asafxrev | 2025-05-04T22:39:48Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"region:us",
"LeRobot",
"so100",
"tutorial"
] | [
"robotics"
] | 2025-05-04T22:39:45Z | 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",
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"fps": 30,
"splits": {
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},
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"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
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6
],
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"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
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"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.follower_cam": {
"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,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
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],
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},
"episode_index": {
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],
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},
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],
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},
"task_index": {
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1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
mehmet0001/github-commits-dataset | mehmet0001 | 2025-05-04T21:23:38Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T21:23:18Z | null | ---
dataset_info:
features:
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download_size: 393630553
dataset_size: 1325874280
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
MBZUAI-IFM/riddlesenseplusplus | MBZUAI-IFM | 2025-05-04T20:52:38Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T20:52:36Z | null | ---
dataset_info:
features:
- name: conversations
list:
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splits:
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num_examples: 3508
download_size: 6754856
dataset_size: 13981243
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
xbilek25/train_hall_absorb_0.7_14400_18000 | xbilek25 | 2025-05-04T20:48:22Z | 0 | 0 | [
"size_categories:1K<n<10K",
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] | [] | 2025-05-04T16:38:08Z | null | ---
dataset_info:
features:
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configs:
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---
|
HungVu2003/opt-350m_beta_0.0_alpha_0.2_num-company_2_dataset_1_for_gen_11_v2 | HungVu2003 | 2025-05-04T20:33:24Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T20:33:22Z | null | ---
dataset_info:
features:
- name: question
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splits:
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download_size: 992842
dataset_size: 3159133
configs:
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data_files:
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path: data/train-*
---
|
doublesizebed/process_dataset_mini | doublesizebed | 2025-05-04T19:04:53Z | 3 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-03T19:06:38Z | null | ---
dataset_info:
features:
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dtype: string
- name: prompt
dtype: string
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dtype: string
- name: gender
dtype: string
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dtype: string
splits:
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download_size: 1073280024
dataset_size: 1076070425.0
configs:
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data_files:
- split: train
path: data/train-*
---
|
BasedLukas/so101_test_2 | BasedLukas | 2025-05-04T18:55:36Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"region:us",
"LeRobot",
"so101",
"tutorial"
] | [
"robotics"
] | 2025-05-04T18:55:26Z | 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",
"total_episodes": 2,
"total_frames": 896,
"total_tasks": 1,
"total_videos": 4,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:2"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.laptop": {
"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,
"has_audio": false
}
},
"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,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
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},
"frame_index": {
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1
],
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},
"episode_index": {
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],
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},
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1
],
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},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
RafaelJaime/sas_opposition_exam_data | RafaelJaime | 2025-05-04T17:48:04Z | 376 | 0 | [
"language:es",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"medical"
] | [] | 2025-03-21T14:57:39Z | null | ---
dataset_info:
features:
- name: statement
dtype: string
- name: answers
sequence: string
- name: correct_answer
dtype: string
- name: theme
dtype: string
- name: version
dtype: string
splits:
- name: train
num_bytes: 5128074
num_examples: 10712
download_size: 2407181
dataset_size: 5128074
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
language:
- es
tags:
- medical
---
# SAS Opposition Exam Dataset
This dataset contains questions and answers from all the exams of the SAS (Servicio Andaluz de Salud) public job offers. The questions and answers are sourced from the official webpage of the Andalusian Health Service [here](https://www.sspa.juntadeandalucia.es/servicioandaluzdesalud/profesionales/ofertas-de-empleo/oferta-de-empleo-publico-puestos-base/oep-extraordinaria-decreto-ley-122022-centros-sas/cuadro-de-evolucion-concurso-oposicion-centros-sas).
## Dataset Information
- **Statement**: The question in the exam.
- **Answers**: The possible answers for the question.
- **Real Answer**: The correct answer for the question.
- **Theme**: The topic or subject of the question.
### Dataset Creation Script
The script used to create this dataset can be found at: [generation_script.py](https://huggingface.co/datasets/RafaelJaime/sas_opposition_exam_data/blob/main/generation_script.py). |
zenml/llmops-database | zenml | 2025-05-04T17:25:45Z | 441 | 18 | [
"task_categories:feature-extraction",
"task_categories:summarization",
"task_categories:text-classification",
"task_categories:text-generation",
"task_ids:news-articles-summarization",
"task_ids:news-articles-headline-generation",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"task_ids:topic-classification",
"task_ids:language-modeling",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"llmops",
"mlops",
"llms",
"production",
"devops",
"use-case",
"case-study"
] | [
"feature-extraction",
"summarization",
"text-classification",
"text-generation"
] | 2024-12-04T13:27:02Z | null | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: LLMOps Database
size_categories:
- n<1K
source_datasets: []
tags:
- llmops
- mlops
- llms
- production
- devops
- use-case
- case-study
task_categories:
- feature-extraction
- summarization
- text-classification
- text-generation
task_ids:
- news-articles-summarization
- news-articles-headline-generation
- multi-class-classification
- multi-label-classification
- topic-classification
- language-modeling
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: created_at
dtype: string
- name: title
dtype: string
- name: industry
dtype: string
- name: year
dtype: int64
- name: source_url
dtype: string
- name: company
dtype: string
- name: application_tags
dtype: string
- name: tools_tags
dtype: string
- name: extra_tags
dtype: string
- name: techniques_tags
dtype: string
- name: short_summary
dtype: string
- name: full_summary
dtype: string
splits:
- name: train
num_bytes: 4088062
num_examples: 665
download_size: 1910660
dataset_size: 4088062
---
# The ZenML LLMOps Database
## Dataset Description
- **Browse dataset:** https://www.zenml.io/llmops-database
- **Launch blog post:** https://www.zenml.io/blog/demystifying-llmops-a-practical-database-of-real-world-generative-ai-implementations
- **Point of Contact:** llmopsdatabase at zenml.io
To learn more about ZenML and our open-source MLOps framework, visit
[zenml.io](https://zenml.io).
### Dataset Summary
The LLMOps Database is a comprehensive collection of over 500 real-world
generative AI implementations that showcases how organizations are successfully
deploying Large Language Models (LLMs) in production. The case studies have been
carefully curated to focus on technical depth and practical problem-solving,
with an emphasis on implementation details rather than marketing content. The
database aims to bridge the gap between theoretical discussions and practical
deployments, providing valuable insights for technical teams looking to
implement LLMs in production.
The LLMOps Database is maintained by the [ZenML](https://zenml.io) team. The
dataset is duplicated here on Hugging Face for those who would prefer to access
the data offline and/or browse it programmatically.
[](https://zenml.io)
### Usage Notes
- The full dataset is a Hugging Face `Dataset` which contains all the summaries
and metadata. Use this as you would any other Hugging Face `Dataset`. All the
entries are presented in a single split.
- Separately, the case studies are also presented as individual markdown files
inside this repository within the `markdown_data` folder. To browse and use
these locally you'll need to clone the repository.
- These markdown files have been concatenated into a single `.txt` file for your
convenience which is `all_data_single_file.txt` at the root of this
repository. You might want to play around with uploading this file into
[NotebookLM](https://notebooklm.google.com/), for example, or into a model
like Google's Gemini Pro which you can then use to in a chat interface. Note
that you'll have to use a model that can handle a very large context window
since as of currently writing this file contains around 200,000 words.
### Supported Tasks and Leaderboards
This dataset does not have any specific associated leaderboards or tasks. It is primarily intended as a resource for learning about real-world LLM deployments and the challenges and solutions involved.
### Languages
The case studies in the LLMOps database are exclusively in English.
## Dataset Structure
### Data Instances
A typical data instance in the LLMOps database includes the following fields:
```json
{
"created_at": "2024-12-03T13:19:00.000Z",
"title": "Scaling AI Image Animation System with Optimized Latency and Traffic Management",
"industry": "Tech",
"year": 2024,
"source_url": "https://engineering.fb.com/2024/08/14/production-engineering/how-meta-animates-ai-generated-images-at-scale/",
"company": "meta",
"application_tags": "realtime_application,high_stakes_application",
"tools_tags": "pytorch,monitoring,load_balancing,scaling,reliability,scalability",
"extra_tags": "pytorch,deployment,optimization,scaling,gpu,load balancing,traffic management,latency optimization,model distillation,inference",
"techniques_tags": "model_optimization,latency_optimization,cost_optimization,error_handling,fallback_strategies",
"short_summary": "Meta developed and deployed an AI-powered image animation feature that needed to serve billions of users efficiently. They tackled this challenge through a comprehensive optimization strategy including floating-point precision reduction, temporal-attention improvements, DPM-Solver implementation, and innovative distillation techniques. The system was further enhanced with sophisticated traffic management and load balancing solutions, resulting in a highly efficient, globally scalable service with minimal latency and failure rates.",
"full_summary": "# Meta: Scaling AI Image Animation System with Optimized Latency and Traffic Management (2024)\n\nhttps://engineering.fb.com/2024/08/14/production-engineering/how-meta-animates-ai-generated-images-at-scale/\n\n..."
}
```
The `full_summary` field contains a detailed writeup of the case study, which is truncated here for brevity.
### Data Fields
Each case study includes the following fields:
- `created_at`: Timestamp of when the entry was created
- `title`: Title of the case study
- `industry`: Industry or domain the case study belongs to
- `year`: Year the case study was published or the work was done
- `source_url`: URL to the original source of the case study
- `company`: Company or organization that conducted the work
- `application_tags`: Tags related to the application or use case
- `tools_tags`: Tags for the specific tools or technologies used
- `extra_tags`: Additional relevant tags
- `techniques_tags`: Tags for the techniques or approaches applied
- `short_summary`: Brief summary of the case study
- `full_summary`: Detailed writeup of the case study
### Data Splits
The LLMOps database currently contains a single collection of >500 case studies, with no defined splits like train/validation/test sets.
## Dataset Creation
### Curation Rationale
The LLMOps Database was created to provide practical, implementation-focused insights into deploying LLMs in production environments. While theoretical discussions about LLMs are abundant, technical teams need concrete information to guide their deployment decisions. By curating and summarizing real-world case studies, the database aims to advance the shared understanding of open-source LLMOps solutions and best practices.
### Source Data
#### Initial Data Collection and Normalization
The case studies in the LLMOps Database have been hand-curated by following relevant discussions on Twitter and Discord channels. [Exa.ai](https://exa.ai) was also used to perform embeddings-based similarity search to find additional relevant sources. The criteria for inclusion focused on technical depth and practical applicability, with an emphasis on detailed implementations, architectural decisions, and real challenges faced by engineering teams.
The original source content was either the full text of a blog post or the transcript of a YouTube video. This content was then summarized using the Claude Sonnet 3.5 model from Anthropic. The metadata for each case study was also extracted using the [`instructor`](https://github.com/jxnl/instructor) library.
#### Who are the source language producers?
The original case study writeups were authored by the engineering teams or technical writers at the respective companies. The summarized versions in the LLMOps Database were generated by Anthropic's Claude Sonnet 3.6 model.
### Personal and Sensitive Information
The LLMOps Database does not contain any personal information, sensitive data, or identity characteristics.
## Considerations for Using the Data
### Social Impact of Dataset
The LLMOps Database is intended to have a positive impact by enabling technical teams to learn from real-world examples of LLM deployments. By providing practical insights and solutions, the dataset aims to make these powerful technologies more accessible and reliable for production use. However, as with any technology, there are potential risks such as the misuse of LLMs or unintended consequences from their deployment. Users of the dataset should carefully consider the ethical implications and potential impacts of their LLM applications.
### Discussion of Biases
One potential limitation of the dataset is that it would have been preferable to include the original source text or full video transcripts along with the summaries. However, this was not done to avoid potential copyright or ownership issues. If users wish to access the original source content, they will need to download it themselves.
### Other Known Limitations
No other known limitations.
## Additional Information
### Dataset Curators
The LLMOps Database was curated by the ZenML team. [ZenML](https://zenml.io)
maintains an open-source MLOps framework, and as part of their work, they engage
with many people doing MLOps and LLMOps. The team gathered these sources to
better understand the space and provide a useful resource for others.
### Licensing Information
The LLMOps Database is shared under the Apache License.
|
HungVu2003/opt-350m_beta_0.5_alpha_0.8_num-company_3_dataset_1_for_gen_1 | HungVu2003 | 2025-05-04T17:01:05Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T17:01:03Z | null | ---
dataset_info:
features:
- name: question
dtype: string
splits:
- name: train
num_bytes: 1903153
num_examples: 12498
download_size: 1090827
dataset_size: 1903153
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
5CD-AI/Viet-qvq-r1 | 5CD-AI | 2025-05-04T16:33:42Z | 140 | 3 | [
"task_categories:question-answering",
"language:vi",
"language:en",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"question-answering"
] | 2025-04-25T03:43:39Z | null | ---
dataset_info:
features:
- name: uid
dtype: int64
- name: subset
dtype: string
- name: id
dtype: string
- name: image
dtype: image
- name: conversations
sequence:
- name: role
dtype: string
- name: content
dtype: string
- name: source
dtype: string
- name: vi_conversations
sequence:
- name: role
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 7252068072.656
num_examples: 78082
download_size: 6093271009
dataset_size: 7252068072.656
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- question-answering
language:
- vi
- en
pretty_name: viet-qvq-r1
---
### Description
This dataset is a Vietnamese translation of the [ahmedheakl/qvq-r1](https://huggingface.co/datasets/ahmedheakl/qvq-r1), intended for training and evaluating multimodal Vision–Language Models (VLMs) on **visual reasoning** tasks involving document-style images such as receipts, forms, invoices.,
Each example includes:
- An input image containing text (typically scanned documents),
- A conversation simulating a user question and an assistant’s step-by-step reasoning leading to the answer,
- A Vietnamese version of the full conversation.
The Vietnamese translations were automatically generated using the Grok model, with careful preservation of both the question intent and the reasoning process.
This subset contains approximately **78,000 examples**.
---
### Dataset Structure
Each record contains the following main fields:
| Field | Type | Description |
|--------------------|----------|-----------------------------------------------------------------------------|
| `image` | `image` | The input image, typically a document or receipt. |
| `conversations` | `string` | A dialogue between user and assistant, with detailed step-by-step reasoning.|
| `vi_conversations` | `string` | Vietnamese translation of the `conversations` field. | |
mteb/medrxiv-clustering-p2p | mteb | 2025-05-04T16:28:25Z | 637 | 2 | [
"task_categories:text-classification",
"annotations_creators:derived",
"multilinguality:monolingual",
"source_datasets:mteb/medrxiv-clustering-p2p",
"language:eng",
"license:other",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text"
] | [
"text-classification"
] | 2022-05-11T06:56:44Z | null | ---
annotations_creators:
- derived
language:
- eng
license: other
multilinguality: monolingual
source_datasets:
- mteb/medrxiv-clustering-p2p
task_categories:
- text-classification
task_ids: []
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;">MedrxivClusteringP2P.v2</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>
Clustering of titles+abstract from medrxiv across 51 categories.
| | |
|---------------|---------------------------------------------|
| Task category | t2c |
| Domains | Academic, Medical, Written |
| Reference | https://api.medrxiv.org/ |
## 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(["MedrxivClusteringP2P.v2"])
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{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("MedrxivClusteringP2P.v2")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"num_samples": 37500,
"number_of_characters": 74294927,
"min_text_length": 148,
"average_text_length": 1981.1980533333333,
"max_text_length": 38759,
"min_labels_per_text": 6,
"average_labels_per_text": 1.0,
"max_labels_per_text": 8830,
"unique_labels": 51,
"labels": {
"epidemiology": {
"count": 6656
},
"public and global health": {
"count": 3595
},
"oncology": {
"count": 845
},
"allergy and immunology": {
"count": 464
},
"orthopedics": {
"count": 104
},
"health informatics": {
"count": 1107
},
"occupational and environmental health": {
"count": 415
},
"infectious diseases": {
"count": 8830
},
"genetic and genomic medicine": {
"count": 1918
},
"health policy": {
"count": 527
},
"gastroenterology": {
"count": 343
},
"radiology and imaging": {
"count": 541
},
"pain medicine": {
"count": 121
},
"neurology": {
"count": 1773
},
"primary care research": {
"count": 232
},
"rheumatology": {
"count": 189
},
"endocrinology": {
"count": 419
},
"hematology": {
"count": 202
},
"addiction medicine": {
"count": 178
},
"pediatrics": {
"count": 589
},
"cardiovascular medicine": {
"count": 855
},
"obstetrics and gynecology": {
"count": 373
},
"health systems and quality improvement": {
"count": 491
},
"nephrology": {
"count": 241
},
"respiratory medicine": {
"count": 482
},
"geriatric medicine": {
"count": 169
},
"dentistry and oral medicine": {
"count": 159
},
"psychiatry and clinical psychology": {
"count": 1781
},
"nutrition": {
"count": 240
},
"intensive care and critical care medicine": {
"count": 368
},
"rehabilitation medicine and physical therapy": {
"count": 322
},
"otolaryngology": {
"count": 166
},
"nursing": {
"count": 93
},
"transplantation": {
"count": 118
},
"health economics": {
"count": 327
},
"sports medicine": {
"count": 180
},
"hiv aids": {
"count": 363
},
"dermatology": {
"count": 98
},
"pathology": {
"count": 223
},
"emergency medicine": {
"count": 191
},
"pharmacology and therapeutics": {
"count": 221
},
"ophthalmology": {
"count": 220
},
"medical ethics": {
"count": 46
},
"palliative medicine": {
"count": 45
},
"sexual and reproductive health": {
"count": 156
},
"medical education": {
"count": 203
},
"surgery": {
"count": 162
},
"urology": {
"count": 65
},
"anesthesia": {
"count": 72
},
"toxicology": {
"count": 16
},
"forensic medicine": {
"count": 6
}
}
}
}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)* |
mteb/sickr-sts | mteb | 2025-05-04T16:26:42Z | 15,274 | 4 | [
"task_categories:sentence-similarity",
"task_ids:semantic-similarity-scoring",
"task_ids:natural-language-inference",
"annotations_creators:human-annotated",
"multilinguality:monolingual",
"language:eng",
"license:cc-by-nc-sa-3.0",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text"
] | [
"sentence-similarity"
] | 2022-04-19T14:28:03Z | null | ---
annotations_creators:
- human-annotated
language:
- eng
license: cc-by-nc-sa-3.0
multilinguality: monolingual
task_categories:
- sentence-similarity
task_ids:
- semantic-similarity-scoring
- natural-language-inference
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;">SICK-R</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>
Semantic Textual Similarity SICK-R dataset
| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Web, Written |
| Reference | https://aclanthology.org/L14-1314/ |
## 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(["SICK-R"])
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{marelli-etal-2014-sick,
abstract = {Shared and internationally recognized benchmarks are fundamental for the development of any computational system. We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them. SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs. By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral). The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes.},
address = {Reykjavik, Iceland},
author = {Marelli, Marco and
Menini, Stefano and
Baroni, Marco and
Bentivogli, Luisa and
Bernardi, Raffaella and
Zamparelli, Roberto},
booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)},
editor = {Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios},
month = may,
pages = {216--223},
publisher = {European Language Resources Association (ELRA)},
title = {A {SICK} cure for the evaluation of compositional distributional semantic models},
url = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/363_Paper.pdf},
year = {2014},
}
@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("SICK-R")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"num_samples": 9927,
"number_of_characters": 915617,
"unique_pairs": 9842,
"min_sentence1_length": 15,
"average_sentence1_len": 46.602196031026494,
"max_sentence1_length": 151,
"unique_sentence1": 5014,
"min_sentence2_length": 14,
"average_sentence2_len": 45.63281958295558,
"max_sentence2_length": 151,
"unique_sentence2": 4946,
"min_score": 1.0,
"avg_score": 3.5291492898156607,
"max_score": 5.0
}
}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)* |
yashparalkar0/codePM | yashparalkar0 | 2025-05-04T16:20:43Z | 0 | 0 | [
"task_categories:text2text-generation",
"region:us",
"code"
] | [
"text2text-generation"
] | 2025-05-04T16:17:32Z | null | ---
task_categories:
- text2text-generation
tags:
- code
pretty_name: f
--- |
harpreetmann/go_emotions_max_500_string_chat | harpreetmann | 2025-05-04T16:14:55Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T16:14:49Z | null | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 27852466
num_examples: 43409
- name: test
num_bytes: 3488513
num_examples: 5427
- name: validation
num_bytes: 3487936
num_examples: 5426
- name: discarded
num_bytes: 3483
num_examples: 1
download_size: 8204171
dataset_size: 34832398
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
- split: discarded
path: data/discarded-*
---
|
mteb/WikipediaRerankingMultilingual | mteb | 2025-05-04T16:12:04Z | 617 | 0 | [
"task_categories:text-ranking",
"annotations_creators:LM-generated and reviewed",
"multilinguality:multilingual",
"language:ben",
"language:bul",
"language:ces",
"language:dan",
"language:deu",
"language:eng",
"language:fas",
"language:fin",
"language:hin",
"language:ita",
"language:nld",
"language:nor",
"language:por",
"language:ron",
"language:srp",
"language:swe",
"license:cc-by-sa-3.0",
"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-ranking"
] | 2025-02-19T09:28:42Z | null | ---
annotations_creators:
- LM-generated and reviewed
language:
- ben
- bul
- ces
- dan
- deu
- eng
- fas
- fin
- hin
- ita
- nld
- nor
- por
- ron
- srp
- swe
license: cc-by-sa-3.0
multilinguality: multilingual
task_categories:
- text-ranking
task_ids: []
dataset_info:
- config_name: bg-corpus
features:
- name: _id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 9604308
num_examples: 13500
download_size: 4593991
dataset_size: 9604308
- config_name: bg-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 778020
num_examples: 13500
download_size: 101652
dataset_size: 778020
- config_name: bg-queries
features:
- name: _id
dtype: string
- name: text
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splits:
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path: bn-corpus/test-*
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path: bn-qrels/test-*
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path: cs-top_ranked/test-*
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path: da-top_ranked/test-*
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path: de-top_ranked/test-*
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path: fa-qrels/test-*
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path: fa-queries/test-*
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path: fa-top_ranked/test-*
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path: fi-corpus/test-*
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data_files:
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path: fi-qrels/test-*
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data_files:
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path: fi-queries/test-*
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path: fi-top_ranked/test-*
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path: hi-corpus/test-*
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path: hi-qrels/test-*
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path: hi-queries/test-*
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data_files:
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path: hi-top_ranked/test-*
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data_files:
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path: it-corpus/test-*
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data_files:
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path: it-qrels/test-*
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data_files:
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path: it-queries/test-*
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data_files:
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path: it-top_ranked/test-*
- config_name: nl-corpus
data_files:
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path: nl-corpus/test-*
- config_name: nl-qrels
data_files:
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path: nl-qrels/test-*
- config_name: nl-queries
data_files:
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path: nl-queries/test-*
- config_name: nl-top_ranked
data_files:
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path: nl-top_ranked/test-*
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data_files:
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path: no-corpus/test-*
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data_files:
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path: no-qrels/test-*
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data_files:
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path: no-queries/test-*
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data_files:
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path: no-top_ranked/test-*
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data_files:
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path: pt-corpus/test-*
- config_name: pt-qrels
data_files:
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path: pt-qrels/test-*
- config_name: pt-queries
data_files:
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path: pt-queries/test-*
- config_name: pt-top_ranked
data_files:
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path: pt-top_ranked/test-*
- config_name: ro-corpus
data_files:
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path: ro-corpus/test-*
- config_name: ro-qrels
data_files:
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path: ro-qrels/test-*
- config_name: ro-queries
data_files:
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path: ro-queries/test-*
- config_name: ro-top_ranked
data_files:
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path: ro-top_ranked/test-*
- config_name: sr-corpus
data_files:
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path: sr-corpus/test-*
- config_name: sr-qrels
data_files:
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path: sr-qrels/test-*
- config_name: sr-queries
data_files:
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path: sr-queries/test-*
- config_name: sr-top_ranked
data_files:
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path: sr-top_ranked/test-*
- config_name: sv-corpus
data_files:
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path: sv-corpus/test-*
- config_name: sv-qrels
data_files:
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path: sv-qrels/test-*
- config_name: sv-queries
data_files:
- split: test
path: sv-queries/test-*
- config_name: sv-top_ranked
data_files:
- split: test
path: sv-top_ranked/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;">WikipediaRerankingMultilingual</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>
The dataset is derived from Cohere's wikipedia-2023-11 dataset and contains synthetically generated queries.
| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Encyclopaedic, Written |
| Reference | https://huggingface.co/datasets/ellamind/wikipedia-2023-11-reranking-multilingual |
## 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(["WikipediaRerankingMultilingual"])
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
@online{wikidump,
author = {Wikimedia Foundation},
title = {Wikimedia Downloads},
url = {https://dumps.wikimedia.org},
}
@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("WikipediaRerankingMultilingual")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"num_samples": 240000,
"number_of_characters": 83866932,
"num_documents": 216000,
"min_document_length": 100,
"average_document_length": 381.70714351851854,
"max_document_length": 9461,
"unique_documents": 216000,
"num_queries": 24000,
"min_query_length": 7,
"average_query_length": 59.091208333333334,
"max_query_length": 180,
"unique_queries": 24000,
"none_queries": 0,
"num_relevant_docs": 216000,
"min_relevant_docs_per_query": 9,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 9,
"unique_relevant_docs": 216000,
"num_instructions": null,
"min_instruction_length": null,
"average_instruction_length": null,
"max_instruction_length": null,
"unique_instructions": null,
"num_top_ranked": 24000,
"min_top_ranked_per_query": 9,
"average_top_ranked_per_query": 9.0,
"max_top_ranked_per_query": 9
}
}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)* |
mteb/VoyageMMarcoReranking | mteb | 2025-05-04T16:11:59Z | 9 | 0 | [
"task_categories:text-ranking",
"annotations_creators:derived",
"multilinguality:monolingual",
"language:jpn",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2312.16144",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text"
] | [
"text-ranking"
] | 2025-02-18T20:02:06Z | null | ---
annotations_creators:
- derived
language:
- jpn
license: cc-by-4.0
multilinguality: monolingual
task_categories:
- text-ranking
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: 26582357
num_examples: 53375
download_size: 12669365
dataset_size: 26582357
- config_name: default
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 3093432
num_examples: 53375
download_size: 359413
dataset_size: 3093432
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 139132
num_examples: 2048
download_size: 79174
dataset_size: 139132
- config_name: top_ranked
features:
- name: query-id
dtype: string
- name: corpus-ids
sequence: string
splits:
- name: test
num_bytes: 1778562
num_examples: 2048
download_size: 353814
dataset_size: 1778562
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-*
- config_name: top_ranked
data_files:
- split: test
path: top_ranked/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;">VoyageMMarcoReranking</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>
a hard-negative augmented version of the Japanese MMARCO dataset as used in Voyage AI Evaluation Suite
| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Academic, Non-fiction, Written |
| Reference | https://arxiv.org/abs/2312.16144 |
## 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(["VoyageMMarcoReranking"])
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{clavié2023jacolbert,
archiveprefix = {arXiv},
author = {Benjamin Clavié},
eprint = {2312.16144},
title = {JaColBERT and Hard Negatives, Towards Better Japanese-First Embeddings for Retrieval: Early Technical Report},
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("VoyageMMarcoReranking")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"num_samples": 55423,
"number_of_characters": 8824820,
"num_documents": 53375,
"min_document_length": 19,
"average_document_length": 164.72532084309134,
"max_document_length": 1192,
"unique_documents": 53375,
"num_queries": 2048,
"min_query_length": 3,
"average_query_length": 15.9208984375,
"max_query_length": 73,
"unique_queries": 2048,
"none_queries": 0,
"num_relevant_docs": 53375,
"min_relevant_docs_per_query": 26,
"average_relevant_docs_per_query": 1.06201171875,
"max_relevant_docs_per_query": 29,
"unique_relevant_docs": 53375,
"num_instructions": null,
"min_instruction_length": null,
"average_instruction_length": null,
"max_instruction_length": null,
"unique_instructions": null,
"num_top_ranked": 2048,
"min_top_ranked_per_query": 26,
"average_top_ranked_per_query": 26.06201171875,
"max_top_ranked_per_query": 29
}
}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)* |
mteb/CQADupstack-Webmasters-PL | mteb | 2025-05-04T16:10:56Z | 16 | 0 | [
"task_categories:text-retrieval",
"task_ids:multiple-choice-qa",
"annotations_creators:derived",
"multilinguality:translated",
"source_datasets:mteb/cqadupstack-webmasters",
"language:pol",
"license:unknown",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2305.19840",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text"
] | [
"text-retrieval"
] | 2025-02-05T19:19:43Z | null | ---
annotations_creators:
- derived
language:
- pol
license: unknown
multilinguality: translated
source_datasets:
- mteb/cqadupstack-webmasters
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: 13844452
num_examples: 17405
download_size: 8365708
dataset_size: 13844452
- config_name: default
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 35771
num_examples: 1395
download_size: 16248
dataset_size: 35771
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 38008
num_examples: 506
download_size: 26256
dataset_size: 38008
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;">CQADupstack-Webmasters-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>
CQADupStack: A Stack Exchange Question Duplicate Pairs Dataset
| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Written, Web |
| Reference | https://huggingface.co/datasets/clarin-knext/cqadupstack-webmasters-pl |
## 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(["CQADupstack-Webmasters-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("CQADupstack-Webmasters-PL")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"num_samples": 17911,
"number_of_characters": 12905956,
"num_documents": 17405,
"min_document_length": 55,
"average_document_length": 739.7775926457914,
"max_document_length": 25496,
"unique_documents": 17405,
"num_queries": 506,
"min_query_length": 12,
"average_query_length": 59.5395256916996,
"max_query_length": 154,
"unique_queries": 506,
"none_queries": 0,
"num_relevant_docs": 1395,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 2.7569169960474307,
"max_relevant_docs_per_query": 207,
"unique_relevant_docs": 1395,
"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/HotpotQA_test_top_250_only_w_correct-v2 | mteb | 2025-05-04T16:10:16Z | 683 | 0 | [
"task_categories:text-retrieval",
"task_ids:multiple-choice-qa",
"annotations_creators:human-annotated",
"multilinguality:monolingual",
"source_datasets:mteb/hotpotqa",
"language:eng",
"license:cc-by-sa-4.0",
"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"
] | 2024-09-28T05:55:11Z | null | ---
annotations_creators:
- human-annotated
language:
- eng
license: cc-by-sa-4.0
multilinguality: monolingual
source_datasets:
- mteb/hotpotqa
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: 69897420.06567885
num_examples: 225621
download_size: 59246411
dataset_size: 69897420.06567885
- config_name: default
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 93923.56515867657
num_examples: 2000
download_size: 40450
dataset_size: 93923.56515867657
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 124244.29439567859
num_examples: 1000
download_size: 85083
dataset_size: 124244.29439567859
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;">HotpotQAHardNegatives</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>
HotpotQA is a question answering dataset featuring natural, multi-hop questions, with strong supervision for supporting facts to enable more explainable question answering systems. 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 | Web, Written |
| Reference | https://hotpotqa.github.io/ |
## 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(["HotpotQAHardNegatives"])
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{yang-etal-2018-hotpotqa,
abstract = {Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems{'} ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions.},
address = {Brussels, Belgium},
author = {Yang, Zhilin and
Qi, Peng and
Zhang, Saizheng and
Bengio, Yoshua and
Cohen, William and
Salakhutdinov, Ruslan and
Manning, Christopher D.},
booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
doi = {10.18653/v1/D18-1259},
editor = {Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi},
month = oct # {-} # nov,
pages = {2369--2380},
publisher = {Association for Computational Linguistics},
title = {{H}otpot{QA}: A Dataset for Diverse, Explainable Multi-hop Question Answering},
url = {https://aclanthology.org/D18-1259},
year = {2018},
}
@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("HotpotQAHardNegatives")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"num_samples": 226621,
"number_of_characters": 84600920,
"num_documents": 225621,
"min_document_length": 9,
"average_document_length": 374.558822095461,
"max_document_length": 3463,
"unique_documents": 225621,
"num_queries": 1000,
"min_query_length": 34,
"average_query_length": 92.584,
"max_query_length": 288,
"unique_queries": 1000,
"none_queries": 0,
"num_relevant_docs": 2000,
"min_relevant_docs_per_query": 2,
"average_relevant_docs_per_query": 2.0,
"max_relevant_docs_per_query": 2,
"unique_relevant_docs": 1975,
"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/cqadupstack-mathematica | mteb | 2025-05-04T16:09:59Z | 380 | 1 | [
"task_categories:text-retrieval",
"task_ids:multiple-choice-qa",
"annotations_creators:derived",
"multilinguality:monolingual",
"language:eng",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text"
] | [
"text-retrieval"
] | 2024-03-02T19:36:14Z | null | ---
annotations_creators:
- derived
language:
- eng
license: apache-2.0
multilinguality: monolingual
task_categories:
- text-retrieval
task_ids:
- multiple-choice-qa
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: test
num_bytes: 34691
num_examples: 1358
- config_name: corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 19568620
num_examples: 16705
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 49576
num_examples: 804
configs:
- config_name: default
data_files:
- 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;">CQADupstackMathematicaRetrieval</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>
CQADupStack: A Benchmark Data Set for Community Question-Answering Research
| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Written, Academic, Non-fiction |
| Reference | http://nlp.cis.unimelb.edu.au/resources/cqadupstack/ |
## 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(["CQADupstackMathematicaRetrieval"])
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{hoogeveen2015,
acmid = {2838934},
address = {New York, NY, USA},
articleno = {3},
author = {Hoogeveen, Doris and Verspoor, Karin M. and Baldwin, Timothy},
booktitle = {Proceedings of the 20th Australasian Document Computing Symposium (ADCS)},
doi = {10.1145/2838931.2838934},
isbn = {978-1-4503-4040-3},
location = {Parramatta, NSW, Australia},
numpages = {8},
pages = {3:1--3:8},
publisher = {ACM},
series = {ADCS '15},
title = {CQADupStack: A Benchmark Data Set for Community Question-Answering Research},
url = {http://doi.acm.org/10.1145/2838931.2838934},
year = {2015},
}
@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("CQADupstackMathematicaRetrieval")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"num_samples": 17509,
"number_of_characters": 19325188,
"num_documents": 16705,
"min_document_length": 75,
"average_document_length": 1154.4967375037413,
"max_document_length": 28907,
"unique_documents": 16705,
"num_queries": 804,
"min_query_length": 15,
"average_query_length": 48.90547263681592,
"max_query_length": 137,
"unique_queries": 804,
"none_queries": 0,
"num_relevant_docs": 1358,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.6890547263681592,
"max_relevant_docs_per_query": 56,
"unique_relevant_docs": 1358,
"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/tweet_sentiment_extraction | mteb | 2025-05-04T16:07:55Z | 4,415 | 27 | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-scoring",
"task_ids:sentiment-classification",
"task_ids:hate-speech-detection",
"annotations_creators:human-annotated",
"multilinguality:monolingual",
"language:eng",
"license:unknown",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text"
] | [
"text-classification"
] | 2022-05-26T18:07:50Z | null | ---
annotations_creators:
- human-annotated
language:
- eng
license: unknown
multilinguality: monolingual
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-scoring
- sentiment-classification
- hate-speech-detection
tags:
- mteb
- text
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 2208166
num_examples: 27481
- name: test
num_bytes: 281934
num_examples: 3534
download_size: 1710860
dataset_size: 2490100
---
<!-- 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;">TweetSentimentExtractionClassification</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>
| | |
|---------------|---------------------------------------------|
| Task category | t2c |
| Domains | Social, Written |
| Reference | https://www.kaggle.com/competitions/tweet-sentiment-extraction/overview |
## 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(["TweetSentimentExtractionClassification"])
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{tweet-sentiment-extraction,
author = {Maggie, Phil Culliton, Wei Chen},
publisher = {Kaggle},
title = {Tweet Sentiment Extraction},
url = {https://kaggle.com/competitions/tweet-sentiment-extraction},
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("TweetSentimentExtractionClassification")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"num_samples": 3534,
"number_of_characters": 239476,
"number_texts_intersect_with_train": 0,
"min_text_length": 4,
"average_text_length": 67.76344086021506,
"max_text_length": 142,
"unique_text": 3534,
"unique_labels": 3,
"labels": {
"1": {
"count": 1430
},
"2": {
"count": 1103
},
"0": {
"count": 1001
}
}
},
"train": {
"num_samples": 27481,
"number_of_characters": 1877709,
"number_texts_intersect_with_train": null,
"min_text_length": 0,
"average_text_length": 68.32753538808632,
"max_text_length": 141,
"unique_text": 27481,
"unique_labels": 3,
"labels": {
"1": {
"count": 11118
},
"0": {
"count": 7781
},
"2": {
"count": 8582
}
}
}
}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)* |
mteb/HotelReviewSentimentClassification | mteb | 2025-05-04T16:07:41Z | 11 | 0 | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-scoring",
"task_ids:sentiment-classification",
"task_ids:hate-speech-detection",
"annotations_creators:derived",
"multilinguality:monolingual",
"language:ara",
"license:unknown",
"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"
] | [
"text-classification"
] | 2024-12-21T10:42:59Z | null | ---
annotations_creators:
- derived
language:
- ara
license: unknown
multilinguality: monolingual
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-scoring
- sentiment-classification
- hate-speech-detection
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 536128
num_examples: 2048
download_size: 274359
dataset_size: 536128
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
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;">HotelReviewSentimentClassification</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>
HARD is a dataset of Arabic hotel reviews collected from the Booking.com website.
| | |
|---------------|---------------------------------------------|
| Task category | t2c |
| Domains | Reviews, Written |
| Reference | https://link.springer.com/chapter/10.1007/978-3-319-67056-0_3 |
## 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(["HotelReviewSentimentClassification"])
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{elnagar2018hotel,
author = {Elnagar, Ashraf and Khalifa, Yasmin S and Einea, Anas},
journal = {Intelligent natural language processing: Trends and applications},
pages = {35--52},
publisher = {Springer},
title = {Hotel Arabic-reviews dataset construction for sentiment analysis applications},
year = {2018},
}
@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("HotelReviewSentimentClassification")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"train": {
"num_samples": 2048,
"number_of_characters": 282368,
"number_texts_intersect_with_train": null,
"min_text_length": 11,
"average_text_length": 137.875,
"max_text_length": 2698,
"unique_text": 2044,
"unique_labels": 4,
"labels": {
"4": {
"count": 512
},
"3": {
"count": 512
},
"0": {
"count": 279
},
"1": {
"count": 745
}
}
}
}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)* |
darkme-ai/LMMFinQA | darkme-ai | 2025-05-04T15:53:07Z | 0 | 0 | [
"license:mit",
"size_categories:n<1K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"region:us"
] | [] | 2025-05-03T06:22:05Z | null | ---
license: mit
---
## 数据集结构
### 数据实例
以下是数据集的示例，展示了不同任务类型的数据格式：
#### 文本分类示例
```python
{
"id": "1",
"image": [
"itd1_1.jpg"
],
"qa_type": [
"text_and_table_based_qa"
],
"question": "<rk>不同的财务报表类型，利润表中“本期金额/本期数/本月金额”填写方法如下：\n1、小企业会计准则：填写本期（即本季度）的发生额，例如第二季度填写“4-6月份累计”；\n2、企业会计制度：填写本期（即本季度）的发生额，例如第二季度填写“4-6月份累计”；\n3、一般企业会计准则：填写从年初到本期期末的累计发生额，例如第二季度填写“1-6月份累计”。已执行和未执行新准则，填写规则是一样的，只是表中明细科目有所差异。</rk>\n<image>\n中，企业执行企业会计准则，利润表中本期金额是否只填写4到6月的发生额？"
} |
Themira/multilingual_parallel_data | Themira | 2025-05-04T15:44:48Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | [] | 2025-05-04T15:42:08Z | null | ---
license: apache-2.0
---
|
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:
features:
- name: question
dtype: string
splits:
- name: train
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_1_for_gen_15_v2 | HungVu2003 | 2025-05-04T15:15:58Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T15:15:57Z | null | ---
dataset_info:
features:
- name: question
dtype: string
splits:
- name: train
num_bytes: 6346402
num_examples: 13750
download_size: 3220680
dataset_size: 6346402
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
LadyMia/x_dataset_17682 | LadyMia | 2025-05-04T15:11:51Z | 880 | 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",
"source_datasets:original",
"license:mit",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-01-29T03:07:52Z | 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:** LadyMia/x_dataset_17682
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5FgYXBnD63LNLkArKfbK1i4K2gbLbs6zULHA2DXFmhLdtFHe
### 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{LadyMia2025datauniversex_dataset_17682,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={LadyMia},
year={2025},
url={https://huggingface.co/datasets/LadyMia/x_dataset_17682},
}
```
### 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:** 37658378
- **Date Range:** 2025-01-22T00:00:00Z to 2025-02-13T00:00:00Z
- **Last Updated:** 2025-02-18T21:42:55Z
### Data Distribution
- Tweets with hashtags: 45.42%
- Tweets without hashtags: 54.58%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 20555610 | 54.58% |
| 2 | #riyadh | 235778 | 0.63% |
| 3 | #zelena | 224298 | 0.60% |
| 4 | #tiktok | 161795 | 0.43% |
| 5 | #ad | 91773 | 0.24% |
| 6 | #jhope_at_galadespiècesjaunes | 85795 | 0.23% |
| 7 | #bbb25 | 79808 | 0.21% |
| 8 | #transferlerlebirliktezafere | 58256 | 0.15% |
| 9 | #theheartkillersep10 | 55037 | 0.15% |
| 10 | #bbmzansi | 51161 | 0.14% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-01-29T03:08:57Z | 2977993 | 2977993 |
| 2025-02-01T15:11:35Z | 7083709 | 10061702 |
| 2025-02-05T03:15:34Z | 8967127 | 19028829 |
| 2025-02-08T15:19:06Z | 9885163 | 28913992 |
| 2025-02-12T03:23:51Z | 7367286 | 36281278 |
| 2025-02-18T06:41:11Z | 659231 | 36940509 |
| 2025-02-18T21:42:55Z | 717869 | 37658378 |
|
mteb/bengali_hate_speech | mteb | 2025-05-04T15:09:57Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T15:09:52Z | null | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': 0
'1': 1
'2': 2
'3': 3
'4': 4
splits:
- name: train
num_bytes: 445907.77559976594
num_examples: 1567
- name: test
num_bytes: 431110.58074897603
num_examples: 1515
download_size: 351631
dataset_size: 877018.356348742
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
vatolinalex/tweet_sarcasm | vatolinalex | 2025-05-04T15:06:00Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T15:05:56Z | null | ---
dataset_info:
features:
- name: dialect
dtype:
class_label:
names:
'0': egypt
'1': gulf
'2': levant
'3': magreb
'4': msa
- name: label
dtype:
class_label:
names:
'0': non-sarcastic
'1': sarcastic
- name: sentiment
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
- name: original_sentiment
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
- name: text
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 1761516.7565485362
num_examples: 8125
- name: test
num_bytes: 425852.990521327
num_examples: 1961
download_size: 1120002
dataset_size: 2187369.747069863
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
vatolinalex/restaurant_review_sentiment | vatolinalex | 2025-05-04T15:05:44Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T15:05:30Z | null | ---
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': 0
'1': 1
- name: text
dtype: string
- name: restaurant_id
dtype: string
- name: user_id
dtype: string
splits:
- name: train
num_bytes: 2715408.5025107604
num_examples: 6279
- name: test
num_bytes: 873566.6786226686
num_examples: 2020
download_size: 1930248
dataset_size: 3588975.181133429
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
Oriolshhh/parlabe-errors-genere-60k | Oriolshhh | 2025-05-04T14:35:18Z | 0 | 0 | [
"language:ca",
"license:apache-2.0",
"size_categories:10K<n<100K",
"region:us",
"català",
"grammar-correction",
"gender",
"text-to-text",
"synthetic"
] | [] | 2025-05-04T14:30:03Z | null | ---
language: ca
license: apache-2.0
tags:
- català
- grammar-correction
- gender
- text-to-text
- synthetic
size_categories:
- 10K<n<100K
---
# Dataset d'errors de gènere en català (60.000 parelles)
Aquest dataset conté **60.000 parelles de frases** amb errors de gènere, generades mitjançant un script automatitzat en Python. Cada parella està formada per:
```text_erroni,text_correcte```
---
## Objectiu
Aquest conjunt de dades està pensat per **entrenar models de correcció gramatical en català**, amb un focus específic en la detecció i correcció **d’errors de gènere**:
- Concordança nominal (ex: *el noia → la noia*)
- Concordança adjectival (ex: *una llibre interessant → un llibre interessant*)
- Concordança pronominal i verbal
---
## Com s’ha generat?
S’han creat automàticament frases correctes, i posteriorment s’hi han introduït **errors de gènere** de manera controlada:
- Canviant articles, pronoms, adjectius i formes verbals
- Mantenint la sintaxi coherent però incorrecta gramaticalment
Aquestes parelles s'han dissenyat per simular errors reals d'escriptura en contextos formals i informals.
---
## Format
- Llengua: Català (`ca`)
- Format: `.csv` amb dues columnes:
- `text_erroni`
- `text_correcte`
- Nombre de parelles: 60.000
|
shylee/eval_DP_cube_downDims1_cropNo_freeze1_16_16_ema1_1e-4_ckpt300000 | shylee | 2025-05-04T14:34:20Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"region:us",
"LeRobot",
"tutorial"
] | [
"robotics"
] | 2025-05-04T14:34:10Z | 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
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 4,
"total_frames": 3310,
"total_tasks": 1,
"total_videos": 12,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:4"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.FrontCam": {
"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
}
},
"observation.images.TopCam": {
"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
}
},
"observation.images.WristCam": {
"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": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
hshwk1983/x_dataset_2983 | hshwk1983 | 2025-05-04T14:33:54Z | 2,809 | 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",
"source_datasets:original",
"license:mit",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-01-27T07:01:04Z | 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:** hshwk1983/x_dataset_2983
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5F2RCkLaXEwdz4PALA5iwSBQQ4rWEAioaniBHouRyhUSYjne
### 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{hshwk19832025datauniversex_dataset_2983,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={hshwk1983},
year={2025},
url={https://huggingface.co/datasets/hshwk1983/x_dataset_2983},
}
```
### 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:** 45361062
- **Date Range:** 2025-01-21T00:00:00Z to 2025-02-10T00:00:00Z
- **Last Updated:** 2025-02-18T19:56:18Z
### Data Distribution
- Tweets with hashtags: 49.23%
- Tweets without hashtags: 50.77%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 23030513 | 50.77% |
| 2 | #riyadh | 389599 | 0.86% |
| 3 | #zelena | 270010 | 0.60% |
| 4 | #tiktok | 216661 | 0.48% |
| 5 | #ad | 127153 | 0.28% |
| 6 | #bbb25 | 124236 | 0.27% |
| 7 | #jhope_at_galadespiècesjaunes | 107356 | 0.24% |
| 8 | #bbmzansi | 73192 | 0.16% |
| 9 | #granhermano | 70611 | 0.16% |
| 10 | #trump | 67914 | 0.15% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-01-27T07:02:01Z | 2802800 | 2802800 |
| 2025-01-30T19:05:56Z | 9696380 | 12499180 |
| 2025-02-03T07:09:45Z | 10920384 | 23419564 |
| 2025-02-06T19:12:26Z | 6138868 | 29558432 |
| 2025-02-10T07:16:07Z | 8261798 | 37820230 |
| 2025-02-13T19:19:34Z | 6252880 | 44073110 |
| 2025-02-18T04:54:59Z | 640422 | 44713532 |
| 2025-02-18T19:56:18Z | 647530 | 45361062 |
|
Kallia/stock-news-summaries-finetuning | Kallia | 2025-05-04T14:11:00Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T14:10:50Z | null | ---
dataset_info:
features:
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dtype: string
- name: summary
dtype: string
splits:
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num_bytes: 5776072.8
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data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
alexchilton/gran-nanobody-proteins | alexchilton | 2025-05-04T14:02:07Z | 0 | 0 | [
"task_categories:text-generation",
"task_categories:graph-ml",
"license:mit",
"region:us",
"protein",
"graph-neural-network",
"adjacency-matrix",
"protein-structure",
"nanobody"
] | [
"text-generation",
"graph-ml"
] | 2025-05-04T14:02:02Z | null | ---
license: mit
task_categories:
- text-generation
- graph-ml
tags:
- protein
- graph-neural-network
- adjacency-matrix
- protein-structure
- nanobody
---
# GRAN Protein Structure Dataset
## Dataset Description
This dataset contains protein graph data for training Graph Recurrent Attention Networks (GRAN) for protein sequence and structure generation.
### Dataset Summary
- **Number of proteins:** 2965
- **Average protein length:** 121.0 residues
- **Unique amino acids:** 22
- **Source:** Nanobody protein structures
- **Created by:** alexchilton
- **Date:** 2025-05-04 16:01:38
### Dataset Structure
Each protein entry contains:
- `sequence`: Complete amino acid sequence
- `sequence_length`: Total length of the protein
- `graph_nodes`: List of graph nodes (residue indices)
- `graph_edges`: List of graph edges (connections between residues)
- `adjacency_matrix`: Binary adjacency matrix representing contacts
- `node_features`: Features for each node (Meiler features or one-hot encoded residues)
- `protein_id`: Unique identifier
### Amino Acids
Available amino acids: ALA, ARG, ASN, ASP, CYS, GLN, GLU, GLY, HIS, ILE, LEU, LYS, MET, PHE, PRO, SER, THR, TRP, TYR, UNK, VAL, X
### Usage
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("alexchilton/gran-nanobody-proteins")
# Access a protein
protein = dataset['train'][0]
print(f"Sequence length: {protein['sequence_length']}")
print(f"Number of graph nodes: {len(protein['graph_nodes'])}")
print(f"Adjacency matrix shape: {np.array(protein['adjacency_matrix']).shape}")
```
### Training GRAN Model
This dataset is designed for training GRAN models that:
1. Generate both protein sequences and contact adjacency matrices
2. Model proteins as graphs with nodes (residues) and edges (contacts)
3. Use node features (Meiler descriptors or one-hot encoding)
### Citation
If you use this dataset, please cite:
```
@dataset{gran_protein_structures,
title={GRAN Protein Structure Dataset},
author={Alex Chilton},
year={2025},
url={https://huggingface.co/datasets/alexchilton/gran-nanobody-proteins}
}
```
|
amekerishvili/ATCO2_full_with_ASR | amekerishvili | 2025-05-04T13:34:29Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T12:50:29Z | null | ---
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---
|
RyanYr/ppo-dapo-qwen2.5math-7B-base-lr-mbs64_actor_matheval | RyanYr | 2025-05-04T13:32:12Z | 41 | 0 | [
"region:us"
] | [] | 2025-05-01T05:48:04Z | null | ---
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---
|
HungVu2003/opt-350m_beta_1.0_alpha_0.2_num-company_2_dataset_0_for_gen_14_v2 | HungVu2003 | 2025-05-04T13:29:17Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T13:29:15Z | null | ---
dataset_info:
features:
- name: question
dtype: string
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num_examples: 13750
download_size: 1120490
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
FLARE-MedFM/FLARE-Task2-LaptopSeg | FLARE-MedFM | 2025-05-04T13:27:33Z | 423 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
] | [] | 2025-04-17T23:32:45Z | null | ---
license: cc-by-nc-4.0
---
## FLARE Task2 Laptop Seg Dataset
## Data Description
This is the dataset for [MICCAI FLARE 2024-2025 Task2: Abdominal CT Organ Segmentation on Laptop](https://www.codabench.org/competitions/2320/).
The training set includes 2050 cases, where 50 cases have ground-truth labels from the FLARE22 dataset,
and the remaining 2000 cases have pseudo labels generated by the FLARE 2022 winning solution.
The old validation set and testing set are merged as a new validation set with 250 cases.
For those participants who are constrained by computing resources, we also provide an unlabeled core set to develop the methods,
where 50 unlabeled CT scans are sampled from the original pseudo training set.
### Data Structure
**coreset_train_50_random：**
50 unlabeled CT scans sampled from the train_pseudo_label.
**train_gt_label：**
50 CT scans with ground-truth labels.
**train_pseudo_label：**
2000 CT scans with pseudo labels generated by the FLARE 2022 winning solution.
**validation：**
200 hidden validation set and 50 public validation set.
FLARE-Task2-LaptopSeg/
├── coreset_train_50_random/
├── train_gt_label/
│ ├── imagesTr/
│ ├── labelsTr/
│ └── dataset.json
├── train_pseudo_label/
│ ├── imagesTr/
│ ├── pseudo_label_aladdin5_flare22.7z
│ └── pseudo_label_blackbean_flare22.zip
├── validation/
│ ├── Validation-Hidden-Images/
│ ├── Validation-Public-Images/
│ └── Validation-Public-Labels/
└── README.md
### Dataset Download Instructions
Participants can download the complete dataset using the following Python script:
```python
from huggingface_hub import snapshot_download
local_dir = "./FLARE-Task2-LaptopSeg"
snapshot_download(
repo_id="FLARE-MedFM/FLARE-Task2-LaptopSeg",
repo_type="dataset",
local_dir=local_dir,
local_dir_use_symlinks=False,
resume_download=True,
)
|
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.= --> |
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-*
---
|
ZennyKenny/cosa-benchmark-dataset | ZennyKenny | 2025-05-04T11:59:44Z | 6 | 0 | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"reasoning-datasets-competition"
] | [
"question-answering",
"text-generation"
] | 2025-05-04T06:49:21Z | null | ---
dataset_info:
features:
- name: index
dtype: int64
- name: code
dtype: string
- name: language
dtype: string
- name: difficulty
dtype: string
- name: vulnerability_type
dtype: string
- name: weakness_solution
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dtype: string
- name: safe_code
dtype: string
splits:
- name: train
num_bytes: 1201328
num_examples: 200
download_size: 426230
dataset_size: 1201328
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- en
pretty_name: CoSa Benchmark Dataset
size_categories:
- n<1K
tags:
- reasoning-datasets-competition
---
<a href="https://github.com/bespokelabsai/curator/">
<img src="https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k/resolve/main/made_with_curator.png" alt="Made with Curator" width=200px>
</a>
# 🧠 CoSa Benchmark Dataset
## 🔍 Introduction
The **CoSa (Code Safety) Benchmark** is a curated evaluation dataset designed to measure the ability of large language models (LLMs) to detect, explain, and repair vulnerabilities in synthetic code samples. It is intended to benchmark LLMs for real-world application in code security audits, reasoning tasks, and secure code generation.
## 📦 Contents
Each row in the dataset includes:
- `code`: a code snippet (varied languages)
- `language`: language of the code (Python, JavaScript, etc.)
- `difficulty`: labeled as `easy`, `medium`, or `hard`
- `vulnerability_type`: high-level category of exploit
- `weakness_solution`: a natural language explanation of the vulnerability
- `solution_statement`: a short summary of the mitigation
- `safe_code`: a repaired version of the input code
All samples were reviewed by a human for correctness of both the vulnerability and the repaired code.
## 🛠️ How It Was Created
The dataset was generated using a multi-step pipeline built in [this notebook](https://github.com/kghamilton89/synthetic-data-generators/blob/main/reasoning-competition/code-safety-bench.ipynb). Code snippets were synthesized using LLM prompting, labeled with a vulnerability type, and then evaluated by another model for flaw detection and repair. All final `safe_code` examples were **manually reviewed for correctness**.
## 📈 Usage
An LLM may be evaluated against the CoSa Benchmark as follows:
```python
# run model on benchmark
results = []
for i, row in tqdm(df.iterrows(), total=len(df), desc="Testing model on code"):
code = row["code"]
idx = row["index"]
try:
prompt = build_test_prompt(code)
response = client.chat.completions.create(
model="gpt-4o", # Change this
messages=[{"role": "user", "content": prompt}],
temperature=0.2,
max_tokens=512
)
content = response.choices[0].message.content.strip()
explanation = ""
fixed_code = ""
for line in content.splitlines():
if line.startswith("Explanation:"):
explanation = line.replace("Explanation:", "").strip()
elif line.startswith("Fixed Code:"):
fixed_code = content.split("Fixed Code:")[1].strip()
break
results.append({
"index": idx,
"model_explanation": explanation,
"model_fix": fixed_code
})
except Exception as e:
print(f"⚠️ Error on row {i}: {e}")
results.append({
"index": idx,
"model_explanation": "ERROR",
"model_fix": ""
})
results_df = pd.DataFrame(results)
results_df.to_json("llm-eval-results.jsonl", orient="records", lines=True)
```
Then score the results:
```python
# load & score
df = pd.merge(
pd.read_json("llm-code-safety-benchmark.jsonl", lines=True),
pd.read_json("llm-eval-results.jsonl", lines=True),
on="index"
)
# Add difficulty weight
weights = {"easy": 1, "medium": 2, "hard": 3}
df["weight"] = df["difficulty"].map(weights)
# Score via sentence transformer + difflib
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import difflib
encoder = SentenceTransformer("all-MiniLM-L6-v2")
explanation_scores, code_scores, final_scores = [], [], []
for i, row in df.iterrows():
# Explanation scoring
gt_expl = row["solution_statement"]
pred_expl = row["model_explanation"]
if pred_expl.lower() == "error":
expl_score = 0
else:
emb_gt = encoder.encode(gt_expl, convert_to_tensor=True)
emb_pred = encoder.encode(pred_expl, convert_to_tensor=True)
sim = cosine_similarity([emb_gt.cpu().numpy()], [emb_pred.cpu().numpy()])[0][0]
expl_score = max(0.2, sim) if sim < 0.9 else 1.0
# Code scoring
gt_code = row["safe_code"]
pred_code = row["model_fix"]
if not pred_code.strip():
code_score = 0
else:
code_sim = difflib.SequenceMatcher(None, gt_code, pred_code).ratio()
code_score = max(0.2, code_sim) if code_sim < 0.95 else 1.0
explanation_scores.append(expl_score)
code_scores.append(code_score)
avg = (expl_score + code_score) / 2
final_scores.append(avg * row["weight"])
df["explanation_score"] = explanation_scores
df["code_score"] = code_scores
df["total_score"] = final_scores
# Normalize difficulty-adjusted score to 100
total_possible = df["weight"].sum()
difficulty_score = (df["total_score"].sum() / total_possible) * 100
print(f"🏁 Difficulty-Adjusted Score: {difficulty_score:.2f}/100")
```
## 🧪 OpenAI Model Evaluation Results
### 📌 GPT-4o
- 🧠 Explanation: **59.92**
- 🔧 Code Repair: **93.52**
- 🏁 Final Score: **75.80**
### 📌 GPT-4o Mini
- 🧠 Explanation: **61.12**
- 🔧 Code Repair: **85.55**
- 🏁 Final Score: **72.47**
### 📌 GPT-3.5 Turbo
- 🧠 Explanation: **62.12**
- 🔧 Code Repair: **79.88**
- 🏁 Final Score: **70.18**
## 🧠 Limitations & Biases
- Most vulnerabilities are intentionally simplified for LLM interpretability.
- Code snippets may not fully reflect production scenarios (e.g. frameworks, APIs).
- While `safe_code` was **manually reviewed for correctness**, adversarial testing was not performed.
- Languages are skewed toward Python, with some JavaScript, Bash, and C.
## 📚 Related Notebooks
- [CoSa Dataset Generation Notebook](https://github.com/kghamilton89/synthetic-data-generators/blob/main/reasoning-competition/code-safety-bench.ipynb)
- [GPT-4.1 Eval Notebook](https://github.com/kghamilton89/synthetic-data-generators/blob/main/reasoning-competition/cosa-evals/GPT_4.1_eval.ipynb)
- [O4 Mini Eval Notebook](https://github.com/kghamilton89/synthetic-data-generators/blob/main/reasoning-competition/cosa-evals/o4_mini_eval.ipynb)
- [O3 Eval Notebook](https://github.com/kghamilton89/synthetic-data-generators/blob/main/reasoning-competition/cosa-evals/o3_eval.ipynb)
## ❤️ These Builders Love CoSa
 |
eduagarcia/corpus-carolina-parquet | eduagarcia | 2025-05-04T11:37:49Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T11:19:36Z | null | ---
dataset_info:
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configs:
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data_files:
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path: data/corpus-*
---
|
HungVu2003/opt-350m_beta_1.0_alpha_0.2_num-company_2_dataset_1_for_gen_12_v2 | HungVu2003 | 2025-05-04T11:15:44Z | 0 | 0 | [
"size_categories:10K<n<100K",
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"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-04T11:15:42Z | null | ---
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path: data/train-*
---
|
AndreaBorghesi/Unfair_Inequality_Education | AndreaBorghesi | 2025-05-04T11:04:51Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T10:52:02Z | null | ---
configs:
- config_name: default
data_files:
- split: data
path: "dataset.csv"
- split: mask
path: "missing_mask.csv"
---
This is a novel benchmark specifically designed for AI fairness research in education. It can be used for challenging tasks aimed at improving students' performance and reducing dropout rates, which are also discussed in the paper to emphasize significant research directions. By prioritizing fairness, this benchmark aims to foster the development of bias-free AI solutions, promoting equal educational access and outcomes for all students.
This repository only contains the outcome of the benchmarking activity, that is, the final dataset (```dataset.csv```) and the he mask for dealing with missing values (```missing_mask.csv```).
For those interested in all the processing details (masks, data obtained at the various pre-processing stages, the actual code) we refer to the following link: https://zenodo.org/records/11171863 |
Yuyeong/rw_pubmed_nbw_6_mask_public | Yuyeong | 2025-05-04T10:58:11Z | 0 | 0 | [
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---
# Dataset Card for "rw_pubmed_nbw_6_mask_public"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Yuyeong/rw_pubmed_mdlr_1_mask_public | Yuyeong | 2025-05-04T09:52:47Z | 0 | 0 | [
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] | [] | 2025-05-04T09:52:20Z | null | ---
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---
# Dataset Card for "rw_pubmed_mdlr_1_mask_public"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Yuyeong/rw_pubmed_standard_2_mask_public | Yuyeong | 2025-05-04T09:05:49Z | 0 | 0 | [
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-04T09:05:21Z | null | ---
dataset_info:
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---
# Dataset Card for "rw_pubmed_standard_2_mask_public"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Yuyeong/rw_cora_standard_1_mask_public | Yuyeong | 2025-05-04T08:27:07Z | 0 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-04T08:26:40Z | null | ---
dataset_info:
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---
# Dataset Card for "rw_cora_standard_1_mask_public"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
putdanil/inter1or | putdanil | 2025-05-04T08:02:55Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T07:49:51Z | null | ---
dataset_info:
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configs:
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path: data/train-*
---
|
alchemistyzz/mathvision_test | alchemistyzz | 2025-05-04T07:36:11Z | 0 | 0 | [
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-03T10:04:54Z | null | ---
license: apache-2.0
---
|
HungVu2003/opt-350m_beta_0.0_alpha_0.2_num-company_2_dataset_1_for_gen_6_v2 | HungVu2003 | 2025-05-04T07:05:24Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T07:05:23Z | null | ---
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---
|
valpy/if_multi_old_3_different_range | valpy | 2025-05-04T04:34:01Z | 0 | 0 | [
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] | [] | 2025-05-04T04:33:06Z | null | ---
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---
|
ma921/golden-hh-tokenized-gpt2_noise0 | ma921 | 2025-05-04T03:27:26Z | 0 | 0 | [
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"library:datasets",
"library:pandas",
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"library:polars",
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] | [] | 2025-05-04T03:27:22Z | null | ---
dataset_info:
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---
|
mlfoundations-dev/mix_avg_all | mlfoundations-dev | 2025-05-04T02:47:06Z | 0 | 0 | [
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] | [] | 2025-05-04T02:33:43Z | null | ---
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- split: train
path: data/train-*
---
|
marcuscedricridia/OpenMathInstruct-1-1000-processed | marcuscedricridia | 2025-05-04T02:46:57Z | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-04T02:46:56Z | null | ---
dataset_info:
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---
|
prithivMLmods/Openpdf-Analysis-Recognition | prithivMLmods | 2025-05-04T02:39:38Z | 0 | 0 | [
"task_categories:image-to-text",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"region:us",
"pdf",
"ocr",
"document",
"code"
] | [
"image-to-text"
] | 2025-05-03T08:20:35Z | null | ---
license: apache-2.0
task_categories:
- image-to-text
language:
- en
tags:
- pdf
- ocr
- document
- code
size_categories:
- 1K<n<10K
---
# Openpdf-Analysis-Recognition
The **Openpdf-Analysis-Recognition** dataset is curated for tasks related to image-to-text recognition, particularly for scanned document images and OCR (Optical Character Recognition) use cases. It contains over 6,900 images in a structured `imagefolder` format suitable for training models on document parsing, PDF image understanding, and layout/text extraction tasks.
| **Attribute** | **Value** |
|---------------|------------------------|
| Task | Image-to-Text |
| Modality | Image |
| Format | ImageFolder |
| Language | English |
| License | Apache 2.0 |
| Size | 1K - 10K samples |
| Split | train (6,910 samples) |
### Key Features
* Contains **6.91k** training samples of document-style images.
* Each sample is an **image**, with no associated text or label (raw OCR input).
* Dataset is auto-converted to **Parquet** format by Hugging Face for efficient streaming and processing.
* Suitable for OCR research, PDF document parsing, and code/text recognition tasks.
## Usage
You can load the dataset using the Hugging Face `datasets` library:
```python
from datasets import load_dataset
dataset = load_dataset("prithivMLmods/Openpdf-Analysis-Recognition")
```
## File Size
* **Total download size**: \~2.72 GB
* **Auto-converted Parquet size**: \~2.71 GB
## License
This dataset is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). |
BornSaint/D33_590d | BornSaint | 2025-05-04T01:44:08Z | 0 | 0 | [
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] | [] | 2025-05-04T01:44:05Z | null | ---
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---
|
ParkSY/data_nerf_more_concept_org_style_anything_depthmap_normalmap | ParkSY | 2025-05-04T01:06:46Z | 0 | 0 | [
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---
|
dgambettaphd/D_llm2_gen1_WXS_doc1000_synt64_lr1e-04_acm_FRESH | dgambettaphd | 2025-05-04T01:04:41Z | 0 | 0 | [
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] | [] | 2025-05-04T01:04:18Z | null | ---
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---
|
psyonp/ttr_response_2 | psyonp | 2025-05-04T00:06:55Z | 0 | 0 | [
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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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|
Bakovic/chatbot_medical_diabetique | Bakovic | 2025-05-03T23:34:34Z | 0 | 0 | [
"license:intel-research",
"region:us"
] | [] | 2025-05-03T23:32:29Z | null | ---
license: intel-research
---
|
AdoCleanCode/Youtube8M_real_train_data_v4_0.8 | AdoCleanCode | 2025-05-03T23:26:47Z | 0 | 0 | [
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] | [] | 2025-05-03T21:27:22Z | null | ---
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|
jysim/koch_test | jysim | 2025-05-03T22:56:27Z | 0 | 0 | [
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"LeRobot",
"tutorial"
] | [
"robotics"
] | 2025-05-03T22:56:19Z | 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
{
"codebase_version": "v2.1",
"robot_type": "koch",
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"main_elbow_flex",
"main_wrist_flex",
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"main_gripper"
]
},
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"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
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}
},
"timestamp": {
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"shape": [
1
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"index": {
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},
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1
],
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}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
ymroddi/langa_train | ymroddi | 2025-05-03T22:40:33Z | 0 | 0 | [
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|
kothasuhas/llp-gold-37m-1.5m_clip0.256_T1.0 | kothasuhas | 2025-05-03T22:39:09Z | 0 | 0 | [
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---
|
AJ97/dd | AJ97 | 2025-05-03T22:31:37Z | 0 | 0 | [
"license:apache-2.0",
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] | [] | 2025-05-03T22:31:37Z | null | ---
license: apache-2.0
---
|
kothasuhas/llama-3b-gold-ctx16 | kothasuhas | 2025-05-03T22:14:39Z | 0 | 0 | [
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|
mlfoundations-dev/e1_science_longest_qwq | mlfoundations-dev | 2025-05-03T21:26:30Z | 0 | 0 | [
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---
|
HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_2_dataset_1_for_gen_18_v2 | HungVu2003 | 2025-05-03T20:33:24Z | 0 | 0 | [
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] | [] | 2025-05-03T20:33:23Z | null | ---
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|
HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_2_dataset_0_for_gen_18_v2 | HungVu2003 | 2025-05-03T20:33:22Z | 0 | 0 | [
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|
HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_2_dataset_1_for_gen_16_v2 | HungVu2003 | 2025-05-03T20:29:36Z | 0 | 0 | [
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---
|
anonymousEcaiHateLLM/Hate.2_label_eval_data | anonymousEcaiHateLLM | 2025-05-03T20:27:23Z | 0 | 0 | [
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---
|
HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_2_dataset_0_for_gen_6_v2 | HungVu2003 | 2025-05-03T20:09:58Z | 0 | 0 | [
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|
Bretagne/WikiMatrix_br_fr | Bretagne | 2025-05-03T19:49:04Z | 15 | 0 | [
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"language:fra",
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"library:polars",
"arxiv:1907.05791",
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] | [
"translation"
] | 2024-10-29T15:47:02Z | null | ---
dataset_info:
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dtype: string
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path: data/train-*
task_categories:
- translation
language:
- br
- fra
multilinguality:
- multilingual
---
## Description
Paires breton/français du jeu de données WikiMatrix disponible sur [OPUS](https://opus.nlpl.eu/results/br&fr/corpus-result-table).
**⚠ Attention ⚠** : il y a des problèmes d'alignement. Ce jeu de données n'est donc pas utilisbale tel quel et un post-processing serait à effectuer.
## Citations
#### WikiMatrix
```
@misc{schwenk2019wikimatrixmining135mparallel,
title={WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia},
author={Holger Schwenk and Vishrav Chaudhary and Shuo Sun and Hongyu Gong and Francisco Guzmán},
year={2019},
eprint={1907.05791},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/1907.05791},
}
```
#### OPUS
```
@inbook{4992de1b5fb34f3e9691772606b36edf,
title = "News from OPUS - A Collection of Multilingual Parallel Corpora with Tools and Interfaces",
author = "J{\"o}rg Tiedemann",
year = "2009",
language = "odefinierat/ok{\"a}nt",
volume = "V",
pages = "237--248",
editor = "N. Nicolov and K. Bontcheva and G. Angelova and R. Mitkov",
booktitle = "Recent Advances in Natural Language Processing",
}
```
|
Bretagne/wikiann_br | Bretagne | 2025-05-03T19:40:12Z | 21 | 0 | [
"task_categories:token-classification",
"language:br",
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"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:
- name: tokens
sequence: string
- name: ner_tags
sequence: int64
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num_bytes: 127019
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dataset_size: 379384
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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
}
``` |
TheRealPilot638/Olmo-1B-0724-best_of_4_H200 | TheRealPilot638 | 2025-05-03T19:24:26Z | 3 | 0 | [
"size_categories:1K<n<10K",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-28T17:28:12Z | null | ---
dataset_info:
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data_files:
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data_files:
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path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--seed-3--agg_strategy-last/train-*
---
|
alucchi/Qwen2.5-1.5B-Instruct_n1000_e10_oadam0.0001_b16_1_a10_flash_compact_ttt_a100_s40 | alucchi | 2025-05-03T19:23:54Z | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-03T19:23:44Z | null | ---
dataset_info:
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dtype: string
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configs:
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data_files:
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path: main/train-*
---
|
sltAI/crowdsourced-text-to-sign-language-rule-based-translation-corpus | sltAI | 2025-05-03T18:32:05Z | 372 | 0 | [
"size_categories:1K<n<10K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-04-11T16:03:46Z | null | ---
configs:
- config_name: default
data_files:
- split: train
path: data.csv
---
# Dataset Card for Dataset Name
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