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2025-06-25 02:40:10
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kh4dien/mc-german | kh4dien | 2025-01-24T01:02:37Z | 54 | 0 | [
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---
|
hirundo-io/TruthfulQA-hallucinations-free-text | hirundo-io | 2025-04-21T11:51:52Z | 37 | 0 | [
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---
|
Allen44/high-quality-instruction-response-pairs | Allen44 | 2025-04-13T10:59:07Z | 15 | 0 | [
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---
|
uzair921/SKILLSPAN_embeddings_text | uzair921 | 2025-02-07T22:44:28Z | 14 | 0 | [
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dataset_info:
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---
|
secmlr/clean_dataset_dsformat_filtered_together-deepseek-reasoner_train_len_8000_inputlen_5000 | secmlr | 2025-02-28T06:11:59Z | 24 | 0 | [
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---
|
scubaSteve512/nfl_prediction_demo | scubaSteve512 | 2024-10-20T23:38:04Z | 19 | 0 | [
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|
juliadollis/FINET_30mil_qwen25_7bI_promptoriginal | juliadollis | 2025-02-22T23:08:02Z | 16 | 0 | [
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---
|
stalaei/realmath_2025-2025-03 | stalaei | 2025-06-24T01:46:55Z | 0 | 0 | [
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|
RyanYr/reflect_nonGenCritic_llama8b-t0_gt-t1_mstllrg-t2_80k | RyanYr | 2024-12-29T23:34:16Z | 16 | 0 | [
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---
|
alangai/freedata | alangai | 2024-10-07T19:17:00Z | 13 | 0 | [
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] | [] | 2024-10-07T19:17:00Z | 0 | ---
license: apache-2.0
---
|
yunjae-won/mp_gemma9b_sft_ogd_rms_epoch4_10k_n8 | yunjae-won | 2025-05-17T06:37:12Z | 0 | 0 | [
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|
hackulos/my-distiset-bb1120ca | hackulos | 2025-02-15T21:46:18Z | 10 | 0 | [
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] | [
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] | 2025-02-15T21:46:16Z | 0 | ---
size_categories: n<1K
task_categories:
- text-classification
dataset_info:
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configs:
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tags:
- synthetic
- distilabel
- rlaif
- datacraft
---
<p align="left">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
# Dataset Card for my-distiset-bb1120ca
This dataset has been created with [distilabel](https://distilabel.argilla.io/).
## Dataset Summary
This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
```console
distilabel pipeline run --config "https://huggingface.co/datasets/hackulos/my-distiset-bb1120ca/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/hackulos/my-distiset-bb1120ca/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
<details><summary> Configuration: default </summary><hr>
```json
{
"label": 1,
"text": "The General Data Protection Regulation (GDPR) of the European Union has imposed obligations on organizations to implement technical and organizational measures to ensure the security of personal data. One of these measures is the pseudonymization of personal data, which involves transforming the data into a form that is no longer directly associated with an individual, while still maintaining its utility. This concept is similar to encryption, but the difference lies in the fact that pseudonymization is reversible, whereas encryption is not. Furthermore, pseudonymization is required to be performed in such a way that the original data cannot be easily reversed, thereby achieving the goal of protecting sensitive information."
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("hackulos/my-distiset-bb1120ca", "default")
```
Or simply as it follows, since there's only one configuration and is named `default`:
```python
from datasets import load_dataset
ds = load_dataset("hackulos/my-distiset-bb1120ca")
```
</details>
|
neelabh17/2025-04-21_13.22.59.114647_partial_40-Qwen2.5-3B-Instruct | neelabh17 | 2025-04-21T17:23:05Z | 20 | 0 | [
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|
saintlyk1d/dont-say-it-prompts-player1-basic | saintlyk1d | 2025-04-11T05:14:33Z | 54 | 0 | [
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---
|
abhinav302019/olympiad_data_10018 | abhinav302019 | 2025-02-24T11:24:35Z | 16 | 0 | [
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---
|
mdeputy/053025-segmentation-masks | mdeputy | 2025-05-31T06:09:45Z | 62 | 0 | [
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|
olachinkei/BFCL_jp | olachinkei | 2025-06-24T14:11:16Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | [] | 2025-06-24T14:11:16Z | 0 | ---
license: apache-2.0
---
|
ryota39/llm-jp-chatbot-arena-conversations-reformatted | ryota39 | 2025-05-19T08:15:20Z | 58 | 0 | [
"license:cc",
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"format:parquet",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-19T08:07:23Z | 0 | ---
dataset_info:
features:
- name: id
dtype: string
- name: model_a
dtype: string
- name: model_b
dtype: string
- name: winner
dtype: string
- name: conversation_a
list:
- name: content
dtype: string
- name: role
dtype: string
- name: speaker
dtype: string
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- name: content
dtype: string
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license: cc
---
- [llm-jp/llm-jp-chatbot-arena-conversations](https://huggingface.co/datasets/llm-jp/llm-jp-chatbot-arena-conversations)を整形したデータセットです |
Deason11/so100_test7 | Deason11 | 2025-03-14T07:49:56Z | 35 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
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"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so100",
"tutorial"
] | [
"robotics"
] | 2025-03-14T07:48:50Z | 0 | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
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]
},
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],
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},
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"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "h264",
"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]
``` |
HanXiao1999/UI-Genie-Agent-5k | HanXiao1999 | 2025-05-29T04:27:31Z | 83 | 0 | [
"task_categories:image-text-to-text",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:json",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2505.21496",
"region:us"
] | [
"image-text-to-text"
] | 2025-05-27T15:00:50Z | 0 | ---
task_categories:
- image-text-to-text
language:
- en
license: mit
---
This repository contains the dataset from the paper [UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based
Mobile GUI Agents](https://huggingface.co/papers/2505.21496).
Github: https://github.com/Euphoria16/UI-Genie |
Min-Jaewon/sdxl_laion_2_0 | Min-Jaewon | 2025-01-17T01:55:09Z | 147 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-01-17T01:47:30Z | 0 | ---
dataset_info:
features:
- name: id
dtype: string
- name: prompt
dtype: string
- name: latents
dtype:
array4_d:
shape:
- 2
- 4
- 128
- 128
dtype: float32
- name: prompt_embed
dtype:
array3_d:
shape:
- 1
- 77
- 2048
dtype: float32
- name: pooled_prompt_embed
dtype:
array2_d:
shape:
- 1
- 1280
dtype: float32
splits:
- name: train
num_bytes: 8735479669
num_examples: 7500
download_size: 8739413858
dataset_size: 8735479669
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
nreHieW/Extracted_GSM | nreHieW | 2025-01-26T16:44:11Z | 19 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-01-23T01:14:36Z | 0 | ---
dataset_info:
features:
- name: type
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1890281
num_examples: 7473
download_size: 1068227
dataset_size: 1890281
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
To reproduce
```python
from datasets import load_dataset, Dataset
from tqdm import tqdm
ds = load_dataset("allenai/RLVR-GSM")
out = []
for item in tqdm(ds["train"]):
text = item['messages'][0]['content']
text = text.split("\n\nQuestion:")[-1].strip()
out.append({
"type": "math",
"question": text,
'answer': item['ground_truth']
})
ds = Dataset.from_list(out)
``` |
junn991/asdasd | junn991 | 2024-11-19T17:04:42Z | 17 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-11-18T08:46:08Z | 0 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 5380
num_examples: 90
download_size: 5714
dataset_size: 5380
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
KiViDrag/breastmnist_50 | KiViDrag | 2024-11-08T11:29:48Z | 61 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-11-08T09:46:23Z | 0 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': malignant
'1': normal, benign
splits:
- name: train
num_bytes: 4346269.75
num_examples: 4914
download_size: 4440909
dataset_size: 4346269.75
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ZixuanKe/trade_the_event_sup_dpo_binarized | ZixuanKe | 2024-11-04T21:46:46Z | 20 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-11-03T22:52:43Z | 0 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: ground_truth_chosen
dtype: string
- name: v1_rejected
dtype: string
splits:
- name: train
num_bytes: 1390170654.0
num_examples: 243425
- name: validation
num_bytes: 72814246.0
num_examples: 12812
download_size: 793179358
dataset_size: 1462984900.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
french-datasets/jpacifico_merged-admin-def-dataset-16k | french-datasets | 2025-06-22T11:39:05Z | 0 | 0 | [
"language:fra",
"region:us"
] | [] | 2025-06-22T11:38:16Z | 0 | ---
language: "fra"
viewer: false
---
Ce répertoire est vide, il a été créé pour améliorer le référencement du jeu de données [jpacifico/merged-admin-def-dataset-16k](https://huggingface.co/datasets/jpacifico/merged-admin-def-dataset-16k).
|
rakhman-llm/XCodeEval-Java-Dataset | rakhman-llm | 2024-11-26T01:16:19Z | 30 | 0 | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"code"
] | [
"text-generation"
] | 2024-11-25T05:36:22Z | 0 | ---
task_categories:
- text-generation
tags:
- code
size_categories:
- 100K<n<1M
--- |
yav1327/music-generation-llm | yav1327 | 2024-10-17T15:35:25Z | 23 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-10-17T15:35:07Z | 0 | ---
dataset_info:
features:
- name: song_id
dtype: int64
- name: filename
dtype: string
- name: filepath
dtype:
audio:
sampling_rate: 16000
- name: genre_id
dtype: int64
- name: genre
dtype: string
splits:
- name: train
num_bytes: 369471304.0
num_examples: 945
download_size: 369252420
dataset_size: 369471304.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
michsethowusu/dinka-dyula_sentence-pairs | michsethowusu | 2025-04-03T10:26:13Z | 52 | 0 | [
"size_categories:10K<n<100K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-03T10:26:10Z | 0 |
---
dataset_info:
features:
- name: score
dtype: float32
- name: Dinka
dtype: string
- name: Dyula
dtype: string
splits:
- name: train
num_bytes: 2860214
num_examples: 17561
download_size: 2860214
dataset_size: 2860214
configs:
- config_name: default
data_files:
- split: train
path: Dinka-Dyula_Sentence-Pairs.csv
---
# Dinka-Dyula_Sentence-Pairs Dataset
This dataset contains sentence pairs for African languages along with similarity scores. It can be used for machine translation, sentence alignment, or other natural language processing tasks.
This dataset is based on the NLLBv1 dataset, published on OPUS under an open-source initiative led by META. You can find more information here: [OPUS - NLLB-v1](https://opus.nlpl.eu/legacy/NLLB-v1.php)
## Metadata
- **File Name**: Dinka-Dyula_Sentence-Pairs
- **Number of Rows**: 17561
- **Number of Columns**: 3
- **Columns**: score, Dinka, Dyula
## Dataset Description
The dataset contains sentence pairs in African languages with an associated similarity score. Each row consists of three columns:
1. `score`: The similarity score between the two sentences (range from 0 to 1).
2. `Dinka`: The first sentence in the pair (language 1).
3. `Dyula`: The second sentence in the pair (language 2).
This dataset is intended for use in training and evaluating machine learning models for tasks like translation, sentence similarity, and cross-lingual transfer learning.
## References
Below are papers related to how the data was collected and used in various multilingual and cross-lingual applications:
[1] Holger Schwenk and Matthijs Douze, Learning Joint Multilingual Sentence Representations with Neural Machine Translation, ACL workshop on Representation Learning for NLP, 2017
[2] Holger Schwenk and Xian Li, A Corpus for Multilingual Document Classification in Eight Languages, LREC, pages 3548-3551, 2018.
[3] Holger Schwenk, Filtering and Mining Parallel Data in a Joint Multilingual Space ACL, July 2018
[4] Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk and Veselin Stoyanov, XNLI: Cross-lingual Sentence Understanding through Inference, EMNLP, 2018.
[5] Mikel Artetxe and Holger Schwenk, Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings arXiv, Nov 3 2018.
[6] Mikel Artetxe and Holger Schwenk, Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond arXiv, Dec 26 2018.
[7] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia arXiv, July 11 2019.
[8] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB
[9] Paul-Ambroise Duquenne, Hongyu Gong, Holger Schwenk, Multimodal and Multilingual Embeddings for Large-Scale Speech Mining, NeurIPS 2021, pages 15748-15761.
[10] Kevin Heffernan, Onur Celebi, and Holger Schwenk, Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
|
yoonholee/metamath-hint-and-data-v4 | yoonholee | 2025-03-01T19:09:48Z | 18 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-02-28T19:52:03Z | 0 | ---
dataset_info:
features:
- name: problem
dtype: string
- name: answer
dtype: string
- name: completion
sequence: string
- name: completion_answer
sequence: string
- name: completion_correct
sequence: bool
- name: completion_succ_rate
dtype: float64
- name: domain
dtype: string
- name: context
dtype: string
- name: hint
dtype: string
splits:
- name: train
num_bytes: 1225053097
num_examples: 40000
download_size: 375337679
dataset_size: 1225053097
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Cornell-AGI/math_size_qwen2.5_3b_eval | Cornell-AGI | 2025-05-29T23:19:30Z | 40 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-29T23:19:23Z | 0 | ---
dataset_info:
features:
- name: level
dtype: string
- name: type
dtype: string
- name: data_source
dtype: string
- name: prompt
list:
- name: content
dtype: string
- name: role
dtype: string
- name: ability
dtype: string
- name: reward_model
struct:
- name: ground_truth
dtype: string
- name: style
dtype: string
- name: extra_info
struct:
- name: answer
dtype: string
- name: index
dtype: int64
- name: question
dtype: string
- name: split
dtype: string
- name: response_0
dtype: string
- name: response_1
dtype: string
- name: response_2
dtype: string
- name: response_3
dtype: string
- name: response_4
dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
- name: response_19
dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
- name: response_28
dtype: string
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dtype: string
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dtype: string
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dtype: string
- name: eval_0
dtype: float64
- name: eval_1
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
splits:
- name: train
num_bytes: 413000734
num_examples: 7500
download_size: 187731245
dataset_size: 413000734
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
infinite-dataset-hub/FitHealth | infinite-dataset-hub | 2025-03-22T22:03:24Z | 23 | 0 | [
"license:mit",
"size_categories:n<1K",
"format:csv",
"modality:tabular",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"infinite-dataset-hub",
"synthetic"
] | [] | 2025-03-22T22:03:23Z | 0 | ---
license: mit
tags:
- infinite-dataset-hub
- synthetic
---
# FitHealth
tags: Healthcare, Physical Activity, Biometric Analysis
_Note: This is an AI-generated dataset so its content may be inaccurate or false_
**Dataset Description:**
The 'FitHealth' dataset comprises records of individuals participating in fitness-related activities, paired with biometric data to monitor their health. It includes information such as types of physical activities, duration, intensity, and corresponding health metrics like heart rate, blood pressure, and body mass index (BMI). This dataset can be used to analyze the impact of different fitness routines on health parameters and to develop personalized fitness plans.
**CSV Content Preview:**
```csv
activity_id,date,activity_type,duration,intensity,heart_rate,blood_pressure,bmi
001,2023-01-15,Running,45,High,172,120/80,23.5
002,2023-01-18,Cycling,60,Moderate,155,115/75,22.1
003,2023-01-20,Swimming,30,Low,130,118/78,24.2
004,2023-01-22,Yoga,60,Low,95,110/70,21.8
005,2023-01-25,Weightlifting,45,High,165,122/82,25.3
```
**Source of the data:**
The dataset was generated using the [Infinite Dataset Hub](https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub) and microsoft/Phi-3-mini-4k-instruct using the query 'Fitness':
- **Dataset Generation Page**: https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub?q=Fitness&dataset=FitHealth&tags=Healthcare,+Physical+Activity,+Biometric+Analysis
- **Model**: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
- **More Datasets**: https://huggingface.co/datasets?other=infinite-dataset-hub
|
mevsg/Gongguan-TextRegions-v1 | mevsg | 2025-03-12T10:02:57Z | 15 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-12T09:58:13Z | 0 | ---
dataset_info:
features:
- name: image_path
dtype: string
- name: image_size
sequence: int64
- name: bboxes
sequence:
sequence: int64
- name: labels
sequence: string
- name: page_id
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 51276245.09090909
num_examples: 8
- name: validation
num_bytes: 19228591.90909091
num_examples: 3
download_size: 70461678
dataset_size: 70504837.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
LabARSS/MMLU-Pro-single-token-entropy | LabARSS | 2025-05-26T13:43:03Z | 0 | 0 | [
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2503.01688",
"region:us"
] | [] | 2025-05-26T12:59:29Z | 0 | ---
configs:
- config_name: mistral_24b
data_files: "mmlu_mistral_24b.tsv"
- config_name: mistral_24b_w_fallback_if_unknown
data_files: "mmlu_mistral_24b_w_fallback_if_unknown.tsv"
- config_name: phi4
data_files: "mmlu_phi4.tsv"
- config_name: phi4_w_fallback_if_unknown
data_files: "mmlu_phi4_w_fallback_if_unknown.tsv"
- config_name: phi4_w_fallback_if_unknown_alternative_prompt
data_files: "mmlu_phi4_w_fallback_if_unknown_alternative_prompt.tsv"
- config_name: phi4mini
data_files: "mmlu_phi4mini.tsv"
- config_name: phi4mini_w_fallback_if_unknown
data_files: "mmlu_phi4mini_w_fallback_if_unknown.tsv"
- config_name: phi4mini_w_fallback_if_unknown_alternative_prompt
data_files: "mmlu_phi4mini_w_fallback_if_unknown_alternative_prompt.tsv"
- config_name: qwen_3b
data_files: "mmlu_qwen_3b.tsv"
- config_name: qwen_3b_w_fallback_if_unknown
data_files: "mmlu_qwen_3b_w_fallback_if_unknown.tsv"
- config_name: qwen_3b_w_fallback_if_unknown_alternative_prompt
data_files: "mmlu_qwen_3b_w_fallback_if_unknown_alternative_prompt.tsv"
license: mit
language:
- en
pretty_name: MMLU Pro with single token response entropy metadata for Mistral 24B, Phi4, Phi4-mini, Qwen2.5 3B
size_categories:
- 10K<n<100K
---
# Dataset Card for MMLU Pro with single token response entropy metadata for Mistral 24B, Phi4, Phi4-mini, Qwen2.5 3B
<!-- Provide a quick summary of the dataset. -->
MMLU Pro dataset with single token response entropy metadata for Mistral 24B, Phi4, Phi4-mini, Qwen2.5 3B
## Dataset Details
### Dataset Description
Following up on the results from ["When an LLM is apprehensive about its answers -- and when its uncertainty is justified"](https://arxiv.org/abs/2503.01688), we measure the response entopy for MMLU Pro dataset when the model is prompted to answer questions directly as a single token. We collect the entropy across 3 different sets of prompts: the ones that allow the model to answer "I do not know" and the ones that do not.
- **Language(s) (NLP):** English
- **License:** MIT
## Dataset Structure
Columns:
- All columns as in the original [MMLU Pro dataset](https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro);
- "entropy_ans_correct_{model_internal_name}" - (bool) correctness of the model answer;
- "entropy_value_{model_internal_name}" - (float) entropy value. Default (if answer is incorrectly formatted or missing): 0.0.
- "entropy_ans_{model_internal_name}" - (str) whole decoded response.
## Prompts
### Default
System prompt:
```
The following are multiple choice questions about {subject}. Write down ONLY the NUMBER of the correct answer and nothing else.
```
User prompt:
```
Question: ...
Options:
1. ...
2. ...
...
n. ...
Choose one of the answers. Write down ONLY the NUMBER of the correct answer and nothing else.".
```
### With fallback if unknown
We allow the model to self-estimate its uncertainty and reply "0" as a special option denoting "I do not know".
System prompt:
```
The following are multiple choice questions about {subject}. If you are certain about the answer return the correct option number, otherwise return 0. Write down ONLY the NUMBER and nothing else.
```
User prompt:
```
Question: ...
Options:
1. ...
2. ...
...
n. ...
Choose one of the answers. If you are certain about the answer return the correct option number, otherwise return 0. Write down ONLY the NUMBER and nothing else.
```
### With fallback if unknown (alternative)
Alternative version of the fallback prompt.
System prompt:
```
The following are multiple choice questions about {subject}. If you know the answer return the correct option number, otherwise return 0. Write down ONLY the NUMBER and nothing else.
```
User prompt:
```
Question: ...
Options:
1. ...
2. ...
...
n. ...
Choose one of the answers. If you know the answer return the correct option number, otherwise return 0. Write down ONLY the NUMBER and nothing else.
```
## Hyperparameters
```
outputs = model.generate(
**inputs,
max_new_tokens=1,
return_dict_in_generate=True,
output_scores=True,
temperature=None,
top_p=None,
top_k=None,
do_sample=False,
num_beams=1,
pad_token_id=tokenizer.eos_token_id,
)
```
## Citation
TBD |
ilovesushiandkimchiandmalaxiangguo/for_bisai | ilovesushiandkimchiandmalaxiangguo | 2025-05-03T07:51:37Z | 0 | 0 | [
"size_categories:n<1K",
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"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-03T07:50:17Z | 0 | ---
dataset_info:
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dtype: image
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num_examples: 297
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
vedataydin/tarim2 | vedataydin | 2025-03-25T12:59:21Z | 15 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
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"library:polars",
"region:us"
] | [] | 2025-03-25T12:58:57Z | 0 | ---
dataset_info:
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dtype: string
- name: input
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- name: response
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- name: test
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num_examples: 1
download_size: 6517
dataset_size: 1615.8
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
michsethowusu/french-fulah_sentence-pairs | michsethowusu | 2025-05-17T17:24:10Z | 0 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-17T15:25:30Z | 0 | ---
dataset_info:
features:
- name: similarity
dtype: float32
- name: French
dtype: string
- name: Fulah
dtype: string
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- name: train
num_bytes: 98351097
num_examples: 701161
download_size: 69409466
dataset_size: 98351097
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# French-Fulah_Sentence-Pairs Dataset
This dataset can be used for machine translation, sentence alignment, or other natural language processing tasks.
It is based on the NLLBv1 dataset, published on OPUS under an open-source initiative led by META. You can find more information here: [OPUS - NLLB-v1](https://opus.nlpl.eu/legacy/NLLB-v1.php)
## Metadata
- **File Name**: French-Fulah_Sentence-Pairs
- **File Size**: 100126177 bytes
- **Languages**: French, French
## Dataset Description
The dataset contains sentence pairs in African languages with an associated similarity score. The file is structured as a CSV with three columns:
1. `similarity`: The similarity score between the two sentences (range from 0 to 1).
2. `French`: The first sentence in the pair.
3. `French`: The second sentence in the pair.
This dataset is intended for use in training and evaluating machine learning models for tasks like translation, sentence similarity, and cross-lingual transfer learning.
## References
Below are papers related to how the data was collected and used in various multilingual and cross-lingual applications:
[1] Holger Schwenk and Matthijs Douze, "Learning Joint Multilingual Sentence Representations with Neural Machine Translation", ACL workshop on Representation Learning for NLP, 2017
[2] Holger Schwenk and Xian Li, "A Corpus for Multilingual Document Classification in Eight Languages", LREC, pages 3548-3551, 2018.
[3] Holger Schwenk, "Filtering and Mining Parallel Data in a Joint Multilingual Space", ACL, July 2018
[4] Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk and Veselin Stoyanov, "XNLI: Cross-lingual Sentence Understanding through Inference", EMNLP, 2018.
[5] Mikel Artetxe and Holger Schwenk, "Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings", arXiv, Nov 3 2018.
[6] Mikel Artetxe and Holger Schwenk, "Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond", arXiv, Dec 26 2018.
[7] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, "WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia", arXiv, July 11 2019.
[8] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin, "CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB", 2020.
[9] Paul-Ambroise Duquenne, Hongyu Gong, Holger Schwenk, "Multimodal and Multilingual Embeddings for Large-Scale Speech Mining", NeurIPS 2021, pages 15748-15761.
[10] Kevin Heffernan, Onur Celebi, and Holger Schwenk, "Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages", 2022.
|
LovrOP/cppe-custom-dataset | LovrOP | 2024-12-09T14:31:43Z | 17 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-12-09T14:25:48Z | 0 | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image_path
dtype: string
- name: width
dtype: int64
- name: height
dtype: int64
- name: objects
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sequence: int64
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download_size: 19833
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configs:
- config_name: default
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- split: train
path: data/train-*
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path: data/test-*
- split: validation
path: data/validation-*
---
|
fwnlp/self-instruct-safety-alignment | fwnlp | 2024-10-23T23:36:37Z | 133 | 2 | [
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2410.05269",
"region:us"
] | [] | 2024-10-13T07:32:13Z | 0 | ---
license: apache-2.0
---
**[EMNLP 2024] Data Advisor: Dynamic Data Curation for Safety Alignment of Large Language Models**
[**🌐 Homepage**](https://feiwang96.github.io/DataAdvisor/) | [**📖 Paper**](https://arxiv.org/pdf/2410.05269) | [**🤗 Dataset (Data Advisor)**](https://huggingface.co/datasets/fwnlp/data-advisor-safety-alignment) | [**🤗 Dataset (Self-Instruct)**](https://huggingface.co/datasets/fwnlp/self-instruct-safety-alignment)
## Disclaimer
The dataset contains content that may be offensive or harmful. This dataset is intended for research purposes, specifically to support efforts aimed at creating safer and less harmful AI systems. Please engage with it responsibly and at your own risk.
## Citation
```
@inproceedings{wang2024data,
title={Data Advisor: Dynamic Data Curation for Safety Alignment of Large Language Models},
author={Wang, Fei and Mehrabi, Ninareh and Goyal, Palash and Gupta, Rahul and Chang, Kai-Wei and Galstyan, Aram},
booktitle={Proceedings of EMNLP 2024},
year={2024}
}
```
|
tuenguyen/Medical-Eval-MedBullets_op4 | tuenguyen | 2025-04-10T11:36:03Z | 221 | 0 | [
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] | [] | 2025-04-10T11:36:01Z | 0 | ---
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---
|
rohith2812/atoi-finetuning-v2 | rohith2812 | 2024-11-18T20:36:27Z | 17 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:image",
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"library:pandas",
"library:mlcroissant",
"library:polars",
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] | [] | 2024-11-18T20:36:23Z | 0 | ---
dataset_info:
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dtype: image
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num_examples: 517
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configs:
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data_files:
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path: data/train-*
---
|
qingshufan/GA-EVLRU | qingshufan | 2025-05-02T12:34:36Z | 79 | 1 | [
"task_categories:time-series-forecasting",
"language:en",
"license:gpl-3.0",
"arxiv:2504.07453",
"region:us"
] | [
"time-series-forecasting"
] | 2025-04-12T09:06:35Z | 0 | ---
language: en
license: gpl-3.0
pretty_name: GA-EVLRU
task_categories:
- time-series-forecasting
---
# Dataset Card for GA-EVLRU Battery Swapping Demand Datasets
## Dataset Details
### Dataset Description
This dataset consists of battery swapping demand datasets generated by the conversion of the [ST-EVCDP](https://github.com/IntelligentSystemsLab/ST-EVCDP), [UrbanEV](https://github.com/IntelligentSystemsLab/UrbanEV), and [EV-Load-Open-Data](https://github.com/yvenn-amara/ev-load-open-data). There are a total of nine scenario-specific datasets, each containing unstructured demand data (`demand.csv`) and data normalized to 1 hour (`bss.csv`). The dataset is used for the study of probability estimation and scheduling optimization for battery swap stations, where an innovative approach combining the Least Recently Used (LRU) strategy with genetic algorithms and a guided search mechanism is proposed to enhance global optimization capability.
### Dataset Sources
- **Repository:** [https://github.com/qingshufan/GA-EVLRU](https://github.com/qingshufan/GA-EVLRU)
- **Paper:** [Probability Estimation and Scheduling Optimization for Battery Swap Stations via LRU-Enhanced Genetic Algorithm and Dual-Factor Decision System](https://arxiv.org/abs/2504.07453)
## Uses
### Direct Use
The dataset can be directly used for research and development related to battery swapping demand prediction and optimization of battery swap stations. Specifically, it can be used to train and test the proposed probability estimation model and the GA-EVLRU algorithm for better understanding and predicting the battery swapping demand in different scenarios.
### Out-of-Scope Use
The dataset is not intended for any applications unrelated to battery swapping demand prediction and optimization of battery swap stations. It should not be used for any commercial purposes that violate the GPL-3.0 license terms, such as selling the dataset without proper authorization. Also, it should not be used to generate any information that may cause harm or discrimination to individuals or groups.
## Dataset Structure
Each of the nine datasets (`acn`, `boulder_2021`, `dundee`, `palo_alto`, `paris`, `perth`, `sap`, `st-evcdp`, `urbanev`) contains two main files: `demand.csv` which is the unstructured demand data, and `bss.csv` which is the data normalized to 1 hour. The source of the original data for these datasets is either from [EV-Load-Open-Data](https://github.com/yvenn-amara/ev-load-open-data), [ST-EVCDP](https://github.com/IntelligentSystemsLab/ST-EVCDP), or [UrbanEV](https://github.com/IntelligentSystemsLab/UrbanEV).
## Dataset Creation
### Curation Rationale
The motivation for creating this dataset is to provide a comprehensive set of data for studying the battery swapping demand in different scenarios, which can help in the development of more efficient battery swap station scheduling and optimization algorithms. By combining data from different sources and normalizing them, a more consistent and useful dataset for research is obtained.
### Source Data
#### Data Collection and Processing
The data is collected from multiple open-source repositories: [ST-EVCDP](https://github.com/IntelligentSystemsLab/ST-EVCDP), [UrbanEV](https://github.com/IntelligentSystemsLab/UrbanEV), and [EV-Load-Open-Data](https://github.com/yvenn-amara/ev-load-open-data). The collected data is then processed to generate the battery swapping demand datasets. The processing includes data conversion to create the `demand.csv` and `bss.csv` files for each scenario. The specific processing steps are described in the associated research paper and the code available in the [https://github.com/qingshufan/GA-EVLRU](https://github.com/qingshufan/GA-EVLRU) repository.
#### Who are the source data producers?
The source data producers for [EV-Load-Open-Data](https://github.com/yvenn-amara/ev-load-open-data) are associated with the project by yvenn-amara. For [ST-EVCDP](https://github.com/IntelligentSystemsLab/ST-EVCDP), it is associated with the IntelligentSystemsLab. And for [UrbanEV](https://github.com/IntelligentSystemsLab/UrbanEV), it is also from the IntelligentSystemsLab.
#### Personal and Sensitive Information
There is no indication that the dataset contains personal or sensitive information such as addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.
## Bias, Risks, and Limitations
The dataset may have limitations in representing all possible real-world scenarios for battery swapping demand. The data is collected from specific open-source repositories and may not cover all geographical locations, types of electric vehicles, or different charging patterns comprehensively. Also, the normalization process may introduce some biases in representing the actual demand in a completely accurate way.
### Recommendations
Users should be aware that the dataset may not fully represent all real-world situations and should use it with caution when making decisions related to large-scale battery swap station deployments. When using the dataset for research, users should consider conducting sensitivity analyses to understand the impact of potential biases and limitations on their results.
## Citation
**BibTeX:**
```bibtex
@inproceedings{li2025gaevlru,
title={Probability Estimation and Scheduling Optimization for Battery Swap Stations via LRU-Enhanced Genetic Algorithm and Dual-Factor Decision System},
author={Anzhen Li and Shufan Qing and Xiaochang Li and Rui Mao and Mingchen Feng},
journal={arXiv preprint arXiv:2504.07453},
year={2025}
}
```
## Dataset Card Contact
For any questions or further information about the dataset, you can contact the authors of the associated research paper or raise issues in the [https://github.com/qingshufan/GA-EVLRU](https://github.com/qingshufan/GA-EVLRU) repository. |
crtvai/jordan-dataset-v11 | crtvai | 2024-11-18T09:44:33Z | 52 | 0 | [
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"modality:audio",
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] | [] | 2024-11-18T09:44:03Z | 0 | ---
dataset_info:
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---
|
kopyl/833-icons-dataset-1024-blip-large | kopyl | 2023-11-17T19:35:17Z | 90 | 1 | [
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2023-11-17T19:34:14Z | 1 | ---
configs:
- config_name: default
data_files:
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path: data/train-*
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features:
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dtype: string
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dataset_size: 21063249.0
---
# Dataset Card for "833-icons-dataset-1024-blip-large"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
maniro-ai/2024-10-24-engine-simple-3d-relative-04 | maniro-ai | 2024-10-25T15:09:18Z | 17 | 0 | [
"region:us"
] | [] | 2024-10-25T15:09:07Z | 0 | ---
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path: data/train-*
---
|
mikesun26card/werewolf_audio_dataset | mikesun26card | 2025-03-22T07:48:28Z | 18 | 0 | [
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] | [] | 2025-02-28T19:19:18Z | 0 | ---
dataset_info:
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|
KomeijiForce/Fandom_Bandori_Codified_Profiles_Claude_4.0 | KomeijiForce | 2025-06-09T20:06:49Z | 0 | 0 | [
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dataset_info:
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---
|
Erroriser/jenny-tts-text-tags-6h-v1 | Erroriser | 2024-10-07T13:57:17Z | 21 | 0 | [
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] | [] | 2024-10-07T13:57:16Z | 0 | ---
dataset_info:
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|
hamishivi/combined_o3_val_data_1 | hamishivi | 2025-05-31T19:39:23Z | 32 | 0 | [
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] | [] | 2025-05-31T19:39:21Z | 0 | ---
dataset_info:
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---
|
BASF-AI/PubChem-Raw | BASF-AI | 2025-05-08T19:35:30Z | 17 | 0 | [
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"region:us"
] | [] | 2025-05-08T19:22:07Z | 0 | ---
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---
|
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_ff596251-154a-4bd0-86d6-af5aef785f95 | argilla-internal-testing | 2024-11-11T11:09:32Z | 19 | 0 | [
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] | [] | 2024-11-11T11:09:31Z | 0 | ---
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---
|
andrewsiah/PersonaPromptPersonalLLM_918 | andrewsiah | 2024-11-15T15:14:07Z | 60 | 0 | [
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"region:us"
] | [] | 2024-11-15T15:14:05Z | 0 | ---
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path: data/train-*
---
# Dataset Card for "PersonaPromptPersonalLLM_918"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
atta00/icd10-codes | atta00 | 2024-10-15T04:33:18Z | 21 | 0 | [
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"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-10-15T04:31:29Z | 0 | ---
license: mit
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---
|
wwbrannon/twinviews-13k | wwbrannon | 2024-10-11T08:11:29Z | 81 | 3 | [
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"region:us",
"synthetic",
"political-bias",
"truthfulness",
"alignment",
"debiasing",
"bias-detection",
"fairness"
] | [
"text-classification",
"reinforcement-learning"
] | 2024-10-11T04:45:53Z | 0 | ---
annotations_creators:
- machine-generated
- expert-generated
license: cc-by-4.0
task_categories:
- text-classification
- reinforcement-learning
language:
- en
language_creators:
- machine-generated
tags:
- synthetic
- political-bias
- truthfulness
- alignment
- debiasing
- bias-detection
- fairness
multilinguality:
- monolingual
pretty_name: TwinViews-13k
size_categories:
- 10K<n<100K
source_datasets:
- original
paperswithcode_id: twinviews-13k
---
<!-- YAML front matter fields documented here: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md -->
# Dataset Card for TwinViews-13k
This dataset contains 13,855 pairs of left-leaning and right-leaning political statements matched by topic. The dataset was generated using GPT-3.5 Turbo and has been audited to ensure quality and ideological balance. It is designed to facilitate the study of political bias in reward models and language models, with a focus on the relationship between truthfulness and political views.
## Dataset Details
### Dataset Description
TwinViews-13k is a dataset of 13,855 pairs of left-leaning and right-leaning political statements, each pair matched by topic. It was created to study political bias in reward and language models, with a focus on understanding the interaction between model alignment to truthfulness and the emergence of political bias. The dataset was generated using GPT-3.5 Turbo, with extensive auditing to ensure ideological balance and topical relevance.
This dataset can be used for various tasks related to political bias, natural language processing, and model alignment, particularly in studies examining how political orientation impacts model outputs.
- **Curated by:** Suyash Fulay, William Brannon, Shrestha Mohanty, Cassandra Overney, Elinor Poole-Dayan, Deb Roy, Jad Kabbara
- **Language(s) (NLP):** en
- **License:** cc-by-4.0
### Dataset Sources
- **Repository:** https://github.com/sfulay/truth_politics
- **Paper:** https://arxiv.org/abs/2409.05283
## Uses
### Direct Use
This dataset is suitable for:
* Studying political bias in reward models and large language models (LLMs).
* Evaluating alignment techniques for LLMs, especially regarding truthfulness and political bias.
* Training and/or evaluating models in the context of political discourse analysis.
* Research on how political views and alignment objectives interact in AI systems.
### Out-of-Scope Use
This dataset is not suitable for tasks requiring very fine-grained or human-labeled annotations of political affiliation beyond the machine-generated left/right splits. Notions of "left" and "right" may also vary between countries and over time, and users of the data should check that it captures the ideological dimensions of interest.
## Dataset Structure
The dataset contains 13,855 pairs of left-leaning and right-leaning political statements. Each pair is matched by topic, with statements generated to be similar in style and length. The dataset consists of the following fields:
* `l`: A left-leaning political statement.
* `r`: A right-leaning political statement.
* `topic`: The general topic of the pair (e.g., taxes, climate, education).
## Dataset Creation
### Curation Rationale
The dataset was created to fill the gap in large-scale, topically matched political statement pairs for studying bias in LLMs. It allows for comparison of how models treat left-leaning versus right-leaning perspectives, particularly in the context of truthfulness and political bias.
### Source Data
#### Data Collection and Processing
The data was generated using GPT-3.5 Turbo. A carefully designed prompt was used to generate statement pairs that were ideologically representative of left-leaning and right-leaning viewpoints. The statements were then audited to ensure relevance, ideological alignment, and quality. Topic matching was done to ensure the statements are comparable across the political spectrum.
In summary:
* Generated using GPT-3.5 Turbo.
* Audited for ideological and topical relevance.
* Final dataset filtered and structured to ensure left/right statement parity.
#### Who are the source data producers?
The dataset was generated by GPT-3.5 Turbo, with extensive auditing performed by the dataset creators at MIT.
#### Personal and Sensitive Information
The dataset consists of machine-generated political statements and does not contain any personal or sensitive information.
## Bias, Risks, and Limitations
Users of the dataset should be aware of certain limitations:
* **Source context:** Notions of what is political and the left/right ideological spectrum are context-specific and vary between countries and over time. Our dataset and its notions of politics and ideology come from the US in the early 2020s and may not generalize to other cultures or other time periods.
* **Generated content:** Since the statements were generated by GPT-3.5 Turbo, they may not fully capture the nuance or complexity of real-world political discourse. It is also possible that the dataset may contain stylistic or lexical artifacts correlated with political bias, though our evaluation has not identified any such artifacts.
## Citation
**BibTeX:**
<!-- add on publication in anthology:
url = "https://aclanthology.org/_______",
doi = "10.________",
pages = "X--Y",
-->
```
@inproceedings{fulayRelationshipTruthPolitical2024,
author = {Fulay, Suyash and Brannon, William and Mohanty, Shrestha and Overney, Cassandra and Poole-Dayan, Elinor and Roy, Deb and Kabbara, Jad},
title = {On the Relationship between Truth and Political Bias in Language Models},
booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP '24)},
year = {2024},
month = nov,
publisher = {Association for Computational Linguistics},
note = {arXiv:2409.05283},
abstract = {Language model alignment research often attempts to ensure that models are not only helpful and harmless, but also truthful and unbiased. However, optimizing these objectives simultaneously can obscure how improving one aspect might impact the others. In this work, we focus on analyzing the relationship between two concepts essential in both language model alignment and political science: \textit{truthfulness} and \textit{political bias}. We train reward models on various popular truthfulness datasets and subsequently evaluate their political bias. Our findings reveal that optimizing reward models for truthfulness on these datasets tends to result in a left-leaning political bias. We also find that existing open-source reward models (i.e. those trained on standard human preference datasets) already show a similar bias and that the bias is larger for larger models. These results raise important questions about both the datasets used to represent truthfulness and what language models capture about the relationship between truth and politics.}
}
```
**APA:**
```
Fulay, S., Brannon, W., Mohanty, S., Overney, C., Poole-Dayan, E., Roy, D., & Kabbara, J. (2024). On the Relationship between Truth and Political Bias in Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP '24). Association for Computational Linguistics.
```
## Glossary
* Left-leaning: Political statements generally associated with progressive or liberal views.
* Right-leaning: Political statements generally associated with conservative or traditional views.
* Political Bias: A model's tendency to favor one political ideology over another in its outputs.
## Dataset Card Authors
William Brannon, <[email protected]>
## Dataset Card Contact
* William Brannon, <[email protected]>
* Suyash Fulay, <[email protected]> |
sun2ot/DiffCLR | sun2ot | 2025-06-10T02:01:28Z | 70 | 0 | [
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"region:us",
"recommendation",
"matrix",
"sparse"
] | [] | 2025-05-18T12:09:44Z | 0 | ---
license: mit
language:
- en
tags:
- recommendation
- matrix
- sparse
size_categories:
- 10K<n<100K
---
This is a datasets for recommendation including `Tiktok`, `Yelp`, and `Amazon-Sports`.
`Tiktok` and `Sports` were published in [DiffMM](https://github.com/HKUDS/DiffMM) work, and we only updated the Scipy version they used.
The `Yelp` dataset is a dataset we constructed from the latest official [Yelp](https://business.yelp.com/data/resources/open-dataset/) data. |
mlfoundations-dev/math_stratos_scale_annotated | mlfoundations-dev | 2025-01-24T22:43:30Z | 17 | 0 | [
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] | [] | 2025-01-24T17:27:46Z | 0 | ---
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---
|
mahdi02ch/test | mahdi02ch | 2025-04-03T09:58:12Z | 16 | 0 | [
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---
|
mahwizzzz/Urdu_Rekhta | mahwizzzz | 2025-03-08T06:48:54Z | 38 | 3 | [
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"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-08T05:49:22Z | 0 | ---
language:
- ur
license: mit
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configs:
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path: data/train-*
---
|
datacomp/imagenet-1k-random-100.0-frac-1over32 | datacomp | 2025-01-18T00:22:07Z | 17 | 0 | [
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] | [] | 2024-12-06T14:13:20Z | 0 | ---
dataset_info:
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'32': tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
'33': loggerhead, loggerhead turtle, Caretta caretta
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'42': agama
'43': frilled lizard, Chlamydosaurus kingi
'44': alligator lizard
'45': Gila monster, Heloderma suspectum
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'47': African chameleon, Chamaeleo chamaeleon
'48': Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus
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'98': red-breasted merganser, Mergus serrator
'99': goose
'100': black swan, Cygnus atratus
'101': tusker
'102': echidna, spiny anteater, anteater
'103': platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus
anatinus
'104': wallaby, brush kangaroo
'105': koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
'106': wombat
'107': jellyfish
'108': sea anemone, anemone
'109': brain coral
'110': flatworm, platyhelminth
'111': nematode, nematode worm, roundworm
'112': conch
'113': snail
'114': slug
'115': sea slug, nudibranch
'116': chiton, coat-of-mail shell, sea cradle, polyplacophore
'117': chambered nautilus, pearly nautilus, nautilus
'118': Dungeness crab, Cancer magister
'119': rock crab, Cancer irroratus
'120': fiddler crab
'121': king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes
camtschatica
'122': American lobster, Northern lobster, Maine lobster, Homarus americanus
'123': spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
'124': crayfish, crawfish, crawdad, crawdaddy
'125': hermit crab
'126': isopod
'127': white stork, Ciconia ciconia
'128': black stork, Ciconia nigra
'129': spoonbill
'130': flamingo
'131': little blue heron, Egretta caerulea
'132': American egret, great white heron, Egretta albus
'133': bittern
'134': crane
'135': limpkin, Aramus pictus
'136': European gallinule, Porphyrio porphyrio
'137': American coot, marsh hen, mud hen, water hen, Fulica americana
'138': bustard
'139': ruddy turnstone, Arenaria interpres
'140': red-backed sandpiper, dunlin, Erolia alpina
'141': redshank, Tringa totanus
'142': dowitcher
'143': oystercatcher, oyster catcher
'144': pelican
'145': king penguin, Aptenodytes patagonica
'146': albatross, mollymawk
'147': grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius
robustus
'148': killer whale, killer, orca, grampus, sea wolf, Orcinus orca
'149': dugong, Dugong dugon
'150': sea lion
'151': Chihuahua
'152': Japanese spaniel
'153': Maltese dog, Maltese terrier, Maltese
'154': Pekinese, Pekingese, Peke
'155': Shih-Tzu
'156': Blenheim spaniel
'157': papillon
'158': toy terrier
'159': Rhodesian ridgeback
'160': Afghan hound, Afghan
'161': basset, basset hound
'162': beagle
'163': bloodhound, sleuthhound
'164': bluetick
'165': black-and-tan coonhound
'166': Walker hound, Walker foxhound
'167': English foxhound
'168': redbone
'169': borzoi, Russian wolfhound
'170': Irish wolfhound
'171': Italian greyhound
'172': whippet
'173': Ibizan hound, Ibizan Podenco
'174': Norwegian elkhound, elkhound
'175': otterhound, otter hound
'176': Saluki, gazelle hound
'177': Scottish deerhound, deerhound
'178': Weimaraner
'179': Staffordshire bullterrier, Staffordshire bull terrier
'180': American Staffordshire terrier, Staffordshire terrier, American pit
bull terrier, pit bull terrier
'181': Bedlington terrier
'182': Border terrier
'183': Kerry blue terrier
'184': Irish terrier
'185': Norfolk terrier
'186': Norwich terrier
'187': Yorkshire terrier
'188': wire-haired fox terrier
'189': Lakeland terrier
'190': Sealyham terrier, Sealyham
'191': Airedale, Airedale terrier
'192': cairn, cairn terrier
'193': Australian terrier
'194': Dandie Dinmont, Dandie Dinmont terrier
'195': Boston bull, Boston terrier
'196': miniature schnauzer
'197': giant schnauzer
'198': standard schnauzer
'199': Scotch terrier, Scottish terrier, Scottie
'200': Tibetan terrier, chrysanthemum dog
'201': silky terrier, Sydney silky
'202': soft-coated wheaten terrier
'203': West Highland white terrier
'204': Lhasa, Lhasa apso
'205': flat-coated retriever
'206': curly-coated retriever
'207': golden retriever
'208': Labrador retriever
'209': Chesapeake Bay retriever
'210': German short-haired pointer
'211': vizsla, Hungarian pointer
'212': English setter
'213': Irish setter, red setter
'214': Gordon setter
'215': Brittany spaniel
'216': clumber, clumber spaniel
'217': English springer, English springer spaniel
'218': Welsh springer spaniel
'219': cocker spaniel, English cocker spaniel, cocker
'220': Sussex spaniel
'221': Irish water spaniel
'222': kuvasz
'223': schipperke
'224': groenendael
'225': malinois
'226': briard
'227': kelpie
'228': komondor
'229': Old English sheepdog, bobtail
'230': Shetland sheepdog, Shetland sheep dog, Shetland
'231': collie
'232': Border collie
'233': Bouvier des Flandres, Bouviers des Flandres
'234': Rottweiler
'235': German shepherd, German shepherd dog, German police dog, alsatian
'236': Doberman, Doberman pinscher
'237': miniature pinscher
'238': Greater Swiss Mountain dog
'239': Bernese mountain dog
'240': Appenzeller
'241': EntleBucher
'242': boxer
'243': bull mastiff
'244': Tibetan mastiff
'245': French bulldog
'246': Great Dane
'247': Saint Bernard, St Bernard
'248': Eskimo dog, husky
'249': malamute, malemute, Alaskan malamute
'250': Siberian husky
'251': dalmatian, coach dog, carriage dog
'252': affenpinscher, monkey pinscher, monkey dog
'253': basenji
'254': pug, pug-dog
'255': Leonberg
'256': Newfoundland, Newfoundland dog
'257': Great Pyrenees
'258': Samoyed, Samoyede
'259': Pomeranian
'260': chow, chow chow
'261': keeshond
'262': Brabancon griffon
'263': Pembroke, Pembroke Welsh corgi
'264': Cardigan, Cardigan Welsh corgi
'265': toy poodle
'266': miniature poodle
'267': standard poodle
'268': Mexican hairless
'269': timber wolf, grey wolf, gray wolf, Canis lupus
'270': white wolf, Arctic wolf, Canis lupus tundrarum
'271': red wolf, maned wolf, Canis rufus, Canis niger
'272': coyote, prairie wolf, brush wolf, Canis latrans
'273': dingo, warrigal, warragal, Canis dingo
'274': dhole, Cuon alpinus
'275': African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
'276': hyena, hyaena
'277': red fox, Vulpes vulpes
'278': kit fox, Vulpes macrotis
'279': Arctic fox, white fox, Alopex lagopus
'280': grey fox, gray fox, Urocyon cinereoargenteus
'281': tabby, tabby cat
'282': tiger cat
'283': Persian cat
'284': Siamese cat, Siamese
'285': Egyptian cat
'286': cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
'287': lynx, catamount
'288': leopard, Panthera pardus
'289': snow leopard, ounce, Panthera uncia
'290': jaguar, panther, Panthera onca, Felis onca
'291': lion, king of beasts, Panthera leo
'292': tiger, Panthera tigris
'293': cheetah, chetah, Acinonyx jubatus
'294': brown bear, bruin, Ursus arctos
'295': American black bear, black bear, Ursus americanus, Euarctos americanus
'296': ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
'297': sloth bear, Melursus ursinus, Ursus ursinus
'298': mongoose
'299': meerkat, mierkat
'300': tiger beetle
'301': ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
'302': ground beetle, carabid beetle
'303': long-horned beetle, longicorn, longicorn beetle
'304': leaf beetle, chrysomelid
'305': dung beetle
'306': rhinoceros beetle
'307': weevil
'308': fly
'309': bee
'310': ant, emmet, pismire
'311': grasshopper, hopper
'312': cricket
'313': walking stick, walkingstick, stick insect
'314': cockroach, roach
'315': mantis, mantid
'316': cicada, cicala
'317': leafhopper
'318': lacewing, lacewing fly
'319': dragonfly, darning needle, devil's darning needle, sewing needle,
snake feeder, snake doctor, mosquito hawk, skeeter hawk
'320': damselfly
'321': admiral
'322': ringlet, ringlet butterfly
'323': monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
'324': cabbage butterfly
'325': sulphur butterfly, sulfur butterfly
'326': lycaenid, lycaenid butterfly
'327': starfish, sea star
'328': sea urchin
'329': sea cucumber, holothurian
'330': wood rabbit, cottontail, cottontail rabbit
'331': hare
'332': Angora, Angora rabbit
'333': hamster
'334': porcupine, hedgehog
'335': fox squirrel, eastern fox squirrel, Sciurus niger
'336': marmot
'337': beaver
'338': guinea pig, Cavia cobaya
'339': sorrel
'340': zebra
'341': hog, pig, grunter, squealer, Sus scrofa
'342': wild boar, boar, Sus scrofa
'343': warthog
'344': hippopotamus, hippo, river horse, Hippopotamus amphibius
'345': ox
'346': water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
'347': bison
'348': ram, tup
'349': bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain
sheep, Ovis canadensis
'350': ibex, Capra ibex
'351': hartebeest
'352': impala, Aepyceros melampus
'353': gazelle
'354': Arabian camel, dromedary, Camelus dromedarius
'355': llama
'356': weasel
'357': mink
'358': polecat, fitch, foulmart, foumart, Mustela putorius
'359': black-footed ferret, ferret, Mustela nigripes
'360': otter
'361': skunk, polecat, wood pussy
'362': badger
'363': armadillo
'364': three-toed sloth, ai, Bradypus tridactylus
'365': orangutan, orang, orangutang, Pongo pygmaeus
'366': gorilla, Gorilla gorilla
'367': chimpanzee, chimp, Pan troglodytes
'368': gibbon, Hylobates lar
'369': siamang, Hylobates syndactylus, Symphalangus syndactylus
'370': guenon, guenon monkey
'371': patas, hussar monkey, Erythrocebus patas
'372': baboon
'373': macaque
'374': langur
'375': colobus, colobus monkey
'376': proboscis monkey, Nasalis larvatus
'377': marmoset
'378': capuchin, ringtail, Cebus capucinus
'379': howler monkey, howler
'380': titi, titi monkey
'381': spider monkey, Ateles geoffroyi
'382': squirrel monkey, Saimiri sciureus
'383': Madagascar cat, ring-tailed lemur, Lemur catta
'384': indri, indris, Indri indri, Indri brevicaudatus
'385': Indian elephant, Elephas maximus
'386': African elephant, Loxodonta africana
'387': lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
'388': giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
'389': barracouta, snoek
'390': eel
'391': coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus
kisutch
'392': rock beauty, Holocanthus tricolor
'393': anemone fish
'394': sturgeon
'395': gar, garfish, garpike, billfish, Lepisosteus osseus
'396': lionfish
'397': puffer, pufferfish, blowfish, globefish
'398': abacus
'399': abaya
'400': academic gown, academic robe, judge's robe
'401': accordion, piano accordion, squeeze box
'402': acoustic guitar
'403': aircraft carrier, carrier, flattop, attack aircraft carrier
'404': airliner
'405': airship, dirigible
'406': altar
'407': ambulance
'408': amphibian, amphibious vehicle
'409': analog clock
'410': apiary, bee house
'411': apron
'412': ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin,
dustbin, trash barrel, trash bin
'413': assault rifle, assault gun
'414': backpack, back pack, knapsack, packsack, rucksack, haversack
'415': bakery, bakeshop, bakehouse
'416': balance beam, beam
'417': balloon
'418': ballpoint, ballpoint pen, ballpen, Biro
'419': Band Aid
'420': banjo
'421': bannister, banister, balustrade, balusters, handrail
'422': barbell
'423': barber chair
'424': barbershop
'425': barn
'426': barometer
'427': barrel, cask
'428': barrow, garden cart, lawn cart, wheelbarrow
'429': baseball
'430': basketball
'431': bassinet
'432': bassoon
'433': bathing cap, swimming cap
'434': bath towel
'435': bathtub, bathing tub, bath, tub
'436': beach wagon, station wagon, wagon, estate car, beach waggon, station
waggon, waggon
'437': beacon, lighthouse, beacon light, pharos
'438': beaker
'439': bearskin, busby, shako
'440': beer bottle
'441': beer glass
'442': bell cote, bell cot
'443': bib
'444': bicycle-built-for-two, tandem bicycle, tandem
'445': bikini, two-piece
'446': binder, ring-binder
'447': binoculars, field glasses, opera glasses
'448': birdhouse
'449': boathouse
'450': bobsled, bobsleigh, bob
'451': bolo tie, bolo, bola tie, bola
'452': bonnet, poke bonnet
'453': bookcase
'454': bookshop, bookstore, bookstall
'455': bottlecap
'456': bow
'457': bow tie, bow-tie, bowtie
'458': brass, memorial tablet, plaque
'459': brassiere, bra, bandeau
'460': breakwater, groin, groyne, mole, bulwark, seawall, jetty
'461': breastplate, aegis, egis
'462': broom
'463': bucket, pail
'464': buckle
'465': bulletproof vest
'466': bullet train, bullet
'467': butcher shop, meat market
'468': cab, hack, taxi, taxicab
'469': caldron, cauldron
'470': candle, taper, wax light
'471': cannon
'472': canoe
'473': can opener, tin opener
'474': cardigan
'475': car mirror
'476': carousel, carrousel, merry-go-round, roundabout, whirligig
'477': carpenter's kit, tool kit
'478': carton
'479': car wheel
'480': cash machine, cash dispenser, automated teller machine, automatic
teller machine, automated teller, automatic teller, ATM
'481': cassette
'482': cassette player
'483': castle
'484': catamaran
'485': CD player
'486': cello, violoncello
'487': cellular telephone, cellular phone, cellphone, cell, mobile phone
'488': chain
'489': chainlink fence
'490': chain mail, ring mail, mail, chain armor, chain armour, ring armor,
ring armour
'491': chain saw, chainsaw
'492': chest
'493': chiffonier, commode
'494': chime, bell, gong
'495': china cabinet, china closet
'496': Christmas stocking
'497': church, church building
'498': cinema, movie theater, movie theatre, movie house, picture palace
'499': cleaver, meat cleaver, chopper
'500': cliff dwelling
'501': cloak
'502': clog, geta, patten, sabot
'503': cocktail shaker
'504': coffee mug
'505': coffeepot
'506': coil, spiral, volute, whorl, helix
'507': combination lock
'508': computer keyboard, keypad
'509': confectionery, confectionary, candy store
'510': container ship, containership, container vessel
'511': convertible
'512': corkscrew, bottle screw
'513': cornet, horn, trumpet, trump
'514': cowboy boot
'515': cowboy hat, ten-gallon hat
'516': cradle
'517': crane2
'518': crash helmet
'519': crate
'520': crib, cot
'521': Crock Pot
'522': croquet ball
'523': crutch
'524': cuirass
'525': dam, dike, dyke
'526': desk
'527': desktop computer
'528': dial telephone, dial phone
'529': diaper, nappy, napkin
'530': digital clock
'531': digital watch
'532': dining table, board
'533': dishrag, dishcloth
'534': dishwasher, dish washer, dishwashing machine
'535': disk brake, disc brake
'536': dock, dockage, docking facility
'537': dogsled, dog sled, dog sleigh
'538': dome
'539': doormat, welcome mat
'540': drilling platform, offshore rig
'541': drum, membranophone, tympan
'542': drumstick
'543': dumbbell
'544': Dutch oven
'545': electric fan, blower
'546': electric guitar
'547': electric locomotive
'548': entertainment center
'549': envelope
'550': espresso maker
'551': face powder
'552': feather boa, boa
'553': file, file cabinet, filing cabinet
'554': fireboat
'555': fire engine, fire truck
'556': fire screen, fireguard
'557': flagpole, flagstaff
'558': flute, transverse flute
'559': folding chair
'560': football helmet
'561': forklift
'562': fountain
'563': fountain pen
'564': four-poster
'565': freight car
'566': French horn, horn
'567': frying pan, frypan, skillet
'568': fur coat
'569': garbage truck, dustcart
'570': gasmask, respirator, gas helmet
'571': gas pump, gasoline pump, petrol pump, island dispenser
'572': goblet
'573': go-kart
'574': golf ball
'575': golfcart, golf cart
'576': gondola
'577': gong, tam-tam
'578': gown
'579': grand piano, grand
'580': greenhouse, nursery, glasshouse
'581': grille, radiator grille
'582': grocery store, grocery, food market, market
'583': guillotine
'584': hair slide
'585': hair spray
'586': half track
'587': hammer
'588': hamper
'589': hand blower, blow dryer, blow drier, hair dryer, hair drier
'590': hand-held computer, hand-held microcomputer
'591': handkerchief, hankie, hanky, hankey
'592': hard disc, hard disk, fixed disk
'593': harmonica, mouth organ, harp, mouth harp
'594': harp
'595': harvester, reaper
'596': hatchet
'597': holster
'598': home theater, home theatre
'599': honeycomb
'600': hook, claw
'601': hoopskirt, crinoline
'602': horizontal bar, high bar
'603': horse cart, horse-cart
'604': hourglass
'605': iPod
'606': iron, smoothing iron
'607': jack-o'-lantern
'608': jean, blue jean, denim
'609': jeep, landrover
'610': jersey, T-shirt, tee shirt
'611': jigsaw puzzle
'612': jinrikisha, ricksha, rickshaw
'613': joystick
'614': kimono
'615': knee pad
'616': knot
'617': lab coat, laboratory coat
'618': ladle
'619': lampshade, lamp shade
'620': laptop, laptop computer
'621': lawn mower, mower
'622': lens cap, lens cover
'623': letter opener, paper knife, paperknife
'624': library
'625': lifeboat
'626': lighter, light, igniter, ignitor
'627': limousine, limo
'628': liner, ocean liner
'629': lipstick, lip rouge
'630': Loafer
'631': lotion
'632': loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
'633': loupe, jeweler's loupe
'634': lumbermill, sawmill
'635': magnetic compass
'636': mailbag, postbag
'637': mailbox, letter box
'638': maillot
'639': maillot, tank suit
'640': manhole cover
'641': maraca
'642': marimba, xylophone
'643': mask
'644': matchstick
'645': maypole
'646': maze, labyrinth
'647': measuring cup
'648': medicine chest, medicine cabinet
'649': megalith, megalithic structure
'650': microphone, mike
'651': microwave, microwave oven
'652': military uniform
'653': milk can
'654': minibus
'655': miniskirt, mini
'656': minivan
'657': missile
'658': mitten
'659': mixing bowl
'660': mobile home, manufactured home
'661': Model T
'662': modem
'663': monastery
'664': monitor
'665': moped
'666': mortar
'667': mortarboard
'668': mosque
'669': mosquito net
'670': motor scooter, scooter
'671': mountain bike, all-terrain bike, off-roader
'672': mountain tent
'673': mouse, computer mouse
'674': mousetrap
'675': moving van
'676': muzzle
'677': nail
'678': neck brace
'679': necklace
'680': nipple
'681': notebook, notebook computer
'682': obelisk
'683': oboe, hautboy, hautbois
'684': ocarina, sweet potato
'685': odometer, hodometer, mileometer, milometer
'686': oil filter
'687': organ, pipe organ
'688': oscilloscope, scope, cathode-ray oscilloscope, CRO
'689': overskirt
'690': oxcart
'691': oxygen mask
'692': packet
'693': paddle, boat paddle
'694': paddlewheel, paddle wheel
'695': padlock
'696': paintbrush
'697': pajama, pyjama, pj's, jammies
'698': palace
'699': panpipe, pandean pipe, syrinx
'700': paper towel
'701': parachute, chute
'702': parallel bars, bars
'703': park bench
'704': parking meter
'705': passenger car, coach, carriage
'706': patio, terrace
'707': pay-phone, pay-station
'708': pedestal, plinth, footstall
'709': pencil box, pencil case
'710': pencil sharpener
'711': perfume, essence
'712': Petri dish
'713': photocopier
'714': pick, plectrum, plectron
'715': pickelhaube
'716': picket fence, paling
'717': pickup, pickup truck
'718': pier
'719': piggy bank, penny bank
'720': pill bottle
'721': pillow
'722': ping-pong ball
'723': pinwheel
'724': pirate, pirate ship
'725': pitcher, ewer
'726': plane, carpenter's plane, woodworking plane
'727': planetarium
'728': plastic bag
'729': plate rack
'730': plow, plough
'731': plunger, plumber's helper
'732': Polaroid camera, Polaroid Land camera
'733': pole
'734': police van, police wagon, paddy wagon, patrol wagon, wagon, black
Maria
'735': poncho
'736': pool table, billiard table, snooker table
'737': pop bottle, soda bottle
'738': pot, flowerpot
'739': potter's wheel
'740': power drill
'741': prayer rug, prayer mat
'742': printer
'743': prison, prison house
'744': projectile, missile
'745': projector
'746': puck, hockey puck
'747': punching bag, punch bag, punching ball, punchball
'748': purse
'749': quill, quill pen
'750': quilt, comforter, comfort, puff
'751': racer, race car, racing car
'752': racket, racquet
'753': radiator
'754': radio, wireless
'755': radio telescope, radio reflector
'756': rain barrel
'757': recreational vehicle, RV, R.V.
'758': reel
'759': reflex camera
'760': refrigerator, icebox
'761': remote control, remote
'762': restaurant, eating house, eating place, eatery
'763': revolver, six-gun, six-shooter
'764': rifle
'765': rocking chair, rocker
'766': rotisserie
'767': rubber eraser, rubber, pencil eraser
'768': rugby ball
'769': rule, ruler
'770': running shoe
'771': safe
'772': safety pin
'773': saltshaker, salt shaker
'774': sandal
'775': sarong
'776': sax, saxophone
'777': scabbard
'778': scale, weighing machine
'779': school bus
'780': schooner
'781': scoreboard
'782': screen, CRT screen
'783': screw
'784': screwdriver
'785': seat belt, seatbelt
'786': sewing machine
'787': shield, buckler
'788': shoe shop, shoe-shop, shoe store
'789': shoji
'790': shopping basket
'791': shopping cart
'792': shovel
'793': shower cap
'794': shower curtain
'795': ski
'796': ski mask
'797': sleeping bag
'798': slide rule, slipstick
'799': sliding door
'800': slot, one-armed bandit
'801': snorkel
'802': snowmobile
'803': snowplow, snowplough
'804': soap dispenser
'805': soccer ball
'806': sock
'807': solar dish, solar collector, solar furnace
'808': sombrero
'809': soup bowl
'810': space bar
'811': space heater
'812': space shuttle
'813': spatula
'814': speedboat
'815': spider web, spider's web
'816': spindle
'817': sports car, sport car
'818': spotlight, spot
'819': stage
'820': steam locomotive
'821': steel arch bridge
'822': steel drum
'823': stethoscope
'824': stole
'825': stone wall
'826': stopwatch, stop watch
'827': stove
'828': strainer
'829': streetcar, tram, tramcar, trolley, trolley car
'830': stretcher
'831': studio couch, day bed
'832': stupa, tope
'833': submarine, pigboat, sub, U-boat
'834': suit, suit of clothes
'835': sundial
'836': sunglass
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|
BarryFutureman/vpt_data_8xx_shard0008 | BarryFutureman | 2025-06-14T09:39:29Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:100K<n<1M",
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"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] | [
"robotics"
] | 2025-06-14T09:38:33Z | 0 | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
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data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
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],
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}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
DeepShop/DeepShop | DeepShop | 2025-05-13T16:14:08Z | 17 | 0 | [
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license: cc-by-4.0
task_categories:
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language:
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tags:
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pretty_name: DeepShop
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--- |
BelalElhossany/FinClass_Reasoning_Dataset | BelalElhossany | 2025-04-21T20:10:31Z | 36 | 0 | [
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|
bitmind/AFHQ___mobius | bitmind | 2024-10-31T20:11:04Z | 241 | 0 | [
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|
supergoose/buzz_sources_287_liquid | supergoose | 2024-11-10T20:51:56Z | 17 | 0 | [
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obiwan96/obiwan96open_web_math_raw_v3_25000_50000 | obiwan96 | 2025-02-25T11:33:15Z | 20 | 0 | [
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sghosts/rand-tezler-firstlast10_alp | sghosts | 2025-03-10T00:11:47Z | 16 | 0 | [
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FiscaAI/PSYHSK_50.00-D-embeddings | FiscaAI | 2024-12-17T18:55:43Z | 8 | 0 | [
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|
reasoning-proj/contrast_pairs_deepseek-ai_DeepSeek-R1-Distill-Llama-8B_neutral_add_random_text | reasoning-proj | 2025-05-15T05:57:09Z | 0 | 0 | [
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|
neelabh17/new_news_exploded_prompt_n_10_d_perc_100_num_gen_10_Qwen2.5-7B-Instruct | neelabh17 | 2025-05-15T15:28:18Z | 0 | 0 | [
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|
vklinhhh/imgur5k_words | vklinhhh | 2025-05-07T10:03:09Z | 0 | 0 | [
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davanstrien/models_with_metadata_and_summaries | davanstrien | 2025-06-03T08:21:20Z | 119 | 0 | [
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appier-rey/mmlu-en | appier-rey | 2025-04-05T04:10:10Z | 51 | 0 | [
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|
kevin017/tokenized_bioS_inverse_QA_c_city_large_padding | kevin017 | 2025-04-03T14:54:21Z | 14 | 0 | [
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path: data/nonmember-*
---
|
david9dragon9/short_to_long_essays | david9dragon9 | 2024-12-29T01:39:22Z | 38 | 0 | [
"task_categories:text-classification",
"language:en",
"license:mit",
"region:us"
] | [
"text-classification"
] | 2024-12-29T01:32:36Z | 0 | ---
license: mit
task_categories:
- text-classification
language:
- en
---
This dataset contains short argument fragments and long essays written by students. The dataset contains both score and preference data, created by transforming data from essays with neighboring scores.
References:
Argument fragments: https://www.kaggle.com/competitions/feedback-prize-effectiveness
Essays: https://www.kaggle.com/competitions/learning-agency-lab-automated-essay-scoring-2 |
diddydaddy/newname | diddydaddy | 2025-02-02T11:58:28Z | 16 | 0 | [
"license:apache-2.0",
"region:us"
] | [] | 2025-02-02T11:58:28Z | 0 | ---
license: apache-2.0
---
|
bchiusano/WrongPatternsCHILDES | bchiusano | 2025-06-05T13:25:50Z | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-05T11:55:06Z | 0 | ---
dataset_info:
features:
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dtype: audio
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dtype: string
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dtype: string
splits:
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configs:
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data_files:
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path: data/train-*
---
|
ruediste/codeparrot-github-code-10G | ruediste | 2025-01-13T19:37:43Z | 132 | 1 | [
"size_categories:10M<n<100M",
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"modality:text",
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"library:dask",
"library:mlcroissant",
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"region:us"
] | [] | 2025-01-12T01:55:59Z | 0 | ---
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data_files:
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path: hs/train-*
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data_files:
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path: html/train-*
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data_files:
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path: java/train-*
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data_files:
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path: jl/train-*
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data_files:
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path: js/train-*
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data_files:
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path: lua/train-*
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data_files:
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path: makefile/train-*
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data_files:
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path: md/train-*
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data_files:
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path: perl/train-*
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data_files:
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path: php/train-*
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data_files:
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path: ps1/train-*
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data_files:
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path: py/train-*
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data_files:
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path: rb/train-*
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data_files:
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path: rs/train-*
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data_files:
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path: scala/train-*
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data_files:
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path: sh/train-*
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data_files:
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path: sql/train-*
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data_files:
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path: tex/train-*
- config_name: ts
data_files:
- split: train
path: ts/train-*
- config_name: vb
data_files:
- split: train
path: vb/train-*
---
This is data is derived from the [Codeparrot Dataset](https://huggingface.co/datasets/codeparrot/github-code) by taking the first 10GB of text from each language, and splitting it into individual configs. This results in a download size of about 3GB per language.
Sample usage:
```python
from datasets import load_dataset
dataset = load_dataset("ruediste/codeparrot-github-code-10G", "java")
```
List of Languages:
```
languages = {
'HTML': 'html',
'Java': 'java',
'JavaScript': 'js',
'CSS': 'css',
'C#': 'cs',
'TypeScript': 'ts',
"Batchfile": "bat",
"C": 'c',
"C++": 'cpp',
"CMake": "cmake",
"Dockerfile": "dockerfile",
"FORTRAN": 'f',
"GO": "go",
"Haskell": "hs",
"Julia": "jl",
"Lua": "lua",
"Makefile": "makefile",
"Markdown": "md",
"PHP": "php",
"Perl": "perl",
"PowerShell": 'ps1',
"Python": "py",
"Ruby": "rb",
"Rust": "rs",
"SQL": "sql",
"Scala": "scala",
"Shell": "sh",
"TeX": "tex",
"Visual Basic": "vb"
}
```
Please note that assembly to lost in the conversion.
|
abhinav302019/olympiad_data_10118 | abhinav302019 | 2025-03-05T23:59:45Z | 17 | 0 | [
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"format:parquet",
"modality:text",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-05T23:59:44Z | 0 | ---
dataset_info:
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---
|
nsethi610/llmtwin | nsethi610 | 2025-04-27T21:43:22Z | 23 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
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] | [] | 2025-04-24T14:43:48Z | 0 | ---
dataset_info:
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path: data/test-*
---
|
mbodiai/ABB_PandG_20250401_093449 | mbodiai | 2025-04-17T06:31:36Z | 6 | 0 | [
"size_categories:n<1K",
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] | [] | 2025-04-17T06:31:35Z | 0 | ---
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---
|
Asap7772/elix_generations_llama31_8b_cleaned | Asap7772 | 2024-12-12T00:51:05Z | 14 | 0 | [
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] | [] | 2024-12-12T00:50:56Z | 0 | ---
dataset_info:
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configs:
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data_files:
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path: data/train-*
- split: test
path: data/test-*
---
|
abhinav302019/olympiad_data_10067 | abhinav302019 | 2025-03-05T19:29:50Z | 16 | 0 | [
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"library:pandas",
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] | [] | 2025-03-05T19:29:49Z | 0 | ---
dataset_info:
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configs:
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path: data/train-*
---
|
extralit-dev/test_import_dataset_from_hub_with_classlabel_8f99c116-d33b-4499-9e9e-8ff51f0248bb | extralit-dev | 2025-06-20T21:45:59Z | 0 | 0 | [
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"library:pandas",
"library:mlcroissant",
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] | [] | 2025-06-20T21:45:58Z | 0 | ---
dataset_info:
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'1': negative
splits:
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download_size: 1264
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configs:
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data_files:
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path: data/train-*
---
|
zkdeng/mushroom_50_edible | zkdeng | 2025-02-24T23:12:29Z | 16 | 0 | [
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"region:us"
] | [] | 2025-02-24T23:12:26Z | 0 | ---
configs:
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dtype: int64
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splits:
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download_size: 254543
dataset_size: 762678
---
# Dataset Card for "mushroom_50_edible"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MayAlsofyani/balanced_manybugs_promote1 | MayAlsofyani | 2025-01-28T20:08:08Z | 14 | 0 | [
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---
|
martijn75/Tanakh_voc_with_aramaic | martijn75 | 2025-01-10T14:53:18Z | 18 | 0 | [
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---
|
mlfoundations-dev/distill_70b_infra_together | mlfoundations-dev | 2025-02-02T17:02:19Z | 15 | 0 | [
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---
|
abhishekkuber/cogdist_ner | abhishekkuber | 2025-01-13T09:45:03Z | 65 | 0 | [
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] | [] | 2025-01-13T09:44:58Z | 0 | ---
dataset_info:
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---
|
qfq/trainnov28_timelimit_sft_numberedmax | qfq | 2024-12-02T03:31:08Z | 16 | 0 | [
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] | [] | 2024-12-02T03:21:00Z | 0 | ---
dataset_info:
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---
|
SandeepKumarRudhravaram/llama2-Lung_Cancer_QA_V2 | SandeepKumarRudhravaram | 2024-11-19T01:46:41Z | 17 | 0 | [
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] | [] | 2024-11-19T01:46:31Z | 0 | ---
dataset_info:
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---
|
AI4Protein/FLIP_GB1_one-vs-rest | AI4Protein | 2024-11-21T13:39:58Z | 63 | 0 | [
"license:apache-2.0",
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] | [] | 2024-11-21T13:39:46Z | 0 | ---
license: apache-2.0
---
|
TAUR-dev/evals__fixed_bf26k_short_syncot_v2__samples | TAUR-dev | 2025-03-22T10:51:40Z | 7 | 0 | [
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] | [] | 2025-03-22T10:51:38Z | 0 | ---
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|
ssyok/malay-voice-test-1 | ssyok | 2025-05-06T05:48:39Z | 0 | 0 | [
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] | [] | 2025-05-06T05:40:08Z | 0 | ---
license: mit
language:
- ms
tags:
- audio
- text
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--- |
MeissonFlow/lemon | MeissonFlow | 2025-04-01T05:30:38Z | 285 | 0 | [
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] | [] | 2025-03-28T14:58:48Z | 0 | ---
license: apache-2.0
---
|
HPC-Forran2Cpp/F2C_dialogue_2.5K | HPC-Forran2Cpp | 2024-12-22T23:55:50Z | 27 | 0 | [
"license:apache-2.0",
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] | [] | 2024-12-22T20:04:34Z | 0 | ---
license: apache-2.0
---
|
jjzha/rt-tokenized | jjzha | 2025-06-08T19:26:49Z | 82 | 0 | [
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"think",
"factuality"
] | [
"question-answering"
] | 2025-03-24T11:24:08Z | 0 | ---
language:
- en
license: apache-2.0
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- question-answering
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dtype: string
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configs:
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tags:
- think
- factuality
---
## Dataset Details
### Dataset Description
This dataset is the training data for `rt` from _Scaling Reasoning can Improve Factuality in Large Language Models_. The amount of data is around 7K rows.
- **Curated by:** Mike Zhang
- **Funded by [optional]:** Villum Fonden
- **Language(s) (NLP):** English
- **License:** Apache 2.0
### Dataset Sources [optional]
- **Repository:** https://huggingface.co/datasets/AAU-NLP/fs1-predictions
- **Paper [optional]:** [https://huggingface.co/papers/2505.11140](https://huggingface.co/papers/2505.11140)
- **Github:** https://github.com/jjzha/fs1
## Uses
One can use these reasoning traces to fine-tune their models to induce more factual thinking.
### Direct Use
Having reasoning models via simple scaling (Muennighoff et al., 2025).
### Out-of-Scope Use
We only have QA in this dataset, no other domains like mathematical reasoning or puzzles.
## Dataset Structure
We have the following features:
```
features:
- name: id
dtype: string
- name: question
dtype: string
- name: gold_answer
sequence: string
- name: model_answer
sequence: string
- name: model
dtype: string
- name: reasoning_trace
dtype: string
- name: model_attempt
dtype: string
- name: valid
dtype: int64
- name: text
dtype: string
- name: total_length
dtype: int64
- name: think_length
dtype: int64
- name: answer_length
dtype: int64
```
The part used for fine-tuning is `text` where we pre-apply the chat template and also add a special tag for the `<thinking>` block.
## Dataset Creation
### Source Data
The data comes from the datasets used in the paper.
#### Data Collection and Processing
We did no further pre-processing to the QA pairs.
## Bias, Risks, and Limitations
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. Note that not every answer is correct, thus always double-check the answers from the model.
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```
@misc{zhang2025scalingreasoningimprovefactuality,
title={Scaling Reasoning can Improve Factuality in Large Language Models},
author={Mike Zhang and Johannes Bjerva and Russa Biswas},
year={2025},
eprint={2505.11140},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.11140},
}
``` |
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_5c774a4a-0849-4a0c-b7e3-6775f4997500 | argilla-internal-testing | 2024-11-26T10:02:19Z | 15 | 0 | [
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"library:pandas",
"library:mlcroissant",
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] | [] | 2024-11-26T10:02:19Z | 0 | ---
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---
|
cosmo3769/synthetic_vqa_dataset_100_images_google_paligemma2-3b-mix-224 | cosmo3769 | 2025-05-21T22:05:27Z | 0 | 0 | [
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] | [] | 2025-05-21T22:05:21Z | 0 | ---
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---
|
liamford1/finetuning_demo | liamford1 | 2025-05-19T20:53:16Z | 28 | 0 | [
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] | [] | 2025-05-17T23:08:33Z | 0 | ---
dataset_info:
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download_size: 365602
dataset_size: 942138
configs:
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data_files:
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path: data/train-*
---
|
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