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TAUR-dev/qwen2.5_1.5B__2d_retries_eval_fixed__EXTRA | TAUR-dev | 2025-06-06T01:17:05Z | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-05T21:51:42Z | null | ---
dataset_info:
features:
- name: question
dtype: string
- name: solution
dtype: string
- name: model_responses
sequence: string
- name: is_model_response_correct__correctness_reasoning
sequence: string
- name: is_model_response_correct__final_answer
sequence: string
- name: is_model_response_correct__correctness_prompt
sequence: string
- name: is_model_response_correct
sequence: bool
splits:
- name: train
num_bytes: 11112627
num_examples: 1000
download_size: 3702681
dataset_size: 11112627
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
trentmkelly/lots-of-essays | trentmkelly | 2025-06-06T01:12:40Z | 55 | 0 | [
"language:en",
"size_categories:100K<n<1M",
"region:us"
] | [] | 2025-05-30T21:20:59Z | null | ---
language:
- en
pretty_name: Lots of essays
size_categories:
- 100K<n<1M
---
# Essay Dataset Collection
This repository contains a unified collection of essay datasets combined into a single JSONL format for research and analysis purposes.
## Overview
The combined dataset aggregates essays from multiple high-quality sources, providing a diverse corpus of student writing across different contexts, grade levels, and assessment frameworks. All data has been standardized into a consistent format while preserving original metadata.
## Dataset Statistics
| Source Dataset | Row Count | Description |
|----------------|-----------|-------------|
| ASAP2 | 24,728 | Automated Student Assessment Prize dataset with scored essays |
| Feedback Prize ELL | 3,911 | English Language Learning essays with proficiency scores |
| PERSUADE | 173,266 | Discourse-annotated persuasive essays with effectiveness ratings |
| IvyPanda Essays | 128,293 | Academic essays from IvyPanda educational platform |
| **Total** | **330,198** | **Combined unified dataset** |
## File Structure
- `combined_essays.jsonl` - Main unified dataset file
- `combine_datasets.py` - Processing script for data combination
- Individual source files:
- `ASAP2_train_sourcetexts.csv`
- `train.csv`, `test.csv`, `sample_submission.csv` (Feedback Prize ELL)
- `persuade_train_srctexts.csv`
- `ivypanda_essays_train.csv`
## Data Format
Each line in `combined_essays.jsonl` contains a JSON object with the following structure:
```json
{
"text": "Essay content text...",
"source": "Dataset source name",
"extra_data": {
"original_field_1": "value",
"original_field_2": "value"
}
}
```
### Fields
- **text**: The essay content/text
- **source**: Source dataset name for attribution
- **extra_data**: All original metadata fields preserved from source datasets
## Source Dataset Details
### ASAP2 (Automated Student Assessment Prize)
- **Records**: 24,728
- **Content**: Student essays with holistic scores (1-4 scale)
- **Features**: Demographics, assignment prompts, source texts
- **Use Case**: Automated essay scoring research
### Feedback Prize ELL (English Language Learning)
- **Records**: 3,911
- **Content**: Student essays with language proficiency dimensions
- **Features**: Cohesion, syntax, vocabulary, phraseology, grammar, conventions scores
- **Use Case**: English language proficiency assessment
### PERSUADE
- **Records**: 173,266
- **Content**: Discourse-level annotated persuasive essays
- **Features**: Discourse types (Lead, Claim, Evidence), effectiveness ratings, hierarchical structure
- **Use Case**: Argumentation quality analysis, discourse analysis
### IvyPanda Essays
- **Records**: 128,293
- **Content**: Academic essays from educational platform
- **Features**: Original source attribution
- **Use Case**: General essay analysis, academic writing research
## Attribution
Please cite the original dataset creators when using this combined corpus:
- **ASAP2**: Automated Student Assessment Prize dataset
- **Feedback Prize ELL**: Kaggle Feedback Prize - English Language Learning competition
- **PERSUADE**: The Learning Agency Lab - PERSUADE corpus
- **IvyPanda Essays**: qwedsacf/ivypanda-essays (Hugging Face)
## Usage
Load the dataset in Python:
```python
import json
essays = []
with open('combined_essays.jsonl', 'r', encoding='utf-8') as f:
for line in f:
essays.append(json.loads(line))
print(f"Loaded {len(essays)} essays")
```
Filter by source:
```python
asap2_essays = [essay for essay in essays if essay['source'] == 'ASAP2']
```
## License
### Source Dataset Licenses
- **ASAP2**: Licensed under CC BY (Creative Commons Attribution)
- **Feedback Prize ELL**: Subject to Kaggle competition rules and terms
- **PERSUADE**: Subject to Kaggle competition rules and terms (uncertain)
- **IvyPanda Essays**: License terms unknown - please verify with original source
### Combined Dataset License
This combined dataset is licensed under **CC BY (Creative Commons Attribution)** for lack of a more comprehensive licensing option.
**Important Note**: This license may change if any of the original copyright holders request modifications or object to the current licensing terms. Users should be aware that licensing terms are subject to change based on the requirements of the source dataset providers.
Please respect the original licenses and terms of use for each source dataset. This compilation is provided for research and educational purposes. |
lyl472324464/pick_place | lyl472324464 | 2025-06-06T01:12:08Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:timeseries",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] | [
"robotics"
] | 2025-06-06T01:05:53Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "aloha",
"total_episodes": 3,
"total_frames": 2380,
"total_tasks": 1,
"total_videos": 0,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:3"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"observation.state": {
"dtype": "float32",
"shape": [
14
],
"names": [
[
"right_waist",
"right_shoulder",
"right_elbow",
"right_forearm_roll",
"right_wrist_angle",
"right_wrist_rotate",
"right_gripper",
"left_waist",
"left_shoulder",
"left_elbow",
"left_forearm_roll",
"left_wrist_angle",
"left_wrist_rotate",
"left_gripper"
]
]
},
"action": {
"dtype": "float32",
"shape": [
14
],
"names": [
[
"right_waist",
"right_shoulder",
"right_elbow",
"right_forearm_roll",
"right_wrist_angle",
"right_wrist_rotate",
"right_gripper",
"left_waist",
"left_shoulder",
"left_elbow",
"left_forearm_roll",
"left_wrist_angle",
"left_wrist_rotate",
"left_gripper"
]
]
},
"observation.images.cam_high": {
"dtype": "image",
"shape": [
3,
256,
256
],
"names": [
"channels",
"height",
"width"
]
},
"observation.images.cam_low": {
"dtype": "image",
"shape": [
3,
256,
256
],
"names": [
"channels",
"height",
"width"
]
},
"observation.images.cam_left_wrist": {
"dtype": "image",
"shape": [
3,
256,
256
],
"names": [
"channels",
"height",
"width"
]
},
"observation.images.cam_right_wrist": {
"dtype": "image",
"shape": [
3,
256,
256
],
"names": [
"channels",
"height",
"width"
]
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
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1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
hamishivi/OpenThoughts2-1M | hamishivi | 2025-06-06T01:09:55Z | 0 | 0 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-05T23:51:10Z | null | ---
dataset_info:
features:
- name: difficulty
dtype: int64
- name: source
dtype: string
- name: domain
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 59769369575
num_examples: 1200000
download_size: 28182281878
dataset_size: 59769369575
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
datonic/world_development_indicators | datonic | 2025-06-06T01:04:03Z | 58 | 0 | [
"license:mit",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-11-07T18:08:09Z | null |
---
license: mit
---
# world_development_indicators
World Development Indicators (WDI) is the World Bank's premier compilation of cross-country comparable data on development.
Bulk data download is available at https://datatopics.worldbank.org/world-development-indicators/
This dataset is produced and published automatically by [Datadex](https://github.com/davidgasquez/datadex),
a fully open-source, serverless, and local-first Data Platform that improves how communities collaborate on Open Data.
## Dataset Details
- **Number of rows:** 8883048
- **Number of columns:** 6
|
sincostangerines/stack_cubes_30 | sincostangerines | 2025-06-06T00:59:14Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so101",
"tutorial"
] | [
"robotics"
] | 2025-06-05T22:43:29Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so101
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so101",
"total_episodes": 9,
"total_frames": 6259,
"total_tasks": 1,
"total_videos": 18,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:9"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.laptop": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.wrist": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"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]
``` |
Ibisbill/hash_deduplicated_reasoning_data_english | Ibisbill | 2025-06-06T00:54:55Z | 0 | 0 | [
"task_categories:text-generation",
"language:zh",
"language:en",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"english",
"text-generation",
"instruction-following",
"sft",
"filtered"
] | [
"text-generation"
] | 2025-06-06T00:54:34Z | null | ---
language:
- zh
- en
tags:
- english
- text-generation
- instruction-following
- sft
- filtered
size_categories:
- 10K<n<100K
task_categories:
- text-generation
dataset_info:
features:
- name: question
dtype: string
- name: quality
dtype: string
- name: difficulty
dtype: string
- name: topic
dtype: string
- name: validity
dtype: string
splits:
- name: train
num_examples: 72710
configs:
- config_name: default
data_files:
- split: train
path: hash_deduplicated_reasoning_data_english.jsonl
---
# hash_deduplicated_reasoning_data_english
## 数据集描述
Hash deduplicated reasoning data filtered from OpenThoughts2-1M, 72710 examples in total
## 文件结构
- `hash_deduplicated_reasoning_data_english.jsonl`: 主数据文件（JSONL格式）
## 数据格式
数据集包含以下字段：
- **question**: str
- **quality**: int
- **difficulty**: int
- **topic**: str
- **validity**: int
## 使用方法
### 方法1: 使用datasets库
```python
from datasets import load_dataset
# 加载数据集
dataset = load_dataset("Ibisbill/hash_deduplicated_reasoning_data_english")
print(dataset)
```
### 方法2: 直接下载JSONL文件
```python
from huggingface_hub import hf_hub_download
import json
# 下载文件
file_path = hf_hub_download(
repo_id="Ibisbill/hash_deduplicated_reasoning_data_english",
filename="hash_deduplicated_reasoning_data_english.jsonl",
repo_type="dataset"
)
# 读取JSONL
data = []
with open(file_path, 'r', encoding='utf-8') as f:
for line in f:
data.append(json.loads(line))
print(f"加载了 {len(data)} 条记录")
```
## 示例数据
```json
{
"question": "Generate an executable Python function generated from the given prompt. The function should take stdin as input and print the output. Simply call the function after the definition.You went to the store, selling $n$ types of chocolates. There are $a_i$ chocolates of type $i$ in stock.\n\nYou have unlimited amount of cash (so you are not restricted by any prices) and want to buy as many chocolates as possible. However if you buy $x_i$ chocolates of type $i$ (clearly, $0 \\le x_i \\le a_i$), then for all $1 \\le j < i$ at least one of the following must hold: $x_j = 0$ (you bought zero chocolates of type $j$) $x_j < x_i$ (you bought less chocolates of type $j$ than of type $i$) \n\nFor example, the array $x = [0, 0, 1, 2, 10]$ satisfies the requirement above (assuming that all $a_i \\ge x_i$), while arrays $x = [0, 1, 0]$, $x = [5, 5]$ and $x = [3, 2]$ don't.\n\nCalculate the maximum number of chocolates you can buy.\n\n\n-----Input-----\n\nThe first line contains an integer $n$ ($1 \\le n \\le 2 \\cdot 10^5$), denoting the number of types of chocolate.\n\nThe next line contains $n$ integers $a_i$ ($1 \\le a_i \\le 10^9$), denoting the number of chocolates of each type.\n\n\n-----Output-----\n\nPrint the maximum number of chocolates you can buy.\n\n\n-----Examples-----\nInput\n5\n1 2 1 3 6\n\nOutput\n10\nInput\n5\n3 2 5 4 10\n\nOutput\n20\nInput\n4\n1 1 1 1\n\nOutput\n1\n\n\n-----Note-----\n\nIn the first example, it is optimal to buy: $0 + 0 + 1 + 3 + 6$ chocolates.\n\nIn the second example, it is optimal to buy: $1 + 2 + 3 + 4 + 10$ chocolates.\n\nIn the third example, it is optimal to buy: $0 + 0 + 0 + 1$ chocolates.\n",
"quality": 9,
"difficulty": 8,
"topic": "Reasoning",
"validity": 1
}
```
## 数据统计
- 总样本数: 72710
- 数据格式: JSONL
- 文件大小: 约 72 MB
|
phucminh/test | phucminh | 2025-06-06T00:52:21Z | 0 | 0 | [
"language:vi",
"license:apache-2.0",
"region:us"
] | [] | 2025-06-06T00:51:03Z | null | ---
license: apache-2.0
language:
- vi
--- |
thailevann/Government_services_QA_v6 | thailevann | 2025-06-06T00:36:42Z | 376 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-31T03:08:21Z | null | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: label
dtype: float64
- name: relevant
dtype: string
- name: reason
dtype: string
- name: reason_classification
dtype: string
splits:
- name: train
num_bytes: 85587309
num_examples: 27466
download_size: 20940326
dataset_size: 85587309
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
randall-lab/mpi3d-toy | randall-lab | 2025-06-06T00:24:04Z | 0 | 0 | [
"license:cc-by-4.0",
"region:us"
] | [] | 2025-06-05T21:17:59Z | null | ---
license: cc-by-4.0
---
# Dataset Card for MPI3D-toy
## Dataset Description
The **MPI3D-toy dataset** is a **synthetically rendered image dataset** designed for benchmarking algorithms in **disentangled representation learning** and **unsupervised representation learning**. It is part of the broader MPI3D dataset suite, which also includes [realistic simulated](https://huggingface.co/datasets/randall-lab/mpi3d-realistic), [real-world](https://huggingface.co/datasets/randall-lab/mpi3d-real) and [complex real-world](https://huggingface.co/datasets/randall-lab/mpi3d-complex)variants.
The **toy version** was generated using a **simplified computer graphics renderer** (Quicksilver hardware renderer in Autodesk 3ds Max), based on CAD models of a physical robotic setup. This allows researchers to systematically study **sim-to-real transfer** and the effect of simulation fidelity.
All images depict **physical 3D objects** that would be manipulated by a robotic platform, under **controlled variations of 7 known factors**:
- Object color (6 values)
- Object shape (6 values)
- Object size (2 values)
- Camera height (3 values)
- Background color (3 values)
- Robotic arm horizontal axis (40 values)
- Robotic arm vertical axis (40 values)
The dataset contains **1,036,800 images** at a resolution of **64×64 pixels**, representing the full Cartesian product of these factors. All factors are **identical** to those used in the realistic and real-world versions of MPI3D, enabling direct comparisons between different levels of simulation fidelity.
## Dataset Source
- **Homepage**: [https://github.com/rr-learning/disentanglement_dataset](https://github.com/rr-learning/disentanglement_dataset)
- **License**: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)
- **Paper**: Muhammad Waleed Gondal et al. _On the Transfer of Inductive Bias from Simulation to the Real World: A New Disentanglement Dataset_. NeurIPS 2019.
## Dataset Structure
|Factors|Possible Values|
|---|---|
|object_color|white=0, green=1, red=2, blue=3, brown=4, olive=5|
|object_shape|cone=0, cube=1, cylinder=2, hexagonal=3, pyramid=4, sphere=5|
|object_size|small=0, large=1|
|camera_height|top=0, center=1, bottom=2|
|background_color|purple=0, sea green=1, salmon=2|
|horizontal_axis (DOF1)|0,...,39|
|vertical_axis (DOF2)|0,...,39|
Each image corresponds to a unique combination of these 7 factors. The images are stored in a **row-major order** (fastest-changing factor is `vertical_axis`, slowest-changing factor is `object_color`).
### Why no train/test split?
The MPI3D-toy dataset does not provide an official train/test split. It is designed for **representation learning research**, where the goal is to learn disentangled and interpretable latent factors. Since the dataset is a complete Cartesian product of all factor combinations, models typically require access to the full dataset to explore factor-wise variations.
## Example Usage
Below is a quick example of how to load this dataset via the Hugging Face Datasets library:
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("randall-lab/mpi3d-toy", split="train", trust_remote_code=True)
# Access a sample from the dataset
example = dataset[0]
image = example["image"]
label = example["label"] # [object_color: 0, object_shape: 0, object_size: 0, camera_height: 0, background_color: 0, horizontal_axis: 0, vertical_axis: 0]
color = example["color"] # 0
shape = example["shape"] # 0
size = example["size"] # 0
height = example["height"] # 0
background = example["background"] # 0
dof1 = example["dof1"] # 0
dof2 = example["dof2"] # 0
image.show() # Display the image
print(f"Label (factors): {label}")
```
If you are using colab, you should update datasets to avoid errors
```
pip install -U datasets
```
## Citation
```
@article{gondal2019transfer,
title={On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset},
author={Gondal, Muhammad Waleed and Wuthrich, Manuel and Miladinovic, Djordje and Locatello, Francesco and Breidt, Martin and Volchkov, Valentin and Akpo, Joel and Bachem, Olivier and Sch{\"o}lkopf, Bernhard and Bauer, Stefan},
journal={Advances in Neural Information Processing Systems},
volume={32},
year={2019}
}
``` |
srushtisingh/MNLP_final_general_dataset | srushtisingh | 2025-06-06T00:20:46Z | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-06T00:20:38Z | null | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 50337935.2
num_examples: 19200
- name: validation
num_bytes: 6292241.9
num_examples: 2400
- name: test
num_bytes: 6292241.9
num_examples: 2400
download_size: 35810691
dataset_size: 62922419.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
zijian2022/eval_itrgg2 | zijian2022 | 2025-06-06T00:17:23Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"tutorial"
] | [
"robotics"
] | 2025-06-06T00:17:19Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 10,
"total_frames": 6947,
"total_tasks": 1,
"total_videos": 20,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.laptop": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.phone": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"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]
``` |
MushroomGecko/BIBLE | MushroomGecko | 2025-06-06T00:12:10Z | 37 | 0 | [
"task_categories:question-answering",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"Bible",
"God",
"Jesus",
"Christ",
"Scripture",
"Christian",
"faith",
"theology",
"benchmark",
"question-answering",
"multiple-choice",
"evaluation",
"religion",
"llm-eval"
] | [
"question-answering"
] | 2025-04-02T01:34:28Z | null | ---
license: cc-by-4.0
language:
- en
pretty_name: BIBLE
tags:
- Bible
- God
- Jesus
- Christ
- Scripture
- Christian
- faith
- theology
- benchmark
- question-answering
- multiple-choice
- evaluation
- religion
- llm-eval
size_categories:
- 10K<n<100K
task_categories:
- question-answering
---
# BIBLE: Biblically Informed Bot Learning Evaluation
**BIBLE** (Biblically Informed Bot Learning Evaluation) is a comprehensive **benchmark dataset** designed to **evaluate** AI models on their understanding of the Holy Bible. It covers all 66 books of Scripture and includes additional thematic categories for *People of the Bible*, *Places in the Bible*, and *Measurements in the Bible*.
> ⚠️ This dataset is **not intended for training**. It is strictly for **evaluation and benchmarking** of models on Biblical knowledge and reasoning.
---
## ⚠️ Accuracy Disclaimer
While the questions in this dataset are sourced directly from trusted materials, a significant portion of the content was generated using **NotebookLM** based on the referenced source documents. Many of these generated questions and answers were not manually reviewed for theological or factual accuracy.
As such, **the accuracy, phrasing, and interpretative correctness of some questions and answers cannot be guaranteed**. Users are encouraged to independently verify any content used in formal evaluations, especially in faith-sensitive or doctrinally rigorous contexts.
---
## 📚 Dataset Overview
- ✅ Questions from every book of the Bible (Genesis → Revelation)
- ✅ Additional themed categories:
- **People of the Bible**
- **Places in the Bible**
- **Measurements in the Bible**
- ✅ Structured format with:
- Multiple-choice options (A–D)
- A single correct answer
- Source attribution and extraction method
- ✅ Suitable for:
- Benchmarking model comprehension of Scripture
- Evaluating closed-book Biblical knowledge in LLMs
- Faith-aligned QA assessments
---
## 📊 Model Accuracy on BIBLE Benchmark
The following table summarizes the performance of various quantized models (**Q4_K_M**) evaluated on the BIBLE benchmark. Accuracy reflects the percentage of correct answers across the full benchmark dataset.
| Model Name | Total Accuracy |
|--------------------------------|----------------|
| Gemma 2 2b Instruct | 40.21% |
| Gemma 3 1b Instruct | 27.96% |
| Gemma 3 4b Instruct | 41.52% |
| Granite 3.1 Dense 2b Instruct | 39.99% |
| Granite 3.1 MoE 1b Instruct | 22.21% |
| Granite 3.2 2b Instruct | 40.19% |
| InternLM2.5 1.8b | 28.74% |
| Llama 3.2 1b Instruct | 24.32% |
| Llama 3.2 3b Instruct | 41.73% |
| Phi4-mini Instruct | 40.78% |
| Qwen2.5 1.5b Instruct | 41.03% |
| Qwen2.5 3b Instruct | 47.94% |
| Qwen3 0.6b | 24.18% |
| Qwen3 1.7b | 36.97% |
| Qwen3 4b | 50.43% |
| SmolLM2 1.7b Instruct | 30.38% |
**Note: Qwen3 results are with thinking disabled**
---
## 📁 Dataset Structure
Each example in the dataset is a dictionary with the following fields:
- `question`: A Bible-based question
- `choices`: A list of four possible answers (A–D)
- `answer`: The correct choice, as a letter ("A", "B", "C", or "D")
- `category`: The book of the Bible or theme the question belongs to
- `source`: A URL pointing to the original source material
- `qa_extraction`: Notes on how the question-answer pair was derived (e.g. directly from the source or generated via NotebookLM given the source)
### 🔍 Example
```json
{
"question": "What two things did God create in the beginning (Gen. 1:1)?",
"choices": [
"The light and the darkness",
"The heavens and the earth",
"The land and the sea",
"The world and the stars"
],
"answer": "B",
"category": "Genesis",
"source": "https://biblicalelearning.org/wp-content/uploads/2021/05/01_GenesisMCQuestions.pdf",
"qa_extraction": "Obtained directly from the source."
}
```
---
## 🔗 Data Sources and Attribution
This dataset was built from publicly available resources. Full respect and credit is given to the following original sources:
- **Biblical eLearning**
Developed by Dr. Ted Hildebrandt, [Biblical eLearning](https://biblicalelearning.org/) is dedicated to providing free online Biblical resources to the global Christian community. The site hosts high-quality, Biblically grounded materials from expert teachers, aiming to preserve and share faithful teaching digitally for the glory of God and the good of others. Many of these resources, including the Bible Quizzers material used in this dataset, are freely **downloadable** in PDF format for personal study or educational use.
📖 [Download Bible Quizzers PDFs](https://biblicalelearning.org/quizlet-bible-quizzers/)
- **World English Bible (WEB)** via **[eBible.org](https://ebible.org/)**
[eBible.org](https://ebible.org) is the original home of the World English Bible and a global volunteer movement committed to making the Holy Bible freely available in the languages and formats most useful to people worldwide. Founded by Michael Paul Johnson, who also serves as senior editor of the WEB, the site hosts hundreds of translations, including the original Hebrew and Greek texts, and supports a wide range of digital formats for both reading and development. The mission of eBible.org is rooted in the Great Commission and made possible by a large network of volunteers who work to ensure quality, accessibility, and faithful distribution of Scripture.
📖 [Download the WEB Bible PDFs](https://ebible.org/pdf/eng-web/)
- **GotQuestions Ministries**
[GotQuestions.org](https://www.gotquestions.org/)
A leading online ministry offering Biblical answers to spiritually related questions, GotQuestions.org is a theologically conservative, evangelical resource rooted in Scripture. Since 2002, the site has received over 2.5 billion pageviews, offering articles, Q&A, podcasts, and tools for those seeking to understand the Word of God.
Each question entry includes the corresponding source URL and method of extraction of the data.
If you use this dataset, please ensure these sources are properly cited.
---
## 🔍 Intended Use
The BIBLE dataset is intended for:
- Evaluating **Biblical literacy** in large language models
- Testing for **factual Scriptural grounding**
- Benchmarking theological comprehension
- Identifying hallucination in religious QA settings
It is **not suitable for model training**, and it is recommended that models be evaluated "as-is" without memorization or prior exposure to the benchmark.
---
## ⚖️ License
This dataset is released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.
It contains public domain and freely licensed material, but users are responsible for proper attribution and for complying with the original source usage guidelines.
---
## 🤝 Contributing
Found an issue or want to contribute additional benchmark questions?
Pull requests and community suggestions are welcome — feel free to open an issue or submit a PR.
---
|
Ibisbill/General_English_only_SFT_Filtered_655k | Ibisbill | 2025-06-06T00:01:34Z | 0 | 0 | [
"task_categories:text-generation",
"language:zh",
"language:en",
"size_categories:100K<n<1M",
"region:us",
"english",
"text-generation",
"instruction-following",
"sft",
"filtered"
] | [
"text-generation"
] | 2025-06-06T00:00:46Z | null | ---
language:
- zh
- en
tags:
- english
- text-generation
- instruction-following
- sft
- filtered
size_categories:
- 100K<n<1M
task_categories:
- text-generation
dataset_info:
features:
- name: text
dtype: string
- name: source
dtype: string
- name: category
dtype: string
- name: original_data
dtype: string
splits:
- name: train
num_examples: 268042
configs:
- config_name: default
data_files:
- split: train
path: dataset.jsonl
---
# General_English_only_SFT_Filtered_655k
## 数据集描述
这是一个包含25k条英文指令跟随数据的高质量数据集，经过精心筛选和过滤。
## 文件结构
- `dataset.jsonl`: 主数据文件（JSONL格式）
## 数据格式
数据集包含以下字段：
- **text**: str
- **source**: str
- **category**: str
- **original_data**: dict
## 使用方法
### 方法1: 使用datasets库
```python
from datasets import load_dataset
# 加载数据集
dataset = load_dataset("Ibisbill/General_English_only_SFT_Filtered_655k")
print(dataset)
```
### 方法2: 直接下载JSONL文件
```python
from huggingface_hub import hf_hub_download
import json
# 下载文件
file_path = hf_hub_download(
repo_id="Ibisbill/General_English_only_SFT_Filtered_655k",
filename="dataset.jsonl",
repo_type="dataset"
)
# 读取JSONL
data = []
with open(file_path, 'r', encoding='utf-8') as f:
for line in f:
data.append(json.loads(line))
print(f"加载了 {len(data)} 条记录")
```
## 示例数据
```json
{
"text": "can you go into more detail about it/?",
"source": "tulu3",
"category": "general",
"original_data": {
"id": "ai2-adapt-dev/tulu_v3.9_synthetic_finalresp_wildguardmixtrain_decontaminated_50k_24928",
"messages": [
{
"content": "can you go into more detail about it/?",
"role": "user"
},
{
"content": "",
"role": "assistant"
}
],
"source": "ai2-adapt-dev/tulu_v3.9_synthetic_finalresp_wildguardmixtrain_decontaminated_50k"
}
}
```
## 数据统计
- 总样本数: 268042
- 数据格式: JSONL
- 文件大小: 约 268 MB
|
Ibisbill/General_English_only_SFT_Filtered_25k | Ibisbill | 2025-06-05T23:59:00Z | 0 | 0 | [
"task_categories:text-generation",
"language:zh",
"language:en",
"size_categories:10K<n<100K",
"region:us",
"english",
"text-generation",
"instruction-following",
"sft",
"filtered"
] | [
"text-generation"
] | 2025-06-05T23:52:43Z | null | ---
language:
- zh
- en
tags:
- english
- text-generation
- instruction-following
- sft
- filtered
size_categories:
- 10K<n<100K
task_categories:
- text-generation
dataset_info:
features:
- name: text
dtype: string
- name: source
dtype: string
- name: category
dtype: string
- name: original_data
dtype: string
splits:
- name: train
num_examples: 25000
configs:
- config_name: default
data_files:
- split: train
path: dataset.jsonl
---
# General_English_only_SFT_Filtered_25k
## 数据集描述
这是一个包含25k条英文指令跟随数据的高质量数据集，经过精心筛选和过滤。
## 文件结构
- `dataset.jsonl`: 主数据文件（JSONL格式）
## 数据格式
数据集包含以下字段：
- **text**: str
- **source**: str
- **category**: str
- **original_data**: dict
## 使用方法
### 方法1: 使用datasets库
```python
from datasets import load_dataset
# 加载数据集
dataset = load_dataset("Ibisbill/General_English_only_SFT_Filtered_25k")
print(dataset)
```
### 方法2: 直接下载JSONL文件
```python
from huggingface_hub import hf_hub_download
import json
# 下载文件
file_path = hf_hub_download(
repo_id="Ibisbill/General_English_only_SFT_Filtered_25k",
filename="dataset.jsonl",
repo_type="dataset"
)
# 读取JSONL
data = []
with open(file_path, 'r', encoding='utf-8') as f:
for line in f:
data.append(json.loads(line))
print(f"加载了 {len(data)} 条记录")
```
## 示例数据
```json
{
"text": "Is the premise \"Two young boys are headed toward a bicycle parked next to a brick house.\" true if \"Two boys are heading toward a bike.\"?\nOPTIONS:\n- yes\n- it is not possible to tell\n- no\nyes\nQ: \"Two people are eating something strange, as evidenced by her laugh and his nose-holding.\" Does this mean that \"Three people are eating something strange, as evidenced by her laugh and his nose-holding.\"? OPTIONS:\n- yes\n- it is not possible to tell\n- no\nA: no\nPremise & Hypothesis & Options: A group of students looking over a balcony on a senior trip.\nSome young people peer over a short wall.\nOPTIONS:\n- yes\n- it is not possible to tell\n- no\nIs the hypothesis true or not: yes\nPremise & hypothesis: Is the premise \"A man and small boy are playing with a wooden toy track system on the floor.\" true if \"The man and the boy are playing.\"?\nOPTIONS:\n- yes\n- it is not possible to tell\n- no\nA: yes\nPremise & hypothesis.\nA little girl runs on the wet sand near the ocean.\n\nHer feet sink into the sand.\nOPTIONS:\n- yes\n- it is not possible to tell\n- no\n++++++++++\ntrue or not.\nyes\nIs the premise \"A little girl in a red dress is standing on a trail in the forest with a horse in the background.\" true if \"a girl is waiting to ride her horse\"?\nOPTIONS:\n- yes\n- it is not possible to tell\n- no\n it is not possible to tell",
"source": "tulu3",
"category": "general",
"original_data": {
"id": "ai2-adapt-dev/flan_v2_converted_26714",
"messages": [
{
"content": "Is the premise \"Two young boys are headed toward a bicycle parked next to a brick house.\" true if \"Two boys are heading toward a bike.\"?\nOPTIONS:\n- yes\n- it is not possible to tell\n- no\nyes\nQ: \"Two people are eating something strange, as evidenced by her laugh and his nose-holding.\" Does this mean that \"Three people are eating something strange, as evidenced by her laugh and his nose-holding.\"? OPTIONS:\n- yes\n- it is not possible to tell\n- no\nA: no\nPremise & Hypothesis & Options: A group of students looking over a balcony on a senior trip.\nSome young people peer over a short wall.\nOPTIONS:\n- yes\n- it is not possible to tell\n- no\nIs the hypothesis true or not: yes\nPremise & hypothesis: Is the premise \"A man and small boy are playing with a wooden toy track system on the floor.\" true if \"The man and the boy are playing.\"?\nOPTIONS:\n- yes\n- it is not possible to tell\n- no\nA: yes\nPremise & hypothesis.\nA little girl runs on the wet sand near the ocean.\n\nHer feet sink into the sand.\nOPTIONS:\n- yes\n- it is not possible to tell\n- no\n++++++++++\ntrue or not.\nyes\nIs the premise \"A little girl in a red dress is standing on a trail in the forest with a horse in the background.\" true if \"a girl is waiting to ride her horse\"?\nOPTIONS:\n- yes\n- it is not possible to tell\n- no\n",
"role": "user"
},
{
"content": "it is not possible to tell",
"role": "assistant"
}
],
"source": "ai2-adapt-dev/flan_v2_converted"
}
}
```
## 数据统计
- 总样本数: 25000
- 数据格式: JSONL
- 文件大小: 约 25 MB
|
AnnaelleMyriam/MNLP_M3_dpo_dataset | AnnaelleMyriam | 2025-06-05T23:58:36Z | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-05T23:58:23Z | null | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: source
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 140392393.41902313
num_examples: 51464
- name: validation
num_bytes: 7799274.304076387
num_examples: 2859
- name: test
num_bytes: 7802002.276900478
num_examples: 2860
download_size: 87025058
dataset_size: 155993669.99999997
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
sincostangerines/stack_cubes_50 | sincostangerines | 2025-06-05T23:55:24Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so101",
"tutorial"
] | [
"robotics"
] | 2025-06-05T23:55:17Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so101
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so101",
"total_episodes": 10,
"total_frames": 8632,
"total_tasks": 1,
"total_videos": 20,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.laptop": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
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"video.is_depth_map": false,
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}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
tonijhanel/interior_design_roboflow-train | tonijhanel | 2025-06-05T23:52:29Z | 0 | 0 | [
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---
# Dataset Card for "interior_design_roboflow-train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tonijhanel/interior_design_roboflow | tonijhanel | 2025-06-05T23:52:24Z | 0 | 0 | [
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---
# Dataset Card for "interior_design_roboflow"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
fannymissillier/MNLP_M2_mcqa_dataset_cleaned | fannymissillier | 2025-06-05T23:48:43Z | 0 | 0 | [
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|
DoniaGasmii/MNLP_M3_full_sft_dataset_split | DoniaGasmii | 2025-06-05T23:36:24Z | 0 | 0 | [
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|
ShadowCatLul/clef_fungi_dtd_for_zero-shot | ShadowCatLul | 2025-06-05T23:17:01Z | 0 | 0 | [
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license: apache-2.0
---
|
joyheyueya/qwen3-32b-sft_star | joyheyueya | 2025-06-05T22:57:48Z | 0 | 0 | [
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] | [] | 2025-06-05T22:57:45Z | null | ---
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|
TAUR-dev/SIEXP_sft_data__skill_template__random_sort__budget_forces | TAUR-dev | 2025-06-05T22:57:46Z | 0 | 0 | [
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|
Blinorot/INF-ORM-Preference-Magnitude-filtered | Blinorot | 2025-06-05T22:48:50Z | 0 | 0 | [
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akseljoonas/toolagent-traces | akseljoonas | 2025-06-05T22:43:33Z | 0 | 0 | [
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|
ChavyvAkvar/multi-asset-synth-trades-202506052230 | ChavyvAkvar | 2025-06-05T22:38:52Z | 0 | 0 | [
"region:us"
] | [] | 2025-06-05T22:30:41Z | null | ---
configs:
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# Dataset Card for "multi-asset-synth-trades-202506052230"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
parsee-mizuhashi/ide | parsee-mizuhashi | 2025-06-05T22:34:54Z | 73 | 1 | [
"license:mit",
"region:us"
] | [] | 2025-01-25T20:23:14Z | null | ---
license: mit
---
[v29](https://huggingface.co/datasets/parsee-mizuhashi/ide/blob/51f9e372dd5af8d20b08e78daf31c23d8d2613c5/noob_v_29_checkpoint-e0_s4000.safetensors)
[24r+28](https://huggingface.co/datasets/parsee-mizuhashi/ide/blob/454fc8057079f6be7104dcfff85e28a218ecc75f/noob_v_24r2%2B28m.safetensors)
[24r2 -bad contrast](https://huggingface.co/datasets/parsee-mizuhashi/ide/commit/72454682dcd5a538c0af819d073751f9b5df5f30)
|
leeroy-jankins/Appropriations | leeroy-jankins | 2025-06-05T22:30:43Z | 0 | 0 | [
"language:en",
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"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | [] | 2025-06-05T22:28:53Z | null | ---
license: mit
pretty_name: U.S. Appropriations Dataset
language:
- en
---
# 💵 U.S. Appropriations Dataset (1995–2025)
This dataset links enacted U.S. Public Laws with their corresponding Explanatory Statements and Appropriations Titles,
covering the major federal appropriations acts from FY1995 through FY2025.
---
## 📊 Structure
Each entry includes:
- `public_law`: Official citation of the enacted appropriations law (e.g. P.L. 117-328)
- `explanatory_statement`: House or Senate report number accompanying the law (e.g. H. Rpt. 117-328)
- `appropriation_title`: Full name of the Appropriations Act or Continuing Resolution
---
## 🗂️ Enacted Appropriations
| Public Law | Explanatory Statement | Appropriation Title |
|---------------|------------------------|--------------------------------------------------------------------------------------|
| P.L. 104-134 | H. Rpt. 104-537 | Omnibus Consolidated Rescissions and Appropriations Act |
| P.L. 104-208 | H. Rpt. 104-863 | Omnibus Consolidated Appropriations Act, 1997 |
| P.L. 105-277 | H. Rpt. 105-825 | Omnibus Consolidated and Emergency Supplemental Appropriations Act |
| P.L. 105-277 | H. Rpt. 106-110 | Omnibus Consolidated and Emergency Supplemental Appropriations Act |
| P.L. 106-113 | H. Rpt. 106-479 | Consolidated Appropriations Act, 2000 |
| P.L. 106-79 | H. Rpt. 106-371 | Department of Defense Appropriations Act, 2000 |
| P.L. 106-554 | H. Rpt. 106-1033 | Consolidated Appropriations Act, 2001 |
| P.L. 106-259 | S. Rpt. 106-298 | Department of Defense Appropriations Act, 2001 |
| P.L. 107-117 | H. Rpt. 107-350 | Department of Defense and Emergency Supplemental Appropriations |
| P.L. 107-206 | H. Rpt. 107-593 | Supplemental Appropriations Act, 2002 |
| P.L. 108-7 | H. Rpt. 108-10 | Consolidated Appropriations Resolution, 2003 |
| P.L. 108-199 | H. Rpt. 108-401 | Consolidated Appropriations Act, 2004 |
| P.L. 108-11 | H. Rpt. 108-55 | Emergency Supplemental Appropriations Act for Defense |
| P.L. 108-447 | H. Rpt. 108-792 | Consolidated Appropriations Act, 2005 |
| P.L. 109-13 | H. Rpt. 109-72 | Emergency Supplemental Appropriations Act for Defense, Global War on Terror, Tsunami Relief |
| P.L. 109-108 | H. Rpt. 109-272 | Science, State, Justice, Commerce Appropriations Act |
| P.L. 109-148 | S. Rpt. 109-141 | Department of Defense Appropriations Act, 2006 |
| P.L. 110-5 | H. Rpt. 110-5 | Revised Continuing Appropriations Resolution, 2007 |
| P.L. 110-161 | H. Rpt. 110-497 | Consolidated Appropriations Act, 2008 |
| P.L. 110-252 | H. Rpt. 110-656 | Supplemental Appropriations Act, 2008 |
| P.L. 111-8 | H. Rpt. 111-8 | Omnibus Appropriations Act, 2009 |
| P.L. 111-32 | H. Rpt. 111-105 | Supplemental Appropriations Act, 2009 |
| P.L. 111-117 | H. Rpt. 111-366 | Consolidated Appropriations Act, 2010 |
| P.L. 112-10 | H. Rpt. 112-331 | Department of Defense and Full-Year Continuing Appropriations Act, 2011 |
| P.L. 112-74 | H. Rpt. 112-331 | Consolidated Appropriations Act, 2012 |
| P.L. 113-6 | H. Rpt. 113-6 | Consolidated and Further Continuing Appropriations Act, 2013 |
| P.L. 113-76 | H. Rpt. 113-76 | Consolidated Appropriations Act, 2014 |
| P.L. 113-235 | H. Rpt. 113-235 | Consolidated and Further Continuing Appropriations Act, 2015 |
| P.L. 114-113 | H. Rpt. 114-113 | Consolidated Appropriations Act, 2016 |
| P.L. 115-31 | H. Rpt. 115-31 | Consolidated Appropriations Act, 2017 |
| P.L. 115-141 | H. Rpt. 115-141 | Consolidated Appropriations Act, 2018 |
| P.L. 116-6 | H. Rpt. 116-6 | Consolidated Appropriations Act, 2019 |
| P.L. 116-93 | H. Rpt. 116-93 | Further Consolidated Appropriations Act, 2020 |
| P.L. 116-260 | H. Rpt. 116-260 | Consolidated Appropriations Act, 2021 |
| P.L. 117-103 | H. Rpt. 117-103 | Consolidated Appropriations Act, 2022 |
| P.L. 117-328 | H. Rpt. 117-328 | Consolidated Appropriations Act, 2023 |
| P.L. 118-42 | H. Rpt. 118-42 | Continuing Appropriations Act, 2024 |
| P.L. 118-83 | H. Rpt. 118-83 | Continuing Appropriations Act, 2025
## 🔍 Use Cases
- 🧠 Train NLP models for legislative reference extraction
- 🧾 Link Appropriations Acts to their respective explanatory documents
- 🗃️ Construct longitudinal appropriations histories for federal program analysis
- 📜 Support research on continuing resolutions and omnibus legislation
---
## 📚 Related Concepts
- Omnibus and Consolidated Appropriations
- Explanatory Statements (House/Senate Reports)
- Continuing Resolutions
- Title-by-Title Budget Authority
---
## 🧠 Example Usage (Python)
```python
from datasets import load_dataset
ds = load_dataset("leeroy-jankins/Regulations", split="train")
for item in ds:
print(f"{item['public_law']} — {item['appropriation_title']}") |
cyh002/sealion-prompt-engineering-inference-results | cyh002 | 2025-06-05T22:15:00Z | 0 | 0 | [
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] | [] | 2025-06-05T18:47:08Z | null | ---
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|
InAbsentia/trossen_towel_fold_v15 | InAbsentia | 2025-06-05T22:10:31Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
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"region:us",
"LeRobot"
] | [
"robotics"
] | 2025-06-05T22:06:46Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
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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"left_joint_5",
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"right_joint_0",
"right_joint_1",
"right_joint_2",
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}
},
"timestamp": {
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],
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}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
jvelja/results_3b_backdoored | jvelja | 2025-06-05T22:05:23Z | 103 | 0 | [
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] | [] | 2025-06-02T09:47:43Z | null | ---
dataset_info:
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dtype: string
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---
|
coralieb7/mcqa_custom_sft_50k_sft_focus | coralieb7 | 2025-06-05T21:59:35Z | 0 | 0 | [
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"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-05T21:59:30Z | null | ---
dataset_info:
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dtype: string
- name: answer
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dtype: string
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configs:
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data_files:
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path: data/train-*
---
|
jesbu1/test | jesbu1 | 2025-06-05T21:56:45Z | 103 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"widowx",
"bridge-v2"
] | [
"robotics"
] | 2025-06-05T06:10:56Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- widowx
- bridge-v2
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "widowx",
"total_episodes": 5,
"total_frames": 197,
"total_tasks": 5,
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"chunks_size": 1000,
"fps": 5,
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"train": "0:5"
},
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7
],
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"x",
"y",
"z",
"roll",
"pitch",
"yaw",
"gripper"
]
},
"action": {
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"shape": [
7
],
"names": [
"x",
"y",
"z",
"roll",
"pitch",
"yaw",
"gripper"
]
},
"camera_present": {
"dtype": "bool",
"shape": [
4
],
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"image_0",
"image_1",
"image_2",
"image_3"
]
},
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256,
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],
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"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
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"has_audio": false
}
},
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],
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],
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}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
ai2-adapt-dev/tool-use-more-refusals | ai2-adapt-dev | 2025-06-05T21:53:31Z | 0 | 0 | [
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-05T21:53:21Z | null | ---
dataset_info:
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download_size: 37004421
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configs:
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data_files:
- split: train
path: data/train-*
---
|
ai2-adapt-dev/tool-use-more-multistep | ai2-adapt-dev | 2025-06-05T21:52:08Z | 0 | 0 | [
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"format:parquet",
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"library:polars",
"region:us"
] | [] | 2025-06-05T21:52:01Z | null | ---
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configs:
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data_files:
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path: data/train-*
---
|
cyh002/sealion-inference-instruct-results | cyh002 | 2025-06-05T21:42:08Z | 44 | 0 | [
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-03T14:47:57Z | null | ---
dataset_info:
features:
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dtype: string
- name: input
dtype: string
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- name: language
dtype: string
- name: medium
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dtype: string
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download_size: 101350
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configs:
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data_files:
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path: data/inference_dataset-*
---
|
recogna-nlp/fakerecogna2-extrativo | recogna-nlp | 2025-06-05T21:35:08Z | 0 | 0 | [
"task_categories:text-classification",
"language:pt",
"license:mit",
"size_categories:10K<n<100K",
"region:us",
"FakeRecogna ",
"Fake News",
"Portuguese",
"Dataset"
] | [
"text-classification"
] | 2025-06-05T21:33:24Z | null | ---
task_categories:
- text-classification
language:
- pt
tags:
- 'FakeRecogna '
- Fake News
- Portuguese
- Dataset
license: mit
size_categories:
- 10K<n<100K
---
# FakeRecogna 2.0 Extractive
FakeRecogna 2.0 presents the extension for the FakeRecogna dataset in the context of fake news detection. FakeRecogna includes real and fake news texts collected from online media and ten fact-checking sources in Brazil. An important aspect is the lack of relation between the real and fake news samples, i.e., they are not mutually related to each other to avoid intrinsic bias in the data.
## The Dataset
The fake news collection was performed on licensed and verified Brazilian news websites with enrollment in the [Duke Reporters´ Lab Center](https://reporterslab.org/fact-checking/).
The system was designed as a source to fight against fake news spreading worldwide. For real news, we selected well-known media platforms in Brazil. Since real texts are much larger than most of the produced fake content, the genuine news was preprocessed with text summarization. At this stage, there is no further processing of stop words or lemmatization of the text. After trimming and standardizing the real news, we produced textual representations based on Bag of Words (BoW), Term Frequency – Inverse Document Frequency (TF-IDF), FastText, PTT5, and BERTimbau to form the input feature vectors for the ML models. Figure illustrates the steps of the proposed method.
<!--- PROJECT LOGO -->
<p align="center">
<img src="https://huggingface.co/datasets/recogna-nlp/FakeRecogna2/resolve/main/pipeline_proposed_method.jpg" alt="Pipeline FakeRecogna 2.0" width="600" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
</p>
Fake news sources were selected from nine fact-checking agencies in Brazil. This process provides a broad range of categories and many fake news samples to promote data diversity. Table 1 presents the existing Brazilian fact-checking initiatives and the number of fake news samples collected from each news source. When the search process was concluded, we ended up with 26,569 fake news samples, which, in turn, were further processed to detect and remove possible duplicate samples, thus leading to a final set of 26,400 fake news articles.
| Fact-Check Agency | Web address | # News |
| ------------------ | ------------------------------------ | ------ |
| AFP Checamos | https://checamos.afp.com/afp-brasil | 1,587 |
| Agência Lupa | https://piaui.folha.uol.com.br/lupa | 3,147 |
| Aos Fatos | https://aosfatos.org | 2,720 |
| Boatos.org | https://boatos.org | 8,654 |
| Estadão Verifica | https://politica.estadao.com.br/blogs/estadao-verifica | 1,405 |
| E-farsas | https://www.e-farsas.com | 3,330 |
| Fato ou Fake | https://oglobo.globo.com/fato-ou-fake| 2,270 |
| Projeto Comprova | https://checamos.afp.com/afp-brasil | 887 |
| UOL Confere | https://noticias.uol.com.br/confere | 2,579 |
| Total | -------------------------------------| 26, 569|
## More informations
The FakeRecogna 2 dataset is a single XLSX file that contains 8 columns for the metadata, and each row represents a sample (real or fake news), as described in Table 2.
| Columns | Description |
| ------------------------ | ------------------------------------------ |
| Title | Title of article |
| Sub-title (if available) | Brief description of news |
| News | Information about the article |
| Category | News grouped according to your information |
| Author | Publication author |
| Date | Publication date |
| URL | Article web address |
| Label | 0 for real news and 1 for fake news |
### FakeRecogna v2 - Abstrative
The abstrative summarization version of FakeRecogna 2 can be found [here](https://huggingface.co/datasets/recogna-nlp/fakerecogna2-abstrativo).
# Citation
@inproceedings{garcia-etal-2024-text,
title = "Text Summarization and Temporal Learning Models Applied to {P}ortuguese Fake News Detection in a Novel {B}razilian Corpus Dataset",
author = "Garcia, Gabriel Lino and
Paiola, Pedro Henrique and
Jodas, Danilo Samuel and
Sugi, Luis Afonso and
Papa, Jo{\~a}o Paulo",
editor = "Gamallo, Pablo and
Claro, Daniela and
Teixeira, Ant{\'o}nio and
Real, Livy and
Garcia, Marcos and
Oliveira, Hugo Gon{\c{c}}alo and
Amaro, Raquel",
booktitle = "Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 1",
month = mar,
year = "2024",
address = "Santiago de Compostela, Galicia/Spain",
publisher = "Association for Computational Lingustics",
url = "https://aclanthology.org/2024.propor-1.9/",
pages = "86--96"
} |
Lithium73fr/TEST7split1 | Lithium73fr | 2025-06-05T21:29:38Z | 0 | 0 | [
"task_categories:robotics",
"size_categories:n<1K",
"modality:video",
"library:datasets",
"library:mlcroissant",
"region:us",
"phosphobot",
"so100",
"phospho-dk"
] | [
"robotics"
] | 2025-06-05T21:29:27Z | null |
---
tags:
- phosphobot
- so100
- phospho-dk
task_categories:
- robotics
---
# TEST7split1
**This dataset was generated using a [phospho starter pack](https://robots.phospho.ai).**
This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot and RLDS.
|
Haribot099/so101_d61 | Haribot099 | 2025-06-05T21:20:39Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so101"
] | [
"robotics"
] | 2025-06-05T19:13:19Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so101
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.0",
"robot_type": "so101",
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"video_path": null,
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"names": {
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"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
}
},
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"shape": [
6
],
"names": {
"motors": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
}
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1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
LocalResearchGroup/split-avelina-python-edu | LocalResearchGroup | 2025-06-05T21:20:16Z | 42 | 0 | [
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"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-12T05:34:36Z | null | ---
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dataset_size: 13498307033.0
configs:
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data_files:
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path: 100k/train-*
- split: test
path: 100k/test-*
- config_name: 10k
data_files:
- split: train
path: 10k/train-*
- split: test
path: 10k/test-*
- config_name: 1M
data_files:
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path: 1M/train-*
- split: test
path: 1M/test-*
- config_name: 1k
data_files:
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path: 1k/train-*
- split: test
path: 1k/test-*
- config_name: full
data_files:
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path: full/train-*
- split: test
path: full/test-*
---
|
royrin/KLOM-models | royrin | 2025-06-05T21:13:12Z | 869 | 2 | [
"license:mit",
"size_categories:10K<n<100K",
"arxiv:2410.23232",
"region:us"
] | [] | 2025-05-04T19:36:56Z | null | ---
license: mit
size_categories:
- 10K<n<100K
---
Dataset for the evaluation of data-unlearning techniques using KLOM (KL-divergence of Margins).
# How KLOM works:
KLOM works by:
1. training N models (original models)
2. Training N fully-retrained models (oracles) on forget set F
3. unlearning forget set F from the original models
4. Comparing the outputs of the unlearned models from the retrained models on different points
(specifically, computing the KL divergence between the distribution of _margins_ of oracle models and distribution of _margins_ of the unlearned models)
Originally proposed in the work Attribute-to-Delete: Machine Unlearning via Datamodel Matching (https://arxiv.org/abs/2410.23232), described in detail in E.1.
**Outline of how KLOM works:**
**Algorithm Description:**
# Structure of Data
The overal structure is as follows:
```
full_models
├── CIFAR10
├── CIFAR10_augmented
└── LIVING17
oracles
└── CIFAR10
├── forget_set_1
├── forget_set_2
├── forget_set_3
├── forget_set_4
├── forget_set_5
├── forget_set_6
├── forget_set_7
├── forget_set_8
├── forget_set_9
└── forget_set_10
```
Each folder has
* train_logits_##.pt - logits at the end of training for model `##` for validation points
* val_logits_##.pt - logits at the end of training for model `##` for train points
* `##__val_margins_#.npy` - margins of model `##` at epoch `#` (this is derived from logits)
* `sd_##____epoch_#.pt` - model `##` checkpoint at epoch `#`
# How to download
Create script `download_folder.sh`
```
#!/bin/bash
REPO_URL=https://huggingface.co/datasets/royrin/KLOM-models
TARGET_DIR=KLOM-models # name it what you wish
FOLDER=$1 # e.g., "oracles/CIFAR10/forget_set_3"
mkdir -p $TARGET_DIR
git clone --filter=blob:none --no-checkout $REPO_URL $TARGET_DIR
cd $TARGET_DIR
git sparse-checkout init --cone
git sparse-checkout set $FOLDER
git checkout main
```
Example how to run script:
```
bash download_folder.sh oracles/CIFAR10/forget_set_3
```
## How forget sets generated
We have 10 different forget sets: sets 1,2,3 are random forget sets of sizes 10,100,1000 respectively; sets 4-9 correspond to semantically coherent subpopulations of examples (e.g., all dogs facing a similar direction) identified using clustering methods.
Specifically, we take a $n \times n$ datamodel matrix constructed by concatenating ``train x train`` datamodels ($n=50,000$). Next, we compute the top principal components (PCs) of the influence matrix and construct the following forget sets:
* Forget set 1: 10 random samples
* Forget set 2: 100 random samples
* Forget set 3: 500 random samples
* Forget set 4: 10 samples with the highest projection onto the 1st PC
* Forget set 5: 100 samples with the highest projection onto the 1st PC
* Forget set 6: 250 samples with the highest projection onto the 1st PC and 250 with lowest projection
* Forget set 7: 10 samples with the highest projection onto the 2nd PC
* Forget set 8: 100 samples with the highest projection onto the 2nd PC
* Forget set 9: 250 samples with the highest projection onto the 2nd PC and 250 with the lowest projection.
* Forget set 10: 100 samples closest in CLIP image space to training example 6 (a cassowary)
\paragraph{ImageNet Living-17.} We use three different forget sets:
* Forget set 1 is random of size 500;
* Forget sets 2 and 3 correspond to 200 examples from a certain subpopulation (corresponding to a single original ImageNet class) within the Living-17 superclass.
|
phospho-ai/dataset_for_testing | phospho-ai | 2025-06-05T21:06:10Z | 968 | 0 | [
"task_categories:robotics",
"size_categories:n<1K",
"modality:video",
"library:datasets",
"library:mlcroissant",
"region:us",
"phosphobot",
"so100",
"phospho-dk"
] | [
"robotics"
] | 2025-02-07T16:59:08Z | null |
---
tags:
- phosphobot
- so100
- phospho-dk
task_categories:
- robotics
---
# dataset_for_testing
**This dataset was generated using a [phospho starter pack](https://robots.phospho.ai).**
This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot and RLDS.
|
DoniaGasmii/final_project_milestone1_preference_pairs | DoniaGasmii | 2025-06-05T21:03:11Z | 55 | 0 | [
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"region:us"
] | [] | 2025-06-03T19:47:38Z | null | ---
dataset_info:
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---
|
david-thomas/yourbench | david-thomas | 2025-06-05T20:53:52Z | 0 | 0 | [
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] | [] | 2025-06-05T20:52:11Z | null | ---
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---
|
TAUR-dev/SIE_EVAL__SIEXP_concat_all_lm2d__sft__samples | TAUR-dev | 2025-06-05T20:48:55Z | 0 | 0 | [
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] | [] | 2025-06-05T20:48:52Z | null | ---
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---
|
ChavyvAkvar/multi-asset-synth-trades-202506052030 | ChavyvAkvar | 2025-06-05T20:38:51Z | 0 | 0 | [
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"library:polars",
"region:us"
] | [] | 2025-06-05T20:30:28Z | null | ---
configs:
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---
# Dataset Card for "multi-asset-synth-trades-202506052030"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TAUR-dev/SIE_EVAL__SIEXP_concat_until_correct_and_filter_lm2d__sft__results | TAUR-dev | 2025-06-05T20:35:07Z | 0 | 0 | [
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] | [] | 2025-06-05T20:35:06Z | null | ---
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|
TAUR-dev/SIE_EVAL__SIEXP_concat_until_correct_lm2d__sft__samples | TAUR-dev | 2025-06-05T20:34:40Z | 0 | 0 | [
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---
|
psg777/gluepickup106 | psg777 | 2025-06-05T20:32:11Z | 90 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so101",
"tutorial"
] | [
"robotics"
] | 2025-06-04T17:45:36Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so101
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.2",
"robot_type": "so101",
"total_episodes": 50,
"total_frames": 35078,
"total_tasks": 1,
"total_videos": 150,
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"chunks_size": 1000,
"fps": 30,
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"train": "0:50"
},
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"main_shoulder_lift",
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"main_wrist_flex",
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]
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6
],
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"main_shoulder_lift",
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"main_wrist_roll",
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"width",
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},
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480,
640,
3
],
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"height",
"width",
"channels"
],
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"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
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}
},
"timestamp": {
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"names": null
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1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
clyyuanzi/so101_test | clyyuanzi | 2025-06-05T20:22:49Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
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"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so101",
"tutorial"
] | [
"robotics"
] | 2025-06-05T20:22:41Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so101
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so101",
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6
],
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"main_shoulder_pan",
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}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
NaykinYT/reward-bench-allenai_2 | NaykinYT | 2025-06-05T20:16:44Z | 0 | 0 | [
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|
rosbotmay/mnlp_M3_big_corpus_no_filter | rosbotmay | 2025-06-05T20:15:07Z | 0 | 0 | [
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|
ChavyvAkvar/multi-asset-synth-trades-202506052003 | ChavyvAkvar | 2025-06-05T20:11:46Z | 0 | 0 | [
"region:us"
] | [] | 2025-06-05T20:03:24Z | null | ---
configs:
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---
# Dataset Card for "multi-asset-synth-trades-202506052003"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
andresnowak/MNLP_MCQA_dataset | andresnowak | 2025-06-05T20:06:10Z | 404 | 0 | [
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path: all/test-*
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path: medmcqa/train-*
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path: medmcqa/validation-*
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data_files:
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path: mmlu/validation-*
- split: test
path: mmlu/test-*
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data_files:
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path: mmlu-auxiliary-train-auto-labelled/train-*
- config_name: mmlu_auxiliary_train_stem_10_choices
data_files:
- split: train
path: mmlu_auxiliary_train_stem_10_choices/train-*
- config_name: openbookqa
data_files:
- split: train
path: openbookqa/train-*
- split: validation
path: openbookqa/validation-*
- split: test
path: openbookqa/test-*
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data_files:
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path: sciq/train-*
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path: sciq/validation-*
- split: test
path: sciq/test-*
task_categories:
- question-answering
language:
- en
size_categories:
- 100K<n<1M
---
This MCQA dataset (of only single answer) contains a mixture of train, validation and test from this datasets (**test and validation are only used for testing not for training**):
- [mmlu auxiliary train](https://huggingface.co/datasets/kz919/mmlu-auxiliary-train-auto-labelled) Only the stem subset is used
- [mmlu](https://huggingface.co/datasets/cais/mmlu) Only the stem subset is used
- [mmlu 10 choices auxiliary train stem](https://huggingface.co/datasets/andresnowak/mmlu-auxiliary-train-10-choices)
- [ai2_arc](https://huggingface.co/datasets/allenai/ai2_arc)
- [ScienceQA](https://huggingface.co/datasets/derek-thomas/ScienceQA)
- [math_qa](https://huggingface.co/datasets/allenai/math_qa)
- [openbook_qa](https://huggingface.co/datasets/allenai/openbookqa)
- [sciq](https://huggingface.co/datasets/allenai/sciq)
- [medmcqa](https://huggingface.co/datasets/openlifescienceai/medmcqa) A 26,000 random subset (seed 42) |
aranemini/fleurs-kmr | aranemini | 2025-06-05T20:04:40Z | 0 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
] | [] | 2025-06-05T20:04:40Z | null | ---
license: cc-by-nc-4.0
---
|
ChavyvAkvar/multi-asset-synth-trades-202506051936 | ChavyvAkvar | 2025-06-05T19:45:00Z | 0 | 0 | [
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] | [] | 2025-06-05T19:36:12Z | null | ---
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---
# Dataset Card for "multi-asset-synth-trades-202506051936"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
VinceEPFL/mmlu_mathphys_only_subset | VinceEPFL | 2025-06-05T19:35:44Z | 0 | 0 | [
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|
CohenQu/HintGen-withSol.01.01 | CohenQu | 2025-06-05T19:32:40Z | 0 | 0 | [
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---
|
svjack/genshin_impact_mavuika_audio_sample | svjack | 2025-06-05T19:28:23Z | 0 | 0 | [
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] | [] | 2025-06-05T19:23:40Z | null | ---
configs:
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--- |
rweics5cs7/exo3-original-PlotQA-text-deg | rweics5cs7 | 2025-06-05T19:09:52Z | 0 | 0 | [
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|
MING-ZCH/MetaphorQA | MING-ZCH | 2025-06-05T19:04:55Z | 0 | 0 | [
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---
# MetaphorQA
The True-False Question(TFQ) about image implication.
- train: 984
- test: 492
|
koreankiwi99/mnlp_stem_reasoning | koreankiwi99 | 2025-06-05T19:01:35Z | 0 | 0 | [
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|
koreankiwi99/mnlp_stem_math_only | koreankiwi99 | 2025-06-05T19:00:11Z | 0 | 0 | [
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|
rweics5cs7/exo3-original-ArxivQA-text-deg | rweics5cs7 | 2025-06-05T18:48:17Z | 0 | 0 | [
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] | [] | 2025-06-05T18:48:14Z | null | ---
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|
danelbaz/some_name_for_hub | danelbaz | 2025-06-05T18:44:32Z | 761 | 0 | [
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"library:mlcroissant",
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] | [] | 2025-03-17T07:37:09Z | null | ---
dataset_info:
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---
|
zijian2022/itrgg | zijian2022 | 2025-06-05T18:43:21Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
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"region:us",
"LeRobot",
"so100",
"tutorial"
] | [
"robotics"
] | 2025-06-05T18:36:51Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- tutorial
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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"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
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"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
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}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
WillHeld/paloma_subreddits | WillHeld | 2025-06-05T18:35:12Z | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-05T17:55:25Z | null | ---
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---
|
sy1998/EarthMind-Bench | sy1998 | 2025-06-05T18:34:50Z | 0 | 1 | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"region:us"
] | [] | 2025-06-05T11:58:20Z | 1 | ---
license: apache-2.0
---
|
Koushim/en-te-kn-translation-dataset | Koushim | 2025-06-05T18:34:48Z | 0 | 0 | [
"task_categories:translation",
"annotations_creators:manual",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:ai4bharat/samanantar",
"language:en",
"language:te",
"language:kn",
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"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"translation"
] | 2025-06-05T18:26:50Z | null | ---
annotations_creators:
- manual
language_creators:
- found
language:
- en
- te
- kn
license: cc-by-4.0
multilinguality:
- multilingual
pretty_name: Multilingual English-Telugu-Kannada Translation Dataset
size_categories:
- 1M<n<10M
source_datasets:
- ai4bharat/samanantar
task_categories:
- translation
task_ids:
- translation
dataset_info:
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dtype: string
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download_size: 700766001
dataset_size: 2231633137
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# 📚 Multilingual English-Telugu-Kannada Translation Dataset
This dataset is a curated and preprocessed subset of the [AI4Bharat Samanantar](https://huggingface.co/datasets/ai4bharat/samanantar) dataset focused on multilingual translation tasks between English, Telugu (`te_IN`), and Kannada (`kn_IN`).
## ✨ Dataset Features
- Language pairs:
- `en ↔ te_IN`
- `en ↔ kn_IN`
- Preprocessed:
- Filtered for sentence length (min=3, max=128 words)
- Cleaned and normalized
- Tokenized using Hugging Face Transformers tokenizers:
- M2M100Tokenizer (for `en↔kn`)
- MBart50TokenizerFast (for `en↔te`)
## 📦 Dataset Structure
The dataset contains the following fields:
- `src_texts`: Source language sentence (English)
- `tgt_texts`: Target language sentence (Telugu or Kannada)
- `labels`: Tokenized target sequence for model training
- `input_ids`, `attention_mask`: Tokenized source sentence
The dataset is split into:
- `train`: Training samples
- `validation`: Small subset for evaluation
## 📊 Size
- ~7.9M total sentence pairs
- Supports batch training and multilingual fine-tuning
## 💡 Usage Example
```python
from datasets import load_dataset
dataset = load_dataset("Koushim/en-te-kn-translation-dataset")
print(dataset["train"][0])
````
## 🧠 Intended Uses
* Train multilingual translation models (MBart, Marian, M2M100)
* Fine-tune LLMs on Indic translation
* Evaluate BLEU or other metrics for low-resource translation
## 📜 License
CC-BY-4.0
## ✍️ Author
Koushik Reddy
[GitHub](https://github.com/Koushik7893) | [Hugging Face](https://huggingface.co/datasets/Koushim)
## 🙏 Acknowledgements
Thanks to AI4Bharat for providing the [Samanantar](https://huggingface.co/datasets/ai4bharat/samanantar) dataset which served as the base for this project. |
IndoorOutdoor/results | IndoorOutdoor | 2025-06-05T18:32:08Z | 221 | 0 | [
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"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-03T01:00:13Z | null | ---
dataset_info:
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num_bytes: 1434
num_examples: 21
download_size: 3913
dataset_size: 1434
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
rweics5cs7/exo3-original-MP-DocVQA-text | rweics5cs7 | 2025-06-05T18:30:19Z | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-05T18:29:58Z | null | ---
dataset_info:
config_name: corpus
features:
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dtype: string
- name: text
dtype: string
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num_bytes: 1302324
num_examples: 741
download_size: 746965
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configs:
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data_files:
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path: corpus/train-*
---
|
VGS-AI/OpenR1-Cleaned | VGS-AI | 2025-06-05T18:26:19Z | 89 | 0 | [
"task_categories:question-answering",
"license:cc-by-nc-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2505.17373",
"region:us"
] | [
"question-answering"
] | 2025-05-22T21:30:00Z | null | ---
license: cc-by-nc-4.0
task_categories:
- question-answering
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features:
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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: source
dtype: string
- name: uuid
dtype: string
- name: is_reasoning_complete
sequence: bool
- name: generations
sequence: string
- name: correctness_math_verify
sequence: bool
- name: correctness_llama
sequence: bool
- name: finish_reasons
sequence: string
- name: correctness_count
dtype: int64
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
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num_bytes: 2634118946
num_examples: 48394
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num_bytes: 28313171
num_examples: 500
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num_bytes: 27765044
num_examples: 500
download_size: 1162355943
dataset_size: 2690197161
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
This dataset is used in the paper [Value-Guided Search for Efficient Chain-of-Thought Reasoning](https://huggingface.co/papers/2505.17373). It contains data for training and evaluating value models for improved long-context reasoning.
GitHub Repository: https://github.com/kaiwenw/value-guided-search
Related resources:
* **Dataset (OpenR1-Cleaned):** https://huggingface.co/datasets/VGS-AI/OpenR1-Cleaned
* **Dataset (OpenR1-VM):** https://huggingface.co/datasets/VGS-AI/OpenR1-VM
* **Value Model (DeepSeek-VM-1.5B):** https://huggingface.co/VGS-AI/DeepSeek-VM-1.5B
**How to use:**
To load the released value model, you can use the following code snippet:
```python
import classifier_lib
import torch
model_loading_kwargs = dict(attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, use_cache=False)
classifier = classifier_lib.Qwen2ForClassifier.from_pretrained("VGS-AI/DeepSeek-VM-1.5B", **model_loading_kwargs)
device = torch.device("cuda")
# your input_ids
input_ids = torch.tensor([151646, 151644, 18, 13, 47238, ...], dtype=torch.long, device=device)
attention_mask = torch.ones_like(input_ids)
classifier_outputs = classifier(input_ids.unsqueeze(0), attention_mask=attention_mask.unsqueeze(0))
# use last index of the sequence
scores = classifier_outputs.success_probs.squeeze(0)[-1].item()
``` |
salamnocap/ml-figs | salamnocap | 2025-06-05T18:24:51Z | 43 | 0 | [
"language:en",
"license:cc-by-nc-4.0",
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"doi:10.57967/hf/5251",
"region:us",
"education",
"figure",
"caption",
"books"
] | [] | 2025-04-25T19:34:49Z | null | ---
license: cc-by-nc-4.0
language:
- en
tags:
- education
- figure
- caption
- books
pretty_name: ML-Figs
size_categories:
- 1K<n<10K
---
[](https://github.com/salamnocap/ml-figs-ldm)
# ML-FIGS 📚📊
This dataset comprises a collection of **4,256 figures** and corresponding metadata extracted from **43 different machine learning books**. Each directory represents one book and contains a subdirectory with **images** and a **JSON file** holding metadata for each figure. The dataset is organized hierarchically as follows:
```python
ml-figs/
├── Book_1/
│ ├── image/
│ │ ├── Figure1.png
│ │ ├── Figure2.png
│ │ └── ...
│ └── Book_1.json
├── Book_2/
│ ├── image/
│ │ ├── Figure1.png
│ │ ├── Figure2.png
│ │ └── ...
│ └── Book_2.json
├── ...
│
├── mlfigs_train.json
│
└── mlfigs_test.json
```
Each **JSON file** in the dataset represents the metadata for all the figures within a particular book. A typical entry for a figure in the JSON file includes the following attributes:
- **caption**: Describes the content of the figure. For example, "Figure 25: Gradient Descent (4/4)".
- **captionBoundary**: The bounding box of the caption text within the page, represented as a dictionary:
- **x1, x2**: Horizontal boundaries of the caption.
- **y1, y2**: Vertical boundaries of the caption.
- **figType**: The type of figure (usually "Figure", "Table", or other structural elements).
- **imageText**: A list of any text recognized within the figure image.
- **name**: The unique identifier for each figure within the book (e.g., "25").
- **page**: The page number on which the figure appears.
- **regionBoundary**: The bounding box of the entire figure within the page, defined as:
- **x1, x2**: Horizontal boundaries of the figure.
- **y1, y2**: Vertical boundaries of the figure.
- **renderDpi**: The DPI (dots per inch) resolution used when rendering the image.
- **renderURL**: The path to the corresponding figure image file within the image/ directory.
- **ocr**: OCR (Optical Character Recognition) data, capturing any text detected within the figure.
- **text** : A list of strings, representing the recognized text within the figure. These are typically individual words or symbols extracted by the OCR system.
- **left** : Horizontal coordinates (in pixels) representing the left edge of each recognized text element.
- **top** : Vertical coordinates (in pixels) representing the top edge of each recognized text element.
- **width** : Width of each recognized text element, providing the horizontal span of the text box.
- **height** : Height of each recognized text element.
- **conf** : Each recognized text element is assigned a confidence score ranging from 0 to 100, indicating the OCR system's confidence in its recognition.
# 📕 Citation
```bibtex
@misc{salamat_kuantaiuly_2025,
author = { Salamat Kuantaiuly },
title = { ml-figs (Revision 8695d1b) },
year = 2025,
url = { https://huggingface.co/datasets/salamnocap/ml-figs },
doi = { 10.57967/hf/5251 },
publisher = { Hugging Face }
}
``` |
uhuru-jonathan/lekiwi_test | uhuru-jonathan | 2025-06-05T18:19:46Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"tutorial"
] | [
"robotics"
] | 2025-06-05T18:20:45Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "lekiwi",
"total_episodes": 7,
"total_frames": 5356,
"total_tasks": 1,
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"total_chunks": 1,
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"fps": 30,
"splits": {
"train": "0:4"
},
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"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
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"shape": [
9
],
"names": [
"shoulder_pan",
"shoulder_lift",
"elbow_flex",
"wrist_flex",
"wrist_roll",
"gripper",
"x_mm",
"y_mm",
"theta"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
9
],
"names": [
"shoulder_pan",
"shoulder_lift",
"elbow_flex",
"wrist_flex",
"wrist_roll",
"gripper",
"x_mm",
"y_mm",
"theta"
]
},
"observation.images.front": {
"dtype": "video",
"shape": [
640,
480,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
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"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"observation.images.wrist": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 30.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
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"shape": [
1
],
"names": null
},
"episode_index": {
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"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]
``` |
Jgold90/sweep_mano | Jgold90 | 2025-06-05T18:16:48Z | 81 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:timeseries",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] | [
"robotics"
] | 2025-05-27T16:07:09Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "human",
"total_episodes": 498,
"total_frames": 96415,
"total_tasks": 1,
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"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:498"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": null,
"features": {
"lang": {
"dtype": "float32",
"shape": [
512
],
"names": null
},
"observation.image.low": {
"dtype": "image",
"shape": [
480,
640,
3
],
"names": [
"width",
"height",
"channels"
]
},
"observation.image.wrist": {
"dtype": "image",
"shape": [
256,
256,
3
],
"names": [
"width",
"height",
"channels"
]
},
"observation.state.box.center": {
"dtype": "float32",
"shape": [
2
]
},
"observation.state.box.kp2d": {
"dtype": "float32",
"shape": [
21,
2
]
},
"observation.state.box.size": {
"dtype": "float32",
"shape": [
1
]
},
"observation.state.kp2d": {
"dtype": "float32",
"shape": [
21,
2
]
},
"observation.state.kp3d": {
"dtype": "float32",
"shape": [
21,
3
]
},
"observation.state.mano.betas": {
"dtype": "float32",
"shape": [
10
]
},
"observation.state.mano.global_orient": {
"dtype": "float32",
"shape": [
3,
3
]
},
"observation.state.mano.hand_pose": {
"dtype": "float32",
"shape": [
15,
3,
3
]
},
"observation.state.right": {
"dtype": "float32",
"shape": [
1
]
},
"observation.state.scaled_focal_length": {
"dtype": "float32",
"shape": [
1
]
},
"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]
``` |
Haribot099/so101_15 | Haribot099 | 2025-06-05T18:01:09Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so101",
"tutorial"
] | [
"robotics"
] | 2025-06-05T17:44:21Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so101
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so101",
"total_episodes": 2,
"total_frames": 892,
"total_tasks": 1,
"total_videos": 6,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:2"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.wrist": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.phone": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.laptop": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"episode_index": {
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"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]
``` |
super-pingouin/wikipedia-stem-articles | super-pingouin | 2025-06-05T17:46:36Z | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-05T17:46:33Z | null | ---
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 4911800
num_examples: 887
download_size: 2610695
dataset_size: 4911800
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
BlueSeaHoneyBee/13TEST_Rated_HSE_QA_Reviewed | BlueSeaHoneyBee | 2025-06-05T17:44:43Z | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-05T17:44:41Z | null | ---
dataset_info:
features:
- name: question
dtype: string
- name: thinking_step
dtype: string
- name: answer
dtype: string
- name: rating
dtype: int64
splits:
- name: train
num_bytes: 2009
num_examples: 3
download_size: 5717
dataset_size: 2009
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Cameronbarry/cams | Cameronbarry | 2025-06-05T17:39:42Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | [] | 2025-06-05T17:39:41Z | null | ---
license: apache-2.0
---
|
jlbaker361/clip-art_coco_captioned-1000 | jlbaker361 | 2025-06-05T17:33:10Z | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-05T17:32:57Z | null | ---
dataset_info:
features:
- name: image
dtype: image
- name: embedding
sequence:
sequence:
sequence: float32
- name: text
sequence:
sequence:
sequence: float16
- name: prompt
dtype: string
- name: posterior
sequence:
sequence:
sequence: float16
splits:
- name: train
num_bytes: 249480845.0
num_examples: 1000
download_size: 242482521
dataset_size: 249480845.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
jlbaker361/ssl-coco_captioned-1000 | jlbaker361 | 2025-06-05T17:33:01Z | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-05T15:46:54Z | null | ---
dataset_info:
features:
- name: image
dtype: image
- name: embedding
sequence:
sequence:
sequence: float32
- name: text
sequence:
sequence:
sequence: float16
- name: prompt
dtype: string
- name: posterior
sequence:
sequence:
sequence: float16
splits:
- name: train
num_bytes: 259178898.0
num_examples: 1000
download_size: 254013098
dataset_size: 259178898.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
slprl/TinyStress-15K | slprl | 2025-06-05T17:18:46Z | 119 | 3 | [
"task_categories:audio-classification",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2505.19103",
"region:us"
] | [
"audio-classification"
] | 2025-05-25T07:23:06Z | 2 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: original_sample_index
dtype: int64
- name: sentence_index
dtype: int64
- name: transcription
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 48000
- name: ssml
dtype: string
- name: emphasis_indices
sequence: int64
- name: metadata
struct:
- name: gender
dtype: int64
- name: language_code
dtype: string
- name: voice_name
dtype: string
- name: word_start_timestamps
sequence: float64
- name: aligned_whisper_transcriptions
dtype: string
splits:
- name: train
num_bytes: 5215476174
num_examples: 15000
- name: test
num_bytes: 337636506
num_examples: 1000
download_size: 4817381967
dataset_size: 5553112680
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
language:
- en
task_categories:
- audio-classification
license: cc-by-nc-4.0
---
# 📚 TinyStress-15K Dataset
TinyStress-15K is a synthetic dataset developed as part of our paper: "[***WhiStress***](https://arxiv.org/abs/2505.19103): *Enriching Transcriptions with Sentence Stress Detection*". It is designed to support research of models that understand sentence stress i.e., emphasis on specific words that affect sentence meaning.
Check out our [project page](https://pages.cs.huji.ac.il/adiyoss-lab/whistress/) to access more resources.
## 📦 Dataset Summary
- **Name**: `TinyStress-15K`
- **Type**: Synthetic speech dataset with stress annotations
- **Samples**: 15,000 training and 1,000 testing examples
- **Sampling Rate**: 48 kHz
- **Texts**: Derived from [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories)
---
## 🧩 Dataset Structure
Each sample contains:
| Feature | Description |
|--------|-------------|
| `id` | Unique sample identifier |
| `original_sample_index` | Index of the original TinyStories sample (story) |
| `sentence_index` | Position of the sentence in the original story |
| `transcription` | Text transcription of the spoken audio |
| `audio` | Audio waveform (`.wav`), sampled at 48kHz |
| `ssml` | SSML-formatted version used to manipulate prosodic features |
| `emphasis_indices` | List of word indices in the transcription that contain emphasis |
| `metadata.gender` | Speaker gender (integer-coded) |
| `metadata.language_code` | Language tag (e.g., `"en"`) |
| `metadata.voice_name` | Synthetic voice name |
| `word_start_timestamps` | Start times (in seconds) for each word |
| `aligned_whisper_transcriptions` | Whisper generated transcription |
---
## 📥 How to Use
```python
from datasets import load_dataset
dataset = load_dataset("slprl/TinyStress-15K", split="train")
sample = dataset[0]
words = sample["transcription"].split()
stressed_words = [words[i] for i in sample["emphasis_indices"]]
print(sample["transcription"])
print(sample["emphasis_indices"])
print(stressed_words)
```
---
## Notes
The data is intended for research purposes only.
---
## 🧠 Citation
If you our use our dataset, please cite our work:
```bibtex
@misc{yosha2025whistress,
title={WHISTRESS: Enriching Transcriptions with Sentence Stress Detection},
author={Iddo Yosha and Dorin Shteyman and Yossi Adi},
year={2025},
eprint={2505.19103},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.19103},
}
``` |
livecodebench/code_generation_lite | livecodebench | 2025-06-05T17:18:27Z | 47,439 | 42 | [
"license:cc",
"size_categories:n<1K",
"arxiv:2403.07974",
"region:us",
"code",
"code generation"
] | [] | 2024-04-16T04:46:53Z | null | ---
license: cc
tags:
- code
- code generation
pretty_name: LiveCodeBench
size_categories:
- n<1K
---
## LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
<p align="center">
<a href="https://livecodebench.github.io/">🏠 Home Page</a> •
<a href="https://github.com/LiveCodeBench/LiveCodeBench">💻 GitHub Repository </a> •
<a href="https://livecodebench.github.io/leaderboard.html">🏆 Leaderboard</a> •
<a href="https://arxiv.org/abs/2403.07974">📄 Paper </a>
</p>
## Change Log
Since LiveCodeBench is a continuously updated benchmark, we provide different versions of the dataset. Particularly, we provide the following versions of the dataset:
- `release_v1`: The initial release of the dataset with problems released between May 2023 and Mar 2024 containing 400 problems.
- `release_v2`: The updated release of the dataset with problems released between May 2023 and May 2024 containing 511 problems.
- `release_v3`: The updated release of the dataset with problems released between May 2023 and Jul 2024 containing 612 problems.
- `release_v4`: The updated release of the dataset with problems released between May 2023 and Sep 2024 containing 713 problems.
- `release_v5`: The updated release of the dataset with problems released between May 2023 and Jan 2025 containing 880 problems.
You can use the `version_tag` argument to load the desired version of the dataset. Additionally, you can use version tags like `v1`, `v2`, `v1_v3`, `v4_v5` to get the problems released in a specific version.
## Dataset Description
LiveCodeBench is a "live" updating benchmark for holistically evaluating code related capabilities of LLMs.
Particularly, it evaluates LLMs across a range of capabilties including code generation, self-repair, test output prediction, and code execution.
This is the code generation scenario of LiveCodeBench. It is also used for evaluating self-repair using test case feedback.
LiveCodeBench problems are collected from competition programming websites with particular focus on maintaining problem quality, test case quality, and problem difficulty diversity.
This scenario currently hosts over 500 problems from LeetCode, AtCoder, and Codeforces.
Each problem instance consists of a problem description, input/output examples, and hidden test cases.
Additionally, every problem is tagged with its difficulty level and release date, which allows measuring model performance across different time windows.
The goal is to generate a correct and efficient solution for each problem instance.
The initial code_generation dataset included a larger number of test cases which leads to a substantially large dataset size. This (lite) version has pruned and sampled tests while trying to ensure similar performances with the original dataset. Going forward, livecodebench will be using this lite version for code generation evaluations.
## Usage
You can use the dataset by loading it from the Hugging Face datasets library. Additionally, the version tag "release_v1" is used to specify the (temporal) version of the dataset. "v1" corresponds to the initial release of the dataset and "release_v2" is the second version.
```python
from datasets import load_dataset
lcb_codegen = load_dataset("livecodebench/code_generation_lite", version_tag="release_v2")
``` |
deepghs/game_characters | deepghs | 2025-06-05T17:15:16Z | 1,441 | 27 | [
"license:apache-2.0",
"region:us"
] | [] | 2023-01-28T11:12:51Z | null | ---
license: apache-2.0
---
# Database of Characters in Mobile Games
[](https://huggingface.co/datasets/deepghs/game_characters)
All the character in the following games are supported:
* Arknights (crawled from [https://prts.wiki](https://prts.wiki))
* Fate/Grand Order (crawled from [https://fgo.wiki](https://fgo.wiki))
* Azur Lane (crawled from [https://wiki.biligame.com/blhx](https://wiki.biligame.com/blhx))
* Girls' Front-Line (crawled from [https://iopwiki.com/](https://iopwiki.com/))
* Genshin Impact (crawled from [https://genshin-impact.fandom.com/ja/wiki/%E5%8E%9F%E7%A5%9E_Wiki](https://genshin-impact.fandom.com/ja/wiki/%E5%8E%9F%E7%A5%9E_Wiki))
The source code and python library is hosted on [narugo1992/gchar](https://github.com/narugo1992/gchar), and the scheduled job is configured on Github Action, so the data will be automatically updated to the latest version once a day.
More character data for other games is coming...
|
BlueSeaHoneyBee/10TEST_Rated_HSE_QA_Reviewed | BlueSeaHoneyBee | 2025-06-05T17:12:13Z | 0 | 0 | [
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|
harrisonenyeartkaleido/defi_training_dataset_6_5_25 | harrisonenyeartkaleido | 2025-06-05T17:10:08Z | 0 | 0 | [
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] | [] | 2025-06-05T17:09:25Z | null | ---
language:
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tags:
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--- |
revyu/pulze_intent_unwrapped_and_ranked | revyu | 2025-06-05T16:59:47Z | 0 | 0 | [
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] | [] | 2025-06-05T16:59:41Z | null | ---
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|
cristiano-sartori/rag_ft | cristiano-sartori | 2025-06-05T16:54:20Z | 0 | 0 | [
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] | [] | 2025-06-05T16:54:17Z | null | ---
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|
fintech-final/FRESH-all_products-with_missing_products-with_cold_item | fintech-final | 2025-06-05T16:48:09Z | 0 | 0 | [
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] | [] | 2025-06-05T13:33:39Z | null | ---
dataset_info:
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|
carminho/piqa-mt-pt | carminho | 2025-06-05T16:45:51Z | 0 | 0 | [
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] | [] | 2025-06-05T12:57:09Z | null | ---
language:
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path: piqa_train_pt.jsonl
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path: piqa_validation_pt.jsonl
--- |
carminho/siqa-mt-pt | carminho | 2025-06-05T16:43:19Z | 0 | 0 | [
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] | [] | 2025-06-05T13:21:38Z | null | ---
language:
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configs:
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path: siqa_train_pt.jsonl
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path: siqa_validation_pt.jsonl
--- |
fannymissillier/balanced-mcqa-dataset-cleaned | fannymissillier | 2025-06-05T16:41:33Z | 0 | 0 | [
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dataset_info:
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|
sugarquark/temp-fashion-1m | sugarquark | 2025-06-05T16:40:31Z | 0 | 0 | [
"region:us"
] | [] | 2025-06-05T16:39:07Z | null | ---
viewer: false
---
Fashion shows from 1990 to 2025. Cloned from and owned by tonyassi.
|
cristiano-sartori/open_mnlp_m1 | cristiano-sartori | 2025-06-05T16:39:07Z | 0 | 0 | [
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] | [] | 2025-06-05T16:39:04Z | null | ---
dataset_info:
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|
Gatescrispy/Largest_therapeutic_molecule_dataset_with_1.4M_compounds_for_scientific_research | Gatescrispy | 2025-06-05T16:34:18Z | 14 | 0 | [
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"phytotherapy",
"therapeutic-compounds",
"natural-products",
"machine-learning",
"pharmaceutical-research",
"cheminformatics",
"qsar",
"drug-repurposing"
] | [
"feature-extraction",
"text-classification"
] | 2025-06-05T06:49:33Z | null | ---
license: cc-by-4.0
task_categories:
- feature-extraction
- text-classification
tags:
- chemistry
- drug-discovery
- molecules
- bioactivity
- traditional-medicine
- phytotherapy
- therapeutic-compounds
- natural-products
- machine-learning
- pharmaceutical-research
- cheminformatics
- qsar
- drug-repurposing
language:
- en
size_categories:
- 1M<n<10M
pretty_name: "PhytoAI MEGA Dataset - 1.4M Therapeutic Molecules"
---
# 🧬 PhytoAI MEGA Dataset - 1.4M Therapeutic Molecules
<div align="center">
**A Comprehensive Dataset of Therapeutic Molecules for AI Drug Discovery Research**
</div>
## 🌟 Overview
The **PhytoAI MEGA Dataset** contains **1,600,000+ therapeutic molecules** with comprehensive molecular properties, bioactivity data, and traditional medicine annotations. This dataset bridges traditional pharmaceutical knowledge and modern computational methods, enabling research opportunities in drug discovery.
### 🏆 Dataset Features
- **Large Scale**: 1,600,000+ unique therapeutic molecules
- **Comprehensive Coverage**: Traditional medicine systems + modern pharmacology
- **High Quality**: Curated and validated molecular data
- **Optimized Format**: Apache Arrow for efficient processing
- **Open Access**: CC BY 4.0 license for research and commercial use
## 📊 Dataset Composition
### Scale & Statistics
| Metric | Value | Description |
|--------|--------|-------------|
| **Total Molecules** | 1,600,000+ | Unique therapeutic compounds |
| **Data Size** | 759.9 MB | Optimized Apache Arrow format |
| **Splits** | train/validation/test | 80%/10%/10% distribution |
| **Format** | Apache Arrow | High-performance columnar format |
| **License** | CC BY 4.0 | Open for research and commercial use |
### Data Sources
Our data integration includes:
- **🔬 Scientific Literature**: PubMed research papers
- **💊 Bioactivity Databases**: ChEMBL validated bioactivities
- **🌿 Traditional Medicine**: Traditional use records
- **📚 Pharmacopoeias**: International pharmacopoeias
## 🗂️ Dataset Structure
### File Organization
```
📁 PhytoAI-MEGA-Dataset/
├── 🗂️ train/ # Training split (80% - ~1,280,000 molecules)
│ ├── data-00000-of-00002.arrow (304 MB)
│ └── data-00001-of-00002.arrow (304 MB)
├── 🗂️ validation/ # Validation split (10% - ~160,000 molecules)
│ └── data-00000-of-00001.arrow (76 MB)
├── 🗂️ test/ # Test split (10% - ~160,000 molecules)
│ └── data-00000-of-00001.arrow (76 MB)
└── 📄 README.md # This documentation
```
**Total Size**: 759.9 MB of molecular data
### Molecular Features Schema
Each molecule contains:
```json
{
"id": "unique_identifier",
"name": "compound_name",
"molecular_weight": float, // Molecular weight in Daltons
"molecular_formula": "string", // Chemical formula (e.g., C21H30O2)
"smiles": "string", // Canonical SMILES notation
"inchi": "string", // InChI identifier
"logp": float, // Lipophilicity (octanol-water partition)
"hbd": int, // Hydrogen bond donors
"hba": int, // Hydrogen bond acceptors
"tpsa": float, // Topological polar surface area
"rotatable_bonds": int, // Number of rotatable bonds
"bioactivity_score": float, // Predicted therapeutic potential (0-1)
"safety_index": float, // Predicted safety profile (0-1)
"traditional_use": "string", // Historical therapeutic applications
"bioactivities": ["array"], // Biological activities
"targets": ["array"], // Molecular targets
"pathways": ["array"], // Biological pathways
"collection_date": "iso_date", // Data integration timestamp
"is_champion": boolean, // High therapeutic potential flag
"literature_refs": ["array"], // Supporting research papers
"source_database": "string" // Original data source
}
```
## 🎯 Therapeutic Coverage
### Major Therapeutic Categories
Distribution across therapeutic areas:
| Therapeutic Area | Molecules | Percentage | Key Targets |
|------------------|-----------|------------|-------------|
| **Anti-inflammatory** | ~180,000 | 11.2% | COX-1/2, NF-κB, TNF-α |
| **Antioxidant** | ~220,000 | 13.8% | ROS scavenging, SOD, catalase |
| **Cardiovascular** | ~150,000 | 9.4% | ACE, β-blockers, calcium channels |
| **Neuroprotective** | ~130,000 | 8.1% | AChE, MAO, NMDA receptors |
| **Anti-cancer** | ~160,000 | 10.0% | p53, MDR1, apoptosis pathways |
| **Antimicrobial** | ~140,000 | 8.8% | Cell wall synthesis, protein synthesis |
| **Multi-target** | ~200,000 | 12.5% | Complex polypharmacology |
| **Other activities** | ~420,000 | 26.2% | Metabolic, endocrine, immune |
### Drug-likeness Assessment
Molecular properties distribution:
- **Lipinski's Rule of Five**: 89.3% compliance
- **Veber Rules**: 92.1% compliance
- **PAINS Filters**: 96.8% pass rate
- **Lead-like Properties**: 78.4% compliance
## 💻 Usage Guide
### Quick Start
```python
from datasets import load_dataset
import pandas as pd
# Load the complete dataset
dataset = load_dataset("Gatescrispy/Largest_therapeutic_molecule_dataset_with_1.4M_compounds_for_scientific_research")
# Access different splits
train_data = dataset['train']
validation_data = dataset['validation']
test_data = dataset['test']
print(f"Training molecules: {len(train_data):,}")
print(f"Validation molecules: {len(validation_data):,}")
print(f"Test molecules: {len(test_data):,}")
print(f"Total molecules: {len(train_data) + len(validation_data) + len(test_data):,}")
```
### Analysis Examples
#### Molecular Property Analysis
```python
# Convert to pandas for analysis
df = train_data.to_pandas()
# Molecular weight distribution
import matplotlib.pyplot as plt
plt.hist(df['molecular_weight'], bins=50, alpha=0.7)
plt.xlabel('Molecular Weight (Da)')
plt.ylabel('Frequency')
plt.title('Molecular Weight Distribution')
plt.axvline(500, color='red', linestyle='--', label='Lipinski Limit')
plt.legend()
plt.show()
# Drug-likeness assessment
lipinski_compliant = (
(df['molecular_weight'] <= 500) &
(df['logp'] <= 5) &
(df['hbd'] <= 5) &
(df['hba'] <= 10)
)
print(f"Lipinski compliant: {lipinski_compliant.sum():,} ({lipinski_compliant.mean()*100:.1f}%)")
```
#### Bioactivity Analysis
```python
# Extract bioactivities
bioactivities = df['bioactivities'].explode().value_counts()
print("Top 10 bioactivities:")
print(bioactivities.head(10))
# High-potential compounds
champions = df[df['is_champion'] == True]
print(f"Champion molecules: {len(champions):,}")
# Traditional use categories
traditional_uses = df['traditional_use'].value_counts()
print("Traditional use categories:")
print(traditional_uses.head(10))
```
#### Machine Learning Pipeline
```python
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
# Prepare features for bioactivity prediction
features = ['molecular_weight', 'logp', 'hbd', 'hba', 'tpsa', 'rotatable_bonds']
X = df[features].fillna(df[features].median())
y = df['bioactivity_score']
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Evaluate
y_pred = model.predict(X_test)
print(f"R² Score: {r2_score(y_test, y_pred):.3f}")
print(f"RMSE: {mean_squared_error(y_test, y_pred, squared=False):.3f}")
```
## 🤝 Citation
### Recommended Citation
```bibtex
@dataset{phytoai_mega_1_6m_2025,
title={PhytoAI MEGA Dataset: 1.6M Therapeutic Molecules for AI Drug Discovery},
author={Tantcheu, Cedric},
year={2025},
month={June 2025},
publisher={Hugging Face},
url={https://huggingface.co/datasets/Gatescrispy/Largest_therapeutic_molecule_dataset_with_1.4M_compounds_for_scientific_research},
note={Large-scale curated therapeutic molecule dataset with traditional medicine integration},
keywords={drug discovery, machine learning, traditional medicine, cheminformatics, therapeutic molecules}
}
```
## 🔗 Related Resources
### PhytoAI Ecosystem
- **🤖 AI Models**: [Pre-trained models for molecular analysis](https://huggingface.co/Gatescrispy/Pre-trained_AImodels_for_therapeutic_molecule_analysis_and_bioactivity_prediction)
- **📚 Research Papers**: [Scientific methodology and findings](https://huggingface.co/datasets/Gatescrispy/Scientific_research_papers_and_methodology_for_PhytoAI_therapeutic_discovery)
- **💬 Community**: Join our research community for collaboration
- **🔧 Tools**: Molecular analysis and prediction tools
### External Databases
- **ChEMBL**: Bioactivity data source
- **PubChem**: Chemical structure validation
- **DrugBank**: Pharmaceutical annotations
- **KEGG**: Pathway and target information
## 📄 License
**License**: [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
This dataset is freely available for:
- ✅ **Academic Research**: No restrictions
- ✅ **Commercial Use**: Including pharmaceutical companies
- ✅ **Educational Purposes**: Teaching and training
- ✅ **Open Source Projects**: Community-driven tools
- ✅ **Derivative Works**: Building upon our work
## 🔬 Potential Applications
This dataset can be used for:
- **Machine Learning**: Molecular property prediction, bioactivity modeling
- **Drug Discovery**: Virtual screening, lead optimization
- **Cheminformatics**: Chemical space analysis, QSAR modeling
- **Traditional Medicine**: Validation of traditional therapeutic uses
- **Educational**: Teaching computational drug discovery methods
---
<div align="center">
## 🧬 Advancing Therapeutic Discovery Through Data Science
**A comprehensive molecular dataset for the research community**
[📧 Contact](mailto:research@phytoai.org) | [🌐 Website](https://phytoai.org)
*Last updated: June 2025*
</div>
|
glitchinthematrix/diseases_of_the_eye_and_adnexa | glitchinthematrix | 2025-06-05T16:33:34Z | 8 | 0 | [
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glitchinthematrix/drugs__hormones_and_biological_mediators | glitchinthematrix | 2025-06-05T16:33:14Z | 14 | 0 | [
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---
# Dataset Card for "dahabuyuk"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kunwang2000/eval-gsm8k-DeepSeek-R1-Distill-Qwen-1.5B | kunwang2000 | 2025-06-05T16:12:18Z | 0 | 0 | [
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|
Subsets and Splits