Hub Card Data
Collection
2 items • Updated • 4
Datasets:
datasetId large_stringlengths 6 123 | author large_stringlengths 2 42 | last_modified large_stringdate 2021-02-22 10:20:34 2026-04-01 02:08:25 | downloads int64 0 2.75M | likes int64 0 9.62k | tags large listlengths 1 6.16k | task_categories large listlengths 0 0 | createdAt large_stringdate 2022-03-02 23:29:22 2026-04-01 02:04:38 | trending_score float64 0 200 | card large_stringlengths 31 29.7M |
|---|---|---|---|---|---|---|---|---|---|
DorayakiLin/eval_so100_pick_charger_on_tissue_last | DorayakiLin | 2025-03-06T03:32:34Z | 7 | 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",
"eval_demo1_charger"
] | [] | 2025-03-06T03:32:06Z | 0 | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- eval_demo1_charger
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": 7852,
"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"
]
},
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],
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"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
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],
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}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
plungedplummer/BallReturn2 | plungedplummer | 2025-10-14T07:25:22Z | 43 | 0 | [
"task_categories:robotics",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"phosphobot",
"so100",
"phospho-dk"
] | [] | 2025-10-11T22:43:34Z | 0 |
---
tags:
- phosphobot
- so100
- phospho-dk
task_categories:
- robotics
---
# BallReturn2
**This dataset was generated using [phosphobot](https://docs.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.
To get started in robotics, [get your own phospho starter pack.](https://robots.phospho.ai).
|
MartialTerran/GPT_Duels | MartialTerran | 2025-07-09T22:21:02Z | 7 | 0 | [
"region:us"
] | [] | 2025-06-04T03:48:29Z | 0 | "Tic Tac Toe Tricks
There are several distinct strategies that can be employed to ensure victory when playing tic tac toe, but there are also a few simple tricks that new players can use to help their chances.
Remember, this game is known as a 'solved game', which means that there is a definite strategy that can be employed to win every single time. However, if both players employ that same unbeatable strategy, the game will result in a draw every time."
https://www.siammandalay.com/2021/05/18/how-to-win-tic-tac-toe-tricks-to-always-win-noughts-crosses/
model initialized on cpu.
ReplayBuffer initialized with capacity: 50000
ReplayBuffer initialized with capacity: 50000
Loaded 12 seed examples for player X (after augmentation if any) into ReplayBuffer.
Loaded 12 seed examples for player O (after augmentation if any) into ReplayBuffer.
Loaded 8 seed examples for player X (after augmentation if any) into ReplayBuffer.
Loaded 8 seed examples for player O (after augmentation if any) into ReplayBuffer.
PygameDisplay initialized.
GameLogger initialized. Logging to: ttt_runs_output_optimized\run_optimized_v1.0_20250605_091852\game_logs, Images to: ttt_runs_output_optimized\run_optimized_v1.0_20250605_091852\image_frames
--- Evaluating models after game 100 ---
Evaluation (50 games): X Wins: 0, O Wins: 50, Draws: 0. X Win Rate: 0.00
--- Evaluating models after game 200 ---
Evaluation (50 games): X Wins: 0, O Wins: 50, Draws: 0. X Win Rate: 0.00
--- Evaluating models after game 300 ---
Evaluation (50 games): X Wins: 0, O Wins: 50, Draws: 0. X Win Rate: 0.00
--- Evaluating models after game 400 ---
Evaluation (50 games): X Wins: 0, O Wins: 50, Draws: 0. X Win Rate: 0.00
--- Starting Game 500/10000 ---
LRs: X=1.0e-04, O=1.0e-04. Buffers: X=1655, O=1751
Training after game 500: Avg Loss X: 0.6662, Avg Loss O: 0.9334
--- Evaluating models after game 500 ---
Evaluation (50 games): X Wins: 25, O Wins: 25, Draws: 0. X Win Rate: 0.50
--- Evaluating models after game 600 ---
Evaluation (50 games): X Wins: 25, O Wins: 25, Draws: 0. X Win Rate: 0.50
--- Evaluating models after game 700 ---
Evaluation (50 games): X Wins: 25, O Wins: 25, Draws: 0. X Win Rate: 0.50
--- Evaluating models after game 800 ---
Evaluation (50 games): X Wins: 25, O Wins: 25, Draws: 0. X Win Rate: 0.50
--- Evaluating models after game 900 ---
Evaluation (50 games): X Wins: 0, O Wins: 25, Draws: 25. X Win Rate: 0.00
--- Starting Game 1000/10000 ---
LRs: X=1.0e-04, O=1.0e-04. Buffers: X=3333, O=3516
Training after game 1000: Avg Loss X: 0.5366, Avg Loss O: 0.7208
--- Evaluating models after game 1000 ---
Evaluation (50 games): X Wins: 0, O Wins: 25, Draws: 25. X Win Rate: 0.00
--- Evaluating models after game 1100 ---
Evaluation (50 games): X Wins: 0, O Wins: 25, Draws: 25. X Win Rate: 0.00
--- Evaluating models after game 1200 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 1300 ---
Evaluation (50 games): X Wins: 0, O Wins: 25, Draws: 25. X Win Rate: 0.00
--- Evaluating models after game 1400 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Starting Game 1500/10000 ---
LRs: X=1.0e-04, O=1.0e-04. Buffers: X=5099, O=5332
Training after game 1500: Avg Loss X: 0.4487, Avg Loss O: 0.6971
--- Evaluating models after game 1500 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 1600 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 1700 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 1800 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 1900 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Starting Game 2000/10000 ---
LRs: X=1.0e-04, O=1.0e-04. Buffers: X=6989, O=7211
Training after game 2000: Avg Loss X: 0.4598, Avg Loss O: 0.5945
--- Evaluating models after game 2000 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 2100 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 2200 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 2300 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 2400 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Starting Game 2500/10000 ---
LRs: X=1.0e-04, O=1.0e-04. Buffers: X=8869, O=9108
Training after game 2500: Avg Loss X: 0.4708, Avg Loss O: 0.5109
--- Evaluating models after game 2500 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 2600 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 2700 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 2800 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 2900 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Starting Game 3000/10000 ---
LRs: X=1.0e-04, O=1.0e-04. Buffers: X=10761, O=10998
Training after game 3000: Avg Loss X: 0.4827, Avg Loss O: 0.5137
--- Evaluating models after game 3000 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 3100 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 3200 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 3300 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 3400 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Starting Game 3500/10000 ---
LRs: X=1.0e-04, O=1.0e-04. Buffers: X=12651, O=12881
Training after game 3500: Avg Loss X: 0.3450, Avg Loss O: 0.4849
--- Evaluating models after game 3500 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 3600 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 3700 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 3800 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 3900 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Starting Game 4000/10000 ---
LRs: X=1.0e-04, O=1.0e-04. Buffers: X=14521, O=14750
Training after game 4000: Avg Loss X: 0.4175, Avg Loss O: 0.5450
--- Evaluating models after game 4000 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 4100 ---
Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00 |
ThunderDrag/Peru-Stock-Symbols-and-Metadata | ThunderDrag | 2025-09-20T16:38:09Z | 4 | 0 | [
"license:cc0-1.0",
"size_categories:n<1K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"finance",
"code",
"agent"
] | [] | 2025-09-20T16:38:06Z | 0 | ---
license: cc0-1.0
tags:
- finance
- code
- agent
pretty_name: Peru Stock Symbols And Metadata
size_categories:
- n<1K
---
# Peru Stock Symbols & Company Metadata
This dataset contains stock symbols and basic company metadata for all listed companies in **Peru**.
It is updated **weekly** if new changes are there.
---
## 📊 Dataset Contents
The dataset is provided as a CSV file with the following columns:
| Column | Description |
| -------- | --------------------------------------------- |
| `name` | Full company name |
| `ticker` | Stock ticker symbol (e.g., AAPL, MSFT) |
| `market` | The exchange/market where the stock is listed |
| `sector` | The primary business sector of the company |
---
## 📜 License
This dataset is released under the **CC0 1.0 Universal (Public Domain Dedication)** license.
You are free to use, modify, and share it without restriction. Attribution is appreciated but not required.
---
## 🌍 Related Datasets
You can find similar symbol datasets for other countries on my profile.
---
|
zjhhhh/iter2_7b_fullcheck_perprompt_scores_adversary_36 | zjhhhh | 2026-01-07T20:23:03Z | 6 | 0 | [
"size_categories:n<1K",
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"modality:tabular",
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"library:datasets",
"library:pandas",
"library:polars",
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] | [] | 2026-01-07T20:23:00Z | 0 | ---
dataset_info:
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configs:
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path: data/train-*
---
|
viols/rag_maybe_final_dataset_51.2M | viols | 2025-06-07T15:32:09Z | 4 | 0 | [
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-07T11:07:58Z | 0 | ---
dataset_info:
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dtype: string
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dtype: string
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configs:
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data_files:
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path: data/train-*
---
|
TeichAI/kimi-k2-thinking-250x | TeichAI | 2025-11-12T19:14:16Z | 271 | 3 | [
"language:en",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-11-09T17:33:53Z | 0 | ---
language:
- en
---
This is a reasoning dataset created using [Kimi k2 thinking](https://openrouter.ai/moonshotai/kimi-k2-thinking) from MoonshotAI. Some of these questions are from reedmayhew and the rest were generated.
Most of the questions cover the following topics: Web Development, Logic, Math, Embedded Systems, Web Design and Python Scripting.
The dataset is meant for creating distilled versions of Kimi k2 thinking by fine-tuning already existing open-source LLMs.
|
ahmedselhady/pm-questions | ahmedselhady | 2025-09-11T19:48:35Z | 10 | 0 | [
"size_categories:n<1K",
"modality:tabular",
"modality:text",
"region:us"
] | [] | 2025-09-11T19:45:06Z | 0 | ---
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---
|
microsoft/NOTSOFAR | microsoft | 2025-01-23T17:23:49Z | 397 | 12 | [
"size_categories:1K<n<10K",
"format:audiofolder",
"modality:audio",
"library:datasets",
"library:mlcroissant",
"region:us"
] | [] | 2025-01-22T01:32:55Z | 0 | [![Slack][slack-badge]][slack-invite]
[slack-badge]: https://img.shields.io/badge/slack-chat-green.svg?logo=slack
[slack-invite]: https://join.slack.com/t/chime-fey5388/shared_invite/zt-1oha0gedv-JEUr1mSztR7~iK9AxM4HOA
# Introduction
Welcome to the "NOTSOFAR-1: Distant Meeting Transcription with a Single Device" Challenge.
This repo contains the baseline system code for the NOTSOFAR-1 Challenge.
- For more information about NOTSOFAR, visit [CHiME's official challenge website](https://www.chimechallenge.org/current/task2/index)
- [Register](https://www.chimechallenge.org/current/task2/submission) to participate.
- [Baseline system description](https://www.chimechallenge.org/current/task2/baseline).
- Contact us: join the `chime-8-notsofar` channel on the [CHiME Slack](https://join.slack.com/t/chime-fey5388/shared_invite/zt-1oha0gedv-JEUr1mSztR7~iK9AxM4HOA), or open a [GitHub issue](https://github.com/microsoft/NOTSOFAR1-Challenge/issues).
### 📊 Baseline Results on NOTSOFAR dev-set-1
Values are presented in `tcpWER / tcORC-WER (session count)` format.
<br>
As mentioned in the [official website](https://www.chimechallenge.org/current/task2/index#tracks),
systems are ranked based on the speaker-attributed
[tcpWER](https://github.com/fgnt/meeteval/blob/main/doc/tcpwer.md)
, while the speaker-agnostic [tcORC-WER](https://github.com/fgnt/meeteval) serves as a supplementary metric for analysis.
<br>
We include analysis based on a selection of hashtags from our [metadata](https://www.chimechallenge.org/current/task2/data#metadata), providing insights into how different conditions affect system performance.
| | Single-Channel | Multi-Channel |
|----------------------|-----------------------|-----------------------|
| All Sessions | **46.8** / 38.5 (177) | **32.4** / 26.7 (106) |
| #NaturalMeeting | 47.6 / 40.2 (30) | 32.3 / 26.2 (18) |
| #DebateOverlaps | 54.9 / 44.7 (39) | 38.0 / 31.4 (24) |
| #TurnsNoOverlap | 32.4 / 29.7 (10) | 21.2 / 18.8 (6) |
| #TransientNoise=high | 51.0 / 43.7 (10) | 33.6 / 29.1 (5) |
| #TalkNearWhiteboard | 55.4 / 43.9 (40) | 39.9 / 31.2 (22) |
# Project Setup
The following steps will guide you through setting up the project on your machine. <br>
### Windows Users
This project is compatible with **Linux** environments. Windows users can refer to [Docker](#docker) or
[Devcontainer](#devcontainer) sections. <br>
Alternatively, install WSL2 by following the [WSL2 Installation Guide](https://learn.microsoft.com/en-us/windows/wsl/install), then install Ubuntu 20.04 from the [Microsoft Store](https://www.microsoft.com/en-us/p/ubuntu-2004-lts/9n6svws3rx71?activetab=pivot:overviewtab). <br>
## Cloning the Repository
Clone the `NOTSOFAR1-Challenge` repository from GitHub. Open your terminal and run the following command:
```bash
sudo apt-get install git
cd path/to/your/projects/directory
git clone https://github.com/microsoft/NOTSOFAR1-Challenge.git
```
## Setting up the environment
### Conda
#### Step 1: Install Conda
Conda is a package manager that is used to install Python and other dependencies.<br>
To install Miniconda, which is a minimal version of Conda, run the following commands:
```bash
miniconda_dir="$HOME/miniconda3"
script="Miniconda3-latest-Linux-$(uname -m).sh"
wget --tries=3 "https://repo.anaconda.com/miniconda/${script}"
bash "${script}" -b -p "${miniconda_dir}"
export PATH="${miniconda_dir}/bin:$PATH"
````
*** You may change the `miniconda_dir` variable to install Miniconda in a different directory.
#### Step 2: Create a Conda Environment
Conda Environments are used to isolate Python dependencies. <br>
To set it up, run the following commands:
```bash
source "/path/to/conda/dir/etc/profile.d/conda.sh"
conda create --name notsofar python=3.10 -y
conda activate notsofar
cd /path/to/NOTSOFAR1-Challenge
python -m pip install --upgrade pip
pip install --upgrade setuptools wheel Cython fasttext-wheel
pip install -r requirements.txt
conda install ffmpeg -c conda-forge -y
```
### PIP
#### Step 1: Install Python 3.10
Python 3.10 is required to run the project. To install it, run the following commands:
```bash
sudo apt update && sudo apt upgrade
sudo add-apt-repository ppa:deadsnakes/ppa -y
sudo apt update
sudo apt install python3.10
```
#### Step 2: Set Up the Python Virtual Environment
Python virtual environments are used to isolate Python dependencies. <br>
To set it up, run the following commands:
```bash
sudo apt-get install python3.10-venv
python3.10 -m venv /path/to/virtualenvs/NOTSOFAR
source /path/to/virtualenvs/NOTSOFAR/bin/activate
```
#### Step 3: Install Python Dependencies
Navigate to the cloned repository and install the required Python dependencies:
```bash
cd /path/to/NOTSOFAR1-Challenge
python -m pip install --upgrade pip
pip install --upgrade setuptools wheel Cython fasttext-wheel
sudo apt-get install python3.10-dev ffmpeg build-essential
pip install -r requirements.txt
```
### Docker
Refer to the `Dockerfile` in the project's root for dependencies setup. To use Docker, ensure you have Docker installed on your system and configured to use Linux containers.
### Devcontainer
With the provided `devcontainer.json` you can run and work on the project in a [devctonainer](https://containers.dev/) using, for example, the [Dev Containers VSCode Extension](https://code.visualstudio.com/docs/devcontainers/containers).
# Running evaluation - the inference pipeline
The following command will download the **entire dev-set** of the recorded meeting dataset and run the inference pipeline
according to selected configuration. The default is configured to `--config-name dev_set_1_mc_debug` for quick debugging,
running on a single session with the Whisper 'tiny' model.
```bash
cd /path/to/NOTSOFAR1-Challenge
python run_inference.py
```
To run on all multi-channel or single-channel dev-set sessions, use the following commands respectively:
```bash
python run_inference.py --config-name full_dev_set_mc
python run_inference.py --config-name full_dev_set_sc
```
The first time `run_inference.py` runs, it will automatically download these required models and datasets from blob storage:
1. The development set of the meeting dataset (dev-set) will be stored in the `artifacts/meeting_data` directory.
2. The CSS models required to run the inference pipeline will be stored in the `artifacts/css_models` directory.
Outputs will be written to the `artifacts/outputs` directory.
The `session_query` argument found in the yaml config file (e.g. `configs/inference/inference_v1.yaml`) offers more control over filtering meetings.
Note that to submit results on the dev-set, you must evaluate on the full set (`full_dev_set_mc` or `full_dev_set_sc`) and no filtering must be performed.
# Integrating your own models
The inference pipeline is modular, designed for easy research and extension.
Begin by exploring the following components:
- **Continuous Speech Separation (CSS)**: See `css_inference` in `css.py` . We provide a model pre-trained on NOTSOFAR's simulated training dataset, as well as inference and training code. For more information, refer to the [CSS section](#running-css-continuous-speech-separation-training).
- **Automatic Speech Recognition (ASR)**: See `asr_inference` in `asr.py`. The baseline implementation relies on [Whisper](https://github.com/openai/whisper).
- **Speaker Diarization**: See `diarization_inference` in `diarization.py`. The baseline implementation relies on the [NeMo toolkit](https://github.com/NVIDIA/NeMo).
### Training datasets
For training and fine-tuning your models, NOTSOFAR offers the **simulated training set** and the training portion of the
**recorded meeting dataset**. Refer to the `download_simulated_subset` and `download_meeting_subset` functions in
[utils/azure_storage.py](https://github.com/microsoft/NOTSOFAR1-Challenge/blob/main/utils/azure_storage.py#L109),
or the [NOTSOFAR-1 Datasets](#notsofar-1-datasets---download-instructions) section.
# Running CSS (continuous speech separation) training
## 1. Local training on a data sample for development and debugging
The following command will run CSS training on the 10-second simulated training data sample in `sample_data/css_train_set`.
```bash
cd /path/to/NOTSOFAR1-Challenge
python run_training_css_local.py
```
## 2. Training on the full simulated training dataset
### Step 1: Download the simulated training dataset
You can use the `download_simulated_subset` function in
[utils/azure_storage.py](https://github.com/microsoft/NOTSOFAR1-Challenge/blob/main/utils/azure_storage.py)
to download the training dataset from blob storage.
You have the option to download either the complete dataset, comprising almost 1000 hours, or a smaller, 200-hour subset.
Examples:
```python
ver='v1.5' # this should point to the lateset and greatest version of the dataset.
# Option 1: Download the training and validation sets of the entire 1000-hour dataset.
train_set_path = download_simulated_subset(
version=ver, volume='1000hrs', subset_name='train', destination_dir=os.path.join(my_dir, 'train'))
val_set_path = download_simulated_subset(
version=ver, volume='1000hrs', subset_name='val', destination_dir=os.path.join(my_dir, 'val'))
# Option 2: Download the training and validation sets of the smaller 200-hour dataset.
train_set_path = download_simulated_subset(
version=ver, volume='200hrs', subset_name='train', destination_dir=os.path.join(my_dir, 'train'))
val_set_path = download_simulated_subset(
version=ver, volume='200hrs', subset_name='val', destination_dir=os.path.join(my_dir, 'val'))
```
### Step 2: Run CSS training
Once you have downloaded the training dataset, you can run CSS training on it using the `run_training_css` function in `css/training/train.py`.
The `main` function in `run_training_css.py` provides an entry point with `conf`, `data_root_in`, and `data_root_out` arguments that you can use to configure the run.
It is important to note that the setup and provisioning of a compute cloud environment for running this training process is the responsibility of the user. Our code is designed to support **PyTorch's Distributed Data Parallel (DDP)** framework. This means you can leverage multiple GPUs across several nodes efficiently.
### Step 3: Customizing the CSS model
To add a new CSS model, you need to do the following:
1. Have your model implement the same interface as our baseline CSS model class `ConformerCssWrapper` which is located
in `css/training/conformer_wrapper.py`. Note that in addition to the `forward` method, it must also implement the
`separate`, `stft`, and `istft` methods. The latter three methods will be used in the inference pipeline and to
calculate the loss when training.
2. Create a configuration dataclass for your model. Add it as a member of the `TrainCfg` dataclass in
`css/training/train.py`.
3. Add your model to the `get_model` function in `css/training/train.py`.
# NOTSOFAR-1 Datasets - Download Instructions
This section is for those specifically interested in downloading the NOTSOFAR datasets.<br>
The NOTSOFAR-1 Challenge provides two datasets: a recorded meeting dataset and a simulated training dataset. <br>
The datasets are stored in Azure Blob Storage, to download them, you will need to setup [AzCopy](https://learn.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10#download-azcopy).
You can use either the python utilities in `utils/azure_storage.py` or the `AzCopy` command to download the datasets as described below.
### Meeting Dataset for Benchmarking and Training
The NOTSOFAR-1 Recorded Meeting Dataset is a collection of 315 meetings, each averaging 6 minutes, recorded across 30 conference rooms with 4-8 attendees, featuring a total of 35 unique speakers. This dataset captures a broad spectrum of real-world acoustic conditions and conversational dynamics.
### Download
To download the dataset, you can call the python function `download_meeting_subset` within `utils/azure_storage.py`.
Alternatively, using AzCopy CLI, set these arguments and run the following command:
- `subset_name`: name of split to download (`dev_set` / `eval_set` / `train_set`).
- `version`: version to download (`240103g` / etc.). Use the latest version.
- `datasets_path` - path to the directory where you want to download the benchmarking dataset (destination directory must exist). <br>
Train, dev, and eval sets for the NOTSOFAR challenge are released in stages.
See release timeline on the [NOTSOFAR page](https://www.chimechallenge.org/current/task2/index#dates).
See doc in `download_meeting_subset` function in
[utils/azure_storage.py](https://github.com/microsoft/NOTSOFAR1-Challenge/blob/main/utils/azure_storage.py#L109)
for latest available versions.
```bash
azcopy copy https://notsofarsa.blob.core.windows.net/benchmark-datasets/<subset_name>/<version>/MTG <datasets_path>/benchmark --recursive
```
Example:
```bash
azcopy copy https://notsofarsa.blob.core.windows.net/benchmark-datasets/dev_set/240415.2_dev/MTG . --recursive
````
### Simulated Training Dataset
The NOTSOFAR-1 Training Dataset is a 1000-hour simulated training dataset, synthesized with enhanced authenticity for real-world generalization, incorporating 15,000 real acoustic transfer functions.
### Download
To download the dataset, you can call the python function `download_simulated_subset` within `utils/azure_storage.py`.
Alternatively, using AzCopy CLI, set these arguments and run the following command:
- `version`: version of the train data to download (`v1.1` / `v1.2` / `v1.3` / `1.4` / `1.5` / etc.).
See doc in `download_simulated_subset` function in `utils/azure_storage.py` for latest available versions.
- `volume` - volume of the train data to download (`200hrs` / `1000hrs`)
- `subset_name`: train data type to download (`train` / `val`)
- `datasets_path` - path to the directory where you want to download the simulated dataset (destination directory must exist). <br>
```bash
azcopy copy https://notsofarsa.blob.core.windows.net/css-datasets/<version>/<volume>/<subset_name> <datasets_path>/benchmark --recursive
```
Example:
```bash
azcopy copy https://notsofarsa.blob.core.windows.net/css-datasets/v1.5/200hrs/train . --recursive
```
## Data License
This public data is currently licensed for use exclusively in the NOTSOFAR challenge event.
We appreciate your understanding that it is not yet available for academic or commercial use.
However, we are actively working towards expanding its availability for these purposes.
We anticipate a forthcoming announcement that will enable broader and more impactful use of this data. Stay tuned for updates.
Thank you for your interest and patience.
# 🤝 Contribute
Please refer to our [contributing guide](CONTRIBUTING.md) for more information on how to contribute!
|
myselfrew/new_prompt_llama31_math_2 | myselfrew | 2024-12-17T20:16:11Z | 7 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-12-17T20:15:20Z | 0 | ---
dataset_info:
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dtype: int64
- name: question
dtype: string
- name: gt_cot
dtype: string
- name: gt
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- name: code
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---
# Dataset Card for "new_prompt_llama31_math_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
french-open-data/le-marche-du-haut-et-tres-haut-debit-fixe-deploiements | french-open-data | 2025-11-21T19:43:31Z | 8 | 0 | [
"language:fra",
"region:us",
"dataset_for_agent"
] | [] | 2025-11-21T19:43:30Z | 0 | ---
language:
- fra
tags:
- dataset_for_agent
---
# Le marché du haut et très haut débit fixe (déploiements)
> [!NOTE]
> Ce jeu de données Hugging Face est vide. Cette carte sert seulement à référencer le jeu de données **Le marché du haut et très haut débit fixe (déploiements)** qui est disponible à l'adresse https://www.data.gouv.fr/datasets/547d8d7ac751df405d090fcb
## Description
**[Cartefibre.arcep.fr](https://cartefibre.arcep.fr/)** est l'**outil cartographique** de l'Arcep qui vise à améliorer l’information du grand public et des professionnels sur les déploiements des réseaux de fibre optique jusqu’à l’abonné. Établie tant à la maille de la commune qu’à la maille technique (généralement infracommunale), elle fait partie de [l’observatoire du haut et du très haut débit fixe](https://www.arcep.fr/cartes-et-donnees/nos-publications-chiffrees/observatoire-des-abonnements-et-deploiements-du-haut-et-tres-haut-debit/derniers-chiffres.html) mis en place par l’Autorité.
Le suivi des déploiements sur le marché du haut et très haut débit fixes est issu de la collecte trimestrielle "Observatoire de gros HD/THD" au titre des décisions n°2018-0170 et n°2023-0981.
### Données publiées :
L'ensemble des données publiées sur [cartefibre.arcep.fr](https://cartefibre.arcep.fr/) est également disponible en open-data ci-dessous au sein de 6 ressources distinctes.
5 d'entre elles sont actualisées chaque trimestre (*AAAA* : année, *TT* : trimestre) :
- __*AAAATT*-obs-hd-thd-deploiement__ : tableur présentant en plusieurs onglets
- l'évolution du dégroupage et du marché de gros
- l'évolution de l'éligibilité et des abonnements sur les réseaux à très haut débit (ventilation par grandes zones)
- l'évolution du nombre d'accès cuivre vendus sur le marché de gros
- l'évolution du nombre de locaux raccordables FTTH (ventilation par grandes zones et par opérateurs d'infrastructure)
- l'évolution du marché de gros en génie civil
- l'évolution du nombre de locaux raccordables FTTH (ventilation à différentes mailles géographiques)
- __*AAAATT*-Departement__ : fichier cartographique départemental incluant le volume de locaux raccordables FTTH.
- __*AAAATT*-Commune__ : fichier cartographique communal incluant le volume de locaux raccordables FTTH, les opérateurs d'infrastructure présents et la zone de déploiement.
- __*AAAATT*-Immeuble__ : fichier texte listant l'ensemble des immeubles recensés par les opérateurs d'infrastructure.
- __*AAAATT*-ZAPM__ : fichier cartographique délimitant les contours des zones arrières des PM.
Deux autres ressource sont actualisées ponctuellement, en cas d'évolution du zonage correspondant, et datée par année ou par trimestre d'édition (*AAAA* : année, *TT* : trimestre) :
- __*AAAA* - Relevé géographique - données sous-jacentes__ : Fichier tableur indiquant le relevé géographique des déploiements de réseaux de communications électroniques
- __*AAAATT*-Iris__ : Fichier cartographique indiquant le découpage spatial du [zonage réglementaire défini par l'Arcep des Zones très dense (ZTD) en poches de basse et haute densité (basé sur les IRIS)](https://www.arcep.fr/la-regulation/grands-dossiers-reseaux-fixes/la-fibre/le-cadre-relatif-a-la-regulation-du-ftth/les-delimitations-des-poches-de-basse-densite-des-zones-tres-denses.html).
Vous trouverez plus de détails concernant les données, formats et systèmes de projection dans les descriptifs associés à chacune de ces ressources.
### Calendrier des publications
Retrouvez le [calendrier des publications](https://www.arcep.fr/cartes-et-donnees/nos-publications-chiffrees/calendrier-de-publication-des-documents-statistiques-de-larcep.html) de l'Arcep.
### Glossaire :
- __Immeuble__ : désigne un bâtiment de type individuel ou collectif.
- __Locaux__ : Les immeubles contiennent des locaux (logements et établissements professionnels). L'estimation du nombre de locaux au sein de chaque immeuble s’appuie sur les fichiers IPE fournis par les opérateurs d'infrastructures (OI) et sur les données fournies par l'INSEE de 2018
- __PM__ : Point de mutualisation.
- __Local raccordable FTTH__ : Un local est considéré comme raccordable si l'immeuble est "déployé" et le PM est "déployé".
## License
Licence Ouverte / Open Licence
|
stefanocarrera/autophagycode_D_metrics_train_Qwen3-8B_lr0.0001_trust_g4 | stefanocarrera | 2026-03-31T08:29:19Z | 4 | 0 | [
"size_categories:n<1K",
"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | [] | 2026-03-30T17:22:20Z | 0 | ---
dataset_info:
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configs:
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data_files:
- split: train
path: data/train-*
---
|
CAIR-HKISI/clevr | CAIR-HKISI | 2026-01-07T09:41:43Z | 7 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | [] | 2026-01-07T09:40:24Z | 0 | ---
dataset_info:
features:
- name: problem
dtype: string
- name: answer
dtype: string
- name: images
list: image
- name: messages
list:
- name: content
dtype: string
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splits:
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configs:
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data_files:
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path: data/train-*
- split: test
path: data/test-*
---
|
mjpsm/risk_tolerance_dataset_v1 | mjpsm | 2025-08-30T04:03:50Z | 6 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-08-30T04:03:43Z | 0 | ---
dataset_info:
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- name: runway_months
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dtype: float32
- name: comfort_with_failure
dtype: int32
- name: entrepreneurial_experience_level
dtype: int32
- name: investment_risk_history
dtype: int32
- name: short_term_vs_long_term_preference
dtype: int32
- name: risk_tolerance
dtype:
class_label:
names:
'0': Low
'1': Medium
'2': High
- name: risk_tolerance_level
dtype: int32
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- name: validation
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num_examples: 200
download_size: 42308
dataset_size: 88000.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
RebuttalAgent/RebuttalBench | RebuttalAgent | 2025-11-09T14:15:59Z | 40 | 2 | [
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:10K<n<100K",
"region:us",
"dataset",
"academic-rebuttal",
"theory-of-mind"
] | [] | 2025-11-07T03:03:09Z | 0 | ---
license: apache-2.0
dataset_type:
- text
language:
- en
- zh
domain:
- nlp
tags:
- dataset
- academic-rebuttal
- theory-of-mind
pretty_name: RebuttalBench
size_categories:
- 10K<n<100K
---
# RebuttalBench 📚
## 1. Introduction
RebuttalBench is derived from the Re2-rebuttal dataset (Zhang et al., 2025), a comprehensive corpus containing initial scientific papers, their corresponding peer reviews, and the authentic author responses. The raw data undergoes a multi-stage processing pipeline: first, GPT-4.1 parses all the reviews into over 200 K distinct comment-response pairs, achieving a 98 % accuracy as verified by manual sampling; next, each review and comment is programmatically annotated with the hierarchical profiles (macro- and micro-level) defined in Section 4.1; then, to ensure a diverse and balanced training set, we curate a final subset of 70 K comments for the next stage, consisting of 60 K instances filtered by category and 10 K selected randomly. In the final step, our TSR framework synthesizes the complete training instances—each selected comment yields a reviewer analysis, a rebuttal strategy, and a response—and, to mitigate model-specific biases and enrich stylistic variety, a mixture of powerful teacher models is used to generate data. The generated analysis, strategy, and response are concatenated into a single target sequence explicitly demarcated by <Analysis>, <Strategy>, and <Response> tags, thereby giving the agent a holistic learning objective that preserves the entire reasoning chain. Moreover, to train a robust reward model for rebuttal quality, we construct a dataset of over 102 K instances from three sources: (1) 12,000 original author responses as a realistic human baseline, (2) high-quality GPT-4.1–refined responses representing top standards, and (3) diverse model-generated replies (e.g., Qwen2.5-3B, Claude 3.5) for style coverage, using 90 % of the data for training and 10 % for testing. Additionally, our in-domain test set R2-test contains 6,000 comments randomly sampled from the Re2 dataset with no training overlap, spanning 24 conferences and 21 workshops on OpenReview (2017–2023) to cover a broad range of topics and styles, while the out-of-domain RAR-test introduces 2 K manually curated comments from one thousand recent ICLR and NeurIPS reviews (post-2023) to assess generalization capability.
---
## 2. Statistics
### Directory Layout
```text
data/
├─ train_data/
│ ├─ RebuttalBench.json # TSR chains for supervised fine-tuning
│ └─ RM_Bench.json # Triples for reward-model training
└─ test_data/
├─ R2_test.json # in-domain (6 000 comments)
└─ RAR_test.json # out-of-domain (2 000 comments)
```
### Category distribution
| Category | Share |
|-----------------------------|-------|
| Experimental Rigor | 26.9 % |
| Methodological Soundness | 25.0 % |
| Novelty & Significance | 20.1 % |
| Presentation & Clarity | 28.0 % |
## 3. Quick Start
```
from datasets import load_dataset
# 1 TSR training split
tsr = load_dataset(
"Zhitao-He/RebuttalBench",
data_files="data/train_data/RebuttalBench.json"
)
# 2 Reward-model split
rm = load_dataset(
"Zhitao-He/RebuttalBench",
data_files="data/train_data/RM_Bench.json"
)
# 3 Test set
r2 = load_dataset(
"Zhitao-He/RebuttalBench",
data_files="data/test_data/R2_test.json"
)
rar = load_dataset(
"Zhitao-He/RebuttalBench",
data_files="data/test_data/RAR_test.json"
)
```
## 4. Citation
```
@inproceedings{he2025rebuttalagent,
title = {RebuttalAgent: Strategic Persuasion in Academic Rebuttal via Theory of Mind},
author = {Zhitao He and Zongwei Lyu and Wuzhenhai Dai and Yi R. (May) Fung},
year = {2025},
institution = {Hong Kong University of Science and Technology},
url = {https://arxiv.org/abs/YYMM.NNNNN}
}
``` |
CohenQu/POPE-hard-dataset-Qwen3-4B-Instruct-32k-128 | CohenQu | 2025-10-07T14:41:04Z | 4 | 0 | [
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|
karina-zadorozhny/moleculeace | karina-zadorozhny | 2025-07-15T12:27:07Z | 46 | 0 | [
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---
# MoleculeACE Dataset
## Overview
The MoleculeACE (Molecule Activity Cliff Estimation) dataset contains bioactivity data for 30 different ChEMBL targets with corresponding protein sequences. This dataset is designed to assess how well molecular machine learning models can handle activity cliffs - pairs of molecules that are structurally similar but show large differences in biological activity.
## Dataset Structure
The dataset is organized with each ChEMBL target as a separate configuration:
```python
from datasets import load_dataset
# Load a specific task
dataset = load_dataset("your-username/moleculeace", "CHEMBL204_Ki")
# Access train/test splits
train_data = dataset["train"]
test_data = dataset["test"]
```
## Features
Each sample contains:
- **smiles**: SMILES representation of the molecule
- **activity_value**: Activity value (pEC50/pKi)
- **protein_sequence**: Amino acid sequence of the target protein
- **chembl_id**: ChEMBL target identifier
- **task**: Task name
## Available Tasks
- CHEMBL1862_Ki
- CHEMBL1871_Ki
- CHEMBL2034_Ki
- CHEMBL2047_EC50
- CHEMBL204_Ki
- CHEMBL2147_Ki
- CHEMBL214_Ki
- CHEMBL218_EC50
- CHEMBL219_Ki
- CHEMBL228_Ki
- CHEMBL231_Ki
- CHEMBL233_Ki
- CHEMBL234_Ki
- CHEMBL235_EC50
- CHEMBL236_Ki
- CHEMBL237_EC50
- CHEMBL237_Ki
- CHEMBL238_Ki
- CHEMBL239_EC50
- CHEMBL244_Ki
- CHEMBL262_Ki
- CHEMBL264_Ki
- CHEMBL2835_Ki
- CHEMBL287_Ki
- CHEMBL2971_Ki
- CHEMBL3979_EC50
- CHEMBL4005_Ki
- CHEMBL4203_Ki
- CHEMBL4616_EC50
- CHEMBL4792_Ki
## Usage Example
```python
from datasets import load_dataset
chembl204_data = load_dataset("your-username/moleculeace", "CHEMBL204_Ki")
train_samples = chembl204_data["train"]
# Access features
for sample in train_samples:
smiles = sample["smiles"]
activity = sample["activity_value"]
protein_seq = sample["protein_sequence"]
# ... process your data
```
## Dataset Statistics
- **Total tasks**: 30 ChEMBL targets
- **Data splits**: Train/Test for each task
- **Features**: SMILES, activity values, protein sequences, ChEMBL IDs
- **Domain**: Molecular bioactivity prediction
- **Focus**: Activity cliff estimation
## Citation
This dataset was introduced in:
```bibtex
@article{vanTilborg2022,
title={Exposing the Limitations of Molecular Machine Learning with Activity Cliffs},
author={van Tilborg, Derek and Alenicheva, Alisa and Grisoni, Francesca},
journal={Journal of Chemical Information and Modeling},
year={2022},
publisher={ACS Publications}
}
```
## Data Source
- Original MoleculeACE data: [GitHub Repository](https://github.com/molML/MoleculeACE)
- Protein sequences: ChEMBL Database
- Reference: https://pubs.acs.org/doi/10.1021/acs.jcim.2c01073
## License
Please refer to the original MoleculeACE repository for licensing information.
|
zhan1993/coconot_experts_train | zhan1993 | 2025-06-25T02:44:25Z | 10 | 0 | [
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|
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---
|
michsethowusu/kongo-twi_sentence-pairs | michsethowusu | 2025-03-30T19:53:43Z | 4 | 0 | [
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download_size: 14554069
dataset_size: 14554069
configs:
- config_name: default
data_files:
- split: train
path: Kongo-Twi_Sentence-Pairs.csv
---
# Kongo-Twi_Sentence-Pairs Dataset
This dataset contains sentence pairs for African languages along with similarity scores. It can be used for machine translation, sentence alignment, or other natural language processing tasks.
This dataset is based on the NLLBv1 dataset, published on OPUS under an open-source initiative led by META. You can find more information here: [OPUS - NLLB-v1](https://opus.nlpl.eu/legacy/NLLB-v1.php)
## Metadata
- **File Name**: Kongo-Twi_Sentence-Pairs
- **Number of Rows**: 117796
- **Number of Columns**: 3
- **Columns**: score, Kongo, Twi
## Dataset Description
The dataset contains sentence pairs in African languages with an associated similarity score. Each row consists of three columns:
1. `score`: The similarity score between the two sentences (range from 0 to 1).
2. `Kongo`: The first sentence in the pair (language 1).
3. `Twi`: The second sentence in the pair (language 2).
This dataset is intended for use in training and evaluating machine learning models for tasks like translation, sentence similarity, and cross-lingual transfer learning.
## References
Below are papers related to how the data was collected and used in various multilingual and cross-lingual applications:
[1] Holger Schwenk and Matthijs Douze, Learning Joint Multilingual Sentence Representations with Neural Machine Translation, ACL workshop on Representation Learning for NLP, 2017
[2] Holger Schwenk and Xian Li, A Corpus for Multilingual Document Classification in Eight Languages, LREC, pages 3548-3551, 2018.
[3] Holger Schwenk, Filtering and Mining Parallel Data in a Joint Multilingual Space ACL, July 2018
[4] Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk and Veselin Stoyanov, XNLI: Cross-lingual Sentence Understanding through Inference, EMNLP, 2018.
[5] Mikel Artetxe and Holger Schwenk, Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings arXiv, Nov 3 2018.
[6] Mikel Artetxe and Holger Schwenk, Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond arXiv, Dec 26 2018.
[7] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia arXiv, July 11 2019.
[8] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB
[9] Paul-Ambroise Duquenne, Hongyu Gong, Holger Schwenk, Multimodal and Multilingual Embeddings for Large-Scale Speech Mining, NeurIPS 2021, pages 15748-15761.
[10] Kevin Heffernan, Onur Celebi, and Holger Schwenk, Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
|
wls04/prm-alfworld-state-qwen3 | wls04 | 2026-03-17T10:15:49Z | 12 | 0 | [
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"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | [] | 2026-03-17T10:15:42Z | 0 | ---
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data_files:
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path: data/train-*
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path: data/test-*
---
|
Gusanidas/countdown-tasks-dataset-med-vl3 | Gusanidas | 2025-06-02T10:25:50Z | 5 | 0 | [
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] | [] | 2025-06-02T10:25:47Z | 0 | ---
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configs:
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path: data/train-*
---
|
rayonlabs/wmt19-lt-en | rayonlabs | 2025-05-02T17:13:45Z | 4 | 0 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
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"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-02T17:13:34Z | 0 | ---
dataset_info:
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download_size: 283045892
dataset_size: 513083086
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AlexHung29629/alpaca_oss_sample | AlexHung29629 | 2025-10-28T05:36:27Z | 7 | 0 | [
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"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | [] | 2025-10-28T05:36:13Z | 0 | ---
dataset_info:
features:
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---
|
mlfoundations-dev/b2_train_fasttext_code_pos_taco_neg_sqls | mlfoundations-dev | 2025-04-21T04:48:43Z | 8 | 0 | [
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] | [] | 2025-04-21T04:48:40Z | 0 | ---
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configs:
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data_files:
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path: data/train-*
---
|
shylee/so100_pengripA | shylee | 2025-05-05T13:29:45Z | 15 | 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",
"so100",
"pengrip"
] | [] | 2025-05-05T12:57:01Z | 0 | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- pengrip
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": 25,
"total_frames": 6007,
"total_tasks": 1,
"total_videos": 75,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:25"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.FrontCam": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 30.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"observation.images.TopCam": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 30.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"observation.images.WristCam": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 30.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
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1
],
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},
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},
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1
],
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}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
dreamerparadise/eval_pick-place-fevi-stik-dataset_v8_ACT | dreamerparadise | 2026-03-19T09:35:00Z | 11 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"LeRobot"
] | [] | 2026-03-19T09:28:01Z | 0 | ---
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": "v3.0",
"robot_type": "so101_follower",
"total_episodes": 10,
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"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 30,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
"features": {
"action": {
"dtype": "float32",
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
],
"shape": [
6
]
},
"observation.state": {
"dtype": "float32",
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
],
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6
]
},
"observation.images.wrist": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
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"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.front": {
"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": {
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"shape": [
1
],
"names": null
},
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1
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1
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},
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],
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},
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1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
sergioestebanb/medieval | sergioestebanb | 2026-01-20T21:45:54Z | 342 | 0 | [
"task_categories:image-to-text",
"language:fr",
"language:en",
"language:nl",
"language:it",
"language:es",
"language:ca",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"libra... | [] | 2026-01-20T21:45:53Z | 0 | ---
license: cc-by-4.0
task_categories:
- image-to-text
version: "1.6.0"
language:
- fr
- en
- nl
- it
- es
- ca
pretty_name: CATMuS Medieval
size_categories:
- 100K<n<1M
tags:
- optical-character-recognition
- humanities
- handwritten-text-recognition
---
# Dataset Card for CATMuS Medieval
Join our Discord to ask questions about the dataset: [](https://discord.gg/J38xgNEsGk)
## Dataset Details
Handwritten Text Recognition (HTR) has emerged as a crucial tool for converting manuscripts images into machine-readable formats,
enabling researchers and scholars to analyse vast collections efficiently.
Despite significant technological progress, establishing consistent ground truth across projects for HTR tasks,
particularly for complex and heterogeneous historical sources like medieval manuscripts in Latin scripts (8th-15th century CE), remains nonetheless challenging.
We introduce the **Consistent Approaches to Transcribing Manuscripts (CATMuS)** dataset for medieval manuscripts,
which offers:
1. a uniform framework for annotation practices for medieval manuscripts,
2. a benchmarking environment for evaluating automatic text recognition models across multiple dimensions thanks to rich metadata (century of production,
language, genre, script, etc.),
3. a benchmarking environment for other tasks (such as script classification or dating approaches),
4. a benchmarking environment and finally for exploratory work pertaining to computer vision and digital paleography around line-based tasks, such as generative approaches.
Developed through collaboration among various institutions and projects, CATMuS provides an inter-compatible dataset spanning more than 200 manuscripts and incunabula in 10
different languages, comprising over 160,000 lines of text and 5 million characters spanning from the 8th century to the 16th.
The dataset's consistency in transcription approaches aims to mitigate challenges arising from the diversity in standards for medieval manuscript transcriptions,
providing a comprehensive benchmark for evaluating HTR models on historical sources.
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** Thibault Clérice
- **Funded by:** BnF Datalab, Biblissima +, DIM PAMIR
- **Language(s) (NLP):** Middle and Old French, Middle Dutch, Catalan, Spanish, Navarese, Italian, Venitian, Old English, Latin
- **License:** CC-BY
<!--
### Dataset Sources [optional]
Provide the basic links for the dataset.
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
-->
## Uses
### Direct Use
- Handwritten Text Recognition
- Date classification
- Script classification
### Out-of-Scope Use
- Text-To-Image
## Dataset Structure
- Data contains the main `split` that is loaded through `load_dataset("CATMuS/medieval")`
- Data can be split with each manuscript inside train, val and test using the `gen_split` columns which results in a 90/5/5 split
- The image is in the `im` column, and the text in the `text` column
<!--
## Dataset Creation
### Curation Rationale
Motivation for the creation of this dataset.
[More Information Needed]
### Source Data
This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...).
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc.
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if
this information is available.
[More Information Needed]
-->
### Annotations [optional]
#### Annotation process
The annotation process is described in the [dataset paper](https://inria.hal.science/hal-04453952).
#### Who are the annotators?
- Pinche, Ariane
- Clérice, Thibault
- Chagué, Alix
- Camps, Jean-Baptiste
- Vlachou-Efstathiou, Malamatenia
- Gille Levenson, Matthias
- Brisville-Fertin, Olivier
- Boschetti, Federico
- Fischer, Franz
- Gervers, Michael
- Boutreux, Agnès
- Manton, Avery
- Gabay, Simon
- Bordier, Julie
- Glaise, Anthony
- Alba, Rachele
- Rubin, Giorgia
- White, Nick
- Karaisl, Antonia
- Leroy, Noé
- Maulu, Marco
- Biay, Sébastien
- Cappe, Zoé
- Konstantinova, Kristina
- Boby, Victor
- Christensen, Kelly
- Pierreville, Corinne
- Aruta, Davide
- Lenzi, Martina
- Le Huëron, Armelle
- Possamaï, Marylène
- Duval, Frédéric
- Mariotti, Violetta
- Morreale, Laura
- Nolibois, Alice
- Foehr-Janssens, Yasmina
- Deleville, Prunelle
- Carnaille, Camille
- Lecomte, Sophie
- Meylan, Aminoel
- Ventura, Simone
- Dugaz, Lucien
## Bias, Risks, and Limitations
The data are skewed toward Old French, Middle Dutch and Spanish, specifically from the 14th century.
The only language that is represented over all centuries is Latin, and in each scripts. The other language with a coverage close to Latin is Old French.
Only one document is available in Old English.
## Citation
**BibTeX:**
```tex
@unpublished{clerice:hal-04453952,
TITLE = {{CATMuS Medieval: A multilingual large-scale cross-century dataset in Latin script for handwritten text recognition and beyond}},
AUTHOR = {Cl{\'e}rice, Thibault and Pinche, Ariane and Vlachou-Efstathiou, Malamatenia and Chagu{\'e}, Alix and Camps, Jean-Baptiste and Gille-Levenson, Matthias and Brisville-Fertin, Olivier and Fischer, Franz and Gervers, Michaels and Boutreux, Agn{\`e}s and Manton, Avery and Gabay, Simon and O'Connor, Patricia and Haverals, Wouter and Kestemont, Mike and Vandyck, Caroline and Kiessling, Benjamin},
URL = {https://inria.hal.science/hal-04453952},
NOTE = {working paper or preprint},
YEAR = {2024},
MONTH = Feb,
KEYWORDS = {Historical sources ; medieval manuscripts ; Latin scripts ; benchmarking dataset ; multilingual ; handwritten text recognition},
PDF = {https://inria.hal.science/hal-04453952/file/ICDAR24___CATMUS_Medieval-1.pdf},
HAL_ID = {hal-04453952},
HAL_VERSION = {v1},
}
```
**APA:**
> Thibault Clérice, Ariane Pinche, Malamatenia Vlachou-Efstathiou, Alix Chagué, Jean-Baptiste Camps, et al.. CATMuS Medieval: A multilingual large-scale cross-century dataset in Latin script for handwritten text recognition and beyond. 2024. ⟨hal-04453952⟩
## Glossary
- Scripts: In the middle ages, the writing style changed over time, specifically in "litterary" manuscripts, for which we call the general scripts "Bookscripts". This is what CATMuS Medieval covers at the time
## Dataset Card Contact
Thibault Clérice (first.last@inria.fr)
|
TheFactoryX/edition_2976_jxcai-scale-hle-public-questions-readymade | TheFactoryX | 2026-02-21T14:22:54Z | 86 | 0 | [
"license:other",
"size_categories:n<1K",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"readymades",
"art",
"shuffled",
"duchamp"
] | [] | 2026-02-21T14:22:52Z | 0 | ---
tags:
- readymades
- art
- shuffled
- duchamp
license: other
---
# edition_2976_jxcai-scale-hle-public-questions-readymade
**A Readymade by TheFactoryX**
## Original Dataset
[jxcai-scale/hle-public-questions](https://huggingface.co/datasets/jxcai-scale/hle-public-questions)
## Process
This dataset is a "readymade" - inspired by Marcel Duchamp's concept of taking everyday objects and recontextualizing them as art.
**What we did:**
1. Selected the original dataset from Hugging Face
2. Shuffled each column independently
3. Destroyed all row-wise relationships
4. Preserved structure, removed meaning
**The result:**
Same data. Wrong order. New meaning. No meaning.
## Purpose
This is art. This is not useful. This is the point.
Column relationships have been completely destroyed. The data maintains its types and values, but all semantic meaning has been removed.
---
Part of the [Readymades](https://github.com/TheFactoryX/readymades) project by [TheFactoryX](https://github.com/TheFactoryX).
> _"I am a machine."_ — Andy Warhol
|
22t1044/shodo1-10 | 22t1044 | 2025-12-06T18:07:20Z | 7 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] | [] | 2025-12-06T18:07:13Z | 0 | ---
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": "v3.0",
"robot_type": "so101_follower",
"total_episodes": 10,
"total_frames": 4948,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 500,
"fps": 30,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
"features": {
"action": {
"dtype": "float32",
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
],
"shape": [
6
]
},
"observation.state": {
"dtype": "float32",
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
],
"shape": [
6
]
},
"observation.images.front": {
"dtype": "video",
"shape": [
1080,
1920,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 1080,
"video.width": 1920,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.top": {
"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]
``` |
stephanedonna/english_mofu | stephanedonna | 2025-01-09T12:16:01Z | 4 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-01-09T12:15:59Z | 0 | ---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 1790690
num_examples: 6332
- name: test
num_bytes: 228466
num_examples: 792
- name: validation
num_bytes: 219868
num_examples: 792
download_size: 1273938
dataset_size: 2239024
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
shlomoc/learn_hf_food_not_food_image_captions | shlomoc | 2024-10-01T18:31:04Z | 8 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-10-01T18:00:33Z | 0 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 20765
num_examples: 270
download_size: 12206
dataset_size: 20765
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
xiaolingao/random100_ipc1 | xiaolingao | 2024-09-17T04:53:00Z | 4 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-09-17T04:52:56Z | 0 | ---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 1383617.0
num_examples: 100
download_size: 1380295
dataset_size: 1383617.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Charles95/sentiment-trl-style | Charles95 | 2024-08-06T09:09:14Z | 29 | 0 | [
"language:en",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1909.08593",
"region:us"
] | [] | 2024-08-05T02:46:05Z | 0 | ---
language:
- en
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 4323778
num_examples: 4992
- name: test
num_bytes: 424977
num_examples: 488
download_size: 3114394
dataset_size: 4748755
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# TRL's Preference Dataset: sentiment
The dataset comes from https://huggingface.co/papers/1909.08593, one of the earliest RLHF work from OpenAI.
We preprocess the dataset using our standard `prompt, chosen, rejected` format.
## Reproduce this dataset
1. Download the `sentiment_descriptiveness.py` from the https://huggingface.co/datasets/Charles95/sentiment-trl-style/tree/0.1.0.
2. Run `python ./sentiment_descriptiveness.py --hf_repo_id sentiment-trl-style --task sentiment --push_to_hub`
|
SEACrowd/newsph | SEACrowd | 2024-06-24T13:27:09Z | 8 | 0 | [
"language:fil",
"language:tgl",
"license:gpl-3.0",
"arxiv:2406.10118",
"region:us",
"self-supervised-pretraining"
] | [] | 2024-06-24T12:12:02Z | 0 |
---
license: gpl-3.0
language:
- fil
- tgl
pretty_name: Newsph
task_categories:
- self-supervised-pretraining
tags:
- self-supervised-pretraining
---
Raw collection of news articles in Filipino which can be used for language modelling.
## Languages
fil, tgl
## Supported Tasks
Self Supervised Pretraining
## Dataset Usage
### Using `datasets` library
```
from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/newsph", trust_remote_code=True)
```
### Using `seacrowd` library
```import seacrowd as sc
# Load the dataset using the default config
dset = sc.load_dataset("newsph", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("newsph"))
# Load the dataset using a specific config
dset = sc.load_dataset_by_config_name(config_name="<config_name>")
```
More details on how to load the `seacrowd` library can be found [here](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use).
## Dataset Homepage
[https://huggingface.co/datasets/newsph](https://huggingface.co/datasets/newsph)
## Dataset Version
Source: 1.0.0. SEACrowd: 2024.06.20.
## Dataset License
GNU General Public License v3.0 (gpl-3.0)
## Citation
If you are using the **Newsph** dataloader in your work, please cite the following:
```
@inproceedings{cruz2021exploiting,
title={Exploiting news article structure for automatic corpus generation of entailment datasets},
author={Cruz, Jan Christian Blaise and Resabal, Jose Kristian and Lin, James and Velasco, Dan John and Cheng, Charibeth},
booktitle={PRICAI 2021: Trends in Artificial Intelligence: 18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021, Hanoi, Vietnam, November 8--12, 2021, Proceedings, Part II 18},
pages={86--99},
year={2021},
organization={Springer}
}
@article{lovenia2024seacrowd,
title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages},
author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
year={2024},
eprint={2406.10118},
journal={arXiv preprint arXiv: 2406.10118}
}
``` |
roger070604/sampleTest | roger070604 | 2024-05-02T16:43:45Z | 5 | 0 | [
"region:us"
] | [] | 2024-04-30T18:02:54Z | 0 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input_image
dtype: image
- name: ground_truth_image
dtype: image
splits:
- name: train
num_bytes: 29263773.0
num_examples: 33
download_size: 29139222
dataset_size: 29263773.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
dvdmrs09/patents | dvdmrs09 | 2024-05-07T17:57:09Z | 95 | 3 | [
"license:apache-2.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-05-07T16:56:39Z | 0 | ---
license: apache-2.0
configs:
- config_name: patents_data
data_files: "*_patents.parquet"
- config_name: patents_metadata
data_files: "*_metadata.parquet"
--- |
alucchi/Qwen3-4B_n-1_e2_oadam0.0001_b1_4_a5_g00001_10186_gpu2_half_e3p0_bestofN_128_0.2 | alucchi | 2026-03-26T10:55:21Z | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | [] | 2026-03-26T10:55:00Z | 0 | ---
dataset_info:
- config_name: default
features:
- name: task_id
dtype: string
- name: prompt
dtype: string
- name: generated_text
dtype: string
- name: generated_grid_rect
sequence:
sequence: int64
- name: task_solution
sequence:
sequence:
sequence: int64
- name: score
dtype: float64
- name: n_try
dtype: int64
- name: temperature
dtype: float64
- name: top_p
dtype: float64
- name: candidate_texts
sequence: string
- name: candidate_sum_logprobs
sequence: float64
- name: best_candidate_sum_logprob
dtype: float64
splits:
- name: train
num_bytes: 8612823
num_examples: 120
download_size: 283059
dataset_size: 8612823
- config_name: main
features:
- name: task_id
dtype: string
- name: prompt
dtype: string
- name: generated_text
dtype: string
- name: generated_grid_rect
sequence:
sequence: int64
- name: task_solution
sequence:
sequence:
sequence: int64
- name: score
dtype: float64
- name: n_try
dtype: int64
- name: temperature
dtype: float64
- name: top_p
dtype: float64
- name: candidate_texts
sequence: string
- name: candidate_sum_logprobs
sequence: float64
- name: best_candidate_sum_logprob
dtype: float64
splits:
- name: train
num_bytes: 8612823
num_examples: 120
download_size: 283059
dataset_size: 8612823
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- config_name: main
data_files:
- split: train
path: main/train-*
---
|
TheFactoryX/edition_2874_shi-labs-oneformer_demo-readymade | TheFactoryX | 2026-02-13T08:26:57Z | 9 | 0 | [
"license:other",
"size_categories:n<1K",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"readymades",
"art",
"shuffled",
"duchamp"
] | [] | 2026-02-13T08:26:54Z | 0 | ---
tags:
- readymades
- art
- shuffled
- duchamp
license: other
---
# edition_2874_shi-labs-oneformer_demo-readymade
**A Readymade by TheFactoryX**
## Original Dataset
[shi-labs/oneformer_demo](https://huggingface.co/datasets/shi-labs/oneformer_demo)
## Process
This dataset is a "readymade" - inspired by Marcel Duchamp's concept of taking everyday objects and recontextualizing them as art.
**What we did:**
1. Selected the original dataset from Hugging Face
2. Shuffled each column independently
3. Destroyed all row-wise relationships
4. Preserved structure, removed meaning
**The result:**
Same data. Wrong order. New meaning. No meaning.
## Purpose
This is art. This is not useful. This is the point.
Column relationships have been completely destroyed. The data maintains its types and values, but all semantic meaning has been removed.
---
Part of the [Readymades](https://github.com/TheFactoryX/readymades) project by [TheFactoryX](https://github.com/TheFactoryX).
> _"I am a machine."_ — Andy Warhol
|
hooriehsabzevari/Daniar_3__5_turns_only_ckp_2 | hooriehsabzevari | 2025-12-07T08:06:52Z | 4 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-12-07T08:06:51Z | 0 | ---
dataset_info:
features:
- name: trajectory
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 92321
num_examples: 20
download_size: 31436
dataset_size: 92321
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
isaiahbjork/waveui-grpo-1k | isaiahbjork | 2025-02-28T22:41:29Z | 5 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-02-28T21:52:32Z | 0 | ---
dataset_info:
features:
- name: conversation
list:
- name: content
list:
- name: text
dtype: string
- name: type
dtype: string
- name: role
dtype: string
- name: image
dtype: string
splits:
- name: train
num_bytes: 644365359
num_examples: 1000
download_size: 145819112
dataset_size: 644365359
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CharlesLi/ultrafeedback_max_min_reward_llama3_1_8b | CharlesLi | 2024-09-20T07:57:17Z | 7 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-09-20T07:56:26Z | 0 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 780541853
num_examples: 61124
download_size: 500067143
dataset_size: 780541853
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
rl-rag/browsecomp-qwen35-35b-a3b-nothink | rl-rag | 2026-03-01T07:40:07Z | 37 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"deep-research",
"tool-use",
"evaluation"
] | [] | 2026-03-01T07:40:05Z | 0 | ---
tags:
- deep-research
- tool-use
- evaluation
---
# browsecomp-qwen35-35b-a3b-nothink
Deep research agent evaluation on **data/browsecomp.jsonl** (normal split).
## Results
| Metric | Value |
|--------|-------|
| **pass@4** | **32.3%** |
| **avg@4** | **16.4%** |
| Trajectory accuracy | 16.4% (830/5064) |
| Questions | 1266 |
| Trajectories | 5064 (4 per question) |
| Avg tool calls | 36.2 |
| Full conversations | ❌ |
## Model & Setup
| | |
|---|---|
| **Model** | `Qwen3.5-35B-A3B` |
| **Judge** | `gpt-4o` |
| **Max tool calls** | 50 |
| **Temperature** | 0.7 |
| **Blocked domains** | huggingface.co |
## Tool Usage
| Tool | Calls | % |
|------|------:|--:|
| `browser.search` | 176,326 | 96% |
| `browser.open` | 6,457 | 4% |
| `functions.paper_search` | 403 | 0.2% |
| `browser.find` | 151 | 0.1% |
| `functions.pubmed_search` | 59 | 0.0% |
**Total: 183,396 tool calls** (36.2 per trajectory)
## Columns
| Column | Description |
|--------|-------------|
| `qid` | Question ID |
| `traj_idx` | Trajectory index (0-3) |
| `question` | Input question |
| `reference_answer` | Ground truth answer |
| `boxed_answer` | Model's extracted `\boxed{}` answer |
| `correct` | GPT-4o judge verdict |
| `judge_explanation` | Judge's reasoning |
| `question_accuracy` | Fraction of trajectories correct for this question (difficulty: 0=hardest, 1=easiest) |
| `num_tool_calls` | Number of tool calls in trajectory |
| `tool_calls` | Tool call log (JSON) |
| `conversation` | Full trajectory with reasoning + tool responses (JSON) |
| `contaminated` | Whether search results contained evaluation dataset pages |
| `latency_s` | Generation time in seconds |
|
vipinkatara/TranslationAlpaca | vipinkatara | 2024-05-08T09:23:26Z | 8 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-05-08T09:20:42Z | 0 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 239507833
num_examples: 63234
- name: test
num_bytes: 67816357
num_examples: 18010
download_size: 154745720
dataset_size: 307324190
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
open-llm-leaderboard-old/details_nbeerbower__flammen15X-mistral-7B | open-llm-leaderboard-old | 2024-04-16T19:33:31Z | 5 | 0 | [
"region:us"
] | [] | 2024-04-16T19:33:06Z | 0 | ---
pretty_name: Evaluation run of nbeerbower/flammen15X-mistral-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [nbeerbower/flammen15X-mistral-7B](https://huggingface.co/nbeerbower/flammen15X-mistral-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_nbeerbower__flammen15X-mistral-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-16T19:30:51.983957](https://huggingface.co/datasets/open-llm-leaderboard/details_nbeerbower__flammen15X-mistral-7B/blob/main/results_2024-04-16T19-30-51.983957.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6545384862633296,\n\
\ \"acc_stderr\": 0.032103832783516625,\n \"acc_norm\": 0.6541190355196339,\n\
\ \"acc_norm_stderr\": 0.03277069107471931,\n \"mc1\": 0.5642594859241126,\n\
\ \"mc1_stderr\": 0.01735834539886313,\n \"mc2\": 0.72331475655426,\n\
\ \"mc2_stderr\": 0.014625134855007837\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6988054607508533,\n \"acc_stderr\": 0.01340674176784763,\n\
\ \"acc_norm\": 0.7167235494880546,\n \"acc_norm_stderr\": 0.013167478735134575\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7094204341764588,\n\
\ \"acc_stderr\": 0.004531019159414103,\n \"acc_norm\": 0.8829914359689305,\n\
\ \"acc_norm_stderr\": 0.0032077357692780447\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6518518518518519,\n\
\ \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n\
\ \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7302631578947368,\n \"acc_stderr\": 0.03611780560284898,\n\
\ \"acc_norm\": 0.7302631578947368,\n \"acc_norm_stderr\": 0.03611780560284898\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\
\ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \
\ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n\
\ \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7847222222222222,\n\
\ \"acc_stderr\": 0.03437079344106135,\n \"acc_norm\": 0.7847222222222222,\n\
\ \"acc_norm_stderr\": 0.03437079344106135\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\"\
: 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n\
\ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\
\ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\
\ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.032232762667117124,\n\
\ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.032232762667117124\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41005291005291006,\n \"acc_stderr\": 0.025331202438944433,\n \"\
acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.025331202438944433\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n\
\ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\
\ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7806451612903226,\n \"acc_stderr\": 0.023540799358723295,\n \"\
acc_norm\": 0.7806451612903226,\n \"acc_norm_stderr\": 0.023540799358723295\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"\
acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n\
\ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"\
acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\
\ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6564102564102564,\n \"acc_stderr\": 0.024078696580635477,\n\
\ \"acc_norm\": 0.6564102564102564,\n \"acc_norm_stderr\": 0.024078696580635477\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34444444444444444,\n \"acc_stderr\": 0.028972648884844267,\n \
\ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.028972648884844267\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \
\ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\
acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8403669724770643,\n \"acc_stderr\": 0.015703498348461766,\n \"\
acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.015703498348461766\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\
acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8431372549019608,\n \"acc_stderr\": 0.025524722324553346,\n \"\
acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.025524722324553346\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944856,\n \
\ \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944856\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.03498149385462472,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.03498149385462472\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\
: 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.032910995786157686,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.032910995786157686\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n\
\ \"acc_stderr\": 0.022209309073165612,\n \"acc_norm\": 0.8675213675213675,\n\
\ \"acc_norm_stderr\": 0.022209309073165612\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8314176245210728,\n\
\ \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n\
\ \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500104,\n\
\ \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500104\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4301675977653631,\n\
\ \"acc_stderr\": 0.01655860163604103,\n \"acc_norm\": 0.4301675977653631,\n\
\ \"acc_norm_stderr\": 0.01655860163604103\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7156862745098039,\n \"acc_stderr\": 0.025829163272757482,\n\
\ \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.025829163272757482\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\
\ \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n\
\ \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7654320987654321,\n \"acc_stderr\": 0.02357688174400572,\n\
\ \"acc_norm\": 0.7654320987654321,\n \"acc_norm_stderr\": 0.02357688174400572\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\"\
: 0.5,\n \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness|hendrycksTest-professional_law|5\"\
: {\n \"acc\": 0.4784876140808344,\n \"acc_stderr\": 0.012758410941038913,\n\
\ \"acc_norm\": 0.4784876140808344,\n \"acc_norm_stderr\": 0.012758410941038913\n\
\ },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\"\
: 0.6801470588235294,\n \"acc_stderr\": 0.028332959514031208,\n \"\
acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.028332959514031208\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6830065359477124,\n \"acc_stderr\": 0.018824219512706207,\n \
\ \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.018824219512706207\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7428571428571429,\n \"acc_stderr\": 0.02797982353874455,\n\
\ \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.02797982353874455\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\
\ \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n\
\ \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5642594859241126,\n\
\ \"mc1_stderr\": 0.01735834539886313,\n \"mc2\": 0.72331475655426,\n\
\ \"mc2_stderr\": 0.014625134855007837\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.824782951854775,\n \"acc_stderr\": 0.010684179227706177\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7005307050796058,\n \
\ \"acc_stderr\": 0.012616300735519658\n }\n}\n```"
repo_url: https://huggingface.co/nbeerbower/flammen15X-mistral-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|arc:challenge|25_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|gsm8k|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hellaswag|10_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-16T19-30-51.983957.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-16T19-30-51.983957.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- '**/details_harness|winogrande|5_2024-04-16T19-30-51.983957.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-16T19-30-51.983957.parquet'
- config_name: results
data_files:
- split: 2024_04_16T19_30_51.983957
path:
- results_2024-04-16T19-30-51.983957.parquet
- split: latest
path:
- results_2024-04-16T19-30-51.983957.parquet
---
# Dataset Card for Evaluation run of nbeerbower/flammen15X-mistral-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [nbeerbower/flammen15X-mistral-7B](https://huggingface.co/nbeerbower/flammen15X-mistral-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_nbeerbower__flammen15X-mistral-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-16T19:30:51.983957](https://huggingface.co/datasets/open-llm-leaderboard/details_nbeerbower__flammen15X-mistral-7B/blob/main/results_2024-04-16T19-30-51.983957.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6545384862633296,
"acc_stderr": 0.032103832783516625,
"acc_norm": 0.6541190355196339,
"acc_norm_stderr": 0.03277069107471931,
"mc1": 0.5642594859241126,
"mc1_stderr": 0.01735834539886313,
"mc2": 0.72331475655426,
"mc2_stderr": 0.014625134855007837
},
"harness|arc:challenge|25": {
"acc": 0.6988054607508533,
"acc_stderr": 0.01340674176784763,
"acc_norm": 0.7167235494880546,
"acc_norm_stderr": 0.013167478735134575
},
"harness|hellaswag|10": {
"acc": 0.7094204341764588,
"acc_stderr": 0.004531019159414103,
"acc_norm": 0.8829914359689305,
"acc_norm_stderr": 0.0032077357692780447
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6518518518518519,
"acc_stderr": 0.041153246103369526,
"acc_norm": 0.6518518518518519,
"acc_norm_stderr": 0.041153246103369526
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7302631578947368,
"acc_stderr": 0.03611780560284898,
"acc_norm": 0.7302631578947368,
"acc_norm_stderr": 0.03611780560284898
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.690566037735849,
"acc_stderr": 0.028450154794118637,
"acc_norm": 0.690566037735849,
"acc_norm_stderr": 0.028450154794118637
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7847222222222222,
"acc_stderr": 0.03437079344106135,
"acc_norm": 0.7847222222222222,
"acc_norm_stderr": 0.03437079344106135
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.03599586301247077,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.03599586301247077
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4215686274509804,
"acc_stderr": 0.04913595201274498,
"acc_norm": 0.4215686274509804,
"acc_norm_stderr": 0.04913595201274498
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5829787234042553,
"acc_stderr": 0.032232762667117124,
"acc_norm": 0.5829787234042553,
"acc_norm_stderr": 0.032232762667117124
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5087719298245614,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.5087719298245614,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5655172413793104,
"acc_stderr": 0.04130740879555498,
"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.04130740879555498
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41005291005291006,
"acc_stderr": 0.025331202438944433,
"acc_norm": 0.41005291005291006,
"acc_norm_stderr": 0.025331202438944433
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.47619047619047616,
"acc_stderr": 0.04467062628403273,
"acc_norm": 0.47619047619047616,
"acc_norm_stderr": 0.04467062628403273
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7806451612903226,
"acc_stderr": 0.023540799358723295,
"acc_norm": 0.7806451612903226,
"acc_norm_stderr": 0.023540799358723295
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4975369458128079,
"acc_stderr": 0.03517945038691063,
"acc_norm": 0.4975369458128079,
"acc_norm_stderr": 0.03517945038691063
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7515151515151515,
"acc_stderr": 0.033744026441394036,
"acc_norm": 0.7515151515151515,
"acc_norm_stderr": 0.033744026441394036
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.029126522834586815,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.029126522834586815
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8963730569948186,
"acc_stderr": 0.02199531196364424,
"acc_norm": 0.8963730569948186,
"acc_norm_stderr": 0.02199531196364424
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6564102564102564,
"acc_stderr": 0.024078696580635477,
"acc_norm": 0.6564102564102564,
"acc_norm_stderr": 0.024078696580635477
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34444444444444444,
"acc_stderr": 0.028972648884844267,
"acc_norm": 0.34444444444444444,
"acc_norm_stderr": 0.028972648884844267
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6722689075630253,
"acc_stderr": 0.03048991141767323,
"acc_norm": 0.6722689075630253,
"acc_norm_stderr": 0.03048991141767323
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.36423841059602646,
"acc_stderr": 0.03929111781242742,
"acc_norm": 0.36423841059602646,
"acc_norm_stderr": 0.03929111781242742
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8403669724770643,
"acc_stderr": 0.015703498348461766,
"acc_norm": 0.8403669724770643,
"acc_norm_stderr": 0.015703498348461766
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5231481481481481,
"acc_stderr": 0.03406315360711507,
"acc_norm": 0.5231481481481481,
"acc_norm_stderr": 0.03406315360711507
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8431372549019608,
"acc_stderr": 0.025524722324553346,
"acc_norm": 0.8431372549019608,
"acc_norm_stderr": 0.025524722324553346
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8143459915611815,
"acc_stderr": 0.025310495376944856,
"acc_norm": 0.8143459915611815,
"acc_norm_stderr": 0.025310495376944856
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6905829596412556,
"acc_stderr": 0.03102441174057221,
"acc_norm": 0.6905829596412556,
"acc_norm_stderr": 0.03102441174057221
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8015267175572519,
"acc_stderr": 0.03498149385462472,
"acc_norm": 0.8015267175572519,
"acc_norm_stderr": 0.03498149385462472
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.768595041322314,
"acc_stderr": 0.03849856098794088,
"acc_norm": 0.768595041322314,
"acc_norm_stderr": 0.03849856098794088
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7870370370370371,
"acc_stderr": 0.0395783547198098,
"acc_norm": 0.7870370370370371,
"acc_norm_stderr": 0.0395783547198098
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7730061349693251,
"acc_stderr": 0.032910995786157686,
"acc_norm": 0.7730061349693251,
"acc_norm_stderr": 0.032910995786157686
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.44642857142857145,
"acc_stderr": 0.04718471485219588,
"acc_norm": 0.44642857142857145,
"acc_norm_stderr": 0.04718471485219588
},
"harness|hendrycksTest-management|5": {
"acc": 0.7766990291262136,
"acc_stderr": 0.04123553189891431,
"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8675213675213675,
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
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### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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#### Who are the source data producers?
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
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#### Who are the annotators?
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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**BibTeX:**
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## Glossary [optional]
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## Dataset Card Contact
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End of preview. Expand in Data Studio
Dataset Card for Hugging Face Hub Dataset Cards
This datasets consists of dataset cards for models hosted on the Hugging Face Hub. The dataset cards are created by the community and provide information about datasets hosted on the Hugging Face Hub. This dataset is updated on a daily basis and includes publicly available datasets on the Hugging Face Hub.
This dataset is made available to help support users wanting to work with a large number of Dataset Cards from the Hub. We hope that this dataset will help support research in the area of Dataset Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new discussion.
Dataset Details
Uses
There are a number of potential uses for this dataset including:
- text mining to find common themes in dataset cards
- analysis of the dataset card format/content
- topic modelling of dataset cards
- training language models on the dataset cards
Out-of-Scope Use
[More Information Needed]
Dataset Structure
This dataset has a single split.
Dataset Creation
Curation Rationale
The dataset was created to assist people in working with dataset cards. In particular it was created to support research in the area of dataset cards and their use. It is possible to use the Hugging Face Hub API or client library to download dataset cards and this option may be preferable if you have a very specific use case or require a different format.
Source Data
The source data is README.md files for datasets hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the dataset directory.
Data Collection and Processing
The data is downloaded using a CRON job on a daily basis.
Who are the source data producers?
The source data producers are the creators of the dataset cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the dataset card in this repository although this information can be gathered from the Hugging Face Hub API.
Annotations [optional]
There are no additional annotations in this dataset beyond the dataset card content.
Annotation process
N/A
Who are the annotators?
N/A
Personal and Sensitive Information
We make no effort to anonymize the data. Whilst we don't expect the majority of dataset cards to contain personal or sensitive information, it is possible that some dataset cards may contain this information. Dataset cards may also link to websites or email addresses.
Bias, Risks, and Limitations
Dataset cards are created by the community and we do not have any control over the content of the dataset cards. We do not review the content of the dataset cards and we do not make any claims about the accuracy of the information in the dataset cards. Some dataset cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the dataset. As a result this dataset may contain examples of bias.
Whilst we do not directly download any images linked to in the dataset cards, some dataset cards may include images. Some of these images may not be suitable for all audiences.
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Citation
No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page.
Dataset Card Authors
Dataset Card Contact
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Curated by:
Daniel van Strien
Language(s) (NLP):
Dataset cards on the Hugging Face Hub are predominantly in English but may include other languages.
Size of downloaded dataset files:
549 MB
Size of the auto-converted Parquet files:
549 MB
Number of rows:
420,001