datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
m-a-p/COIG-Kun | ---
task_categories:
- question-answering
language:
- zh
size_categories:
- 100K<n<1M
configs:
- config_name: default
data_files:
- split: wudao
path: wudao_v1.jsonl
- split: wanjuan
path: wanjuan_v1.jsonl
- split: skypile
path: skypile_v1.jsonl
---
<div align="center">
<img src="Yi_logo.svg" width="150px" style="display: inline-block;">
<img src="m-a-p.png" width="150px" style="display: inline-block;">
</div>
# Kun: Answer Polishment Saves Your Time for Using Intruction Backtranslation on Self-Alignment
## Table of Contents
- [Overview](#overview)
- [Dataset Description](#dataset-description)
- [Usage](#usage)
- [Citation](#citation)
- [Acknowledgments](#acknowledgments)
## Overview
The COIG-Kun dataset, part of the [COIG-Kun GitHub](https://github.com/Zheng0428/COIG-Kun) project, consists of instructional data used for training language models. This dataset was developed following the methodology inspired by Meta's "Self-Alignment with Instruction Backtranslation" and adapted for optimal performance in training label, point, and answer models.
## Dataset Description
### Language
- The dataset contains instructions primarily in Chinese.
### Dataset Structure
- **Data Instances**: Each data instance is structured in a JSON format with two fields: `instruction` and `output`.
- Example: `{"instruction": "如何评价祁又一自编自导的电影《鸽子小姐》？", "output": "《鸽子小姐》是一部由祁又一自编自导的电影。..."}`
- **Data Split**: The dataset is comprised of three subsets:
- `wudao.jsonl`: 139,852 instances
- `wanjuan.jsonl`: 328,294 instances
- `skypile.jsonl`: 71,560 instances
### Data Characteristics
- The dataset is designed to provide high-quality instructional data for language model training, focusing on enhancing the quality and applicability of the data.
## Methodology
Our approach closely follows the self-alignment method ådescribed by Meta, with adaptations to optimize the process:
1. **Seed Data Selection and Model Training**: Initially, appropriate seed data are selected and inverted to train a Label Model on a base model(Yi Base). Concurrently, using the same seed data, a Primary Chat model is trained following the Supervised Fine-Tuning (SFT) method typical of chat models.
3. **Labeling Unlabeled Data**: The Label Model is then used to annotate preliminarily cleansed Primary data. Cleansing involves filtering based on perplexity (ppl) and length, discarding data exceeding 512 tokens.
4. **Instruction Data Generation**: Post-annotation, we obtain our first version of Labeled data. Unlike the original project where both instruction and output data pairs are fed into Primary Chat Model for scoring, our replication revealed limitations in Primary Chat's ability to discern high-quality instructions. We innovated by scoring only the instruction component, effectively filtering out noise and selecting high-quality instructions.
5. **Output Data Refinement**: Upon manual inspection, we identified a mismatch between the Primary Data (used as output) and the standard requirements for output in instruction data. To address this, we introduced an additional step: refining the output data. Using Primary Chat's capabilities, the output (originally unlabeled data) is adjusted according to the instructions, making it more suitable as output for the instruction data.
6. **Framework Completion**: Our methodology concludes with the acquisition of a substantial volume of instructional data, achieved with minimal resource expenditure.
## Usage
### Using the Data
- The dataset can be used for training and fine-tuning language models, specifically focusing on instruction understanding and response generation.
- Users are encouraged to refer to the project documentation for detailed instructions on utilizing the dataset in the training process.
## Citation
If you use this dataset in your research, please cite it as follows:
```bibtex
@misc{COIG-Kun,
title={Kun: Answer Polishment Saves Your Time for Using Intruction Backtranslation on Self-Alignment},
author={Tianyu, Zheng* and Shuyue, Guo* and Xingwei, Qu and Xinrun, Du and Wenhu, Chen and Jie, Fu and Wenhao, Huang and Ge, Zhang},
year={2023},
publisher={GitHub},
journal={GitHub repository},
howpublished={https://github.com/Zheng0428/COIG-Kun}
}
```
## Acknowledgments
This dataset was created by a dedicated team at [M-A-P]. We acknowledge the contributions of all individuals and organizations that made this project possible.
|
mjbuehler/GPTSilkomePretrained | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 213036133
num_examples: 731354
download_size: 203708011
dataset_size: 213036133
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "GPTSilkomePretrained"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mHossain/final_train_v2_150000 | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: input_text
dtype: string
- name: target_text
dtype: string
- name: prefix
dtype: string
splits:
- name: train
num_bytes: 9145459.8
num_examples: 27000
- name: test
num_bytes: 1016162.2
num_examples: 3000
download_size: 4456343
dataset_size: 10161622.0
---
# Dataset Card for "final_train_v2_150000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kabir5297/CV_Eng_train_specialCharsRemoved | ---
dataset_info:
features:
- name: filename
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 822619
num_examples: 11281
download_size: 409770
dataset_size: 822619
---
# Dataset Card for "CV_Eng_train_specialCharsRemoved"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mikhail-panzo/fil-fleur | ---
dataset_info:
features:
- name: speaker_embeddings
sequence: float32
- name: input_ids
sequence: int32
- name: labels
sequence:
sequence: float32
splits:
- name: train
num_bytes: 820263620
num_examples: 2619
download_size: 813554622
dataset_size: 820263620
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
laurent255/EU_digital_acts | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2432593
num_examples: 1
download_size: 1447042
dataset_size: 2432593
---
# Dataset Card for "EU_digital_acts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_AiMavenAi__MavenWest | ---
pretty_name: Evaluation run of AiMavenAi/MavenWest
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [AiMavenAi/MavenWest](https://huggingface.co/AiMavenAi/MavenWest) 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_AiMavenAi__MavenWest\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-23T22:26:42.328277](https://huggingface.co/datasets/open-llm-leaderboard/details_AiMavenAi__MavenWest/blob/main/results_2024-01-23T22-26-42.328277.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\": 0.6519949800012913,\n\
\ \"acc_stderr\": 0.03226904531191905,\n \"acc_norm\": 0.651558948732219,\n\
\ \"acc_norm_stderr\": 0.032941911568037885,\n \"mc1\": 0.5055079559363526,\n\
\ \"mc1_stderr\": 0.01750243899045107,\n \"mc2\": 0.6529155692943942,\n\
\ \"mc2_stderr\": 0.015412828995723143\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6953924914675768,\n \"acc_stderr\": 0.013449522109932489,\n\
\ \"acc_norm\": 0.7158703071672355,\n \"acc_norm_stderr\": 0.013179442447653886\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7135032861979685,\n\
\ \"acc_stderr\": 0.004512002459757957,\n \"acc_norm\": 0.8843855805616411,\n\
\ \"acc_norm_stderr\": 0.003191084792793155\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\": 0.6222222222222222,\n\
\ \"acc_stderr\": 0.04188307537595853,\n \"acc_norm\": 0.6222222222222222,\n\
\ \"acc_norm_stderr\": 0.04188307537595853\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.038424985593952694,\n\
\ \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.038424985593952694\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\
\ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \
\ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249387,\n\
\ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249387\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\"\
: 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\n\
\ \"acc_stderr\": 0.03669072477416906,\n \"acc_norm\": 0.6358381502890174,\n\
\ \"acc_norm_stderr\": 0.03669072477416906\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\
\ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\
\ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\
\ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.543859649122807,\n\
\ \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.543859649122807,\n\
\ \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370332,\n\
\ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370332\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4312169312169312,\n \"acc_stderr\": 0.02550648169813821,\n \"\
acc_norm\": 0.4312169312169312,\n \"acc_norm_stderr\": 0.02550648169813821\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\
\ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\
\ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7709677419354839,\n\
\ \"acc_stderr\": 0.023904914311782655,\n \"acc_norm\": 0.7709677419354839,\n\
\ \"acc_norm_stderr\": 0.023904914311782655\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\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\"\
: 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.032568666616811015,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.032568666616811015\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8131313131313131,\n \"acc_stderr\": 0.027772533334218967,\n \"\
acc_norm\": 0.8131313131313131,\n \"acc_norm_stderr\": 0.027772533334218967\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.023381935348121434,\n\
\ \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.023381935348121434\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6512820512820513,\n \"acc_stderr\": 0.02416278028401772,\n \
\ \"acc_norm\": 0.6512820512820513,\n \"acc_norm_stderr\": 0.02416278028401772\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \
\ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n\
\ \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.03958027231121569,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.03958027231121569\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8403669724770643,\n \"acc_stderr\": 0.015703498348461783,\n \"\
acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.015703498348461783\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"\
acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8284313725490197,\n \"acc_stderr\": 0.026460569561240644,\n \"\
acc_norm\": 0.8284313725490197,\n \"acc_norm_stderr\": 0.026460569561240644\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601443,\n \
\ \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601443\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\
\ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\
\ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.743801652892562,\n \"acc_stderr\": 0.03984979653302872,\n \"acc_norm\"\
: 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302872\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n\
\ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.041858325989283136,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.041858325989283136\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.02158649400128137,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.02158649400128137\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8199233716475096,\n\
\ \"acc_stderr\": 0.013740797258579825,\n \"acc_norm\": 0.8199233716475096,\n\
\ \"acc_norm_stderr\": 0.013740797258579825\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.023948512905468365,\n\
\ \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.023948512905468365\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.464804469273743,\n\
\ \"acc_stderr\": 0.01668102093107665,\n \"acc_norm\": 0.464804469273743,\n\
\ \"acc_norm_stderr\": 0.01668102093107665\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7091503267973857,\n \"acc_stderr\": 0.02600480036395213,\n\
\ \"acc_norm\": 0.7091503267973857,\n \"acc_norm_stderr\": 0.02600480036395213\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n\
\ \"acc_stderr\": 0.025755865922632952,\n \"acc_norm\": 0.7106109324758842,\n\
\ \"acc_norm_stderr\": 0.025755865922632952\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.02438366553103545,\n\
\ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.02438366553103545\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \
\ \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4667535853976532,\n\
\ \"acc_stderr\": 0.01274197433389723,\n \"acc_norm\": 0.4667535853976532,\n\
\ \"acc_norm_stderr\": 0.01274197433389723\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.028418208619406755,\n\
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.028418208619406755\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6699346405228758,\n \"acc_stderr\": 0.019023726160724553,\n \
\ \"acc_norm\": 0.6699346405228758,\n \"acc_norm_stderr\": 0.019023726160724553\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\
\ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\
\ \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.038823108508905954\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\": 0.5055079559363526,\n\
\ \"mc1_stderr\": 0.01750243899045107,\n \"mc2\": 0.6529155692943942,\n\
\ \"mc2_stderr\": 0.015412828995723143\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8326756116811366,\n \"acc_stderr\": 0.010490608806828075\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6884003032600455,\n \
\ \"acc_stderr\": 0.012757375376754941\n }\n}\n```"
repo_url: https://huggingface.co/AiMavenAi/MavenWest
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_01_23T22_26_42.328277
path:
- '**/details_harness|arc:challenge|25_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|gsm8k|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hellaswag|10_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T22-26-42.328277.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-23T22-26-42.328277.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- '**/details_harness|winogrande|5_2024-01-23T22-26-42.328277.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-23T22-26-42.328277.parquet'
- config_name: results
data_files:
- split: 2024_01_23T22_26_42.328277
path:
- results_2024-01-23T22-26-42.328277.parquet
- split: latest
path:
- results_2024-01-23T22-26-42.328277.parquet
---
# Dataset Card for Evaluation run of AiMavenAi/MavenWest
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [AiMavenAi/MavenWest](https://huggingface.co/AiMavenAi/MavenWest) 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_AiMavenAi__MavenWest",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-23T22:26:42.328277](https://huggingface.co/datasets/open-llm-leaderboard/details_AiMavenAi__MavenWest/blob/main/results_2024-01-23T22-26-42.328277.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": 0.6519949800012913,
"acc_stderr": 0.03226904531191905,
"acc_norm": 0.651558948732219,
"acc_norm_stderr": 0.032941911568037885,
"mc1": 0.5055079559363526,
"mc1_stderr": 0.01750243899045107,
"mc2": 0.6529155692943942,
"mc2_stderr": 0.015412828995723143
},
"harness|arc:challenge|25": {
"acc": 0.6953924914675768,
"acc_stderr": 0.013449522109932489,
"acc_norm": 0.7158703071672355,
"acc_norm_stderr": 0.013179442447653886
},
"harness|hellaswag|10": {
"acc": 0.7135032861979685,
"acc_stderr": 0.004512002459757957,
"acc_norm": 0.8843855805616411,
"acc_norm_stderr": 0.003191084792793155
},
"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": 0.6222222222222222,
"acc_stderr": 0.04188307537595853,
"acc_norm": 0.6222222222222222,
"acc_norm_stderr": 0.04188307537595853
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6644736842105263,
"acc_stderr": 0.038424985593952694,
"acc_norm": 0.6644736842105263,
"acc_norm_stderr": 0.038424985593952694
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.02815283794249387,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.02815283794249387
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.57,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.57,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6358381502890174,
"acc_stderr": 0.03669072477416906,
"acc_norm": 0.6358381502890174,
"acc_norm_stderr": 0.03669072477416906
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.43137254901960786,
"acc_stderr": 0.04928099597287534,
"acc_norm": 0.43137254901960786,
"acc_norm_stderr": 0.04928099597287534
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.79,
"acc_stderr": 0.04093601807403326,
"acc_norm": 0.79,
"acc_norm_stderr": 0.04093601807403326
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5829787234042553,
"acc_stderr": 0.03223276266711712,
"acc_norm": 0.5829787234042553,
"acc_norm_stderr": 0.03223276266711712
},
"harness|hendrycksTest-econometrics|5": {
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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
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[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. -->
[More Information Needed]
## 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]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### 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. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
owanr/r2_iterater | ---
dataset_info:
features:
- name: src
dtype: string
- name: tgt
dtype: string
- name: instructions
dtype: string
splits:
- name: train
num_bytes: 2856363.0
num_examples: 7210
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num_examples: 909
- name: test
num_bytes: 411534.0
num_examples: 995
download_size: 2092213
dataset_size: 3639541.0
---
# Dataset Card for "r2_iterater"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
quocanh34/thaivanlinh_w2v_whispertiny | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: gold_text
dtype: string
- name: predicted_text
dtype: string
splits:
- name: train
num_bytes: 89388736.298
num_examples: 1077
download_size: 89351814
dataset_size: 89388736.298
---
# Dataset Card for "thaivanlinh_w2v_whispertiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Plachta/sampled_audio4ft | ---
license: apache-2.0
---
|
liuyanchen1015/MULTI_VALUE_cola_our_us | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
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num_examples: 7
- name: test
num_bytes: 716
num_examples: 9
- name: train
num_bytes: 3099
num_examples: 38
download_size: 8431
dataset_size: 4325
---
# Dataset Card for "MULTI_VALUE_cola_our_us"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
18moumi/trialdata | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 48673
num_examples: 142
download_size: 21464
dataset_size: 48673
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "trialdata"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_mrpc_zero_degree | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
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dtype: int64
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num_examples: 29
- name: train
num_bytes: 19874
num_examples: 76
- name: validation
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num_examples: 7
download_size: 32388
dataset_size: 30312
---
# Dataset Card for "MULTI_VALUE_mrpc_zero_degree"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
SZTAKI-HLT/HunSum-2-extractive | ---
task_categories:
- summarization
language:
- hu
multilinguality:
- monolingual
pretty_name: HunSum-2-extractive
license: cc-by-nc-sa-4.0
size_categories:
- 1M<n<10M
--- |
tyzhu/find_sent_after_sent_train_400_eval_40_last_permute | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: title
dtype: string
- name: context
dtype: string
splits:
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num_bytes: 5866475.834053587
num_examples: 4188
- name: validation
num_bytes: 232483
num_examples: 200
download_size: 1244208
dataset_size: 6098958.834053587
---
# Dataset Card for "find_sent_after_sent_train_400_eval_40_last_permute"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
FreedomIntelligence/MMLU_Hindi | ---
license: mit
---
Hindi version of MMLU dataset tranlasted by gpt-3.5-turbo.
The dataset is used in the research related to [MultilingualSIFT](https://github.com/FreedomIntelligence/MultilingualSIFT). |
YuehHanChen/forecasting_raw | ---
language:
- en
license: apache-2.0
---
<p align="center"><h1>Raw Dataset from "Approaching Human-Level Forecasting with Language Models"</h1></p>
<p>This documentation provides an overview of the raw dataset utilized in our research paper, <strong><a href="https://arxiv.org/abs/2402.18563" target="_blank">Approaching Human-Level Forecasting with Language Models</a></strong>, authored by <a href="mailto:dhalawi@berkeley.edu">Danny Halawi</a>, <a href="mailto:z0@eecs.berkeley.edu">Fred Zhang</a>, <a href="mailto:john0922ucb@berkeley.edu">Chen Yueh-Han</a>, and <a href="mailto:jsteinhardt@berkeley.edu">Jacob Steinhardt</a>.</p>
<h2>Data Source and Format</h2>
<p>The dataset originates from forecasting platforms such as Metaculus, Good Judgment Open, INFER, Polymarket, and Manifold. These platforms engage users in predicting the likelihood of future events by assigning probabilities to various outcomes. The data structure encompasses:</p>
<ul>
<li><strong>Background Description:</strong> Provides context for the forecasting question.</li>
<li><strong>Resolution Criterion:</strong> Defines how and when the question will be resolved.</li>
<li><strong>Timestamps:</strong> Includes the publication date (begin date), the forecast submission deadline (close date), and the resolution date (resolve date).</li>
</ul>
<p>Forecasts can be submitted any time between the begin date and the earlier of the resolve date or close date. Refer to <em>Table 1</em> in the paper for a detailed example of these fields in action.</p>
<h2>Dataset Composition</h2>
<p>Our dataset aggregates forecasting questions from the aforementioned platforms, resulting in a comprehensive collection of:</p>
<ul>
<li><strong>50,343 Questions:</strong> Spanning from 2015 to 2024.</li>
<li><strong>6,534,042 User Forecasts:</strong> Offering a rich dataset for analysis.</li>
<li><strong>Question Types:</strong> Includes 33,664 binary questions, 9,725 multiple-choice questions, 4,019 numerical questions, and 1,346 questions of other types.</li>
</ul>
<p>The questions cover a broad spectrum of topics worldwide, providing a diverse and extensive dataset for forecasting analysis.</p>
<h2>Research Significance</h2>
<p>This dataset plays a crucial role in our study, enabling us to explore the capabilities of language models in forecasting and their potential to achieve human-level performance in predicting future events.</p>
<p>For more details on our methodology and findings, please refer to our paper linked at the beginning of this document.</p>
<h2>How to Cite</h2>
<p>If you find our dataset and research useful for your work, please cite it using the following BibTeX entry:</p>
```bibtex
@misc{halawi2024approaching,
title={Approaching Human-Level Forecasting with Language Models},
author={Danny Halawi and Fred Zhang and Chen Yueh-Han and Jacob Steinhardt},
year={2024},
eprint={2402.18563},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
|
Mihaj/robot_ds | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
splits:
- name: test
num_bytes: 27494365.0
num_examples: 219
- name: train
num_bytes: 60944526.0
num_examples: 487
download_size: 83859664
dataset_size: 88438891.0
---
# Dataset Card for "robot_ds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ChatLoom/ChatLoom_Test | ---
dataset_info:
features:
- name: example
dtype: string
splits:
- name: train
num_bytes: 58817
num_examples: 42
- name: test
num_bytes: 12290
num_examples: 9
download_size: 27257
dataset_size: 71107
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
HuggingFaceM4/COCO | ---
license: cc-by-4.0
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://cocodataset.org/](https://cocodataset.org/)
- **Repository:**
- **Paper:** [Microsoft COCO: Common Objects in Context](https://arxiv.org/abs/1405.0312)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
MS COCO is a large-scale object detection, segmentation, and captioning dataset.
COCO has several features: Object segmentation, Recognition in context, Superpixel stuff segmentation, 330K images (>200K labeled), 1.5 million object instances, 80 object categories, 91 stuff categories, 5 captions per image, 250,000 people with keypoints.
As of now, there is only the 2014 subset (with Karpathy annotations and splits), but feel free to contribute the 2017 subset of COCO!
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Each instance has the following structure:
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7F69C1BA8550>,
'filepath': 'COCO_val2014_000000522418.jpg',
'sentids': [681330, 686718, 688839, 693159, 693204],
'filename': 'COCO_val2014_000000522418.jpg',
'imgid': 1,
'split': 'restval',
'sentences': {
'tokens': ['a', 'woman', 'wearing', 'a', 'net', 'on', 'her', 'head', 'cutting', 'a', 'cake'],
'raw': 'A woman wearing a net on her head cutting a cake. ',
'imgid': 1,
'sentid': 681330
},
'cocoid': 522418
}
```
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.
|
clonandovoz/michaeljackson | ---
license: openrail
---
|
Sleoruiz/disc_cla_cuarta | ---
dataset_info:
features:
- name: text
dtype: string
- name: inputs
struct:
- name: text
dtype: string
- name: prediction
list:
- name: label
dtype: string
- name: score
dtype: float64
- name: prediction_agent
dtype: string
- name: annotation
sequence: string
- name: annotation_agent
dtype: string
- name: multi_label
dtype: bool
- name: explanation
dtype: 'null'
- name: id
dtype: string
- name: metadata
dtype: 'null'
- name: status
dtype: string
- name: event_timestamp
dtype: timestamp[us]
- name: metrics
struct:
- name: text_length
dtype: int64
splits:
- name: train
num_bytes: 14861447
num_examples: 3349
download_size: 7807410
dataset_size: 14861447
---
# Dataset Card for "disc_cla_cuarta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_cola_drop_aux_be_progressive | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 777
num_examples: 11
- name: test
num_bytes: 1458
num_examples: 19
- name: train
num_bytes: 11627
num_examples: 165
download_size: 12361
dataset_size: 13862
---
# Dataset Card for "MULTI_VALUE_cola_drop_aux_be_progressive"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
4eJIoBek/Minecraft-skins-26k | ---
license: unknown
---
|
vidhikatkoria/SGD_Hotels | ---
dataset_info:
features:
- name: domain
dtype: string
- name: context
dtype: string
- name: response
dtype: string
- name: act
dtype: int64
- name: speaker
dtype: int64
splits:
- name: train
num_bytes: 3552843.2265793467
num_examples: 12520
- name: test
num_bytes: 439
num_examples: 1
download_size: 1494564
dataset_size: 3553282.2265793467
---
# Dataset Card for "SGD_Hotels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
juancopi81/orca-math-word-problems-40008_50010 | ---
language:
- en
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 7848507
num_examples: 10002
download_size: 2741050
dataset_size: 7848507
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
heliosprime/twitter_dataset_1712989529 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 7057
num_examples: 15
download_size: 8381
dataset_size: 7057
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1712989529"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Adorg/ToolBench_reproduction | ---
license: apache-2.0
---
|
nlpso/m1_fine_tuning_ref_cmbert_io | ---
language:
- fr
multilinguality:
- monolingual
task_categories:
- token-classification
---
# m1_fine_tuning_ref_cmbert_io
## Introduction
This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ref_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ref_cmbert_io_level_1)
* Level 2 : [nlpso/m1_ind_layers_ref_cmbert_io_level_2](https://huggingface.co/nlpso/m1_ind_layers_ref_cmbert_io_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_fine_tuning_ref_cmbert_io")
|
BirdL/Goya-Dataset | ---
license: other
---
Dataset of Goya Paintings |
revands/revanf | ---
license: mit
---
|
useSword/AnimateDiff-Motion-Module | ---
license: apache-2.0
---
|
jieyuz2/WRENCH | ---
license: apache-2.0
language:
- en
size_categories:
- 100K<n<1M
---
# Dataset Card for WRENCH
**Wrench** is a **benchmark platform** containing diverse weak supervision tasks. It also provides a **common and easy framework** for development and evaluation of your own weak supervision models within the benchmark.
For more information, checkout the [github repo](https://github.com/JieyuZ2/wrench) and our publications:
- [WRENCH: A Comprehensive Benchmark for Weak Supervision](https://arxiv.org/abs/2109.11377) (NeurIPS 2021)
- [A Survey on Programmatic Weak Supervision](https://arxiv.org/pdf/2202.05433.pdf)
If you find this repository helpful, feel free to cite our publication:
```
@inproceedings{
zhang2021wrench,
title={{WRENCH}: A Comprehensive Benchmark for Weak Supervision},
author={Jieyu Zhang and Yue Yu and Yinghao Li and Yujing Wang and Yaming Yang and Mao Yang and Alexander Ratner},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2021},
url={https://openreview.net/forum?id=Q9SKS5k8io}
}
```
|
mmoebis/5gdata_test | ---
dataset_info:
features:
- name: Sentences
dtype: string
- name: Questions
dtype: string
- name: __index_level_0__
dtype: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 157908
num_examples: 199
download_size: 13842
dataset_size: 157908
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Atipico1/NQ-colbert-20k | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: ctxs
list:
- name: hasanswer
dtype: bool
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 66048937.162354276
num_examples: 20000
- name: test
num_bytes: 12000594
num_examples: 3610
download_size: 45706178
dataset_size: 78049531.16235428
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
thavens/ufb_rejected | ---
configs:
- config_name: default
data_files:
- split: train_prefs
path: data/train_prefs-*
- split: test_prefs
path: data/test_prefs-*
dataset_info:
features:
- name: messages
list:
- name: condition
dtype: string
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train_prefs
num_bytes: 113146743
num_examples: 61135
- name: test_prefs
num_bytes: 3646621
num_examples: 2000
download_size: 63064916
dataset_size: 116793364
---
# Dataset Card for "ufb_rejected"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nikad/jugg | ---
viewer: true
dataset_info:
features:
- name: image
dtype: image
- name: additional_feature
dtype: string
splits:
- name: train
num_bytes: 2216090.0
num_examples: 6
download_size: 2215723
dataset_size: 2216090.0
---
# Dataset Card for "jugg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
freshpearYoon/v3_train_free_concat_1 | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 3842673976
num_examples: 2500
download_size: 1985741325
dataset_size: 3842673976
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
tachodex/msbte | ---
license: mit
---
|
louisbrulenaudet/code-domaine-etat-collectivites-mayotte | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code du domaine de l'Etat et des collectivités publiques applicable à la collectivité territoriale de Mayotte
source_datasets:
- original
pretty_name: Code du domaine de l'Etat et des collectivités publiques applicable à la collectivité territoriale de Mayotte
task_categories:
- text-generation
- table-question-answering
- summarization
- text-retrieval
- question-answering
- text-classification
size_categories:
- 1K<n<10K
---
# Code du domaine de l'Etat et des collectivités publiques applicable à la collectivité territoriale de Mayotte, non-instruct (2024-04-15)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Concurrent reading of the LegalKit
To use all the legal data published on LegalKit, you can use this code snippet:
```python
# -*- coding: utf-8 -*-
import concurrent.futures
import os
import datasets
from tqdm.notebook import tqdm
def dataset_loader(
name:str,
streaming:bool=True
) -> datasets.Dataset:
"""
Helper function to load a single dataset in parallel.
Parameters
----------
name : str
Name of the dataset to be loaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
dataset : datasets.Dataset
Loaded dataset object.
Raises
------
Exception
If an error occurs during dataset loading.
"""
try:
return datasets.load_dataset(
name,
split="train",
streaming=streaming
)
except Exception as exc:
logging.error(f"Error loading dataset {name}: {exc}")
return None
def load_datasets(
req:list,
streaming:bool=True
) -> list:
"""
Downloads datasets specified in a list and creates a list of loaded datasets.
Parameters
----------
req : list
A list containing the names of datasets to be downloaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
datasets_list : list
A list containing loaded datasets as per the requested names provided in 'req'.
Raises
------
Exception
If an error occurs during dataset loading or processing.
Examples
--------
>>> datasets = load_datasets(["dataset1", "dataset2"], streaming=False)
"""
datasets_list = []
with concurrent.futures.ThreadPoolExecutor() as executor:
future_to_dataset = {executor.submit(dataset_loader, name): name for name in req}
for future in tqdm(concurrent.futures.as_completed(future_to_dataset), total=len(req)):
name = future_to_dataset[future]
try:
dataset = future.result()
if dataset:
datasets_list.append(dataset)
except Exception as exc:
logging.error(f"Error processing dataset {name}: {exc}")
return datasets_list
req = [
"louisbrulenaudet/code-artisanat",
"louisbrulenaudet/code-action-sociale-familles",
# ...
]
datasets_list = load_datasets(
req=req,
streaming=True
)
dataset = datasets.concatenate_datasets(
datasets_list
)
```
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
- `start`: `string`, the date of entry into force of the article.
- `expiration`: `string`, the date of expiration of the article.
- `num`: `string`, the id of the article.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Feedback
If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com). |
davidscripka/MIT_environmental_impulse_responses | ---
license: unknown
task_categories:
- audio-classification
- automatic-speech-recognition
size_categories:
- n<1K
---
MIT Environmental Impulse Response Dataset
The audio recordings in this dataset are originally created by the Computational Audition Lab at MIT. The source of the data can be found at: [https://mcdermottlab.mit.edu/Reverb/IR_Survey.html](https://mcdermottlab.mit.edu/Reverb/IR_Survey.html).
The audio files in the dataset have been resampled to a sampling rate of 16 kHz. This resampling was done to reduce the size of the dataset while making it more suitable for various tasks, including data augmentation.
The dataset consists of 271 audio files, each in WAV format. These files collectively provide a diverse range of environmental impulse response data.
The license for this dataset is unknown. Please refer to the dataset source for any licensing information or usage restrictions, and cite appropriately. |
joey1895/new04_image | ---
license: apache-2.0
---
|
onuralp/open-otter | ---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
- name: data_source
dtype: string
splits:
- name: train
num_bytes: 48901728
num_examples: 162864
download_size: 22256986
dataset_size: 48901728
task_categories:
- multiple-choice
- question-answering
language:
- en
pretty_name: open-otter
size_categories:
- 100K<n<1M
---
## Table of Contents
- [Dataset Summary](#dataset-summary)
- [Dataset Attribution](#dataset-attribution)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Dataset Use](#dataset-use)
- [Use Cases](#use-cases)
- [Getting Started](#getting-started)
**Disclaimer: this dataset is curated for NeurIPS 2023 LLM efficiency challange, and currently work in progress. Please use at your own risk.**
<a name="dataset-summary"></a>
# Dataset Summary
We curated this dataset to finetune open source base models as part of [NeurIPS 2023 LLM Efficiency Challenge](https://llm-efficiency-challenge.github.io/) (1 LLM + 1 GPU + 1 Day). This challenge requires participants to use open source models and datasets with permissible licenses to encourage wider adoption, use and dissemination of open source contributions in generative AI space. Additionally, LLM generated datasets such as Alpaca and Orca datasets are not allowed.
**Open-Otter** combines the non-LLM generated subset of [Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) datasets with other datasets, and is used for finetuning Llama-2-7b, Llama-2-13b and Mistral-7b-v0.1 base models to perform reasonably well in a suit of reasoning tasks selected by the organizers. Please visit the challenge website for more detailed information on the rules.
<a name="dataset-attribution"></a>
# Dataset Attribution
<a name="languages"></a>
# Languages
Evaluation for the challenge includes only English text. Therefore, Open-Otter includes data sources only in English.
_Note: we are not aware of any compelling literature demonstrating the value of finetuning on multilingual datasets (over datasets in target language). Please leave a comment if you come across any relevant work addressing this question._
<a name="dataset-structure"></a>
# Dataset Structure
<a name="data-fields"></a>
## Data Fields
Data fields follow Alpaca style formatting.
The fields are:
1) 'input', an optional field for providing additional context for response type
2) 'output', response, answer or solution to the corresponding instruction (e.g., a multiple choice question)
3) 'instruction', required field including the question and multiple choice options (when applicable)
4) 'data_source', original dataset and split for the data instance
<a name="dataset-creation"></a>
# Dataset Creation
<a name="curation-rationale"></a>
## Curation Rationale
TODO: NeurIPS 2023 LLM Efficiency Challenge
<a name="source-data"></a>
## Source Data
We have combined the non-LLM-generated subset of Open-Platypus dataset with 4 additional datasets:
- [Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) dataset
- Excluding [airoboros-gpt4-1.4.1](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1) and [PRM800K](https://github.com/openai/prm800k)
- [ARC](https://allenai.org/data/arc) (Allen AI Reasoning Challenge)
- [CommonsenseQA](https://huggingface.co/datasets/commonsense_qa)
- [WinoGrande, debiased](https://huggingface.co/datasets/winogrande)
- [MedMCQA](https://huggingface.co/datasets/medmcqa)
Notably, train, validation and test splits were all included for each dataset. If answer key is not provided, test set is excluded.
<a name="dataset-use"></a>
# Dataset Use
## Getting Started
You can use Hugging Face datasets library to load this dataset.
```python
from datasets import load_dataset
dataset = load_dataset("onuralp/open-otter")
```
# Citation
If you find this dataset useful for your own work and are interested in acknowledging, please use the citation below.
```bibtex
@misc {onuralp2023,
author = { {Onuralp Soylemez} },
title = { open-otter (Revision 17db84f) },
year = 2023,
url = { https://huggingface.co/datasets/onuralp/open-otter },
doi = { 10.57967/hf/1270 },
publisher = { Hugging Face }
}
```
|
techiaith/banc-trawsgrifiadau-bangor | ---
language:
- cy
license: cc0-1.0
size_categories:
- 10K<n<100K
pretty_name: Banc Trawsgrifiadau Bangor
tags:
- verbatim transcriptions
- speech recognition
dataset_info:
features:
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 48000
- name: sentence
dtype: string
splits:
- name: clips
num_bytes: 678448153.375
num_examples: 28277
- name: train
num_bytes: 543955916.375
num_examples: 22621
- name: test
num_bytes: 134492237.0
num_examples: 5656
download_size: 1345245508
dataset_size: 1356896306.75
configs:
- config_name: default
data_files:
- split: clips
path: data/clips-*
- split: train
path: data/train-*
- split: test
path: data/test-*
---
[See below for English](#bangor-transcription-bank)
# Banc Trawsgrifiadau Bangor
Dyma fanc o 35 awr 39 munud a 53 eiliad o segmentau o leferydd naturiol dros hanner cant o gyfranwyr ar ffurf ffeiliau mp3, ynghyd â thrawsgrifiadau 'verbatim' cyfatebol o’r lleferydd ar ffurf ffeil .tsv. Mae'r mwyafrif o'r lleferydd yn leferydd digymell, naturiol. Dosbarthwn y deunydd hwn o dan drwydded agored CC0.
## Pwrpas
Pwrpas y trawsgrifiadau hyn yw gweithredu fel data hyfforddi ar gyfer modelau adnabod lleferydd, gan gynnwys [ein modelau wav2vec](https://github.com/techiaith/docker-wav2vec2-cy). Ar gyfer y diben hwnnw, mae gofyn am drawsgrifiadau mwy verbatim o'r hyn a ddywedwyd na'r hyn a welir mewn trawsgrifiadau traddodiadol ac mewn isdeitlau, felly datblygwyd confensiwn arbennig ar gyfer y gwaith trawsgrifio ([gweler isod](#confensiynau_trawsgrifio)). Gydag ein modelau wav2vec, caiff cydran ychwnaegol, sef 'model iaith' ei defnyddio ar ôl y model adnabod lleferydd i safoni mwy ar allbwn y model iaith i fod yn debycach i drawsgrifiadau traddodiadol ac isdeitlau.
Rydyn ni wedi darparu 3 ffeil .tsv, sef clips.tsv, train.tsv a test.tsv. Mae clips.tsv yn cynnwys ein trawsgrifiadau i gyd. Crëwyd train.tsv a test.tsv er mewn darparu setiau 'safonol' sy'n caniatáu i ddefnyddwyr allu gymharu modelau gan wahanol hyfforddwyr yn deg,hynny yw fe'u crëwyd at bwrpas meincnodi. Mae train.tsv yn cynnwys 80% o'n trawsgrifiadau, a test.tsv yn cynnwys y 20% sy'n weddill.
Dyma enghraifft o gynnwys y data:
```
audio_filename audio_filesize transcript duration
f86a046fd0964e0386d8c1363907183d.mp3 898272 *post industrial* yym a gyda yy dwi'n ca'l deud 5092
f0c2310fdca34faaa83beca5fa7ed212.mp3 809720 sut i ymdopio felly, wedyn erbyn hyn mae o nôl yn y cartra 4590
3eec3feefe254c9790739c22dd63c089.mp3 1335392 Felly ma' hon hefyd yn ddogfen fydd yn trosglwyddo gyda'r plant bobol ifanc o un cam i'r llall ac hefyd erbyn hyn i'r coleg 'lly. 7570
```
Ceir pedair colofn yn y ffeiliau .tsv. Y cyntaf yw enw’r ffeil sain. Maint y ffeil sain yw’r ail. Y trawsgrifiad ei hun sydd yn y drydedd golofn. Hyd y clip sain sydd yn yr olaf.
Dyma'r wybodaeth am y colofnau.
| Maes| Esboniad |
| ------ | ------ |
| `audio_filename`| Enw'r ffeil sain o fewn y ffolder 'clips'|
| `audio_filesize` | Maint y ffeil|
| `transcript` | Trawsgrifiad |
| `duration` | Hyd amser y clip mewn milliseconds. |
## Cyfieithu Is-set
Rydyn ni hefyd wedi cyfieithu 500 o'n trawsgrifiadau i'r Saesneg a chyhoeddi'r cyfieithiadau gyda'u trawsgrifiadau gwreiddiol yn y ffeil translations.tsv.
Dyma enghraifft o gynnwys y data:
```
mp3_filename Original Translation
8d6b7347cae6092930aa9b436045e33d.mp3 fel oedden ni odd yym <anadlu> odd pob pennod yn troi mewn i Ben-Hur rywfaint ag yn yy, odd hi'n eitha anodd as we were um <breath> every episode turned into Ben-Hur, somewhat, and was er, it was quite difficult
ce526eaf61557b8e3eb53eb1a2f55076.mp3 pan ddechreuon ni'r podlediad yma y bwriad odd i ga'l un pennod bob bythefnos <anadlu> ond yy, wrth i ni fynd ymlaen when we started this podcast the intention was to have one episode every two weeks <breath> but er, as we go on
```
Ceir tair colofn yn y ffeil translation.tsv. Y cyntaf yw enw’r ffeil sain. Y trawsgrifiad Cymraeg sydd yn yr ail golofn. Y cyfieithiad Saesneg sydd yn yr olaf.
Dyma'r wybodaeth am y colofnau.
| Maes| Esboniad |
| ------ | ------ |
| `mp3_filename`| Enw'r ffeil sain o fewn y ffolder 'clips'|
| `Original` | Y trawsgrifiad Cymraeg|
| `Translation` | Y cyfieithiad Saesneg|
## Y Broses o Greu’r Adnodd
Casglwyd y ffeiliau sain yn bennaf o bodlediadau Cymraeg gyda chaniatâd eu perchnogion yn ogystal â'r cyfranwyr unigol. Rydym yn ddiolchgar tu hwnt i’r bobl yna. Yn ogystal, crewyd rhywfaint o sgriptiau ar batrwm eitemau newyddion ac erthyglau a'u darllen gan ymchwilwyr yr Uned Technolegau Iaith er mwyn sicrhau bod cynnwys o'r math hwnnw yn y banc.
Gyrrwyd y ffeiliau sain trwy ein trawsgrifiwr awtomataidd mewnol i segmentu’r sain a chreu trawsgrifiadau amrwd. Defnyddiwyd pecyn trawsgrifio Elan 6.4 (ar gael o https://archive.mpi.nl/tla/elan) gan drawsgrifwyr profiadol i wrando ar a chywiro’r trawsgrifiad amrwd.
## Nodyn Ynghylch Anonymeiddio’r Cynnwys
Er tegwch i’r cyfranwyr, rydyn ni wedi anonymeiddio’r trawsgrifiadau. Penderfynwyd anonymeiddio nid yn unig enwau pobl unigol, ond hefyd unrhyw Wybodaeth Bersonol Adnabyddadwy (PII) gan gynnwys, ond nid yn gyfunedig i:
* Rhif ffôn
* Teitlau swyddi/galwedigaethau
* Gweithleoedd
* Enwau mannau cyhoeddus
* Lleoliad daearyddol
* Dyddiadau/amseroedd
Wrth drawsgrifio marciwyd pob segment oedd yn cynnwys PII gyda’r tag \<PII>, yna wnaethom hidlo allan pob segment oedd yn cynnwys tag \<PII> er mwyn sicrhau nad oedd unrhyw wybodaeth bersonol yn cael eu cyhoeddi fel rhan o’r adnodd hwn.
Rydym hefyd wedi newid trefn trawsgrifiadau i fod ar hap, felly nid ydynt wedi'u cyhoeddi yn y drefn y maent yn eu ymddangos yn y ffeiliau sain gwreiddiol.
<a name="confensiynau_trawsgrifio"></a>
## Confensiynau Trawsgrifio
Datblygwyd y confensiynau trawsgrifio hyn er mwyn sicrhau fod y trawsgrifiadau nid yn unig yn verbatim ond hefyd yn gyson. Fe’u datblygwyd trwy gyfeirio at gonfensiynau a ddefnyddir gan yr Uned yn y gorffennol, confensiynau eraill megis y rhai a defnyddiwyd yng nghorpora CorCenCC, Siarad, CIG1 a CIG2, a hefyd trwy broses o ddatblygu parhaol wrth i’r tîm ymgymryd â’r dasg o drawsgrifio.
**NODWCH** - gan ein bod wedi datblygu’r egwyddorion trawsgrifio yn rhannol wrth ymgymryd â’r dasg o drawsgrifio nid yw’r trawsgrifiadau cynnar o reidrwydd yn dilyn yr egwyddorion cant y cant. Bwriadwn wirio’r trawsgrifiadau wedi i ni fireinio’r confensiynau.
### Collnodau
Ni ddefnyddiwyd collnodau i marcio pob un llythyren a hepgorwyd gan siaradwyr. Er enghraifft, _gwitho_ (sef ynganiad o _gweithio_) sy’n gywir, nid _gw’ith’o_
Yn hytrach, defnyddiwyd collnodau i wahaniaethu rhwng gwahanol eiriau oedd yn cael eu sillafu'r union yr un fath fel arall. Er enghraifft rydym yn defnyddio collnod o flaen _’ma_ (sef _yma_) i wahaniaethu rhyngddo â _ma’_ (sef _mae_), _gor’o’_ i wahaniaethu rhwng _gorfod_ a ffurf trydydd person unigol amser dibynnol presennol _gori_, a _pwysa’_ i wahaniaethu rhwng ffurf luosog _pwys_ a nifer o ffurfiau berfol posib _pwyso_.
Fodd bynnag, ceir eithriad i’r rheol hon, a hynny pan fo sillafu gair heb gollnod yn newid sŵn y llythyren cyn neu ar ôl y collnod, ac felly _Cymra’g_ sy’n gywir, nid _Cymrag_.
### Tagiau
Wrth drawsgrifio, defnyddiwyd y tagiau hyn i recordio elfennau oedd y tu hwnt i leferydd yr unigolion:
* \<anadlu>
* \<anadlu i mewn yn sydyn>
* \<aneglur>
* \<cerddoriaeth>
* \<chwerthin>
* \<chwibanu>
* \<chwythu allan>
* \<clapio>
* \<clirio gwddf>
* \<cusanu>
* \<distawrwydd>
* \<ochneidio>
* \<PII>
* \<peswch>
* \<sniffian>
* \<twtian>
Rhagwelwn y bydd y rhestr hon yn chwyddo wrth i ni drawsgrifio mwy o leferydd ac wrth i ni daro ar draws mwy o elfennau sydd y tu hwnt i leferydd unigolion.
### Synau nad ydynt yn eiriol
Ymdrechwyd i drawsgrifio synau nad ydynt yn eiriol yn gyson. Er enghraifft, defnyddiwyd _yy_ bob tro (yn hytrach nag _yrr_, _yr_ neu _err_ neu gymysgedd o’r rheiny) i gynrychioli neu adlewyrchu’r sŵn a wnaethpwyd pan oedd siaradwr yn ceisio meddwl neu oedi wrth siarad.
Defnyddiwyd y canlynol wrth drawsgrifio:
* yy
* yym
* hmm
* m-hm
Eto, rhagwelwn y bydd y rhestr hon yn chwyddo wrth i ni drawsgrifio mwy o leferydd ac wrth i ni daro ar draws mwy o synau nad ydynt yn eiriol.
### Geiriau Saesneg
Rydym wedi amgylchynu bob gair neu ymadrodd Saesneg gyda sêr, er enghraifft:
> Dwi’n deall **\*sort of\***.
### Cymreigio berfenwau
Pan fo siaradwyr yn defnyddio geiriau Saesneg fel berfenwau (trwy ychwanegu _io_ ar ddiwedd y gair er enghraifft) rydym wedi ymdrechu i sillafu’r gair gan ddefnyddio confensiynau sillafu Cymreig yn hytrach nag ychwanegu _io_ at sillafiad Saesneg o’r gair. Er enghraifft rydym wedi trawsgrifio _heitio_ yn hytrach na _hateio_, a _lyfio_ yn hytrach na _loveio_.
### Cywiro cam-siarad
I sicrhau ein bod ni’n glynu at egwyddorion trawsgrifio verbatim penderfynwyd na ddylem gywiro cam-siarad neu gam-ynganu siaradwyr. Er enghraifft, yn y frawddeg ganlynol:
> enfawr fel y diffyg o fwyd yym **efallu** cam-drin
mae'n amlwg mai’r gair _efallai_ sydd dan sylw mewn gwirionedd, ond fe’i trawsgrifiwyd fel ei glywir.
### Atalnodi
Defnyddiwyd atalnodau llawn, marciau cwestiwn ac ebychnodau wrth drawsgrifio’r lleferydd.
Rydym wedi amgylchynu bob gair neu ymadrodd sydd wedi ei dyfynnu gyda _”_, er enghraifft:
> Dywedodd hi **”Dwi’n mynd”** ond aeth hi ddim.
### Nodyn ynghylch ein defnydd o gomas
Gan mai confensiwn ysgrifenedig yw coma yn y bôn, ni ddefnyddiwyd comas cymaint wrth drawsgrifio. Byddai defnyddio coma lle y disgwylir i’w weld mewn testun ysgrifenedig ddim o reidrwydd wedi adlewyrchu lleferydd yr unigolyn. Dylid cadw hynny mewn cof wrth ddarllen y trawsgrifiadau.
### Sillafu llythrennau
Sillafwyd llythrennau unigol yn hytrach na thrawsgrifio’r llythrennau unigol yn unig.
Hynny yw, hyn sy’n gywir:
> Roedd ganddo **ow si di**
**ac nid:**
> Roedd ganddo **O C D**
**na chwaith:**
> Roedd ganddo **OCD**
### Rhifau
Trawsgrifiwyd rhifau fel geiriau yn hytrach na digidau, hynny yw hyn sy’n gywir:
> Y flwyddyn dwy fil ac ugain
**ac nid:**
> Y flwyddyn 2020
### Gorffen gair ar ei hanner
Marciwyd gair oedd wedi ei orffen ar ei hanner gyda `-`. Er enghraifft:
> Ma’n rhaid i mi **ca-** cael diod.
### Gorffen brawddeg ar ei hanner/ailddechrau brawddeg
Marciwyd brawddeg oedd wedi ei gorffen ar ei hanner gyda `...`. Er enghraifft:
> Ma’n rhaid i mi ca’l... Ma’ rhaid i mi brynu diod.
### Siaradwr yn torri ar draws siaradwr arall
Ceir yn y data llawer o enghreifftiau o siaradwr yn torri ar draws y prif leferydd gan ddefnyddio synau nad ydynt yn eiriol, geiriau neu ymadroddion (megis _m-hm_, _ie_, _ydi_, _yn union_ ac ati). Pan oedd y ddau siaradwr i'w clywed yn glir ag ar wahân, rhoddwyd `...` ar ddiwedd rhan gyntaf y lleferydd toredig, a `...` arall ar ddechrau ail ran y lleferydd toredig, fel yn yr enghraifft ganlynol:
> Ond y peth yw... M-hm. ...mae’r ddau yn wir
Pan nad oedd y ddau siaradwyr i'w clywed yn glir ag ar wahân, fe hepgorwyd y lleferydd o’r data.
### Rhegfeydd
Dylid nodi ein bod ni heb hepgor rhegfeydd wrth drawsgrifio.
## Y Dyfodol
Wrth ddefnyddio’r banc trawsgrifiadau dylid cadw mewn cof mai fersiwn cychwynnol ydyw. Bwriadwn fireinio a chysoni ein trawsgrifiadau ymhellach, ac ychwanegu mwy fyth o drawsgrifiadau i’r banc yn rheolaidd dros y flwyddyn nesaf
## Cyfyngiadau
Er mwyn parchu'r cyfrannwyr, wrth lwytho'r data hwn i lawr rydych yn cytuno i beidio â cheisio adnabod y siaradwyr yn y data.
## Diolchiadau
Diolchwn i'r cyfrannwyr am eu caniatâd i ddefnyddio'u lleferydd. Rydym hefyd yn ddiolchgar i Lywodraeth Cymru am ariannu’r gwaith hwn fel rhan o broject Technoleg Testun, Lleferydd a Chyfieithu ar gyfer yr Iaith Gymraeg.
---
# Bangor Transcription Bank
This resource is a bank of 35 hours 39 minutes and 53 seconds of segments of natural speech from over 50 contributors in mp3 file format, together with corresponding 'verbatim' transcripts of the speech in .tsv file format. The majority of the speech is spontaneous, natural speech. We distribute this material under a CC0 open license.
## Purpose
The purpose of these transcripts is to act as training data for speech recognition models, including [our wav2vec models](https://github.com/techiaith/docker-wav2vec2-cy). For that purpose, transcriptions are more verbatim than what is seen in traditional transcriptions and than what is required for subtitling purposes, thus a bespoke set of conventions has been developed for the transcription work ([see below](#transcription_conventions) ). Our wav2vec models use an auxiliary component, namely a 'language model', to further standardize the speech recognition model’s output in order that it be more similar to traditional transcriptions and subtitles.
We have provided 3 .tsv files, namely clips.tsv, train.tsv and test.tsv. clips.tsv contains all of our transcripts. train.tsv and test.tsv were created to provide 'standard' sets that allow users to compare models trained by different trainers fairly, i.e. they were created as a 'benchmark'. train.tsv contains 80% of our transcripts, and test.tsv contains the remaining 20%.
Here is an example of the data content:
```
audio_filename audio_filesize transcript duration
f86a046fd0964e0386d8c1363907183d.mp3 898272 *post industrial* yym a gyda yy dwi'n ca'l deud 5092
f0c2310fdca34faaa83beca5fa7ed212.mp3 809720 sut i ymdopio felly, wedyn erbyn hyn mae o nôl yn y cartra 4590
3eec3feefe254c9790739c22dd63c089.mp3 1335392 Felly ma' hon hefyd yn ddogfen fydd yn trosglwyddo gyda'r plant bobol ifanc o un cam i'r llall ac hefyd erbyn hyn i'r coleg 'lly. 7570
```
There are four columns in the .tsv files. The first is the name of the audio file. The second is the size of the audio file. The transcript itself appears in the third column. The length of the audio clip appears in the last.
Here is the information about the columns.
| Field| Explanation |
| ------ | ------ |
| `audio_filename`| The name of the audio file within the 'clips' folder|
| `audio_filesize` | The size of the file |
| `transcript` | Transcript |
| `duration` | Duration of the clip in milliseconds. |
### Translation of a Sub-set
We have also translated 500 of our transcripts into English and published the translations together with their original transcripts in the translations.tsv file.
Here is an example of the data content:
```
mp3_filename Original Translation
8d6b7347cae6092930aa9b436045e33d.mp3 fel oedden ni odd yym <anadlu> odd pob pennod yn troi mewn i Ben-Hur rywfaint ag yn yy, odd hi'n eitha anodd as we were um <breath> every episode turned into Ben-Hur, somewhat, and was er, it was quite difficult
ce526eaf61557b8e3eb53eb1a2f55076.mp3 pan ddechreuon ni'r podlediad yma y bwriad odd i ga'l un pennod bob bythefnos <anadlu> ond yy, wrth i ni fynd ymlaen when we started this podcast the intention was to have one episode every two weeks <breath> but er, as we go on
```
There are three columns in the translation.tsv file. The first is the name of the audio file. The Welsh transcription is in the second column. The English translation is in the last.
Here is the information about the columns.
| Field| Explanation |
| ------ | ------ |
| `mp3_filename`| The name of the audio file within the 'clips' folder|
| `Original` | The Welsh transcription|
| `Translation` | The English translation|
## The Process of Creating the Resource
The audio files were mainly collected from Welsh podcasts, after having gained the consent of the podcast owners and individual contributors to do so. We are extremely grateful to those people. In addition, some scripts were created which mimicked the pattern of news items and articles. These scripts were then read by Language Technologies Unit researchers in order to ensure that content of that type was included in the bank.
The audio files were run through our in-house automated transcriber to segment the audio and create raw transcripts. Using Elan 6.4 (available from https://archive.mpi.nl/tla/elan), experienced transcribers listened to and corrected the raw transcript.
## A Note About Content Anonymization
Out of respect to the contributors, we have anonymised all transcripts. It was decided to anonymize not only the names of individual people, but also any other Personally Identifiable Information (PII) including, but not limited to:
* Phone number
* Job titles/occupations
* Workplaces
* Names of public places
* Geographical location
* Dates/times
When transcribing, all segments containing PII were marked with the \<PII> tag, we then filtered out all segments containing a \<PII> tag to ensure no personal information was published as part of this resource.
We have also randomized the order of the segments so that they are not published in the order they appeared in the original audio files.
<a name="transcription_conventions"></a>
## Transcription Conventions
These transcription conventions were developed to ensure that the transcriptions were not only verbatim but also consistent. They were developed by referring to conventions used by the Unit in the past, conventions such as those used in the CorCenCC, Siarad, CIG1 and CIG2 corpora, and also through a process of ongoing development as the team undertook the task of transcription.
**NOTE** - as we have partially developed the conventions at the same time as undertaking the task of transcription the early transcriptions may not follow the latest principles faithfully. We intend to check the transcripts after we have refined the conventions.
### Apostrophes
Apostrophes were not used to mark every single letter omitted by speakers. For example, _gwitho_ (which is a pronunciation of _gweithio_) is correct, not _gw’ith'o_.
Rather, apostrophes were used to distinguish between different words that were otherwise spelled identically. For example we use an apostrophe in front of _'ma_ (a pronunciation of _yma_) to distinguish it from _ma'_ (a pronunciation of _mae_), _gor'o'_ to distinguish between _gorfod_ and the third person singular form of the present dependent tense _gori_, and _pwysa'_ to distinguish between the plural form of _pwys_ and a number of possible verb forms of _pwyso_.
However, there is an exception to this rule, that being when spelling a word without an apostrophe would change the sound of the letter before or after the apostrophe, thus _Cymra'g_ is correct, not _Cymrag_.
### Tags
When transcribing, these tags were used to record elements that were external to the speech of the individuals:
* \<anadlu>
* \<anadlu i mewn yn sydyn>
* \<aneglur>
* \<cerddoriaeth>
* \<chwerthin>
* \<chwibanu>
* \<chwythu allan>
* \<clapio>
* \<clirio gwddf>
* \<cusanu>
* \<distawrwydd>
* \<ochneidio>
* \<PII>
* \<peswch>
* \<sniffian>
* \<twtian>
We anticipate that this list will grow as we transcribe more speech and as we come across more elements that are external to the speech of individuals.
### Non-verbal sounds
Efforts were made to transcribe non-verbal sounds consistently. For example, _yy_ was always used (rather than _yrr_, _yr_ or _err_, or a mixture of those) to represent or reflect the sound made when a speaker was trying to think or paused in speaking.
The following were used in transcription:
* yy
* yym
* hmm
* m-hm
Again, we anticipate that this list will grow as we transcribe more speech and as we encounter more non-verbal sounds.
### English words
We have surrounded each English word or phrase with asterixis, for example:
> Dwi’n deall **\*sort of\***.
### Adapting English words as Welsh language infinitives
When speakers use English words as infinitives (by adding _io_ at the end of the word for example) we have endeavoured to spell the word using Welsh spelling conventions rather than adding _io_ to the English spelling of the word. For example we have transcribed _heitio_ instead of _hateio_, and _lyfio_ instead of _loveio_.
### Correction of mis-pronunciations
To ensure that we adhere to the principles of verbatim transcription it was decided that we should not correct speakers' mis-pronunciations. For example, in the following sentence:
> enfawr fel y diffyg o fwyd yym **efallu** cam-drin
it is clear that _efallai_ is the intended word, but it is transcribed as it is heard.
### Punctuation
Full stops, question marks and exclamation marks were used when transcribing the speech.
We have surrounded all quoted words or phrases with _”_, for example:
> Dywedodd hi **”Dwi’n mynd”** ond aeth hi ddim.
### A note about our use of commas
As a comma is essentially a convention used for written text, commas were not used prolifically in transcription. Using a comma where one would expected to see it in a written text during transcription would not necessarily have reflected the individual's speech. This should be borne in mind when reading the transcripts.
### Individual letters
Individual letters were spelled out rather than being transcribed as individual letters.
That is, this is correct:
> Roedd ganddo **ow si di**
**not:**
> Roedd ganddo **O C D**
**nor:**
> Roedd ganddo **OCD**
### Numbers
Numbers were transcribed as words rather than digits, thus this is correct:
> Y flwyddyn dwy fil ac ugain
**rather than:**
> Y flwyddyn 2020
### Half-finished words
Half-finished words are marked with a `-`. For example:
> Ma’n rhaid i mi **ca-** cael diod.
### Half-finished/restarted sentences
Half-finished sentences are marked with a `...`. For example:
> Ma’n rhaid i mi ca’l... Ma’ rhaid i mi brynu diod.
### Speaker interruptions
There are many examples of a speaker interrupting another speaker by using non-verbal sounds, words or phrases (such as _m-hm_, _ie_, _ydi_, _yn union_ etc.) in the data. When the two speakers could be heard clearly and distinctly, a `...` was placed at the end of the first part of the broken speech, and another `...` at the beginning of the second part of the broken speech, as in the following example:
> Ond y peth yw... M-hm. ...mae’r ddau yn wir
When the two speakers could not be heard clearly and distinctly, the speech was omitted from the data.
### Swearwords
It should be noted that we have not omitted swearwords when transcribing.
## The future
That this is an initial version of the transcript bank should be borne in mind when using this resource. We intend to refine and harmonize our transcripts further, and add yet more transcripts to the bank regularly over the next year.
## Restrictions
In order to respect the contributors, by downloading this data you agree not to attempt to identify the speakers in the data.
## Acknowledgements
We thank the contributors for their permission to use their speech. We are also grateful to the Welsh Government for funding this work as part of the Text, Speech and Translation Technology project for the Welsh Language.
|
Asap7772/skewexp_minlength | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: output
dtype: string
- name: text
dtype: string
- name: alpaca_text
dtype: string
- name: prompt
dtype: string
- name: alpaca_prompt
dtype: string
- name: y_ref
dtype: string
- name: y_1
dtype: string
- name: y_2
dtype: string
- name: y_w
dtype: string
- name: y_w_alpaca
dtype: string
- name: y_l
dtype: string
- name: y_l_alpaca
dtype: string
- name: y_w_score
dtype: float64
- name: y_l_score
dtype: float64
- name: score_diff
dtype: float64
splits:
- name: train
num_bytes: 62156813
num_examples: 19000
- name: test
num_bytes: 3233542
num_examples: 1000
download_size: 31144787
dataset_size: 65390355
---
# Dataset Card for "skewexp_minlength"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vikhyatk/synthetic-pepe | ---
pretty_name: "synthetic pepe"
---
by using this dataset you are agreeing to the fact that the pleiades star system is a binary system and any claim otherwise is a lie |
Jackmin108/c4-en-validation-mini | ---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
dataset_info:
features:
- name: text
dtype: string
- name: timestamp
dtype: string
- name: url
dtype: string
splits:
- name: validation
num_bytes: 175483
num_examples: 100
download_size: 116815
dataset_size: 175483
---
# Dataset Card for "c4-en-validation-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
oraul/orca_small_splitted | ---
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 3842159531.8209243
num_examples: 851442
- name: test
num_bytes: 1152649664.5591998
num_examples: 255433
- name: valid
num_bytes: 493995936.6198757
num_examples: 109472
download_size: 2964895612
dataset_size: 5488805133.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
|
open-llm-leaderboard/details_ehartford__WizardLM-33B-V1.0-Uncensored | ---
pretty_name: Evaluation run of ehartford/WizardLM-33B-V1.0-Uncensored
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ehartford/WizardLM-33B-V1.0-Uncensored](https://huggingface.co/ehartford/WizardLM-33B-V1.0-Uncensored)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 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 agregated 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_ehartford__WizardLM-33B-V1.0-Uncensored\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-12T23:21:17.619828](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__WizardLM-33B-V1.0-Uncensored/blob/main/results_2023-10-12T23-21-17.619828.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 \"em\": 0.13328439597315436,\n\
\ \"em_stderr\": 0.0034807081740792067,\n \"f1\": 0.20888108221476515,\n\
\ \"f1_stderr\": 0.003634426964391504,\n \"acc\": 0.48157132744485465,\n\
\ \"acc_stderr\": 0.01121741880244755\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.13328439597315436,\n \"em_stderr\": 0.0034807081740792067,\n\
\ \"f1\": 0.20888108221476515,\n \"f1_stderr\": 0.003634426964391504\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1865049279757392,\n \
\ \"acc_stderr\": 0.010729140039689902\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.77663772691397,\n \"acc_stderr\": 0.011705697565205198\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ehartford/WizardLM-33B-V1.0-Uncensored
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: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|arc:challenge|25_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_12T23_21_17.619828
path:
- '**/details_harness|drop|3_2023-10-12T23-21-17.619828.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-12T23-21-17.619828.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_12T23_21_17.619828
path:
- '**/details_harness|gsm8k|5_2023-10-12T23-21-17.619828.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-12T23-21-17.619828.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hellaswag|10_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T10:34:34.277823.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
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path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
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path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T10:34:34.277823.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T10:34:34.277823.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_12T23_21_17.619828
path:
- '**/details_harness|winogrande|5_2023-10-12T23-21-17.619828.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-12T23-21-17.619828.parquet'
- config_name: results
data_files:
- split: 2023_08_09T10_34_34.277823
path:
- results_2023-08-09T10:34:34.277823.parquet
- split: 2023_10_12T23_21_17.619828
path:
- results_2023-10-12T23-21-17.619828.parquet
- split: latest
path:
- results_2023-10-12T23-21-17.619828.parquet
---
# Dataset Card for Evaluation run of ehartford/WizardLM-33B-V1.0-Uncensored
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ehartford/WizardLM-33B-V1.0-Uncensored
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [ehartford/WizardLM-33B-V1.0-Uncensored](https://huggingface.co/ehartford/WizardLM-33B-V1.0-Uncensored) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 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 agregated 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_ehartford__WizardLM-33B-V1.0-Uncensored",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-12T23:21:17.619828](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__WizardLM-33B-V1.0-Uncensored/blob/main/results_2023-10-12T23-21-17.619828.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": {
"em": 0.13328439597315436,
"em_stderr": 0.0034807081740792067,
"f1": 0.20888108221476515,
"f1_stderr": 0.003634426964391504,
"acc": 0.48157132744485465,
"acc_stderr": 0.01121741880244755
},
"harness|drop|3": {
"em": 0.13328439597315436,
"em_stderr": 0.0034807081740792067,
"f1": 0.20888108221476515,
"f1_stderr": 0.003634426964391504
},
"harness|gsm8k|5": {
"acc": 0.1865049279757392,
"acc_stderr": 0.010729140039689902
},
"harness|winogrande|5": {
"acc": 0.77663772691397,
"acc_stderr": 0.011705697565205198
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
kpriyanshu256/MultiTabQA-multitable_pretraining-train-v2-27000 | ---
dataset_info:
features:
- name: tables
sequence: string
- name: table_names
sequence: string
- name: query
dtype: string
- name: answer
dtype: string
- name: source
dtype: string
- name: target
dtype: string
- name: source_latex
dtype: string
- name: target_latex
dtype: string
- name: source_html
dtype: string
- name: target_html
dtype: string
- name: source_markdown
dtype: string
- name: target_markdown
dtype: string
splits:
- name: train
num_bytes: 2495468704
num_examples: 500
download_size: 528512802
dataset_size: 2495468704
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
louisbrulenaudet/code-civil | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code civil
source_datasets:
- original
pretty_name: Code civil
task_categories:
- text-generation
- table-question-answering
- summarization
- text-retrieval
- question-answering
- text-classification
size_categories:
- 1K<n<10K
---
# Code civil, non-instruct (2024-04-15)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Concurrent reading of the LegalKit
To use all the legal data published on LegalKit, you can use this code snippet:
```python
# -*- coding: utf-8 -*-
import concurrent.futures
import os
import datasets
from tqdm.notebook import tqdm
def dataset_loader(
name:str,
streaming:bool=True
) -> datasets.Dataset:
"""
Helper function to load a single dataset in parallel.
Parameters
----------
name : str
Name of the dataset to be loaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
dataset : datasets.Dataset
Loaded dataset object.
Raises
------
Exception
If an error occurs during dataset loading.
"""
try:
return datasets.load_dataset(
name,
split="train",
streaming=streaming
)
except Exception as exc:
logging.error(f"Error loading dataset {name}: {exc}")
return None
def load_datasets(
req:list,
streaming:bool=True
) -> list:
"""
Downloads datasets specified in a list and creates a list of loaded datasets.
Parameters
----------
req : list
A list containing the names of datasets to be downloaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
datasets_list : list
A list containing loaded datasets as per the requested names provided in 'req'.
Raises
------
Exception
If an error occurs during dataset loading or processing.
Examples
--------
>>> datasets = load_datasets(["dataset1", "dataset2"], streaming=False)
"""
datasets_list = []
with concurrent.futures.ThreadPoolExecutor() as executor:
future_to_dataset = {executor.submit(dataset_loader, name): name for name in req}
for future in tqdm(concurrent.futures.as_completed(future_to_dataset), total=len(req)):
name = future_to_dataset[future]
try:
dataset = future.result()
if dataset:
datasets_list.append(dataset)
except Exception as exc:
logging.error(f"Error processing dataset {name}: {exc}")
return datasets_list
req = [
"louisbrulenaudet/code-artisanat",
"louisbrulenaudet/code-action-sociale-familles",
# ...
]
datasets_list = load_datasets(
req=req,
streaming=True
)
dataset = datasets.concatenate_datasets(
datasets_list
)
```
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
- `start`: `string`, the date of entry into force of the article.
- `expiration`: `string`, the date of expiration of the article.
- `num`: `string`, the id of the article.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Feedback
If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com). |
hmmamalrjoub/Islam_Question_and_Answer | ---
language:
- ar
task_categories:
- question-answering
size_categories:
- n<1K
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## 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
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[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. -->
[More Information Needed]
## 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]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### 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. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
CyberHarem/furutaka_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of furutaka/古鷹 (Kantai Collection)
This is the dataset of furutaka/古鷹 (Kantai Collection), containing 500 images and their tags.
The core tags of this character are `brown_hair, short_hair, yellow_eyes, glowing_eye, heterochromia, hair_ornament, hairclip, brown_eyes, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 432.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furutaka_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 300.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furutaka_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1118 | 610.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furutaka_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 403.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furutaka_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1118 | 777.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furutaka_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/furutaka_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, brown_sweater, glowing, official_alternate_costume, looking_at_viewer, smile, solo, open_mouth, white_shirt, collared_shirt, grey_skirt, heart, simple_background, upper_body |
| 1 | 11 |  |  |  |  |  | 1girl, glowing, serafuku, solo, upper_body, looking_at_viewer, red_neckerchief, blue_sailor_collar, smile, white_background, simple_background |
| 2 | 12 |  |  |  |  |  | 1girl, glowing, serafuku, single_elbow_glove, solo, looking_at_viewer, red_neckerchief, black_gloves, blush, bodysuit, blue_sailor_collar, upper_body, open_mouth, simple_background, short_sleeves, smile, white_background |
| 3 | 10 |  |  |  |  |  | 1girl, blue_skirt, bodysuit, pleated_skirt, red_neckerchief, serafuku, single_elbow_glove, single_thighhigh, solo, blue_sailor_collar, cowboy_shot, glowing, black_gloves, black_thighhighs, covered_navel, simple_background, smile, white_background, hair_between_eyes, looking_at_viewer, blush, short_sleeves |
| 4 | 7 |  |  |  |  |  | 1girl, blue_skirt, glowing, pleated_skirt, serafuku, single_thighhigh, solo, blue_sailor_collar, bodysuit, single_kneehigh, full_body, red_neckerchief, single_elbow_glove, uneven_legwear, white_background, simple_background, smile, black_gloves, looking_at_viewer, sitting |
| 5 | 6 |  |  |  |  |  | 1girl, blue_kimono, obi, smile, hair_between_eyes, solo, yukata, floral_print, looking_at_viewer, official_alternate_costume, wide_sleeves, dated, glowing, hair_flower, open_mouth, twitter_username, upper_body |
| 6 | 7 |  |  |  |  |  | cowboy_shot, 1girl, cleavage, glowing, looking_at_viewer, solo, smile, blue_bikini, collarbone, large_breasts, leaning_forward, medium_breasts, navel, simple_background |
| 7 | 11 |  |  |  |  |  | 1boy, 1girl, blush, hetero, nipples, solo_focus, cum_in_pussy, penis, sex, vaginal, censored, heart, medium_breasts, nude, open_mouth, sweat, glowing, navel, smile, lying, spread_legs |
| 8 | 7 |  |  |  |  |  | detached_collar, fake_animal_ears, glowing, playboy_bunny, rabbit_ears, strapless_leotard, wrist_cuffs, 1girl, black_leotard, solo, cleavage, cowboy_shot, medium_breasts, white_background, alternate_costume, black_bowtie, black_pantyhose, brown_pantyhose, fake_tail, rabbit_tail, smile, twitter_username |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | brown_sweater | glowing | official_alternate_costume | looking_at_viewer | smile | solo | open_mouth | white_shirt | collared_shirt | grey_skirt | heart | simple_background | upper_body | serafuku | red_neckerchief | blue_sailor_collar | white_background | single_elbow_glove | black_gloves | blush | bodysuit | short_sleeves | blue_skirt | pleated_skirt | single_thighhigh | cowboy_shot | black_thighhighs | covered_navel | hair_between_eyes | single_kneehigh | full_body | uneven_legwear | sitting | blue_kimono | obi | yukata | floral_print | wide_sleeves | dated | hair_flower | twitter_username | cleavage | blue_bikini | collarbone | large_breasts | leaning_forward | medium_breasts | navel | 1boy | hetero | nipples | solo_focus | cum_in_pussy | penis | sex | vaginal | censored | nude | sweat | lying | spread_legs | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | strapless_leotard | wrist_cuffs | black_leotard | alternate_costume | black_bowtie | black_pantyhose | brown_pantyhose | fake_tail | rabbit_tail |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------------|:----------|:-----------------------------|:--------------------|:--------|:-------|:-------------|:--------------|:-----------------|:-------------|:--------|:--------------------|:-------------|:-----------|:------------------|:---------------------|:-------------------|:---------------------|:---------------|:--------|:-----------|:----------------|:-------------|:----------------|:-------------------|:--------------|:-------------------|:----------------|:--------------------|:------------------|:------------|:-----------------|:----------|:--------------|:------|:---------|:---------------|:---------------|:--------|:--------------|:-------------------|:-----------|:--------------|:-------------|:----------------|:------------------|:-----------------|:--------|:-------|:---------|:----------|:-------------|:---------------|:--------|:------|:----------|:-----------|:-------|:--------|:--------|:--------------|:------------------|:-------------------|:----------------|:--------------|:--------------------|:--------------|:----------------|:--------------------|:---------------|:------------------|:------------------|:------------|:--------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | X | | X | | X | X | X | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 12 |  |  |  |  |  | X | | X | | X | X | X | X | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 10 |  |  |  |  |  | X | | X | | X | X | X | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 7 |  |  |  |  |  | X | | X | | X | X | X | | | | | | X | | X | X | X | X | X | X | | X | | X | X | X | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | | X | X | X | X | X | X | | | | | | X | | | | | | | | | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 7 |  |  |  |  |  | X | | X | | X | X | X | | | | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 11 |  |  |  |  |  | X | | X | | | X | | X | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 8 | 7 |  |  |  |  |  | X | | X | | | X | X | | | | | | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | X | X | | | | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
irds/mmarco_v2_ja | ---
pretty_name: '`mmarco/v2/ja`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `mmarco/v2/ja`
The `mmarco/v2/ja` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/ja).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_v2_ja_dev`](https://huggingface.co/datasets/irds/mmarco_v2_ja_dev), [`mmarco_v2_ja_train`](https://huggingface.co/datasets/irds/mmarco_v2_ja_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_v2_ja', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
|
alisson40889/cris | ---
license: openrail
---
|
result-kand2-sdxl-wuerst-karlo/f4e64da0 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 171
num_examples: 10
download_size: 1333
dataset_size: 171
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "f4e64da0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ecdr/123 | ---
license: other
---
|
CyberHarem/yuki_haru_theidolmastercinderellagirlsu149 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Yūki Haru
This is the dataset of Yūki Haru, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 461 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 461 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 461 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 461 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/molly_netjuunosusume | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Molly (Net-juu No Susume)
This is the dataset of Molly (Net-juu No Susume), containing 141 images and their tags.
The core tags of this character are `blue_hair, long_hair, blue_eyes, ahoge`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 141 | 103.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/molly_netjuunosusume/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 141 | 103.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/molly_netjuunosusume/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 283 | 192.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/molly_netjuunosusume/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/molly_netjuunosusume',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, blush, open_mouth, outdoors, solo, anime_coloring, day, hair_between_eyes, collarbone, sky, tree, :d, cloud, portrait, bare_shoulders, off_shoulder, shirt |
| 1 | 7 |  |  |  |  |  | 1girl, anime_coloring, blurry_background, blush, hair_between_eyes, portrait, solo, outdoors, parody, open_mouth, tree |
| 2 | 18 |  |  |  |  |  | 1girl, hat, solo, blush, open_mouth, looking_at_viewer, :d |
| 3 | 9 |  |  |  |  |  | 1girl, blush, day, profile, solo, anime_coloring, sky, cloud, hat, outdoors |
| 4 | 8 |  |  |  |  |  | 1girl, bare_shoulders, smile, blush, collarbone, night, map, dress, holding, solo, off_shoulder, sky |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | open_mouth | outdoors | solo | anime_coloring | day | hair_between_eyes | collarbone | sky | tree | :d | cloud | portrait | bare_shoulders | off_shoulder | shirt | blurry_background | parody | hat | looking_at_viewer | profile | smile | night | map | dress | holding |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------------|:-----------|:-------|:-----------------|:------|:--------------------|:-------------|:------|:-------|:-----|:--------|:-----------|:-----------------|:---------------|:--------|:--------------------|:---------|:------|:--------------------|:----------|:--------|:--------|:------|:--------|:----------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | X | X | | X | | | X | | | X | | | | X | X | | | | | | | | |
| 2 | 18 |  |  |  |  |  | X | X | X | | X | | | | | | | X | | | | | | | | X | X | | | | | | |
| 3 | 9 |  |  |  |  |  | X | X | | X | X | X | X | | | X | | | X | | | | | | | X | | X | | | | | |
| 4 | 8 |  |  |  |  |  | X | X | | | X | | | | X | X | | | | | X | X | | | | | | | X | X | X | X | X |
|
Maxwell001/skill_model_formatted_1 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 1061145
num_examples: 1023
download_size: 239351
dataset_size: 1061145
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Aarif1430/english-to-hindi | ---
dataset_info:
features:
- name: english_sentence
dtype: string
- name: hindi_sentence
dtype: string
splits:
- name: train
num_bytes: 41188315
num_examples: 127705
download_size: 21737146
dataset_size: 41188315
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "english-to-hindi"
**Dataset Card: English-to-Hindi Translation**
**Overview:**
- **Dataset Name:** English-to-Hindi Translation
- **Dataset Size:** 128K sentences
- **Source:** Curated list of English sentences paired with their Hindi translations.
- **Use Case:** Training machine translation models, specifically English-to-Hindi translation using transformer architectures.
**Data Collection:**
- **Collection Method:** Manual translation by bilingual speakers.
- **Data Quality:** High quality with accurate translations.
**Dataset Composition:**
- **Language Pair:** English to Hindi
- **Text Type:** General sentences, covering a wide range of topics.
- **Text Length:** Varied lengths of sentences.
**Data Format:**
- **Format:** CSV, each row containing an English sentence and its corresponding Hindi translation.
**Licensing:**
- **License:** MIT
**Dataset Distribution:**
- **Availability:**
```python
from datasets import load_dataset
dataset = load_dataset("Aarif1430/english-to-hindi")
```
```shell
curl -X GET "https://datasets-server.huggingface.co/rows?dataset=Aarif1430%2Fenglish-to-hindi&config=default&split=train&offset=0&length=100"
```
- **Download Size:** 21.7 MB
**Potential Use Cases:**
- Training and evaluating machine translation models.
- Research in natural language processing, specifically in the field of translation.
**Limitations:**
- Limited coverage of domain-specific language or specialized terminology.
**Additional Information:**
- The dataset was created to facilitate research and development in English-to-Hindi machine translation. Researchers and developers are encouraged to contribute to and improve the dataset.
**Citation:**
- If you use this dataset in your work, please cite the dataset using the provided citation information.
**References:**
- https://huggingface.co/datasets/ai4bharat/samanantar
|
SnowZeng/enron_mail | ---
license: apache-2.0
---
|
arianhosseini/comparisons_20k_regen_labeled_dpo1b1 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 36118675
num_examples: 20000
download_size: 20607508
dataset_size: 36118675
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "comparisons_20k_regen_labeled_dpo1b1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
distilled-one-sec-cv12-each-chunk-uniq/chunk_117 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1514961268.0
num_examples: 295199
download_size: 1551150461
dataset_size: 1514961268.0
---
# Dataset Card for "chunk_117"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ConvLab/tm1 | ---
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Taskmaster-1
size_categories:
- 10K<n<100K
task_categories:
- conversational
---
# Dataset Card for Taskmaster-1
- **Repository:** https://github.com/google-research-datasets/Taskmaster/tree/master/TM-1-2019
- **Paper:** https://arxiv.org/pdf/1909.05358.pdf
- **Leaderboard:** None
- **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com)
To use this dataset, you need to install [ConvLab-3](https://github.com/ConvLab/ConvLab-3) platform first. Then you can load the dataset via:
```
from convlab.util import load_dataset, load_ontology, load_database
dataset = load_dataset('tm1')
ontology = load_ontology('tm1')
database = load_database('tm1')
```
For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets).
### Dataset Summary
The original dataset consists of 13,215 task-based dialogs, including 5,507 spoken and 7,708 written dialogs created with two distinct procedures. Each conversation falls into one of six domains: ordering pizza, creating auto repair appointments, setting up ride service, ordering movie tickets, ordering coffee drinks and making restaurant reservations.
- **How to get the transformed data from original data:**
- Download [master.zip](https://github.com/google-research-datasets/Taskmaster/archive/refs/heads/master.zip).
- Run `python preprocess.py` in the current directory.
- **Main changes of the transformation:**
- Remove dialogs that are empty or only contain one speaker.
- Split woz-dialogs into train/validation/test randomly (8:1:1). The split of self-dialogs is followed the original dataset.
- Merge continuous turns by the same speaker (ignore repeated turns).
- Annotate `dialogue acts` according to the original segment annotations. Add `intent` annotation (inform/accept/reject). The type of `dialogue act` is set to `non-categorical` if the original segment annotation includes a specified `slot`. Otherwise, the type is set to `binary` (and the `slot` and `value` are empty) since it means general reference to a transaction, e.g. "OK your pizza has been ordered". If there are multiple spans overlapping, we only keep the shortest one, since we found that this simple strategy can reduce the noise in annotation.
- Add `domain`, `intent`, and `slot` descriptions.
- Add `state` by accumulate `non-categorical dialogue acts` in the order that they appear, except those whose intents are **reject**.
- Keep the first annotation since each conversation was annotated by two workers.
- **Annotations:**
- dialogue acts, state.
### Supported Tasks and Leaderboards
NLU, DST, Policy, NLG
### Languages
English
### Data Splits
| split | dialogues | utterances | avg_utt | avg_tokens | avg_domains | cat slot match(state) | cat slot match(goal) | cat slot match(dialogue act) | non-cat slot span(dialogue act) |
|------------|-------------|--------------|-----------|--------------|---------------|-------------------------|------------------------|--------------------------------|-----------------------------------|
| train | 10535 | 223322 | 21.2 | 8.75 | 1 | - | - | - | 100 |
| validation | 1318 | 27903 | 21.17 | 8.75 | 1 | - | - | - | 100 |
| test | 1322 | 27660 | 20.92 | 8.87 | 1 | - | - | - | 100 |
| all | 13175 | 278885 | 21.17 | 8.76 | 1 | - | - | - | 100 |
6 domains: ['uber_lyft', 'movie_ticket', 'restaurant_reservation', 'coffee_ordering', 'pizza_ordering', 'auto_repair']
- **cat slot match**: how many values of categorical slots are in the possible values of ontology in percentage.
- **non-cat slot span**: how many values of non-categorical slots have span annotation in percentage.
### Citation
```
@inproceedings{byrne-etal-2019-taskmaster,
title = {Taskmaster-1:Toward a Realistic and Diverse Dialog Dataset},
author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},
booktitle = {2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing},
address = {Hong Kong},
year = {2019}
}
```
### Licensing Information
[**CC BY 4.0**](https://creativecommons.org/licenses/by/4.0/) |
aisyahhrazak/crawl-ering.com.my | ---
language:
- ms
---
About
- Data scraped from https://ering.com.my/ |
DKYoon/proofpile2-200k | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 1960404458
num_examples: 200000
download_size: 998683199
dataset_size: 1960404458
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
benayas/banking_chatgpt_10pct_v0 | ---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1103024
num_examples: 10003
download_size: 375650
dataset_size: 1103024
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Gbssreejith/marriage_classification_1738_1951 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': blank
'1': type1
'2': type2
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 139325572.0
num_examples: 425
- name: test
num_bytes: 12347904.0
num_examples: 35
download_size: 145568458
dataset_size: 151673476.0
---
# Dataset Card for "marriage_classification_1738_1951"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vargr/main_instagram | ---
dataset_info:
features:
- name: sid
dtype: int64
- name: sid_profile
dtype: int64
- name: shortcode
dtype: string
- name: profile_id
dtype: int64
- name: date
dtype: string
- name: post_type
dtype: int64
- name: description
dtype: string
- name: likes
dtype: int64
- name: comments
dtype: int64
- name: username
dtype: string
- name: bio
dtype: string
- name: following
dtype: int64
- name: followers
dtype: int64
- name: num_posts
dtype: int64
- name: is_business_account
dtype: bool
- name: lang
dtype: string
- name: description_category
dtype: string
- name: description_grade
dtype: float64
- name: image_grade
dtype: float64
- name: path
dtype: string
splits:
- name: train
num_bytes: 263209721
num_examples: 605868
download_size: 158703728
dataset_size: 263209721
---
# Dataset Card for "main_instagram"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
heliosprime/twitter_dataset_1713000222 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 11935
num_examples: 26
download_size: 10507
dataset_size: 11935
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713000222"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
flaviolima/mp3 | ---
license: openrail
---
|
tinhpx2911/history_book | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 74062733
num_examples: 81
download_size: 37725495
dataset_size: 74062733
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "history_book"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
xfilek4/erp_test | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 7786
num_examples: 32
download_size: 4172
dataset_size: 7786
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
NomaDamas/split_search_qa | ---
license: unknown
dataset_info:
- config_name: corpus
features:
- name: query_id
dtype: string
- name: snippets
dtype: string
- name: air_date
dtype: string
- name: category
dtype: string
- name: value
dtype: string
- name: round
dtype: string
- name: show_number
dtype: int32
- name: doc_id
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 6252715344
num_examples: 14120776
download_size: 3271155810
dataset_size: 6252715344
- config_name: qa_data
features:
- name: query_id
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: search_results
struct:
- name: related_links
sequence: string
- name: snippets
sequence: string
- name: titles
sequence: string
- name: urls
sequence: string
- name: doc_id
sequence: string
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dtype: int64
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num_bytes: 6503932619
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num_bytes: 1830028629
num_examples: 43350
download_size: 5008413626
dataset_size: 8333961248
configs:
- config_name: corpus
data_files:
- split: train
path: corpus/train-*
- config_name: qa_data
data_files:
- split: train
path: qa_data/train-*
- split: test
path: qa_data/test-*
---
# preprocessed_SearchQA
The SearchQA question-answer pairs originate from J! Archive2, which comprehensively archives all question-answer pairs
from the renowned television show Jeopardy! The passages, sourced from Google search web page snippets.
We offer passage metadata, encompassing details like 'air_date,' 'category,' 'value,' 'round,' and 'show_number,'
enabling you to enhance retrieval performance at your discretion.
Should you require further details about SearchQA, please refer to below links.
[Github](https://github.com/nyu-dl/dl4ir-searchQA)<br>
[Paper](https://arxiv.org/abs/1704.05179)<br>
The dataset is derived from [searhQA](https://huggingface.co/datasets/search_qa).<br>
This preprocessed dataset is for RAG. For more information about our task, visit our [repository](https://github.com/NomaDamas/RAGchain)!<br>
Preprocess SearchQA dataset code for RAG benchmark. <br>
More information, refer to this link! [huggingface](https://huggingface.co/datasets/NomaDamas/search_qa_split)
|
vagalume13/dataset | ---
license: openrail
---
|
kalomaze/PaperMarioDecomp_1k | ---
license: apache-2.0
---
A subset of MIPS Assembly instructions with matching reverse engineered C code from Paper Mario.
https://github.com/pmret/papermario |
Fumika/Wikinews-multilingual | ---
license: cc-by-2.5
language:
- en
- es
- fr
- de
- pt
- pl
- it
- zh
- ru
- ja
- nl
- sv
- ta
- sr
- cs
- ca
- he
- tr
- fi
- eo
- el
- hu
- uk
- 'no'
- ar
- fa
- ko
- ro
- bg
- bs
- li
- sq
- th
task_categories:
- text-classification
- feature-extraction
---
# Wikinews - weakly aligned multilingual pararell sentence datasets
This dataset contains 15,200 multilingual WikiNews articles in 33 languages.
Out of 15,200 articles, 9,960 are non-English news and 5240 are English news. All non-English news are linked to one of 5240 English news. Linked articles show the same event.
List of non-English languages are: Spanish, French, German, Portuguese, Polish, Italian, Chinese, Russian, Japanese, Dutch, Swedish, Tamil, Serbian, Czech, Catalan, Hebrew, Turkish, Finnish, Esperanto, Greek, Hungarian, Ukrainian, Norwegian, Arabic, Persian, Korean, Romanian, Bulgarian, Bosnian, Limburgish, Albanian, Thai.
## Dataset Details
### Example raw datasets
| | title | pageid | categories | lang | url | text | date | type |
|---|-------------------------------------------------------------|--------|----------------------------------------------------|------|-----------------------------------------------------------------------------------------|-----------------------------------------------------------|-----------------------------|-----------------|
| 0 | 'Bloody Sunday Inquiry' publishes report into ... | 191513 | [Northern Ireland, Martin McGuinness, Politics...] | en | https://en.wikinews.org/wiki/%27Bloody_Sunday_... | [On Tuesday, the "Bloody Sunday Inquiry" publi... | 2010-06-17 | title |
| 1 | 1972 ”இரத்த ஞாயிறு” படுகொலைகள் தொடர்பில் பிரித... | 191513 | [Northern Ireland, Martin McGuinness, Politics...] | ta | https://ta.wikinews.org/wiki/1972_%E2%80%9D%E0... | [வடக்கு அயர்லாந்தில் 38 ஆண்டுகளுக்கு முன்னர் இ... | வியாழன், சூன் 17, 2010 | interlang link |
| 2 | 'Very serious': Chinese government releases co... | 232226 | [China, December 30, 2010, Politics and confli...] | en | https://en.wikinews.org/wiki/%27Very_serious%2... | [A report by the Chinese government states cor... | 2010-12-30 | title |
| 3 | Čína připustila, že tamní korupce je vážný pro... | 232226 | [China, December 30, 2010, Politics and confli...] | cs | https://cs.wikinews.org/wiki/%C4%8C%C3%ADna_p%... | [Zpráva čínské vlády připouští, že korupce v z... | Středa 29. prosince 2010 | interlang link |
| 4 | China admite que la corrupción en el país es '... | 232226 | [China, December 30, 2010, Politics and confli...] | es | https://es.wikinews.org/wiki/China_admite_que_... | [29 de diciembre de 2010Beijing, China —, Un r... | None | interlang link |
### Variables
Each data point includes following variables:
| Field Name | Description |
|-----------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|
| title | WikiNews article title |
| pageid | pageid defined by the English WikiNews article. Data with the same pageid corresponds to the same news event linked together. |
| categories | list of topics defined by WikiNews. All pages have at least one topic from [Crime and law, Culture and entertainment, Disasters and accidents, Economy and business, Education, Environment, Heath, Obituaries, Politics and conflicts, Science and technology, Sports, Wackynews, Weather] |
| text | content of the article. Some foreign pages have news titles but no content. For those, text is left empty. |
| lang | languages of the article (WP code, check [here](https://en.wikipedia.org/wiki/List_of_Wikipedias#Lists) for lists ) |
| url | articles' URL |
| date | date of publish in YYYY-MM-DD for English pages. Dates in foreign pages were left as it is. To get a date with YYYY-MM-DD format, look for a English page with the same pageid. |
| type | `title` for the English page, `interlang link` for non-English page linked to the English page with the `pageid`
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** Fumika Isono, Primer AI
- **Language(s) (NLP):** en, es, fr, de, pt, pl, it, zh, ru, ja, nl, sv, ta, sr, cs, ca, he, tr, fi, eo, el, hu, uk, 'no', ar, fa, ko, ro, bg, bs, li, sq, th
- **License:** cc-by-2.5
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** [Github](https://github.com/PrimerAI/primer-research/tree/main)
- **Paper:** ArXiv [Linear Cross-Lingual Mapping of Sentence Embeddings](https://arxiv.org/abs/2305.14256)
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Weakly aligned multilingual pararell sentence datasets
Weakly aligned multilingual pararell sentence datasets can be constructed by comparing the titles and/or contents of the WikiNews pages that are linked to the same English WikiNews page (in the dataset, they have the same pageid).
Following is the example case where titles of the same pageid are retrieved. These five phrases (news titles) are the news titles of the same incident.
| News title | Language | type |
|---------------------------------------------------------------|----------|-------------------|
| Bomb blast in Delhi kills 12, injures 62 | English | title |
| چندین کشته بر اثر انفجار بمب در مقابل دادگاه عالی هند | Farsi | title|
| 9 נהרגו בפיגוע מחוץ לבית המשפט העליון של הודו | Hebrew | title|
| У Индији 11 мртвих, 64 повређених | Serbian | title|
| தில்லி உயர்நீதிமன்றத்தில் குண்டு வெடிப்பு, 10 பேர் உயிரிழப்பு | Tamil | title|
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
- Multilingual embeddings
- Language comparison
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
[Wikinews](https://www.wikinews.org/)
## Dataset Card Authors
Fumika Isono
|
havens2/apitext_dirty | ---
dataset_info:
features:
- name: text
dtype: string
splits:
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num_examples: 8830
download_size: 2694540
dataset_size: 6472042
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "apitext_dirty"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dshut002/ActionData | ---
dataset_info:
features:
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dtype: string
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dtype: string
splits:
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num_bytes: 83784
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download_size: 40541
dataset_size: 83784
---
# Dataset Card for "ActionData"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
2A2I/Arabic_Aya | ---
language:
- ar
license: apache-2.0
size_categories:
- 1M<n<10M
task_categories:
- text-classification
- translation
- summarization
pretty_name: 2A
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configs:
- config_name: CohereForAI-aya_collection-aya_dataset
data_files:
- split: train
path: CohereForAI-aya_collection-aya_dataset/train-*
- config_name: CohereForAI-aya_collection-aya_human_annotated
data_files:
- split: test
path: CohereForAI-aya_collection-aya_human_annotated/test-*
- config_name: CohereForAI-aya_collection-templated_afrisenti
data_files:
- split: train
path: CohereForAI-aya_collection-templated_afrisenti/train-*
- split: test
path: CohereForAI-aya_collection-templated_afrisenti/test-*
- split: validation
path: CohereForAI-aya_collection-templated_afrisenti/validation-*
- config_name: CohereForAI-aya_collection-templated_mintaka
data_files:
- split: train
path: CohereForAI-aya_collection-templated_mintaka/train-*
- split: test
path: CohereForAI-aya_collection-templated_mintaka/test-*
- split: validation
path: CohereForAI-aya_collection-templated_mintaka/validation-*
- config_name: CohereForAI-aya_collection-templated_ntx_llm
data_files:
- split: train
path: CohereForAI-aya_collection-templated_ntx_llm/train-*
- config_name: CohereForAI-aya_collection-templated_xcsqa
data_files:
- split: validation
path: CohereForAI-aya_collection-templated_xcsqa/validation-*
- config_name: CohereForAI-aya_collection-templated_xlel_wd
data_files:
- split: train
path: CohereForAI-aya_collection-templated_xlel_wd/train-*
- split: test
path: CohereForAI-aya_collection-templated_xlel_wd/test-*
- split: validation
path: CohereForAI-aya_collection-templated_xlel_wd/validation-*
- config_name: CohereForAI-aya_collection-translated_adversarial_qa
data_files:
- split: train
path: CohereForAI-aya_collection-translated_adversarial_qa/train-*
- split: test
path: CohereForAI-aya_collection-translated_adversarial_qa/test-*
- split: validation
path: CohereForAI-aya_collection-translated_adversarial_qa/validation-*
- config_name: CohereForAI-aya_collection-translated_cnn_dailymail
data_files:
- split: train
path: CohereForAI-aya_collection-translated_cnn_dailymail/train-*
- split: test
path: CohereForAI-aya_collection-translated_cnn_dailymail/test-*
- split: validation
path: CohereForAI-aya_collection-translated_cnn_dailymail/validation-*
- config_name: CohereForAI-aya_collection-translated_dolly
data_files:
- split: train
path: CohereForAI-aya_collection-translated_dolly/train-*
- config_name: CohereForAI-aya_collection-translated_flan_coqa
data_files:
- split: train
path: CohereForAI-aya_collection-translated_flan_coqa/train-*
- config_name: CohereForAI-aya_collection-translated_flan_cot
data_files:
- split: train
path: CohereForAI-aya_collection-translated_flan_cot/train-*
- config_name: CohereForAI-aya_collection-translated_flan_gem_wiki
data_files:
- split: train
path: CohereForAI-aya_collection-translated_flan_gem_wiki/train-*
- config_name: CohereForAI-aya_collection-translated_flan_lambada
data_files:
- split: train
path: CohereForAI-aya_collection-translated_flan_lambada/train-*
- config_name: CohereForAI-aya_collection-translated_flan_qa
data_files:
- split: train
path: CohereForAI-aya_collection-translated_flan_qa/train-*
- config_name: CohereForAI-aya_collection-translated_hotpotqa
data_files:
- split: train
path: CohereForAI-aya_collection-translated_hotpotqa/train-*
- split: validation
path: CohereForAI-aya_collection-translated_hotpotqa/validation-*
- config_name: CohereForAI-aya_collection-translated_joke_explaination
data_files:
- split: train
path: CohereForAI-aya_collection-translated_joke_explaination/train-*
- config_name: CohereForAI-aya_collection-translated_mintaka
data_files:
- split: train
path: CohereForAI-aya_collection-translated_mintaka/train-*
- split: test
path: CohereForAI-aya_collection-translated_mintaka/test-*
- split: validation
path: CohereForAI-aya_collection-translated_mintaka/validation-*
- config_name: CohereForAI-aya_collection-translated_mlqa
data_files:
- split: test
path: CohereForAI-aya_collection-translated_mlqa/test-*
- split: validation
path: CohereForAI-aya_collection-translated_mlqa/validation-*
- config_name: CohereForAI-aya_collection-translated_nqopen
data_files:
- split: train
path: CohereForAI-aya_collection-translated_nqopen/train-*
- split: validation
path: CohereForAI-aya_collection-translated_nqopen/validation-*
- config_name: CohereForAI-aya_collection-translated_paws
data_files:
- split: train
path: CohereForAI-aya_collection-translated_paws/train-*
- split: test
path: CohereForAI-aya_collection-translated_paws/test-*
- split: validation
path: CohereForAI-aya_collection-translated_paws/validation-*
- config_name: CohereForAI-aya_collection-translated_piqa
data_files:
- split: train
path: CohereForAI-aya_collection-translated_piqa/train-*
- split: validation
path: CohereForAI-aya_collection-translated_piqa/validation-*
- config_name: CohereForAI-aya_collection-translated_soda
data_files:
- split: train
path: CohereForAI-aya_collection-translated_soda/train-*
- split: test
path: CohereForAI-aya_collection-translated_soda/test-*
- split: validation
path: CohereForAI-aya_collection-translated_soda/validation-*
- config_name: CohereForAI-aya_collection-translated_wiki_split
data_files:
- split: train
path: CohereForAI-aya_collection-translated_wiki_split/train-*
- split: test
path: CohereForAI-aya_collection-translated_wiki_split/test-*
- split: validation
path: CohereForAI-aya_collection-translated_wiki_split/validation-*
- config_name: CohereForAI-aya_collection-translated_wikiqa
data_files:
- split: train
path: CohereForAI-aya_collection-translated_wikiqa/train-*
- split: test
path: CohereForAI-aya_collection-translated_wikiqa/test-*
- split: validation
path: CohereForAI-aya_collection-translated_wikiqa/validation-*
- config_name: CohereForAI-aya_collection-translated_xlel_wd
data_files:
- split: train
path: CohereForAI-aya_collection-translated_xlel_wd/train-*
- split: test
path: CohereForAI-aya_collection-translated_xlel_wd/test-*
- split: validation
path: CohereForAI-aya_collection-translated_xlel_wd/validation-*
- config_name: CohereForAI-aya_dataset
data_files:
- split: train
path: CohereForAI-aya_dataset/train-*
- split: test
path: CohereForAI-aya_dataset/test-*
- config_name: CohereForAI-aya_evaluation_suite-aya_human_annotated
data_files:
- split: test
path: CohereForAI-aya_evaluation_suite-aya_human_annotated/test-*
- config_name: CohereForAI-aya_evaluation_suite-dolly_human_edited
data_files:
- split: test
path: CohereForAI-aya_evaluation_suite-dolly_human_edited/test-*
- config_name: CohereForAI-aya_evaluation_suite-dolly_machine_translated
data_files:
- split: test
path: CohereForAI-aya_evaluation_suite-dolly_machine_translated/test-*
---
# Dataset Card for : Arabic Aya (2A)
<!-- Provide a quick summary of the dataset. -->
<!-- This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).-->
## **Arabic Aya (2A) : A Curated Subset of the Aya Collection for Arabic Language Processing**
### Dataset Sources & Infos
- **Data Origin**: Derived from 69 subsets of the original Aya datasets : [CohereForAI/aya_collection](https://huggingface.co/datasets/CohereForAI/aya_collection), [CohereForAI/aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset), and [CohereForAI/aya_evaluation_suite](https://huggingface.co/datasets/CohereForAI/aya_evaluation_suite).
- **Languages**: Modern Standard Arabic (MSA) and a variety of Arabic dialects ( 'arb', 'arz', 'ary', 'ars', 'knc', 'acm', 'apc', 'aeb', 'ajp', 'acq' )
- **Applications**: `Language Modeling`, `Text Classification`, `Sentiment Analysis`, `Dialect Identification`, `Translation`
- **Paper:** [2402.06619](https://huggingface.co/papers/2402.06619)
- **Maintainer:** [Elfilali Ali](https://huggingface.co/Ali-C137)
- **License:** Apache-2.0
### Overview
`Arabic Aya` is a meticulously curated dataset derived from the comprehensive Aya collection by [CohereForAI](https://huggingface.co/CohereForAI), specifically focusing on Arabic text data. This dataset aggregates content from the [CohereForAI/aya_collection](https://huggingface.co/datasets/CohereForAI/aya_collection), [CohereForAI/aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset), and [CohereForAI/aya_evaluation_suite](https://huggingface.co/datasets/CohereForAI/aya_evaluation_suite), filtering out all but the Arabic content, including both Modern Standard Arabic (MSA) and various regional dialects.
### Purpose
The aim of 'Arabic Aya' is to provide researchers, technologists, and linguists with a ready-to-use Arabic text resource, significantly reducing the time and effort required for data preprocessing in NLP and AI projects focused on the Arabic language.
- Use the Aya datasets out of the box for your Arabic applications and research 😀
### Usage
This dataset serves as a foundational tool for those embarking on Arabic language projects, from academic research to commercial applications. By providing a pre-filtered source of Arabic text, 'Arabic Aya' enables users to dive straight into model training, analysis, and application development without the preliminary hassle of data cleaning and language filtering.
#### Use with HuggingFace's datasets library
To load this dataset with Datasets, you'll need to install Datasets as `pip install datasets --upgrade` and then use a similar code to the following:
```python
from datasets import load_dataset
dataset = load_dataset("2A2I/Arabic_Aya", "CohereForAI-aya_collection-templated_mintaka")
```
In the above code snippet, "CohereForAI-aya_collection-templated_mintaka" refers to the arabic version (100k rows) of the original "templated_mintaka" subset (780k rows) of the aya_collection. You can load other subsets by specifying its name at the time of loading the dataset.
### Access and Contribution
Available on the Hugging Face Hub under [2A2I/Arabic_Aya](https://huggingface.co/datasets/2A2I/Arabic_Aya), 'Arabic Aya' invites contributions from the community. Users are encouraged to offer feedback, suggest improvements.
### Support and Collaboration
We are committed to fostering an inclusive and supportive environment around Arabic AI and NLP research. For support, collaboration, or queries regarding the dataset, please reach out through the Hugging Face Hub's discussion section or reach out at [2A2I Contact Email](arabic.ai.initiative@gmail.com).
# Original Dataset Card of Aya by CohereForAI
# Dataset Summary
The Aya Collection is a massive multilingual collection consisting of 513 million instances of prompts and completions covering a wide range of tasks.
This collection incorporates instruction-style templates from fluent speakers and applies them to a curated list of datasets, as well as translations of instruction-style datasets into 101 languages. Aya Dataset, a human-curated multilingual instruction and response dataset, is also part of this collection. See our paper for more details regarding the collection.
- **Curated by:** Contributors of [Aya Open Science Intiative](https://cohere.com/research/aya)
- **Language(s):** 115 languages
- **License:** [Apache 2.0](https://opensource.org/license/apache-2-0)
- **Aya Datasets Family:**
| Name | Explanation |
|------|--------------|
| [aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) | Human-annotated multilingual instruction finetuning dataset, comprising over 204K instances across 65 languages. |
| [aya_collection](https://huggingface.co/datasets/CohereForAI/aya_collection) | Created by applying instruction-style templates from fluent speakers to 44 datasets, including translations of 19 instruction-style datasets into 101 languages.|
| [aya_evaluation_suite](https://huggingface.co/datasets/CohereForAI/aya_evaluation_suite) | A diverse evaluation set for multilingual open-ended generation, featuring 250 culturally grounded prompts in 7 languages, 200 translated prompts in 24 languages, and human-edited versions selected for cross-cultural relevance from English Dolly in 6 languages.|
# Dataset
The `Aya Collection` is a comprehensive, large corpus of datasets that can be used by researchers around the world to train multilingual models. Our goal is only to include datasets with permissive licensing for manipulation and redistribution.
The `Aya Collection` consists of three different sources of data:
1. Templated data: We collaborated with fluent speakers to create templates that allowed for the automatic expansion of existing datasets into various languages.
2. Translated data: We translated a hand-selected subset of 19 datasets into 101 languages (114 dialects) using the NLLB 3.3B parameter machine translation model.
3. Aya Dataset: We release the [Aya Dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) as a subset of the overall collection. This is the only dataset in the collection that is human-annotated in its entirety.
## Load with Datasets
To load this dataset with Datasets, you'll need to install Datasets as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
dataset = load_dataset("CohereForAI/aya_collection", "templated_mintaka")
```
In the above code snippet, "templated_mintaka" refers to a subset of the aya_collection. You can load other subsets by specifying its name at the time of loading the dataset.
## Data Instances
An example of a `train` instance looks as follows:
```json
{'id': 246001,
'inputs': 'The following query in English is taken from the geography category. What could be the answer to the question?\nWhat is the seventh tallest mountain in North America?',
'targets': 'The answer is Mount Lucania.',
'dataset_name': 'Mintaka-inst',
'sub_dataset_name': '-',
'task_type': 'question-answering',
'template_id': 3,
'language': 'eng',
'split': 'train',
'script': 'Latn'
}
```
## Data Fields
The data fields are the same among all splits:
- `id:` Unique id of the data point
- `inputs:` Prompt or input to the language model.
- `targets:` Completion or output of the language model.
- `dataset_name:` The name of the source dataset that the data point was taken from
- `sub_dataset_name:` If the source is a collection, this field indicates which part of that collection the data point was taken from. If it is not a collection, this field is left blank.
- `task_type:` The task type that this conversation belongs to.
- `template_id`: The id of the template applied to this data point.
- `language:` The ISO code of the dialect of the conversation.
- `script:` The script of the language.
- `split:` Indicates whether the data point is part of the `train` or the `test` split.
### Statistics
The total number of data points, including the Aya Dataset` is 513,758,189. To view the breakdown of dialect codes and the respective templated and translated data point counts in the Aya Collection , refer to the toggled table below.
<details>
<summary> <b> Breakdown of Aya Collection data point counts grouped by dialects </b> </summary>
|dialect code|language|translated data point count|templated data point count|total count |
|------------|--------|---------------------------|--------------------------|---------------|
|ace |Achinese|8240684 |2000 |8242684 |
|acm |Arabic |4120342 |0 |4120342 |
|acq |Arabic |4120342 |0 |4120342 |
|aeb |Arabic |4120342 |0 |4120342 |
|afr |Afrikaans|4120342 |6108 |4126450 |
|ajp |Arabic |4120342 |0 |4120342 |
|als |Albanian|4120342 |0 |4120342 |
|amh |Amharic |4120342 |25327 |4145669 |
|apc |Arabic |4120342 |0 |4120342 |
|arb |Arabic |6424999 |216430 |6641429 |
|ars |Arabic |4120342 |0 |4120342 |
|ary |Arabic |4120342 |18076 |4138418 |
|arz |Arabic |4120342 |0 |4120342 |
|azb |Azerbaijani|4120342 |0 |4120342 |
|azj |Azerbaijani|4120342 |0 |4120342 |
|bel |Belarusian|4120342 |21273 |4141615 |
|ben |Bengali |4120342 |30661 |4151003 |
|bjn |Banjar |8240684 |2000 |8242684 |
|bul |Bulgarian|4120342 |37722 |4158064 |
|cat |Catalan |4120342 |66900 |4187242 |
|ceb |Cebuano |4120342 |0 |4120342 |
|ces |Czech |4120342 |179604 |4299946 |
|ckb |Kurdish |4120342 |0 |4120342 |
|cym |Welsh |4120342 |0 |4120342 |
|dan |Danish |4120342 |36310 |4156652 |
|deu |German |4120342 |1326722 |5447064 |
|ell |Greek |4120342 |40291 |4160633 |
|eng |English |9771427 |8066678 |17838105 |
|epo |Esperanto|4120342 |0 |4120342 |
|est |Estonian|4120342 |0 |4120342 |
|eus |Basque |4120342 |0 |4120342 |
|fin |Finnish |4120342 |457895 |4578237 |
|fra |French |4120342 |835520 |4955862 |
|gla |Scottish Gaelic|4120342 |0 |4120342 |
|gle |Irish |4120342 |0 |4120342 |
|glg |Galician|4120342 |0 |4120342 |
|guj |Gujarati|4120342 |2157 |4122499 |
|hat |Haitian Creole|4120342 |0 |4120342 |
|hau |Hausa |4120342 |51396 |4171738 |
|heb |Hebrew |4120342 |103466 |4223808 |
|hin |Hindi |4120342 |260387 |4380729 |
|hun |Hungarian|4120342 |82039 |4202381 |
|hye |Armenian|4120342 |7080 |4127422 |
|ibo |Igbo |4120342 |36312 |4156654 |
|ind |Indonesian|4120342 |45709 |4166051 |
|isl |Icelandic|4120342 |0 |4120342 |
|ita |Italian |4120342 |405682 |4526024 |
|jav |Javanese|4120342 |829 |4121171 |
|jpn |Japanese|4120342 |2693177 |6813519 |
|kan |Kannada |4120342 |1156 |4121498 |
|kas |Kashmiri|4120342 |0 |4120342 |
|kat |Georgian|4120342 |0 |4120342 |
|kaz |Kazakh |4120342 |0 |4120342 |
|khk |Mongolian|4120342 |0 |4120342 |
|khm |Khmer |4120342 |0 |4120342 |
|kir |Kyrgyz |4120342 |0 |4120342 |
|kmr |Kurdish |4120342 |0 |4120342 |
|knc |Kanuri |8240684 |0 |8240684 |
|kor |Korean |4120342 |41011 |4161353 |
|lao |Lao |4120342 |0 |4120342 |
|lit |Lithuanian|4120342 |0 |4120342 |
|ltz |Luxembourgish|4120342 |0 |4120342 |
|lvs |Latvian |4120342 |0 |4120342 |
|mal |Malayalam|4120342 |4347 |4124689 |
|mar |Marathi |4120342 |3678 |4124020 |
|min |Minangkabau|6753788 |2000 |6755788 |
|mkd |Macedonian|4120342 |0 |4120342 |
|mlt |Maltese |4120342 |0 |4120342 |
|mni |Manipuri|4120342 |0 |4120342 |
|mri |Maori |4120342 |0 |4120342 |
|mya |Burmese |4120342 |0 |4120342 |
|nld |Dutch |4120342 |220181 |4340523 |
|nno |Norwegian|4120342 |0 |4120342 |
|nob |Norwegian|4120342 |0 |4120342 |
|npi |Nepali |4120342 |0 |4120342 |
|nso |Northern Sotho|4120342 |0 |4120342 |
|pbt |Pashto |4120342 |0 |4120342 |
|pes |Persian |4120342 |245520 |4365862 |
|plt |Malagasy|4120342 |0 |4120342 |
|pol |Polish |4120342 |332503 |4452845 |
|por |Portuguese|4120342 |287432 |4407774 |
|ron |Romanian|4120342 |36359 |4156701 |
|rus |Russian |4120342 |545920 |4666262 |
|sin |Sinhala |4120342 |195 |4120537 |
|slk |Slovak |4120342 |27845 |4148187 |
|slv |Slovenian|4120342 |25731 |4146073 |
|smo |Samoan |4120342 |0 |4120342 |
|sna |Shona |4120342 |3684 |4124026 |
|snd |Sindhi |4120342 |0 |4120342 |
|som |Somali |4120342 |2926 |4123268 |
|sot |Southern Sotho|4120342 |0 |4120342 |
|spa |Spanish |4120342 |379194 |4499536 |
|srp |Serbian |4120342 |77124 |4197466 |
|sun |Sundanese|4120342 |2208 |4122550 |
|swe |Swedish |4120342 |76486 |4196828 |
|swh |Swahili |4120342 |12726 |4133068 |
|tam |Tamil |4120342 |11462 |4131804 |
|taq |Tamasheq|4120342 |0 |4120342 |
|tel |Telugu |4120342 |477821 |4598163 |
|tgk |Tajik |4120342 |0 |4120342 |
|tha |Thai |4120342 |2125180 |6245522 |
|tur |Turkish |4120342 |59932 |4180274 |
|ukr |Ukrainian|4120342 |189384 |4309726 |
|urd |Urdu |4120342 |337739 |4458081 |
|uzn |Uzbek |4120342 |0 |4120342 |
|vie |Vietnamese|4120342 |42232 |4162574 |
|xho |Xhosa |4120342 |2952 |4123294 |
|ydd |Yiddish |4120342 |0 |4120342 |
|yor |Yoruba |4120342 |4907 |4125249 |
|yue |Chinese |4120342 |0 |4120342 |
|zho-Hans |Chinese |4120342 |54528 |4174870 |
|zho-Hant |Chinese |4120342 |0 |4120342 |
|zsm |Malay |4120342 |13950 |4134292 |
|zul |Zulu |4120342 |786 |4121128 |
|arq |Arabic |0 |6046 |6046 |
|ban |Balinese|0 |2000 |2000 |
|bbc |Toba Batak|0 |2000 |2000 |
|bem |Bemba |0 |776 |776 |
|fil |Filipino|0 |220 |220 |
|fon |Fon |0 |845 |845 |
|hrv |Croatian|0 |9007 |9007 |
|kin |Kinyarwanda|0 |11165 |11165 |
|lij |Ligurian|0 |6409 |6409 |
|mad |Madurese|0 |2000 |2000 |
|nij |Ngaju |0 |2000 |2000 |
|nor |Norwegian|0 |72352 |72352 |
|pan |Punjabi |0 |2156 |2156 |
|twi |Twi |0 |10840 |10840 |
|wol |Wolof |0 |785 |785 |
|zho |Chinese |0 |74972 |74972 |
PS: Templated data also includes Mozambican Portuguese, which doesn't have its own ISO language code.
</details>
<br>
# Motivations & Intentions
- **Curation Rationale:** Automatic augmentation of existing datasets serves to enhance the available linguistic resources for multiple languages. The list of languages was initially established from mT5 and aligned with the annotators’ language list and NLLB translation model. The datasets were translated directly from English for all languages.
# Additional Information
## Provenance
- **Methods Used:** A combination of crowd-sourced templating and automatic translation was employed to source this dataset.
- **Methodology Details:**
- *Source:* Existing NLP datasets
- *Dates of Collection:* May 2023 - Dec 2023
## Dataset Version and Maintenance
- **Maintenance Status:** Actively Maintained
- **Version Details:**
- *Current version:* 1.0
- *Last Update:* 02/2024
- *First Release:* 02/2024
## Authorship
- **Publishing Organization:** [Cohere For AI](https://cohere.com/research)
- **Industry Type:** Not-for-profit - Tech
- **Contact Details:** https://cohere.com/research/aya
## Licensing Information
This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Apache 2.0](https://opensource.org/license/apache-2-0) License.
## Citation Information
```bibtex
@misc{singh2024aya,
title={Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning},
author={Shivalika Singh and Freddie Vargus and Daniel Dsouza and Börje F. Karlsson and Abinaya Mahendiran and Wei-Yin Ko and Herumb Shandilya and Jay Patel and Deividas Mataciunas and Laura OMahony and Mike Zhang and Ramith Hettiarachchi and Joseph Wilson and Marina Machado and Luisa Souza Moura and Dominik Krzemiński and Hakimeh Fadaei and Irem Ergün and Ifeoma Okoh and Aisha Alaagib and Oshan Mudannayake and Zaid Alyafeai and Vu Minh Chien and Sebastian Ruder and Surya Guthikonda and Emad A. Alghamdi and Sebastian Gehrmann and Niklas Muennighoff and Max Bartolo and Julia Kreutzer and Ahmet Üstün and Marzieh Fadaee and Sara Hooker},
year={2024},
eprint={2402.06619},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
shidowake/nu-dialogue_jmultiwoz_with_custom_sys_prompt_fixed2 | ---
dataset_info:
features:
- name: dialogue_id
dtype: int64
- name: goal_description
struct:
- name: attraction
sequence: string
- name: general
sequence: string
- name: hotel
sequence: string
- name: restaurant
sequence: string
- name: shopping
sequence: string
- name: taxi
sequence: string
- name: weather
sequence: string
- name: conversations
list:
- name: content
dtype: string
- name: role
dtype: string
- name: goal_description_array
sequence: string
- name: goal_description_concat
dtype: string
- name: system_input
dtype: string
- name: conversations_without_system_prompt
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 34534924
num_examples: 4246
download_size: 6892865
dataset_size: 34534924
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Description
Slightly modified and formatted version of the original datasets for my own purpose.
# Original Dataset
- nu-dialogue/jmultiwoz
The JMultiWOZ dataset is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
- [nu-dialogue/jmultiwoz · Datasets at Hugging Face](https://huggingface.co/datasets/nu-dialogue/jmultiwoz)
- [nu-dialogue/jmultiwoz: JMultiWOZ: A Large-Scale Japanese Multi-Domain Task-Oriented Dialogue Dataset](https://github.com/nu-dialogue/jmultiwoz)
# License
CC BY-ND 4.0 DEED
- [CC BY-ND 4.0 Deed | Attribution-NoDerivs 4.0 International | Creative Commons](https://creativecommons.org/licenses/by-nd/4.0/)
|
Heba30018/chestX-ray | ---
license: llama2
dataset_info:
features:
- name: formatted_text
dtype: string
splits:
- name: train
num_bytes: 8130687
num_examples: 5175
download_size: 1203206
dataset_size: 8130687
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
WDong/test_push | ---
dataset_info:
features:
- name: encoding
sequence:
sequence:
sequence: float64
splits:
- name: train
num_bytes: 872
num_examples: 2
download_size: 0
dataset_size: 872
---
# Dataset Card for "test_push"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
erlend0/dog | ---
dataset_info:
features:
- name: image
dtype: image
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 5591029.0
num_examples: 5
download_size: 5593069
dataset_size: 5591029.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_cloudyu__Venus_DPO_50 | ---
pretty_name: Evaluation run of cloudyu/Venus_DPO_50
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [cloudyu/Venus_DPO_50](https://huggingface.co/cloudyu/Venus_DPO_50) 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_cloudyu__Venus_DPO_50\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-21T17:50:57.443719](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__Venus_DPO_50/blob/main/results_2024-01-21T17-50-57.443719.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\": 0.6666481748258403,\n\
\ \"acc_stderr\": 0.03169619700244902,\n \"acc_norm\": 0.6675473139195528,\n\
\ \"acc_norm_stderr\": 0.032342525727742856,\n \"mc1\": 0.5801713586291309,\n\
\ \"mc1_stderr\": 0.017277030301775766,\n \"mc2\": 0.7263318450468071,\n\
\ \"mc2_stderr\": 0.014889987688937593\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.689419795221843,\n \"acc_stderr\": 0.013522292098053059,\n\
\ \"acc_norm\": 0.7073378839590444,\n \"acc_norm_stderr\": 0.013295916103619427\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7125074686317466,\n\
\ \"acc_stderr\": 0.004516681953879092,\n \"acc_norm\": 0.8846843258315077,\n\
\ \"acc_norm_stderr\": 0.0031874975090874207\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n\
\ \"acc_stderr\": 0.04218506215368879,\n \"acc_norm\": 0.6074074074074074,\n\
\ \"acc_norm_stderr\": 0.04218506215368879\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.03523807393012047,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.03523807393012047\n \
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6867924528301886,\n \"acc_stderr\": 0.028544793319055326,\n\
\ \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.028544793319055326\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\
\ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\
\ \"acc_norm_stderr\": 0.03514697467862388\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\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\"\
: 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.653179190751445,\n\
\ \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n\
\ \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6212765957446809,\n \"acc_stderr\": 0.03170995606040655,\n\
\ \"acc_norm\": 0.6212765957446809,\n \"acc_norm_stderr\": 0.03170995606040655\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6413793103448275,\n \"acc_stderr\": 0.039966295748767186,\n\
\ \"acc_norm\": 0.6413793103448275,\n \"acc_norm_stderr\": 0.039966295748767186\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.49206349206349204,\n \"acc_stderr\": 0.02574806587167328,\n \"\
acc_norm\": 0.49206349206349204,\n \"acc_norm_stderr\": 0.02574806587167328\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\
\ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\
\ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8064516129032258,\n \"acc_stderr\": 0.022475258525536057,\n \"\
acc_norm\": 0.8064516129032258,\n \"acc_norm_stderr\": 0.022475258525536057\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n \"\
acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\"\
: 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8121212121212121,\n \"acc_stderr\": 0.03050193405942914,\n\
\ \"acc_norm\": 0.8121212121212121,\n \"acc_norm_stderr\": 0.03050193405942914\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8636363636363636,\n \"acc_stderr\": 0.024450155973189835,\n \"\
acc_norm\": 0.8636363636363636,\n \"acc_norm_stderr\": 0.024450155973189835\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.021995311963644244,\n\
\ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.021995311963644244\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563976,\n\
\ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563976\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.36666666666666664,\n \"acc_stderr\": 0.029381620726465073,\n \
\ \"acc_norm\": 0.36666666666666664,\n \"acc_norm_stderr\": 0.029381620726465073\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.029344572500634332,\n\
\ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.029344572500634332\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.39072847682119205,\n \"acc_stderr\": 0.03983798306659806,\n \"\
acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.03983798306659806\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8440366972477065,\n \"acc_stderr\": 0.015555802713590177,\n \"\
acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.015555802713590177\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5648148148148148,\n \"acc_stderr\": 0.03381200005643527,\n \"\
acc_norm\": 0.5648148148148148,\n \"acc_norm_stderr\": 0.03381200005643527\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8627450980392157,\n \"acc_stderr\": 0.024152225962801588,\n \"\
acc_norm\": 0.8627450980392157,\n \"acc_norm_stderr\": 0.024152225962801588\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8481012658227848,\n \"acc_stderr\": 0.023363878096632446,\n \
\ \"acc_norm\": 0.8481012658227848,\n \"acc_norm_stderr\": 0.023363878096632446\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\
\ \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n\
\ \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.03768335959728743,\n\
\ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.03768335959728743\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\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\": 0.7484662576687117,\n \"acc_stderr\": 0.03408997886857529,\n\
\ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.03408997886857529\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8543689320388349,\n \"acc_stderr\": 0.03492606476623791,\n\
\ \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.03492606476623791\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n\
\ \"acc_stderr\": 0.02280138253459753,\n \"acc_norm\": 0.8589743589743589,\n\
\ \"acc_norm_stderr\": 0.02280138253459753\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8084291187739464,\n\
\ \"acc_stderr\": 0.014072859310451949,\n \"acc_norm\": 0.8084291187739464,\n\
\ \"acc_norm_stderr\": 0.014072859310451949\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7543352601156069,\n \"acc_stderr\": 0.023176298203992005,\n\
\ \"acc_norm\": 0.7543352601156069,\n \"acc_norm_stderr\": 0.023176298203992005\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4,\n\
\ \"acc_stderr\": 0.01638463841038082,\n \"acc_norm\": 0.4,\n \
\ \"acc_norm_stderr\": 0.01638463841038082\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.02451819564187933,\n\
\ \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.02451819564187933\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\
\ \"acc_stderr\": 0.025583062489984824,\n \"acc_norm\": 0.7170418006430869,\n\
\ \"acc_norm_stderr\": 0.025583062489984824\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7870370370370371,\n \"acc_stderr\": 0.022779719088733396,\n\
\ \"acc_norm\": 0.7870370370370371,\n \"acc_norm_stderr\": 0.022779719088733396\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5070921985815603,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.5070921985815603,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4915254237288136,\n\
\ \"acc_stderr\": 0.01276840169726906,\n \"acc_norm\": 0.4915254237288136,\n\
\ \"acc_norm_stderr\": 0.01276840169726906\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7389705882352942,\n \"acc_stderr\": 0.026679252270103124,\n\
\ \"acc_norm\": 0.7389705882352942,\n \"acc_norm_stderr\": 0.026679252270103124\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6797385620915033,\n \"acc_stderr\": 0.018875682938069446,\n \
\ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.018875682938069446\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\
\ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\
\ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.02783302387139968,\n\
\ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.02783302387139968\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\
\ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\
\ \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \
\ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5843373493975904,\n\
\ \"acc_stderr\": 0.03836722176598053,\n \"acc_norm\": 0.5843373493975904,\n\
\ \"acc_norm_stderr\": 0.03836722176598053\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03188578017686398,\n\
\ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03188578017686398\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5801713586291309,\n\
\ \"mc1_stderr\": 0.017277030301775766,\n \"mc2\": 0.7263318450468071,\n\
\ \"mc2_stderr\": 0.014889987688937593\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8342541436464088,\n \"acc_stderr\": 0.010450899545370632\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6360879454131918,\n \
\ \"acc_stderr\": 0.013252539227966193\n }\n}\n```"
repo_url: https://huggingface.co/cloudyu/Venus_DPO_50
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_01_21T17_50_57.443719
path:
- '**/details_harness|arc:challenge|25_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|gsm8k|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hellaswag|10_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-21T17-50-57.443719.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-21T17-50-57.443719.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- '**/details_harness|winogrande|5_2024-01-21T17-50-57.443719.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-21T17-50-57.443719.parquet'
- config_name: results
data_files:
- split: 2024_01_21T17_50_57.443719
path:
- results_2024-01-21T17-50-57.443719.parquet
- split: latest
path:
- results_2024-01-21T17-50-57.443719.parquet
---
# Dataset Card for Evaluation run of cloudyu/Venus_DPO_50
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [cloudyu/Venus_DPO_50](https://huggingface.co/cloudyu/Venus_DPO_50) 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_cloudyu__Venus_DPO_50",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-21T17:50:57.443719](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__Venus_DPO_50/blob/main/results_2024-01-21T17-50-57.443719.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": 0.6666481748258403,
"acc_stderr": 0.03169619700244902,
"acc_norm": 0.6675473139195528,
"acc_norm_stderr": 0.032342525727742856,
"mc1": 0.5801713586291309,
"mc1_stderr": 0.017277030301775766,
"mc2": 0.7263318450468071,
"mc2_stderr": 0.014889987688937593
},
"harness|arc:challenge|25": {
"acc": 0.689419795221843,
"acc_stderr": 0.013522292098053059,
"acc_norm": 0.7073378839590444,
"acc_norm_stderr": 0.013295916103619427
},
"harness|hellaswag|10": {
"acc": 0.7125074686317466,
"acc_stderr": 0.004516681953879092,
"acc_norm": 0.8846843258315077,
"acc_norm_stderr": 0.0031874975090874207
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6074074074074074,
"acc_stderr": 0.04218506215368879,
"acc_norm": 0.6074074074074074,
"acc_norm_stderr": 0.04218506215368879
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.75,
"acc_stderr": 0.03523807393012047,
"acc_norm": 0.75,
"acc_norm_stderr": 0.03523807393012047
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6867924528301886,
"acc_stderr": 0.028544793319055326,
"acc_norm": 0.6867924528301886,
"acc_norm_stderr": 0.028544793319055326
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7708333333333334,
"acc_stderr": 0.03514697467862388,
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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
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[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. -->
[More Information Needed]
## 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]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### 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. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
miesepeter/selected_cmu-arctic-xvectors | ---
license: mit
---
Dataset containing selected cmu-artic-xvectors; full dataset can be found at: https://huggingface.co/datasets/Matthijs/cmu-arctic-xvectors |
distil-whisper/tedlium-dev-test | ---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: string
- name: gender
dtype:
class_label:
names:
'0': unknown
'1': female
'2': male
- name: file
dtype: string
- name: id
dtype: string
splits:
- name: validation
num_bytes: 197798071.0
num_examples: 591
- name: test
num_bytes: 352803076.375
num_examples: 1469
download_size: 549654154
dataset_size: 550601147.375
---
# Dataset Card for "tedlium-dev-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rvorias/realms_adventurers | ---
license: other
task_categories:
- text-to-image
language:
- en
tags:
- stable-diffusion
- realms
pretty_name: Realms Adventurers Dataset
size_categories:
- n<1K
---
# Realms Adventurer Dataset for Text-to-Image
This dataset contains annotated image-caption pairs with a specific structure.
## Example
```json
{
"file_name": "91200682-07_giants.png",
"sex": "male",
"race": "giant",
"class": "mage",
"inherent_features": "red flowers growing on his skin",
"clothing": "brown leather pants",
"accessories": null,
"background": "between tall red trees",
"shot": "full",
"view": "frontal",
"caption": "a male giant mage with red flowers growing on his skin, wearing brown leather pants, between tall red trees, full, frontal"
}
```
## Usage
```python
import datasets
dataset = datasets.load_dataset("rvorias/realms_adventurers")
dataset["train"][0]
```
## Annotation tooling
Label-studio was used to organize and create annotations. |
arubenruben/primeiro_harem_conll_2003_style | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
splits:
- name: train
num_bytes: 1504058
num_examples: 121
- name: validation
num_bytes: 51150
num_examples: 8
- name: test
num_bytes: 1060266
num_examples: 128
download_size: 528687
dataset_size: 2615474
---
# Dataset Card for "primeiro_harem_conll_2003_style"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
huggingartists/sundara-karma | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/sundara-karma"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.081864 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/afbb51b0dc0e4618f79565e67991a9fd.360x360x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/sundara-karma">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Sundara Karma</div>
<a href="https://genius.com/artists/sundara-karma">
<div style="text-align: center; font-size: 14px;">@sundara-karma</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/sundara-karma).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/sundara-karma")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|46| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/sundara-karma")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
mikhail-panzo/raw_res_ceb | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
- name: speaker_id
dtype: string
splits:
- name: train
num_bytes: 534357620.0
num_examples: 310
download_size: 532856520
dataset_size: 534357620.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
anuragiiser/ILDC_expert | ---
license: mit
---
|
autoevaluate/autoeval-staging-eval-project-squad_v2-e85023ec-11745565 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/roberta-large-squad2
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/roberta-large-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model. |
joey234/medmcqa-neg-answer | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: opa
dtype: string
- name: opb
dtype: string
- name: opc
dtype: string
- name: opd
dtype: string
- name: cop
dtype:
class_label:
names:
'0': a
'1': b
'2': c
'3': d
- name: choice_type
dtype: string
- name: exp
dtype: string
- name: subject_name
dtype: string
- name: topic_name
dtype: string
- name: neg_answer
dtype: string
splits:
- name: validation
num_bytes: 2341249
num_examples: 4183
download_size: 1571269
dataset_size: 2341249
---
# Dataset Card for "medmcqa-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ovior/twitter_dataset_1713056384 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 2603888
num_examples: 8135
download_size: 1471149
dataset_size: 2603888
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
reciprocate/oasst_hh_shp_hellaswag_webgpt_rm_dataset | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: replies
sequence: string
splits:
- name: train
num_bytes: 395107894.0
num_examples: 264534
- name: test
num_bytes: 5859289.0
num_examples: 2874
download_size: 232712113
dataset_size: 400967183.0
---
# Dataset Card for "oasst_hh_shp_hellaswag_webgpt_rm_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_BlouseJury__Mistral-7B-Discord-0.1-DPO | ---
pretty_name: Evaluation run of BlouseJury/Mistral-7B-Discord-0.1-DPO
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [BlouseJury/Mistral-7B-Discord-0.1-DPO](https://huggingface.co/BlouseJury/Mistral-7B-Discord-0.1-DPO)\
\ 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_BlouseJury__Mistral-7B-Discord-0.1-DPO\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-01T17:00:23.691484](https://huggingface.co/datasets/open-llm-leaderboard/details_BlouseJury__Mistral-7B-Discord-0.1-DPO/blob/main/results_2024-02-01T17-00-23.691484.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\": 0.6233951979610222,\n\
\ \"acc_stderr\": 0.032739541113838276,\n \"acc_norm\": 0.6297936917040108,\n\
\ \"acc_norm_stderr\": 0.033418584464628434,\n \"mc1\": 0.38922888616891066,\n\
\ \"mc1_stderr\": 0.017068552680690328,\n \"mc2\": 0.5527536910345616,\n\
\ \"mc2_stderr\": 0.015269414074864143\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6015358361774744,\n \"acc_stderr\": 0.014306946052735563,\n\
\ \"acc_norm\": 0.6322525597269625,\n \"acc_norm_stderr\": 0.014090995618168484\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6394144592710616,\n\
\ \"acc_stderr\": 0.004791890625834196,\n \"acc_norm\": 0.8327026488747261,\n\
\ \"acc_norm_stderr\": 0.003724783389253327\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n\
\ \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n\
\ \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6513157894736842,\n \"acc_stderr\": 0.0387813988879761,\n\
\ \"acc_norm\": 0.6513157894736842,\n \"acc_norm_stderr\": 0.0387813988879761\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.56,\n\
\ \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \
\ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n\
\ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7152777777777778,\n\
\ \"acc_stderr\": 0.037738099906869334,\n \"acc_norm\": 0.7152777777777778,\n\
\ \"acc_norm_stderr\": 0.037738099906869334\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n\
\ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5953757225433526,\n\
\ \"acc_stderr\": 0.03742461193887249,\n \"acc_norm\": 0.5953757225433526,\n\
\ \"acc_norm_stderr\": 0.03742461193887249\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n\
\ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\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\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\
\ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\
\ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\
\ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482758,\n\
\ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482758\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41798941798941797,\n \"acc_stderr\": 0.025402555503260912,\n \"\
acc_norm\": 0.41798941798941797,\n \"acc_norm_stderr\": 0.025402555503260912\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7548387096774194,\n\
\ \"acc_stderr\": 0.02447224384089553,\n \"acc_norm\": 0.7548387096774194,\n\
\ \"acc_norm_stderr\": 0.02447224384089553\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n\
\ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\
: 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7393939393939394,\n \"acc_stderr\": 0.034277431758165236,\n\
\ \"acc_norm\": 0.7393939393939394,\n \"acc_norm_stderr\": 0.034277431758165236\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\
acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8601036269430051,\n \"acc_stderr\": 0.02503387058301518,\n\
\ \"acc_norm\": 0.8601036269430051,\n \"acc_norm_stderr\": 0.02503387058301518\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6333333333333333,\n \"acc_stderr\": 0.02443301646605246,\n \
\ \"acc_norm\": 0.6333333333333333,\n \"acc_norm_stderr\": 0.02443301646605246\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3074074074074074,\n \"acc_stderr\": 0.028133252578815642,\n \
\ \"acc_norm\": 0.3074074074074074,\n \"acc_norm_stderr\": 0.028133252578815642\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.031041941304059278,\n\
\ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.031041941304059278\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\
acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8165137614678899,\n \"acc_stderr\": 0.016595259710399327,\n \"\
acc_norm\": 0.8165137614678899,\n \"acc_norm_stderr\": 0.016595259710399327\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5509259259259259,\n \"acc_stderr\": 0.03392238405321617,\n \"\
acc_norm\": 0.5509259259259259,\n \"acc_norm_stderr\": 0.03392238405321617\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8088235294117647,\n \"acc_stderr\": 0.02759917430064076,\n \"\
acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.02759917430064076\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676173,\n \
\ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676173\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6681614349775785,\n\
\ \"acc_stderr\": 0.03160295143776678,\n \"acc_norm\": 0.6681614349775785,\n\
\ \"acc_norm_stderr\": 0.03160295143776678\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n\
\ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070417,\n \"\
acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070417\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\
\ \"acc_stderr\": 0.041331194402438376,\n \"acc_norm\": 0.7592592592592593,\n\
\ \"acc_norm_stderr\": 0.041331194402438376\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\
\ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.38392857142857145,\n\
\ \"acc_stderr\": 0.04616143075028547,\n \"acc_norm\": 0.38392857142857145,\n\
\ \"acc_norm_stderr\": 0.04616143075028547\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n\
\ \"acc_stderr\": 0.022209309073165616,\n \"acc_norm\": 0.8675213675213675,\n\
\ \"acc_norm_stderr\": 0.022209309073165616\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8109833971902938,\n\
\ \"acc_stderr\": 0.014000791294407003,\n \"acc_norm\": 0.8109833971902938,\n\
\ \"acc_norm_stderr\": 0.014000791294407003\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7023121387283237,\n \"acc_stderr\": 0.024617055388676992,\n\
\ \"acc_norm\": 0.7023121387283237,\n \"acc_norm_stderr\": 0.024617055388676992\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3396648044692737,\n\
\ \"acc_stderr\": 0.015839400406212505,\n \"acc_norm\": 0.3396648044692737,\n\
\ \"acc_norm_stderr\": 0.015839400406212505\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7091503267973857,\n \"acc_stderr\": 0.02600480036395213,\n\
\ \"acc_norm\": 0.7091503267973857,\n \"acc_norm_stderr\": 0.02600480036395213\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\
\ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\
\ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.02474862449053738,\n\
\ \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.02474862449053738\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.46808510638297873,\n \"acc_stderr\": 0.029766675075873866,\n \
\ \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.029766675075873866\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.439374185136897,\n\
\ \"acc_stderr\": 0.012676014778580214,\n \"acc_norm\": 0.439374185136897,\n\
\ \"acc_norm_stderr\": 0.012676014778580214\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.028418208619406755,\n\
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.028418208619406755\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6339869281045751,\n \"acc_stderr\": 0.019488025745529675,\n \
\ \"acc_norm\": 0.6339869281045751,\n \"acc_norm_stderr\": 0.019488025745529675\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\
\ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\
\ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.0289205832206756,\n\
\ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.0289205832206756\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\
\ \"acc_stderr\": 0.026508590656233264,\n \"acc_norm\": 0.8308457711442786,\n\
\ \"acc_norm_stderr\": 0.026508590656233264\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.82,\n \"acc_stderr\": 0.038612291966536955,\n \
\ \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.038612291966536955\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\
\ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\
\ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.029913127232368036,\n\
\ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.029913127232368036\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.38922888616891066,\n\
\ \"mc1_stderr\": 0.017068552680690328,\n \"mc2\": 0.5527536910345616,\n\
\ \"mc2_stderr\": 0.015269414074864143\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7892659826361483,\n \"acc_stderr\": 0.011462046419710681\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.30401819560272936,\n \
\ \"acc_stderr\": 0.012670420440198654\n }\n}\n```"
repo_url: https://huggingface.co/BlouseJury/Mistral-7B-Discord-0.1-DPO
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_02_01T17_00_23.691484
path:
- '**/details_harness|arc:challenge|25_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|gsm8k|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hellaswag|10_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T17-00-23.691484.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-01T17-00-23.691484.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- '**/details_harness|winogrande|5_2024-02-01T17-00-23.691484.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-01T17-00-23.691484.parquet'
- config_name: results
data_files:
- split: 2024_02_01T17_00_23.691484
path:
- results_2024-02-01T17-00-23.691484.parquet
- split: latest
path:
- results_2024-02-01T17-00-23.691484.parquet
---
# Dataset Card for Evaluation run of BlouseJury/Mistral-7B-Discord-0.1-DPO
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [BlouseJury/Mistral-7B-Discord-0.1-DPO](https://huggingface.co/BlouseJury/Mistral-7B-Discord-0.1-DPO) 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_BlouseJury__Mistral-7B-Discord-0.1-DPO",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-01T17:00:23.691484](https://huggingface.co/datasets/open-llm-leaderboard/details_BlouseJury__Mistral-7B-Discord-0.1-DPO/blob/main/results_2024-02-01T17-00-23.691484.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": 0.6233951979610222,
"acc_stderr": 0.032739541113838276,
"acc_norm": 0.6297936917040108,
"acc_norm_stderr": 0.033418584464628434,
"mc1": 0.38922888616891066,
"mc1_stderr": 0.017068552680690328,
"mc2": 0.5527536910345616,
"mc2_stderr": 0.015269414074864143
},
"harness|arc:challenge|25": {
"acc": 0.6015358361774744,
"acc_stderr": 0.014306946052735563,
"acc_norm": 0.6322525597269625,
"acc_norm_stderr": 0.014090995618168484
},
"harness|hellaswag|10": {
"acc": 0.6394144592710616,
"acc_stderr": 0.004791890625834196,
"acc_norm": 0.8327026488747261,
"acc_norm_stderr": 0.003724783389253327
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6074074074074074,
"acc_stderr": 0.0421850621536888,
"acc_norm": 0.6074074074074074,
"acc_norm_stderr": 0.0421850621536888
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6513157894736842,
"acc_stderr": 0.0387813988879761,
"acc_norm": 0.6513157894736842,
"acc_norm_stderr": 0.0387813988879761
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6830188679245283,
"acc_stderr": 0.02863723563980089,
"acc_norm": 0.6830188679245283,
"acc_norm_stderr": 0.02863723563980089
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7152777777777778,
"acc_stderr": 0.037738099906869334,
"acc_norm": 0.7152777777777778,
"acc_norm_stderr": 0.037738099906869334
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.49,
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```
## Dataset Details
### Dataset Description
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## Uses
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### Direct Use
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## Dataset Structure
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### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Personal and Sensitive Information
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ICILS/isco_esco_occupations_taxonomy | ---
# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
pretty_name: ISCO-ESCO Occupations Taxonomy
task_categories:
- text-classification
task_ids:
- multi-class-classification
tags:
- occupation coding
- ESCO
- ISCO-08
source_datasets:
- European Commission ESCO
dataset_info:
- config_name: isco_occupations
features:
- name: ISCO_OCCUPATION
dtype: string
- name: ISCO_CODE
dtype:
class_label:
names:
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'275': '3421'
'276': '3422'
'277': '3423'
'278': '343'
'279': '3431'
'280': '3432'
'281': '3433'
'282': '3434'
'283': '3435'
'284': '35'
'285': '351'
'286': '3511'
'287': '3512'
'288': '3513'
'289': '3514'
'290': '352'
'291': '3521'
'292': '3522'
'293': '4'
'294': '41'
'295': '411'
'296': '4110'
'297': '412'
'298': '4120'
'299': '413'
'300': '4131'
'301': '4132'
'302': '42'
'303': '421'
'304': '4211'
'305': '4212'
'306': '4213'
'307': '4214'
'308': '422'
'309': '4221'
'310': '4222'
'311': '4223'
'312': '4224'
'313': '4225'
'314': '4226'
'315': '4227'
'316': '4229'
'317': '43'
'318': '431'
'319': '4311'
'320': '4312'
'321': '4313'
'322': '432'
'323': '4321'
'324': '4322'
'325': '4323'
'326': '44'
'327': '441'
'328': '4411'
'329': '4412'
'330': '4413'
'331': '4414'
'332': '4415'
'333': '4416'
'334': '4419'
'335': '5'
'336': '51'
'337': '511'
'338': '5111'
'339': '5112'
'340': '5113'
'341': '512'
'342': '5120'
'343': '513'
'344': '5131'
'345': '5132'
'346': '514'
'347': '5141'
'348': '5142'
'349': '515'
'350': '5151'
'351': '5152'
'352': '5153'
'353': '516'
'354': '5161'
'355': '5162'
'356': '5163'
'357': '5164'
'358': '5165'
'359': '5169'
'360': '52'
'361': '521'
'362': '5211'
'363': '5212'
'364': '522'
'365': '5221'
'366': '5222'
'367': '5223'
'368': '523'
'369': '5230'
'370': '524'
'371': '5241'
'372': '5242'
'373': '5243'
'374': '5244'
'375': '5245'
'376': '5246'
'377': '5249'
'378': '53'
'379': '531'
'380': '5311'
'381': '5312'
'382': '532'
'383': '5321'
'384': '5322'
'385': '5329'
'386': '54'
'387': '541'
'388': '5411'
'389': '5412'
'390': '5413'
'391': '5414'
'392': '5419'
'393': '6'
'394': '61'
'395': '611'
'396': '6111'
'397': '6112'
'398': '6113'
'399': '6114'
'400': '612'
'401': '6121'
'402': '6122'
'403': '6123'
'404': '6129'
'405': '613'
'406': '6130'
'407': '62'
'408': '621'
'409': '6210'
'410': '622'
'411': '6221'
'412': '6222'
'413': '6223'
'414': '6224'
'415': '63'
'416': '631'
'417': '6310'
'418': '632'
'419': '6320'
'420': '633'
'421': '6330'
'422': '634'
'423': '6340'
'424': '7'
'425': '71'
'426': '711'
'427': '7111'
'428': '7112'
'429': '7113'
'430': '7114'
'431': '7115'
'432': '7119'
'433': '712'
'434': '7121'
'435': '7122'
'436': '7123'
'437': '7124'
'438': '7125'
'439': '7126'
'440': '7127'
'441': '713'
'442': '7131'
'443': '7132'
'444': '7133'
'445': '72'
'446': '721'
'447': '7211'
'448': '7212'
'449': '7213'
'450': '7214'
'451': '7215'
'452': '722'
'453': '7221'
'454': '7222'
'455': '7223'
'456': '7224'
'457': '723'
'458': '7231'
'459': '7232'
'460': '7233'
'461': '7234'
'462': '73'
'463': '731'
'464': '7311'
'465': '7312'
'466': '7313'
'467': '7314'
'468': '7315'
'469': '7316'
'470': '7317'
'471': '7318'
'472': '7319'
'473': '732'
'474': '7321'
'475': '7322'
'476': '7323'
'477': '74'
'478': '741'
'479': '7411'
'480': '7412'
'481': '7413'
'482': '742'
'483': '7421'
'484': '7422'
'485': '75'
'486': '751'
'487': '7511'
'488': '7512'
'489': '7513'
'490': '7514'
'491': '7515'
'492': '7516'
'493': '752'
'494': '7521'
'495': '7522'
'496': '7523'
'497': '753'
'498': '7531'
'499': '7532'
'500': '7533'
'501': '7534'
'502': '7535'
'503': '7536'
'504': '754'
'505': '7541'
'506': '7542'
'507': '7543'
'508': '7544'
'509': '7549'
'510': '8'
'511': '81'
'512': '811'
'513': '8111'
'514': '8112'
'515': '8113'
'516': '8114'
'517': '812'
'518': '8121'
'519': '8122'
'520': '813'
'521': '8131'
'522': '8132'
'523': '814'
'524': '8141'
'525': '8142'
'526': '8143'
'527': '815'
'528': '8151'
'529': '8152'
'530': '8153'
'531': '8154'
'532': '8155'
'533': '8156'
'534': '8157'
'535': '8159'
'536': '816'
'537': '8160'
'538': '817'
'539': '8171'
'540': '8172'
'541': '818'
'542': '8181'
'543': '8182'
'544': '8183'
'545': '8189'
'546': '82'
'547': '821'
'548': '8211'
'549': '8212'
'550': '8219'
'551': '83'
'552': '831'
'553': '8311'
'554': '8312'
'555': '832'
'556': '8321'
'557': '8322'
'558': '833'
'559': '8331'
'560': '8332'
'561': '834'
'562': '8341'
'563': '8342'
'564': '8343'
'565': '8344'
'566': '835'
'567': '8350'
'568': '9'
'569': '91'
'570': '911'
'571': '9111'
'572': '9112'
'573': '912'
'574': '9121'
'575': '9122'
'576': '9123'
'577': '9129'
'578': '92'
'579': '921'
'580': '9211'
'581': '9212'
'582': '9213'
'583': '9214'
'584': '9215'
'585': '9216'
'586': '93'
'587': '931'
'588': '9311'
'589': '9312'
'590': '9313'
'591': '932'
'592': '9321'
'593': '9329'
'594': '933'
'595': '9331'
'596': '9332'
'597': '9333'
'598': '9334'
'599': '94'
'600': '941'
'601': '9411'
'602': '9412'
'603': '95'
'604': '951'
'605': '9510'
'606': '952'
'607': '9520'
'608': '96'
'609': '961'
'610': '9611'
'611': '9612'
'612': '9613'
'613': '962'
'614': '9621'
'615': '9622'
'616': '9623'
'617': '9624'
'618': '9629'
splits:
- name: train
num_bytes: 248076
num_examples: 7018
configs:
- config_name: isco_occupations
data_files:
- split: train
path: data/isco_occupations.jsonl
default: true
- config_name: isco_taxonomy
data_files:
- split: train
path: data/isco_taxonomy.jsonl
train-eval-index:
- config: isco_occupations
task: text-classification
task_id: multi-class-classification
splits:
train_split: train
col_mapping:
text: ISCO_OCCUPATION
label: ISCO_CODE
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
- type: danieldux/isco_hierarchical_accuracy
name: ISCO Hierarchical Accuracy
---
# Dataset Card for {{ pretty_name | default("Dataset Name", true) }}
<!-- Provide a quick summary of the dataset. -->
{{ dataset_summary | default("", true) }}
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
{{ dataset_description | default("", true) }}
- **Curated by:** {{ curators | default("[More Information Needed]", true)}}
- **Funded by [optional]:** {{ funded_by | default("[More Information Needed]", true)}}
- **Shared by [optional]:** {{ shared_by | default("[More Information Needed]", true)}}
- **Language(s) (NLP):** {{ language | default("[More Information Needed]", true)}}
- **License:** {{ license | default("[More Information Needed]", true)}}
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** {{ repo | default("[More Information Needed]", true)}}
- **Paper [optional]:** {{ paper | default("[More Information Needed]", true)}}
- **Demo [optional]:** {{ demo | default("[More Information Needed]", true)}}
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
{{ direct_use | default("[More Information Needed]", true)}}
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
{{ out_of_scope_use | default("[More Information Needed]", true)}}
## 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. -->
{{ dataset_structure | default("[More Information Needed]", true)}}
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
{{ curation_rationale_section | default("[More Information Needed]", true)}}
### 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. -->
{{ data_collection_and_processing_section | default("[More Information Needed]", true)}}
#### 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. -->
{{ source_data_producers_section | default("[More Information Needed]", true)}}
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
{{ annotation_process_section | default("[More Information Needed]", true)}}
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
{{ who_are_annotators_section | default("[More Information Needed]", true)}}
#### 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. -->
{{ personal_and_sensitive_information | default("[More Information Needed]", true)}}
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
{{ bias_risks_limitations | default("[More Information Needed]", true)}}
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
{{ bias_recommendations | default("Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.", true)}}
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
{{ citation_bibtex | default("[More Information Needed]", true)}}
**APA:**
{{ citation_apa | default("[More Information Needed]", true)}}
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
{{ glossary | default("[More Information Needed]", true)}}
## More Information [optional]
{{ more_information | default("[More Information Needed]", true)}}
## Dataset Card Authors [optional]
{{ dataset_card_authors | default("[More Information Needed]", true)}}
## Dataset Card Contact
{{ dataset_card_contact | default("[More Information Needed]", true)}}
|
darksensei/details_dataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
- name: full_text
dtype: string
splits:
- name: train
num_bytes: 43751142.4
num_examples: 960
- name: test
num_bytes: 10937785.6
num_examples: 240
download_size: 53367810
dataset_size: 54688928.0
---
# Dataset Card for "details_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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