datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
joshuajewell/Openclipart-Oldstyle | ---
license: cc0-1.0
annotations_creators:
- human generated
language:
- en
language_creators:
- other
multilinguality:
- monolingual
pretty_name: Black and White Print Images
size_categories:
- n=103
source_datasets:
- https://openclipart.org/artist/j4p4n
- https://openclipart.org/artist/johnny_automatic
- https://openclipart.org/artist/SnipsAndClips
tags: []
task_categories:
- text-to-image
task_ids: []
---
<h1>Dataset Card for 16th Century(?) Black and White Style</h1>
Dataset used to train/finetune a black and white print style
Captions are generated by hand with the assistance of BLIP.
Images were sourced from:
</br> https://openclipart.org/artist/j4p4n
</br> https://openclipart.org/artist/johnny_automatic
</br> https://openclipart.org/artist/SnipsAndClips
Text file filenames correspond image file filenames as captions. |
Voice-man-76/Molly | ---
license: apache-2.0
---
.gitattributes
2.31 kB
initial commit
10 minutes ago
Oh gee, no party.mp3
31.7 kB
LFS
Upload Oh gee, no party.mp3 |
tr416/dataset_20231006_234856 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 762696.0
num_examples: 297
- name: test
num_bytes: 7704.0
num_examples: 3
download_size: 73719
dataset_size: 770400.0
---
# Dataset Card for "dataset_20231006_234856"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
totally-not-an-llm/collage-40k | ---
license: apache-2.0
---
# Collage 40k Dataset
Collage 40k is a dataset of approximately 40,000 user/assistant conversations generated by GPT-3.5 and GPT-4. This dataset was created by extracting filtered subsets from other datasets.
## Dataset Details
- Total Conversations: 39,819
- GPTeacher general-instruct: 13,567
- ShareGPT: 16,409
- OpenOrca (step by step): 9,843
The dataset has undergone filtering to remove censorship, refusals, alignment, and low-quality conversations.
The data is provided in ShareGPT format.
## Licensing Information
This dataset is licensed under the Apache-2.0 license. However, the OpenOrca and GPTeacher licenses are both MIT licensed.
|
iamkaikai/ELLSWORTH-KELLY-ART | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 3101654.0
num_examples: 226
download_size: 2836219
dataset_size: 3101654.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ELLSWORTH-KELLY-ART"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Nexdata/Chinese_Wake-up_Words_Speech_Data_by_Mobile_Phone | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/Chinese_Wake-up_Words_Speech_Data_by_Mobile_Phone
## 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://www.nexdata.ai/datasets/177?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Chinese wake-up words audio data captured by mobile phone, collected from 200 people, 180 sentences per person, a total length of 24.5 hours; recording staff come from seven dialect regions with balanced gender distribution; collection environment was diversified; recorded text includes wake-up words and colloquial sentences.
For more details, please refer to the link: https://www.nexdata.ai/datasets/177?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Mandarin Chinsese
## 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
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo2_100_kl_0.1_prm_70m_thr_1.0_seed_2 | ---
dataset_info:
config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: preference
dtype: int64
- name: output_1
dtype: string
- name: output_2
dtype: string
- name: reward_model_prompt_format
dtype: string
- name: gen_prompt_format
dtype: string
- name: gen_kwargs
struct:
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dtype: bool
- name: max_new_tokens
dtype: int64
- name: pad_token_id
dtype: int64
- name: top_k
dtype: int64
- name: top_p
dtype: float64
- name: reward_1
dtype: float64
- name: reward_2
dtype: float64
- name: n_samples
dtype: int64
- name: reject_select
dtype: string
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: index
dtype: int64
- name: filtered_epoch
dtype: int64
- name: gen_reward
dtype: float64
- name: gen_response
dtype: string
splits:
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- name: epoch_28
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num_examples: 18929
- name: epoch_29
num_bytes: 44413394
num_examples: 18929
download_size: 1399974044
dataset_size: 1330470418
configs:
- config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
data_files:
- split: epoch_0
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-*
- split: epoch_1
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-*
- split: epoch_2
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-*
- split: epoch_3
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-*
- split: epoch_4
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-*
- split: epoch_5
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-*
- split: epoch_6
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-*
- split: epoch_7
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-*
- split: epoch_8
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-*
- split: epoch_9
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-*
- split: epoch_10
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-*
- split: epoch_11
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-*
- split: epoch_12
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-*
- split: epoch_13
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-*
- split: epoch_14
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-*
- split: epoch_15
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-*
- split: epoch_16
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-*
- split: epoch_17
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-*
- split: epoch_18
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-*
- split: epoch_19
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-*
- split: epoch_20
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-*
- split: epoch_21
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-*
- split: epoch_22
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-*
- split: epoch_23
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-*
- split: epoch_24
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-*
- split: epoch_25
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-*
- split: epoch_26
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-*
- split: epoch_27
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-*
- split: epoch_28
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-*
- split: epoch_29
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-*
---
|
open-llm-leaderboard/details_SJ-Donald__SOLAR-10.7B-slerp | ---
pretty_name: Evaluation run of SJ-Donald/SOLAR-10.7B-slerp
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [SJ-Donald/SOLAR-10.7B-slerp](https://huggingface.co/SJ-Donald/SOLAR-10.7B-slerp)\
\ 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_SJ-Donald__SOLAR-10.7B-slerp\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-25T05:52:30.041619](https://huggingface.co/datasets/open-llm-leaderboard/details_SJ-Donald__SOLAR-10.7B-slerp/blob/main/results_2024-01-25T05-52-30.041619.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\": 0.6693576990621962,\n\
\ \"acc_stderr\": 0.031454814037401475,\n \"acc_norm\": 0.6709568764499055,\n\
\ \"acc_norm_stderr\": 0.03209310283459356,\n \"mc1\": 0.5091799265605875,\n\
\ \"mc1_stderr\": 0.017500550724819756,\n \"mc2\": 0.674246091155489,\n\
\ \"mc2_stderr\": 0.014911205444372602\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6501706484641638,\n \"acc_stderr\": 0.013936809212158296,\n\
\ \"acc_norm\": 0.681740614334471,\n \"acc_norm_stderr\": 0.013611993916971453\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.682832105158335,\n\
\ \"acc_stderr\": 0.004644223294727723,\n \"acc_norm\": 0.8691495717984465,\n\
\ \"acc_norm_stderr\": 0.003365474860676742\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5703703703703704,\n\
\ \"acc_stderr\": 0.042763494943765995,\n \"acc_norm\": 0.5703703703703704,\n\
\ \"acc_norm_stderr\": 0.042763494943765995\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7631578947368421,\n \"acc_stderr\": 0.03459777606810535,\n\
\ \"acc_norm\": 0.7631578947368421,\n \"acc_norm_stderr\": 0.03459777606810535\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-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\": 0.7569444444444444,\n\
\ \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.7569444444444444,\n\
\ \"acc_norm_stderr\": 0.0358687928008034\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\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\"\
: 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6994219653179191,\n\
\ \"acc_stderr\": 0.0349610148119118,\n \"acc_norm\": 0.6994219653179191,\n\
\ \"acc_norm_stderr\": 0.0349610148119118\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663454,\n\
\ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663454\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.042923469599092816\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\": 0.6344827586206897,\n \"acc_stderr\": 0.04013124195424386,\n\
\ \"acc_norm\": 0.6344827586206897,\n \"acc_norm_stderr\": 0.04013124195424386\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.48148148148148145,\n \"acc_stderr\": 0.02573364199183898,\n \"\
acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.02573364199183898\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\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\": 0.47783251231527096,\n \"acc_stderr\": 0.03514528562175008,\n\
\ \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.03514528562175008\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8181818181818182,\n \"acc_stderr\": 0.03011768892950357,\n\
\ \"acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.03011768892950357\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822516,\n \"\
acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822516\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328972,\n\
\ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328972\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.023710888501970562,\n \
\ \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.023710888501970562\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.37407407407407406,\n \"acc_stderr\": 0.029502861128955286,\n \
\ \"acc_norm\": 0.37407407407407406,\n \"acc_norm_stderr\": 0.029502861128955286\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7226890756302521,\n \"acc_stderr\": 0.02907937453948001,\n \
\ \"acc_norm\": 0.7226890756302521,\n \"acc_norm_stderr\": 0.02907937453948001\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\
acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8550458715596331,\n \"acc_stderr\": 0.01509421569970048,\n \"\
acc_norm\": 0.8550458715596331,\n \"acc_norm_stderr\": 0.01509421569970048\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6111111111111112,\n \"acc_stderr\": 0.033247089118091176,\n \"\
acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.033247089118091176\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8676470588235294,\n \"acc_stderr\": 0.023784297520918856,\n \"\
acc_norm\": 0.8676470588235294,\n \"acc_norm_stderr\": 0.023784297520918856\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.869198312236287,\n \"acc_stderr\": 0.02194876605947076,\n \
\ \"acc_norm\": 0.869198312236287,\n \"acc_norm_stderr\": 0.02194876605947076\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7174887892376681,\n\
\ \"acc_stderr\": 0.03021683101150878,\n \"acc_norm\": 0.7174887892376681,\n\
\ \"acc_norm_stderr\": 0.03021683101150878\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\
\ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"\
acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.040191074725573483\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\": 0.49107142857142855,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\
\ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8160919540229885,\n\
\ \"acc_stderr\": 0.013853724170922533,\n \"acc_norm\": 0.8160919540229885,\n\
\ \"acc_norm_stderr\": 0.013853724170922533\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7630057803468208,\n \"acc_stderr\": 0.02289408248992599,\n\
\ \"acc_norm\": 0.7630057803468208,\n \"acc_norm_stderr\": 0.02289408248992599\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.43687150837988825,\n\
\ \"acc_stderr\": 0.01658868086453063,\n \"acc_norm\": 0.43687150837988825,\n\
\ \"acc_norm_stderr\": 0.01658868086453063\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7647058823529411,\n \"acc_stderr\": 0.0242886194660461,\n\
\ \"acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.0242886194660461\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7363344051446945,\n\
\ \"acc_stderr\": 0.02502553850053234,\n \"acc_norm\": 0.7363344051446945,\n\
\ \"acc_norm_stderr\": 0.02502553850053234\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.023132376234543343,\n\
\ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.023132376234543343\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5390070921985816,\n \"acc_stderr\": 0.02973659252642444,\n \
\ \"acc_norm\": 0.5390070921985816,\n \"acc_norm_stderr\": 0.02973659252642444\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5026075619295959,\n\
\ \"acc_stderr\": 0.012770062445433166,\n \"acc_norm\": 0.5026075619295959,\n\
\ \"acc_norm_stderr\": 0.012770062445433166\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.026799562024887667,\n\
\ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.026799562024887667\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7075163398692811,\n \"acc_stderr\": 0.018403415710109797,\n \
\ \"acc_norm\": 0.7075163398692811,\n \"acc_norm_stderr\": 0.018403415710109797\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\
\ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\
\ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7795918367346939,\n \"acc_stderr\": 0.02653704531214529,\n\
\ \"acc_norm\": 0.7795918367346939,\n \"acc_norm_stderr\": 0.02653704531214529\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\
\ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\
\ \"acc_norm_stderr\": 0.02587064676616913\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\": 0.572289156626506,\n\
\ \"acc_stderr\": 0.03851597683718533,\n \"acc_norm\": 0.572289156626506,\n\
\ \"acc_norm_stderr\": 0.03851597683718533\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\
\ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5091799265605875,\n\
\ \"mc1_stderr\": 0.017500550724819756,\n \"mc2\": 0.674246091155489,\n\
\ \"mc2_stderr\": 0.014911205444372602\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.840568271507498,\n \"acc_stderr\": 0.010288617479454764\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.621683093252464,\n \
\ \"acc_stderr\": 0.013358407831777112\n }\n}\n```"
repo_url: https://huggingface.co/SJ-Donald/SOLAR-10.7B-slerp
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_25T05_52_30.041619
path:
- '**/details_harness|arc:challenge|25_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|gsm8k|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hellaswag|10_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-25T05-52-30.041619.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-25T05-52-30.041619.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- '**/details_harness|winogrande|5_2024-01-25T05-52-30.041619.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-25T05-52-30.041619.parquet'
- config_name: results
data_files:
- split: 2024_01_25T05_52_30.041619
path:
- results_2024-01-25T05-52-30.041619.parquet
- split: latest
path:
- results_2024-01-25T05-52-30.041619.parquet
---
# Dataset Card for Evaluation run of SJ-Donald/SOLAR-10.7B-slerp
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [SJ-Donald/SOLAR-10.7B-slerp](https://huggingface.co/SJ-Donald/SOLAR-10.7B-slerp) 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_SJ-Donald__SOLAR-10.7B-slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-25T05:52:30.041619](https://huggingface.co/datasets/open-llm-leaderboard/details_SJ-Donald__SOLAR-10.7B-slerp/blob/main/results_2024-01-25T05-52-30.041619.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": 0.6693576990621962,
"acc_stderr": 0.031454814037401475,
"acc_norm": 0.6709568764499055,
"acc_norm_stderr": 0.03209310283459356,
"mc1": 0.5091799265605875,
"mc1_stderr": 0.017500550724819756,
"mc2": 0.674246091155489,
"mc2_stderr": 0.014911205444372602
},
"harness|arc:challenge|25": {
"acc": 0.6501706484641638,
"acc_stderr": 0.013936809212158296,
"acc_norm": 0.681740614334471,
"acc_norm_stderr": 0.013611993916971453
},
"harness|hellaswag|10": {
"acc": 0.682832105158335,
"acc_stderr": 0.004644223294727723,
"acc_norm": 0.8691495717984465,
"acc_norm_stderr": 0.003365474860676742
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5703703703703704,
"acc_stderr": 0.042763494943765995,
"acc_norm": 0.5703703703703704,
"acc_norm_stderr": 0.042763494943765995
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7631578947368421,
"acc_stderr": 0.03459777606810535,
"acc_norm": 0.7631578947368421,
"acc_norm_stderr": 0.03459777606810535
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.72,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.72,
"acc_norm_stderr": 0.045126085985421276
},
"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": 0.7569444444444444,
"acc_stderr": 0.0358687928008034,
"acc_norm": 0.7569444444444444,
"acc_norm_stderr": 0.0358687928008034
},
"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": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6994219653179191,
"acc_stderr": 0.0349610148119118,
"acc_norm": 0.6994219653179191,
"acc_norm_stderr": 0.0349610148119118
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.39215686274509803,
"acc_stderr": 0.048580835742663454,
"acc_norm": 0.39215686274509803,
"acc_norm_stderr": 0.048580835742663454
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6212765957446809,
"acc_stderr": 0.03170995606040655,
"acc_norm": 0.6212765957446809,
"acc_norm_stderr": 0.03170995606040655
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5,
"acc_stderr": 0.047036043419179864,
"acc_norm": 0.5,
"acc_norm_stderr": 0.047036043419179864
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6344827586206897,
"acc_stderr": 0.04013124195424386,
"acc_norm": 0.6344827586206897,
"acc_norm_stderr": 0.04013124195424386
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.48148148148148145,
"acc_stderr": 0.02573364199183898,
"acc_norm": 0.48148148148148145,
"acc_norm_stderr": 0.02573364199183898
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.044444444444444495,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.044444444444444495
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8064516129032258,
"acc_stderr": 0.022475258525536057,
"acc_norm": 0.8064516129032258,
"acc_norm_stderr": 0.022475258525536057
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.47783251231527096,
"acc_stderr": 0.03514528562175008,
"acc_norm": 0.47783251231527096,
"acc_norm_stderr": 0.03514528562175008
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8181818181818182,
"acc_stderr": 0.03011768892950357,
"acc_norm": 0.8181818181818182,
"acc_norm_stderr": 0.03011768892950357
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8686868686868687,
"acc_stderr": 0.024063156416822516,
"acc_norm": 0.8686868686868687,
"acc_norm_stderr": 0.024063156416822516
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9067357512953368,
"acc_stderr": 0.02098685459328972,
"acc_norm": 0.9067357512953368,
"acc_norm_stderr": 0.02098685459328972
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.676923076923077,
"acc_stderr": 0.023710888501970562,
"acc_norm": 0.676923076923077,
"acc_norm_stderr": 0.023710888501970562
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.37407407407407406,
"acc_stderr": 0.029502861128955286,
"acc_norm": 0.37407407407407406,
"acc_norm_stderr": 0.029502861128955286
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7226890756302521,
"acc_stderr": 0.02907937453948001,
"acc_norm": 0.7226890756302521,
"acc_norm_stderr": 0.02907937453948001
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3509933774834437,
"acc_stderr": 0.03896981964257375,
"acc_norm": 0.3509933774834437,
"acc_norm_stderr": 0.03896981964257375
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8550458715596331,
"acc_stderr": 0.01509421569970048,
"acc_norm": 0.8550458715596331,
"acc_norm_stderr": 0.01509421569970048
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6111111111111112,
"acc_stderr": 0.033247089118091176,
"acc_norm": 0.6111111111111112,
"acc_norm_stderr": 0.033247089118091176
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8676470588235294,
"acc_stderr": 0.023784297520918856,
"acc_norm": 0.8676470588235294,
"acc_norm_stderr": 0.023784297520918856
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.869198312236287,
"acc_stderr": 0.02194876605947076,
"acc_norm": 0.869198312236287,
"acc_norm_stderr": 0.02194876605947076
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7174887892376681,
"acc_stderr": 0.03021683101150878,
"acc_norm": 0.7174887892376681,
"acc_norm_stderr": 0.03021683101150878
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7404580152671756,
"acc_stderr": 0.03844876139785271,
"acc_norm": 0.7404580152671756,
"acc_norm_stderr": 0.03844876139785271
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8099173553719008,
"acc_stderr": 0.03581796951709282,
"acc_norm": 0.8099173553719008,
"acc_norm_stderr": 0.03581796951709282
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.040191074725573483,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.040191074725573483
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7423312883435583,
"acc_stderr": 0.03436150827846917,
"acc_norm": 0.7423312883435583,
"acc_norm_stderr": 0.03436150827846917
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.49107142857142855,
"acc_stderr": 0.04745033255489123,
"acc_norm": 0.49107142857142855,
"acc_norm_stderr": 0.04745033255489123
},
"harness|hendrycksTest-management|5": {
"acc": 0.8155339805825242,
"acc_stderr": 0.03840423627288276,
"acc_norm": 0.8155339805825242,
"acc_norm_stderr": 0.03840423627288276
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8717948717948718,
"acc_stderr": 0.02190190511507333,
"acc_norm": 0.8717948717948718,
"acc_norm_stderr": 0.02190190511507333
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.79,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.79,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8160919540229885,
"acc_stderr": 0.013853724170922533,
"acc_norm": 0.8160919540229885,
"acc_norm_stderr": 0.013853724170922533
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7630057803468208,
"acc_stderr": 0.02289408248992599,
"acc_norm": 0.7630057803468208,
"acc_norm_stderr": 0.02289408248992599
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.43687150837988825,
"acc_stderr": 0.01658868086453063,
"acc_norm": 0.43687150837988825,
"acc_norm_stderr": 0.01658868086453063
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7647058823529411,
"acc_stderr": 0.0242886194660461,
"acc_norm": 0.7647058823529411,
"acc_norm_stderr": 0.0242886194660461
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7363344051446945,
"acc_stderr": 0.02502553850053234,
"acc_norm": 0.7363344051446945,
"acc_norm_stderr": 0.02502553850053234
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.023132376234543343,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.023132376234543343
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5390070921985816,
"acc_stderr": 0.02973659252642444,
"acc_norm": 0.5390070921985816,
"acc_norm_stderr": 0.02973659252642444
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5026075619295959,
"acc_stderr": 0.012770062445433166,
"acc_norm": 0.5026075619295959,
"acc_norm_stderr": 0.012770062445433166
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7352941176470589,
"acc_stderr": 0.026799562024887667,
"acc_norm": 0.7352941176470589,
"acc_norm_stderr": 0.026799562024887667
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.7075163398692811,
"acc_stderr": 0.018403415710109797,
"acc_norm": 0.7075163398692811,
"acc_norm_stderr": 0.018403415710109797
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7090909090909091,
"acc_stderr": 0.04350271442923243,
"acc_norm": 0.7090909090909091,
"acc_norm_stderr": 0.04350271442923243
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7795918367346939,
"acc_stderr": 0.02653704531214529,
"acc_norm": 0.7795918367346939,
"acc_norm_stderr": 0.02653704531214529
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8407960199004975,
"acc_stderr": 0.02587064676616913,
"acc_norm": 0.8407960199004975,
"acc_norm_stderr": 0.02587064676616913
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.89,
"acc_stderr": 0.03144660377352203,
"acc_norm": 0.89,
"acc_norm_stderr": 0.03144660377352203
},
"harness|hendrycksTest-virology|5": {
"acc": 0.572289156626506,
"acc_stderr": 0.03851597683718533,
"acc_norm": 0.572289156626506,
"acc_norm_stderr": 0.03851597683718533
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8070175438596491,
"acc_stderr": 0.030267457554898458,
"acc_norm": 0.8070175438596491,
"acc_norm_stderr": 0.030267457554898458
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5091799265605875,
"mc1_stderr": 0.017500550724819756,
"mc2": 0.674246091155489,
"mc2_stderr": 0.014911205444372602
},
"harness|winogrande|5": {
"acc": 0.840568271507498,
"acc_stderr": 0.010288617479454764
},
"harness|gsm8k|5": {
"acc": 0.621683093252464,
"acc_stderr": 0.013358407831777112
}
}
```
## 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] |
arumaekawa/bookcorpus-tokens-distilgpt2-bart-large-cnn-debug-embeds | ---
dataset_info:
features:
- name: lm.input_ids
sequence: int64
- name: lm.attention_mask
sequence: int64
- name: text
dtype: string
- name: sum.input_ids
sequence: int64
- name: sum.attention_mask
sequence: int64
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 5292550
num_examples: 267
download_size: 1463188
dataset_size: 5292550
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
jan-hq/evol_codealpaca_dpo_binarized | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 252682437.89544156
num_examples: 35893
- name: test
num_bytes: 28082084.10455845
num_examples: 3989
download_size: 139762239
dataset_size: 280764522.0
---
# Dataset Card for "evol_codealpaca_dpo_binarized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
millawell/wikipedia_field_of_science | ---
license: cc-by-sa-3.0
---
|
jxtse/gec_gpt4_evaluation | ---
language:
- en
tags:
- Grammatical Error Correction
---
In the era of Large Language Models, traditional grammatical error correction model martics have gap with human feedback. To solve this problem, we integrated LLMs to simulate human scoring to make models closer to human feedback |
confit/timit | ---
task_categories:
- audio-classification
dataset_info:
- config_name: asr
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: phonetic_detail
sequence:
- name: start
dtype: int64
- name: stop
dtype: int64
- name: utterance
dtype: string
- name: word_detail
sequence:
- name: start
dtype: int64
- name: stop
dtype: int64
- name: utterance
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 453138589.38
num_examples: 4620
- name: test
num_bytes: 168845311.48
num_examples: 1680
download_size: 588447068
dataset_size: 621983900.86
- config_name: si
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: speaker_id
dtype: string
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dtype:
class_label:
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splits:
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num_bytes: 346093330.52
num_examples: 3520
- name: validation
num_bytes: 115074029.92
num_examples: 1172
- name: test
num_bytes: 118118268.94
num_examples: 1172
download_size: 543534867
dataset_size: 579285629.38
configs:
- config_name: asr
data_files:
- split: train
path: asr/train-*
- split: test
path: asr/test-*
- config_name: si
data_files:
- split: train
path: si/train-*
- split: validation
path: si/validation-*
- split: test
path: si/test-*
tags:
- audio
- asr
- speaker
- phonetics
- ipa
---
|
VedCodes/llama2_project | ---
task_categories:
- text-generation
language:
- en
tags:
- medical
size_categories:
- n<1K
pretty_name: boy_hi
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
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).
### 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]
nt is empty. Use the Ed
#### 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] |
liuyanchen1015/MULTI_VALUE_wnli_non_coordinated_subj_obj | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 4614
num_examples: 21
- name: test
num_bytes: 10546
num_examples: 36
- name: train
num_bytes: 32747
num_examples: 145
download_size: 24504
dataset_size: 47907
---
# Dataset Card for "MULTI_VALUE_wnli_non_coordinated_subj_obj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/tohsaka_rin_fatestaynightufotable | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Tohsaka Rin (Fate Stay Night [UFOTABLE])
This is the dataset of Tohsaka Rin (Fate Stay Night [UFOTABLE]), containing 723 images and their tags.
The core tags of this character are `long_hair, black_hair, two_side_up, ribbon, hair_ribbon, blue_eyes`, 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 | 723 | 613.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tohsaka_rin_fatestaynightufotable/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 723 | 613.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tohsaka_rin_fatestaynightufotable/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1402 | 1.06 GiB | [Download](https://huggingface.co/datasets/CyberHarem/tohsaka_rin_fatestaynightufotable/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/tohsaka_rin_fatestaynightufotable',
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 | 12 |  |  |  |  |  | 1girl, brown_vest, homurahara_academy_school_uniform, solo, white_shirt, neck_ribbon, upper_body, brown_hair, looking_at_viewer |
| 1 | 18 |  |  |  |  |  | 1girl, solo, red_jacket, homurahara_academy_school_uniform, looking_at_viewer, upper_body |
| 2 | 11 |  |  |  |  |  | 1girl, anime_coloring, solo, turtleneck, upper_body, looking_at_viewer, brown_hair, parody, green_eyes, open_mouth, sweater |
| 3 | 7 |  |  |  |  |  | 1girl, profile, solo, anime_coloring, from_side, upper_body |
| 4 | 9 |  |  |  |  |  | 1girl, anime_coloring, solo, bow, orange_scarf, parody, portrait, looking_at_viewer, coat, frown |
| 5 | 9 |  |  |  |  |  | 1girl, orange_scarf, solo, red_coat, upper_body, looking_at_viewer, sweatdrop |
| 6 | 8 |  |  |  |  |  | 1girl, green_eyes, orange_scarf, solo, upper_body, anime_coloring, brown_hair, red_coat, frown |
| 7 | 5 |  |  |  |  |  | 1girl, clenched_teeth, solo, anime_coloring, bow, sweatdrop, coat, orange_scarf, parody, upper_body |
| 8 | 44 |  |  |  |  |  | 1girl, solo, black_thighhighs, zettai_ryouiki, black_skirt, pleated_skirt, red_coat, miniskirt, orange_scarf, outdoors, standing, long_sleeves |
| 9 | 5 |  |  |  |  |  | 1girl, black_skirt, black_thighhighs, solo, zettai_ryouiki, anime_coloring, long_legs, pleated_skirt, standing, turtleneck, looking_at_viewer |
| 10 | 10 |  |  |  |  |  | 1girl, black_skirt, black_thighhighs, turtleneck, zettai_ryouiki, anime_coloring, long_sleeves, miniskirt, pleated_skirt, solo, brown_hair, black_ribbon, sitting, indoors, looking_at_viewer, hand_between_legs, red_sweater |
| 11 | 8 |  |  |  |  |  | 1girl, bondage, chair, sitting, solo, black_thighhighs, orange_scarf, rope, zettai_ryouiki, black_skirt, tied_up_(nonsexual), looking_at_viewer, red_coat, brown_hair, from_side, miniskirt, pleated_skirt |
| 12 | 6 |  |  |  |  |  | 1girl, homurahara_academy_school_uniform, solo, tatami, zabuton, seiza, table, cup, black_skirt, pantyhose, vest |
| 13 | 5 |  |  |  |  |  | 1girl, collared_shirt, solo, upper_body, open_mouth, anime_coloring, chair, closed_eyes, hair_down, sitting |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | brown_vest | homurahara_academy_school_uniform | solo | white_shirt | neck_ribbon | upper_body | brown_hair | looking_at_viewer | red_jacket | anime_coloring | turtleneck | parody | green_eyes | open_mouth | sweater | profile | from_side | bow | orange_scarf | portrait | coat | frown | red_coat | sweatdrop | clenched_teeth | black_thighhighs | zettai_ryouiki | black_skirt | pleated_skirt | miniskirt | outdoors | standing | long_sleeves | long_legs | black_ribbon | sitting | indoors | hand_between_legs | red_sweater | bondage | chair | rope | tied_up_(nonsexual) | tatami | zabuton | seiza | table | cup | pantyhose | vest | collared_shirt | closed_eyes | hair_down |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-------------|:------------------------------------|:-------|:--------------|:--------------|:-------------|:-------------|:--------------------|:-------------|:-----------------|:-------------|:---------|:-------------|:-------------|:----------|:----------|:------------|:------|:---------------|:-----------|:-------|:--------|:-----------|:------------|:-----------------|:-------------------|:-----------------|:--------------|:----------------|:------------|:-----------|:-----------|:---------------|:------------|:---------------|:----------|:----------|:--------------------|:--------------|:----------|:--------|:-------|:----------------------|:---------|:----------|:--------|:--------|:------|:------------|:-------|:-----------------|:--------------|:------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 18 |  |  |  |  |  | X | | X | X | | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 11 |  |  |  |  |  | X | | | X | | | X | X | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | X | | | X | | | X | | | | X | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 9 |  |  |  |  |  | X | | | X | | | | | X | | X | | X | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 9 |  |  |  |  |  | X | | | X | | | X | | X | | | | | | | | | | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 8 |  |  |  |  |  | X | | | X | | | X | X | | | X | | | X | | | | | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | X | | | X | | | X | | | | X | | X | | | | | | X | X | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 44 |  |  |  |  |  | X | | | X | | | | | | | | | | | | | | | | X | | | | X | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 9 | 5 |  |  |  |  |  | X | | | X | | | | | X | | X | X | | | | | | | | | | | | | | | X | X | X | X | | | X | | X | | | | | | | | | | | | | | | | | | | |
| 10 | 10 |  |  |  |  |  | X | | | X | | | | X | X | | X | X | | | | | | | | | | | | | | | X | X | X | X | X | | | X | | X | X | X | X | X | | | | | | | | | | | | | | |
| 11 | 8 |  |  |  |  |  | X | | | X | | | | X | X | | | | | | | | | X | | X | | | | X | | | X | X | X | X | X | | | | | | X | | | | X | X | X | X | | | | | | | | | | |
| 12 | 6 |  |  |  |  |  | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | |
| 13 | 5 |  |  |  |  |  | X | | | X | | | X | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | | | | | | | | | | X | X | X |
|
Joanne/Metaphors_and_Analogies | ---
task_categories:
- question-answering
- token-classification
language:
- en
---
# Metaphors and analogies datasets
These datasets contain word pairs and quadruples forming analogies, metaphoric mapping or sematically unacceptable compositions.
- Pair instances are pairs of nouns A and B in a sentence of the form "A is a B".
- Quadruple instances are of the form : < (A,B),(C,D) >
There is an analogy when A is to B what C is to D.
The analogy is also a metaphor when the (A,B) and (C,D) form a metaphoric mapping, usually when they come from different domains.
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
Language : English
### Datasets and paper links
| Name | Size | Labels | Description |
| ---------: | :----- |:-------- | :-------------------------------------------------------------------------- |
| `Cardillo` | 260 *2 | 1, 2 | Pairs of "A is-a B" sentences composed of one metaphoric and one literal sentence. The two sentences of a given pair share the same B term. |
| `Jankowiak`| 120*3 | 0, 1, 2 | Triples of "A is-a/is-like-a B" sentences with exactly one literal, one semantic abnormal and one metaphoric sentence. |
| `Green` | 40*3 | 0, 1, 2 | Triples of proportional analogies, made of 4 terms <A, B, Ci, Di> each. One stem <A,B> is composed with 3 different <Ci,Di> pairs, to form exaclty one near analogy, one far analogy and one non analogic quadruple|
| `Kmiecik` | 720 | 0, 1, 2 | Quadruples <A,B,C,D> labelled as analogy:True/False and far_analogy: True/False|
| `SAT-met` | 160?*5 | 0, 1, 2, 12 | One pair stem <A,B> to combine with 5 different pairs <Ci,Di> and attempt to form proportional analogies. Only one <Ci,Di> forms an analogy with <A,B> We additionally labelled the analogies as **metaphoric**:True/False|
| Name | Paper Citation | Paper link | Dataset link |
| ---------: | :------- | :------------------------------ |-----------------------------------------: |
| `Cardillo` | | [Cardillo (2010)](https://link.springer.com/article/10.3758/s13428-016-0717-1) [Cardillo (2017)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2952404/ ) | |
| `Jankowiak`| | [Jankowiak (2020)]( https://link-springer-com.abc.cardiff.ac.uk/article/10.1007/s10936-020-09695-7) | |
| `Green` | Green, A. E., Kraemer, D. J. M., Fugelsang, J., Gray, J. R., & Dunbar, K. (2010). Connecting Long Distance: Semantic Distance in Analogical Reasoning Modulates Frontopolar Cortex Activity. Cerebral Cortex, 10, 70-76. | [Green (20)]() ||
| `Kmiecik` |Kmiecik, M. J., Brisson, R. J., & Morrison, R. G. (2019). The time course of semantic and relational processing during verbal analogical reasoning. Brain and Cognition, 129, 25-34. | [Kmiecik (20)]() ||
| `SAT-met` | | [Turney (2005)](https://arxiv.org/pdf/cs/0508053.pdf) | |
### Labels :
- Pairs
- **0** : anomaly
- **1** : literal
- **2** : metaphor
- Quadruples :
- **0** : not an analogy
- **1** : an analogy but not a metaphor
- **2** : an analogy and a metaphor or a far analogy
- **12** : maybe a metaphor, somewhere between 1 and 2
### Dataset Splits
- Both lexical and random splits are available for classification experiments.
- Size of the splits :
- **train** : 50 %
- **validation** : 10 %
- **test** : 40 %
- Additionally, for all datasets, the `5-folds` field gives frozen splits for a five-folds cross validation experiment with train/val/test = 70/10/20% of the sets.
# Datasets for Classification
- Task : binary classification or 3-classes classification of pairs or quadruples. Each pair or quadruple is to classify between anomaly, non-metaphoric and metaphoric.
## Pairs
### Datasets names & splits :
| Original set | Dataset name | Split |
|-------------:| :------------ | :------ |
| Cardillo | Pairs\_Cardillo\_random_split | random |
| | Pairs\_Cardillo\_lexical_split | lexical |
| Jankowiac | Pairs\_Jankowiac\_random_split | random |
| | Pairs\_Jankowiac\_lexical_split | lexical |
### Data fields :
| Field | Description | Type |
| -------------:| :------------ | ---- |
| corpus | name of the orgiginal dataset | str |
| id | instance id | str |
| set_id | id of the set containing the given instance in the multiple choice task | int |
| label | 0, 1, 2 | int |
| sentence | A is-a B sentence. | str |
| A | A expression in the sentence | str |
| B | B expression in the sentence | str |
| A\_position | position of A in the sentence | list(int) |
| B\_position | position of B in the sentence | list(int) |
| 5-folds | frozen splits for cross validation | list(str) |
### Examples :
| Name | Example | Label|
| -------: | :------------------------------------- | :-------- |
|Cardillo | | |
|Jankowiac | | |
## Quadruples
### Datasets names & splits
| Original set | dataset name | Split |
| -------: | :------------------------------------- | :-------- |
|Green | Quadruples\_Green\_random_split | random |
| | Quadruples\_Green\_lexical_split | lexical |
|Kmiecik | Quadruples\_Kmiecik\_random_split | random |
| | Quadruples\_Kmiecik\_lexical\_split\_on\_AB | lexical AB |
| | Quadruples\_Kmiecik\_lexical_split\_on\_CD | lexical CD |
|SAT | Quadruples\_SAT\_random\_split | random | random |
| | Quadruples\_SAT\_lexical\_split | lexical | lexical |
### Data fields :
| Field| Description | Type |
| -------------: | :------------ | :------------ |
| corpus | Name of the orgiginal dataset | str |
| id | Element id | str |
| set\_id | Id of the set containing the given instance in the multiple-choice task datasets | int |
| label | 0, 1, 2, 12 | int |
| AB | pair of terms | list(str) |
| CD | pair of terms | list(str) |
| 5-folds | frozen splits for cross validation | list(str) |
### Examples :
| Name | Example | Label|
|-------: | :------------------------------------- | :-------- |
|Green | | |
|Kmiecik | | |
| SAT | | |
# Datasets for multiple choice questions or permutation
- Task : One stem and multiple choices. The stem and its possible combinations are to be combined to form a sentence. The resulting sentence has a label <0,1,2>.
## Pairs
### Datasets names & splits :
| Original set | dataset name | Split |
| -----------|------| :---- |
| Cardillo | Pairs\_Cardillo\_set | test only |
| Jankowiac | Pairs\_Jankowiac\_set |test only |
### Data fields :
| Field | Description | Type |
| -------------: | :------------ | :------------ |
| corpus | Name of the orgiginal dataset | str |
| id | Element id | str |
| pair_ids | Ids of each pair as appearing in the classification datasets. | list(str) |
| labels | 0, 1, 2 | list(int) |
| sentences | List of the sentences composing the set | list(str) |
| A\_positions | Positions of the A's in each sentence | list(list(int)) |
| B\_positions | Positions of the B's in each sentence | list(list(int)) |
| answer | Index of the metaphor | int |
| stem | Term shared between the sentences of the set. | str |
| 5-folds | frozen splits for cross validation | list(str) |
### Examples :
| Name | Stem | Sentences |Label|
|-------: |-------: | :------------------------------------- | :-------- |
|Cardillo | comet | The astronomer's obssession was a comet. | 1 |
| | | The politician's career was a comet. | 2 |
| Jankoviac | harbour | This banana is like a harbour | 0 |
| | | A house is a harbour | 2|
| | | This area is a harbour | 1 |
## Quadruples
### Datasets names & splits :
| Original set | dataset name | Split |
| ----------: | :------| :---- |
| Green | Quadruples\_Green\_set | test only |
| SAT | Quadruples\_SAT\_met_set | test only |
### Data fields :
| Field | Description | Type |
|-------------: | :------------ | :------------ |
| corpus | name of the orgiginal dataset | str |
| id | Element id | str |
| pair\_ids | Ids of the instances as appearing in the clasification datasets | list(str) |
| labels | 0, 1, 2, 12 | list(int) |
| answer | temp | int |
| stem | Word pair to compose with all the other pairs of the set | list(str) |
| pairs | List of word pairs | list(list(str)) |
| 5-folds | Frozen splits for cross validation | list(str) |
### Examples :
| Name | Example | Label|
|-------: | :------------------------------------- | :-------- |
|Green | | |
| | | |
| SAT | | |
|
gustavecortal/DreamBank-annotated | ---
task_categories:
- text-generation
- text2text-generation
- summarization
- text-classification
language:
- en
pretty_name: DreamBank
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
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).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
- alta: 422
- angie: 48
- arlie: 212
- b: 3116
- b2: 1138
- bay_area_girls_456: 234
- bay_area_girls_789: 154
- bea1: 223
- bea2: 63
- blind-f: 238
- blind-m: 143
- bosnak: 53
- chris: 100
- chuck: 75
- college-f: 160
- college-m: 160
- dahlia: 24
- david: 166
- dorothea: 900
- ed: 143
- edna: 19
- elizabeth: 1707
- emma: 1221
- emmas_husband: 72
- esther: 110
- hall_female: 681
- izzy-all: 4352
- jasmine-all: 664
- jeff: 87
- joan: 42
- kenneth: 2022
- lawrence: 206
- mack: 38
- madeline1-hs: 98
- madeline2-dorms: 186
- madeline3-offcampus: 348
- madeline4-postgrad: 294
- mark: 23
- melissa: 89
- melora: 211
- melvin: 128
- merri: 315
- miami-home: 171
- miami-lab: 274
- midwest_teens-f: 111
- midwest_teens-m: 83
- nancy: 44
- natural_scientist: 234
- norman: 1235
- norms-f: 491
- norms-m: 500
- pegasus: 1093
- peru-f: 382
- peru-m: 384
- phil1: 106
- phil2: 220
- phil3: 180
- physiologist: 86
- pregnancy_abortion: 226
- ringo: 16
- sally: 249
- samantha: 63
- seventh_graders: 69
- toby: 33
- tom: 27
- ucsc_women: 81
- van: 192
- vickie: 35
- vietnam_vet: 98
- vietnam_vet2: 32
- vietnam_vet3: 463
- west_coast_teens: 89
### 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] |
huggingartists/ariya | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/ariya"
## 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:** 0.070471 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/975b03ba317602498bed5321f12caebe.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/ariya">
<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">Ария (Ariya)</div>
<a href="https://genius.com/artists/ariya">
<div style="text-align: center; font-size: 14px;">@ariya</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/ariya).
### 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/ariya")
```
## 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|
|------:|---------:|---:|
|22| -| -|
'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/ariya")
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)
|
open-llm-leaderboard/details_CorticalStack__mistral-7b-alpaca-sft | ---
pretty_name: Evaluation run of CorticalStack/mistral-7b-alpaca-sft
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CorticalStack/mistral-7b-alpaca-sft](https://huggingface.co/CorticalStack/mistral-7b-alpaca-sft)\
\ 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_CorticalStack__mistral-7b-alpaca-sft\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-15T19:16:07.365309](https://huggingface.co/datasets/open-llm-leaderboard/details_CorticalStack__mistral-7b-alpaca-sft/blob/main/results_2024-02-15T19-16-07.365309.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\": 0.6146116714025754,\n\
\ \"acc_stderr\": 0.03286709487834751,\n \"acc_norm\": 0.6202418665578125,\n\
\ \"acc_norm_stderr\": 0.03353487447545581,\n \"mc1\": 0.3769889840881273,\n\
\ \"mc1_stderr\": 0.016965517578930354,\n \"mc2\": 0.5359107243003883,\n\
\ \"mc2_stderr\": 0.014857832315965628\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5819112627986348,\n \"acc_stderr\": 0.01441398839699608,\n\
\ \"acc_norm\": 0.6168941979522184,\n \"acc_norm_stderr\": 0.014206472661672876\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.634833698466441,\n\
\ \"acc_stderr\": 0.004804927608773126,\n \"acc_norm\": 0.8355905198167696,\n\
\ \"acc_norm_stderr\": 0.0036988923883801024\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5851851851851851,\n\
\ \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n\
\ \"acc_norm_stderr\": 0.04256193767901408\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6578947368421053,\n \"acc_stderr\": 0.03860731599316092,\n\
\ \"acc_norm\": 0.6578947368421053,\n \"acc_norm_stderr\": 0.03860731599316092\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n\
\ \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\"\
: 0.05\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"\
acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n \
\ \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6944444444444444,\n\
\ \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.6944444444444444,\n\
\ \"acc_norm_stderr\": 0.03852084696008534\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\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\"\
: 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6184971098265896,\n\
\ \"acc_stderr\": 0.03703851193099521,\n \"acc_norm\": 0.6184971098265896,\n\
\ \"acc_norm_stderr\": 0.03703851193099521\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082635,\n\
\ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082635\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n\
\ \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n\
\ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\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\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3968253968253968,\n \"acc_stderr\": 0.02519710107424648,\n \"\
acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.02519710107424648\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\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7322580645161291,\n\
\ \"acc_stderr\": 0.02518900666021238,\n \"acc_norm\": 0.7322580645161291,\n\
\ \"acc_norm_stderr\": 0.02518900666021238\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n\
\ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\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\": 0.7212121212121212,\n \"acc_stderr\": 0.035014387062967806,\n\
\ \"acc_norm\": 0.7212121212121212,\n \"acc_norm_stderr\": 0.035014387062967806\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7828282828282829,\n \"acc_stderr\": 0.029376616484945627,\n \"\
acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.029376616484945627\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8549222797927462,\n \"acc_stderr\": 0.025416343096306433,\n\
\ \"acc_norm\": 0.8549222797927462,\n \"acc_norm_stderr\": 0.025416343096306433\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6205128205128205,\n \"acc_stderr\": 0.024603626924097417,\n\
\ \"acc_norm\": 0.6205128205128205,\n \"acc_norm_stderr\": 0.024603626924097417\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3111111111111111,\n \"acc_stderr\": 0.028226446749683515,\n \
\ \"acc_norm\": 0.3111111111111111,\n \"acc_norm_stderr\": 0.028226446749683515\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6050420168067226,\n \"acc_stderr\": 0.031753678460966245,\n\
\ \"acc_norm\": 0.6050420168067226,\n \"acc_norm_stderr\": 0.031753678460966245\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3973509933774834,\n \"acc_stderr\": 0.0399552400768168,\n \"acc_norm\"\
: 0.3973509933774834,\n \"acc_norm_stderr\": 0.0399552400768168\n },\n\
\ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7981651376146789,\n\
\ \"acc_stderr\": 0.01720857935778758,\n \"acc_norm\": 0.7981651376146789,\n\
\ \"acc_norm_stderr\": 0.01720857935778758\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.034099716973523674,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.034099716973523674\n \
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7745098039215687,\n \"acc_stderr\": 0.02933116229425174,\n \"\
acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.02933116229425174\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7510548523206751,\n \"acc_stderr\": 0.028146970599422644,\n \
\ \"acc_norm\": 0.7510548523206751,\n \"acc_norm_stderr\": 0.028146970599422644\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6502242152466368,\n\
\ \"acc_stderr\": 0.03200736719484503,\n \"acc_norm\": 0.6502242152466368,\n\
\ \"acc_norm_stderr\": 0.03200736719484503\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306085,\n\
\ \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306085\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\
: 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7239263803680982,\n \"acc_stderr\": 0.035123852837050475,\n\
\ \"acc_norm\": 0.7239263803680982,\n \"acc_norm_stderr\": 0.035123852837050475\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\
\ \"acc_stderr\": 0.04726835553719099,\n \"acc_norm\": 0.45535714285714285,\n\
\ \"acc_norm_stderr\": 0.04726835553719099\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\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.021586494001281382,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.021586494001281382\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8045977011494253,\n\
\ \"acc_stderr\": 0.014179171373424384,\n \"acc_norm\": 0.8045977011494253,\n\
\ \"acc_norm_stderr\": 0.014179171373424384\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6820809248554913,\n \"acc_stderr\": 0.02507071371915319,\n\
\ \"acc_norm\": 0.6820809248554913,\n \"acc_norm_stderr\": 0.02507071371915319\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.25921787709497207,\n\
\ \"acc_stderr\": 0.014655780837497733,\n \"acc_norm\": 0.25921787709497207,\n\
\ \"acc_norm_stderr\": 0.014655780837497733\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7254901960784313,\n \"acc_stderr\": 0.025553169991826524,\n\
\ \"acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.025553169991826524\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6816720257234726,\n\
\ \"acc_stderr\": 0.02645722506781103,\n \"acc_norm\": 0.6816720257234726,\n\
\ \"acc_norm_stderr\": 0.02645722506781103\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7129629629629629,\n \"acc_stderr\": 0.02517104191530968,\n\
\ \"acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.02517104191530968\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.450354609929078,\n \"acc_stderr\": 0.029680105565029036,\n \
\ \"acc_norm\": 0.450354609929078,\n \"acc_norm_stderr\": 0.029680105565029036\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4335071707953064,\n\
\ \"acc_stderr\": 0.012656810383983967,\n \"acc_norm\": 0.4335071707953064,\n\
\ \"acc_norm_stderr\": 0.012656810383983967\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6580882352941176,\n \"acc_stderr\": 0.028814722422254187,\n\
\ \"acc_norm\": 0.6580882352941176,\n \"acc_norm_stderr\": 0.028814722422254187\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6209150326797386,\n \"acc_stderr\": 0.019627444748412236,\n \
\ \"acc_norm\": 0.6209150326797386,\n \"acc_norm_stderr\": 0.019627444748412236\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6272727272727273,\n\
\ \"acc_stderr\": 0.04631381319425465,\n \"acc_norm\": 0.6272727272727273,\n\
\ \"acc_norm_stderr\": 0.04631381319425465\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6938775510204082,\n \"acc_stderr\": 0.02950489645459596,\n\
\ \"acc_norm\": 0.6938775510204082,\n \"acc_norm_stderr\": 0.02950489645459596\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\
\ \"acc_stderr\": 0.02519692987482708,\n \"acc_norm\": 0.8507462686567164,\n\
\ \"acc_norm_stderr\": 0.02519692987482708\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\": 0.5180722891566265,\n\
\ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\
\ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.03126781714663179,\n\
\ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.03126781714663179\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3769889840881273,\n\
\ \"mc1_stderr\": 0.016965517578930354,\n \"mc2\": 0.5359107243003883,\n\
\ \"mc2_stderr\": 0.014857832315965628\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7719021310181531,\n \"acc_stderr\": 0.011793015817663595\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.36087945413191813,\n \
\ \"acc_stderr\": 0.013228626753925145\n }\n}\n```"
repo_url: https://huggingface.co/CorticalStack/mistral-7b-alpaca-sft
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_15T19_16_07.365309
path:
- '**/details_harness|arc:challenge|25_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|gsm8k|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hellaswag|10_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-15T19-16-07.365309.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-15T19-16-07.365309.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- '**/details_harness|winogrande|5_2024-02-15T19-16-07.365309.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-15T19-16-07.365309.parquet'
- config_name: results
data_files:
- split: 2024_02_15T19_16_07.365309
path:
- results_2024-02-15T19-16-07.365309.parquet
- split: latest
path:
- results_2024-02-15T19-16-07.365309.parquet
---
# Dataset Card for Evaluation run of CorticalStack/mistral-7b-alpaca-sft
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [CorticalStack/mistral-7b-alpaca-sft](https://huggingface.co/CorticalStack/mistral-7b-alpaca-sft) 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_CorticalStack__mistral-7b-alpaca-sft",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-15T19:16:07.365309](https://huggingface.co/datasets/open-llm-leaderboard/details_CorticalStack__mistral-7b-alpaca-sft/blob/main/results_2024-02-15T19-16-07.365309.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": 0.6146116714025754,
"acc_stderr": 0.03286709487834751,
"acc_norm": 0.6202418665578125,
"acc_norm_stderr": 0.03353487447545581,
"mc1": 0.3769889840881273,
"mc1_stderr": 0.016965517578930354,
"mc2": 0.5359107243003883,
"mc2_stderr": 0.014857832315965628
},
"harness|arc:challenge|25": {
"acc": 0.5819112627986348,
"acc_stderr": 0.01441398839699608,
"acc_norm": 0.6168941979522184,
"acc_norm_stderr": 0.014206472661672876
},
"harness|hellaswag|10": {
"acc": 0.634833698466441,
"acc_stderr": 0.004804927608773126,
"acc_norm": 0.8355905198167696,
"acc_norm_stderr": 0.0036988923883801024
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5851851851851851,
"acc_stderr": 0.04256193767901408,
"acc_norm": 0.5851851851851851,
"acc_norm_stderr": 0.04256193767901408
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6578947368421053,
"acc_stderr": 0.03860731599316092,
"acc_norm": 0.6578947368421053,
"acc_norm_stderr": 0.03860731599316092
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.690566037735849,
"acc_stderr": 0.028450154794118637,
"acc_norm": 0.690566037735849,
"acc_norm_stderr": 0.028450154794118637
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6944444444444444,
"acc_stderr": 0.03852084696008534,
"acc_norm": 0.6944444444444444,
"acc_norm_stderr": 0.03852084696008534
},
"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": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252604,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252604
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6184971098265896,
"acc_stderr": 0.03703851193099521,
"acc_norm": 0.6184971098265896,
"acc_norm_stderr": 0.03703851193099521
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.37254901960784315,
"acc_stderr": 0.04810840148082635,
"acc_norm": 0.37254901960784315,
"acc_norm_stderr": 0.04810840148082635
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.73,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.73,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5659574468085107,
"acc_stderr": 0.03240038086792747,
"acc_norm": 0.5659574468085107,
"acc_norm_stderr": 0.03240038086792747
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4473684210526316,
"acc_stderr": 0.04677473004491199,
"acc_norm": 0.4473684210526316,
"acc_norm_stderr": 0.04677473004491199
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5655172413793104,
"acc_stderr": 0.04130740879555498,
"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.04130740879555498
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3968253968253968,
"acc_stderr": 0.02519710107424648,
"acc_norm": 0.3968253968253968,
"acc_norm_stderr": 0.02519710107424648
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42063492063492064,
"acc_stderr": 0.04415438226743744,
"acc_norm": 0.42063492063492064,
"acc_norm_stderr": 0.04415438226743744
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7322580645161291,
"acc_stderr": 0.02518900666021238,
"acc_norm": 0.7322580645161291,
"acc_norm_stderr": 0.02518900666021238
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4827586206896552,
"acc_stderr": 0.035158955511656986,
"acc_norm": 0.4827586206896552,
"acc_norm_stderr": 0.035158955511656986
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695237,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695237
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7212121212121212,
"acc_stderr": 0.035014387062967806,
"acc_norm": 0.7212121212121212,
"acc_norm_stderr": 0.035014387062967806
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7828282828282829,
"acc_stderr": 0.029376616484945627,
"acc_norm": 0.7828282828282829,
"acc_norm_stderr": 0.029376616484945627
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8549222797927462,
"acc_stderr": 0.025416343096306433,
"acc_norm": 0.8549222797927462,
"acc_norm_stderr": 0.025416343096306433
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6205128205128205,
"acc_stderr": 0.024603626924097417,
"acc_norm": 0.6205128205128205,
"acc_norm_stderr": 0.024603626924097417
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3111111111111111,
"acc_stderr": 0.028226446749683515,
"acc_norm": 0.3111111111111111,
"acc_norm_stderr": 0.028226446749683515
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6050420168067226,
"acc_stderr": 0.031753678460966245,
"acc_norm": 0.6050420168067226,
"acc_norm_stderr": 0.031753678460966245
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3973509933774834,
"acc_stderr": 0.0399552400768168,
"acc_norm": 0.3973509933774834,
"acc_norm_stderr": 0.0399552400768168
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7981651376146789,
"acc_stderr": 0.01720857935778758,
"acc_norm": 0.7981651376146789,
"acc_norm_stderr": 0.01720857935778758
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5,
"acc_stderr": 0.034099716973523674,
"acc_norm": 0.5,
"acc_norm_stderr": 0.034099716973523674
},
"harness|hendrycksTest-high_school_us_history|5": {
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},
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},
"harness|hendrycksTest-international_law|5": {
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},
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"harness|hendrycksTest-management|5": {
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},
"harness|hendrycksTest-medical_genetics|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.25921787709497207,
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"acc_norm": 0.25921787709497207,
"acc_norm_stderr": 0.014655780837497733
},
"harness|hendrycksTest-nutrition|5": {
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"acc_norm": 0.7254901960784313,
"acc_norm_stderr": 0.025553169991826524
},
"harness|hendrycksTest-philosophy|5": {
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},
"harness|hendrycksTest-prehistory|5": {
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},
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},
"harness|hendrycksTest-professional_medicine|5": {
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"acc_norm_stderr": 0.028814722422254187
},
"harness|hendrycksTest-professional_psychology|5": {
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"acc_norm_stderr": 0.019627444748412236
},
"harness|hendrycksTest-public_relations|5": {
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"acc_norm_stderr": 0.04631381319425465
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6938775510204082,
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"acc_norm": 0.6938775510204082,
"acc_norm_stderr": 0.02950489645459596
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8507462686567164,
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"acc_norm_stderr": 0.02519692987482708
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
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"acc_norm": 0.85,
"acc_norm_stderr": 0.03588702812826371
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5180722891566265,
"acc_stderr": 0.03889951252827216,
"acc_norm": 0.5180722891566265,
"acc_norm_stderr": 0.03889951252827216
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7894736842105263,
"acc_stderr": 0.03126781714663179,
"acc_norm": 0.7894736842105263,
"acc_norm_stderr": 0.03126781714663179
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3769889840881273,
"mc1_stderr": 0.016965517578930354,
"mc2": 0.5359107243003883,
"mc2_stderr": 0.014857832315965628
},
"harness|winogrande|5": {
"acc": 0.7719021310181531,
"acc_stderr": 0.011793015817663595
},
"harness|gsm8k|5": {
"acc": 0.36087945413191813,
"acc_stderr": 0.013228626753925145
}
}
```
## 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] |
nliew/767project | ---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 2049608.0
num_examples: 7
download_size: 2040827
dataset_size: 2049608.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
RishabhM/AbrahamLincoln | ---
license: unknown
---
|
Hantao/trainofasys | ---
dataset_info:
features:
- name: '0'
dtype: int64
- name: ocr
dtype: string
- name: caption
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 204855956.375
num_examples: 1325
download_size: 200935734
dataset_size: 204855956.375
---
# Dataset Card for "trainofasys"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ESPEKTRO/meneses | ---
license: openrail
---
|
AneeqMalik/test_audio_clips | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: audio_names
dtype: string
- name: class_label
dtype:
class_label:
names:
'0': bad
'1': okay
'2': good
'3': great
splits:
- name: train
num_bytes: 12388426.0
num_examples: 6
download_size: 12391305
dataset_size: 12388426.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Follow these steps to set up and upload your audio dataset to Hugging Face:
* **Create a Virtual Environment**
- Start by creating a virtual environment on your machine. Run the following commands:
# On Windows
```
python -m venv env
./env/Scripts/activate
```
# On macOS/Linux
```
source env/bin/activate
pip install -r requirements.txt
```
* **Generate a Hugging Face Token**
- To interact with Hugging Face and push datasets, you'll need a Hugging Face access token. Follow these steps to generate one:
- Go to [Hugging Face Settings](https://huggingface.co/settings/tokens).
- Click on "New Token."
- Give the token a name and select the Role as "Write."
- Copy the generated token.
* **Configure Your Token**
- Run the following command, replacing `'YOUR_TOKEN_HERE'` with the token you obtained from Hugging Face:
```bash
python -c "from huggingface_hub.hf_api import HfFolder; HfFolder.save_token('YOUR_TOKEN_HERE')"
```
This command will configure your environment with your Hugging Face token.
* **Modify `main.py`**
- In the `main.py` file, make the following changes:
- Replace `'Enter-Your-hub-name'` with the name of your dataset. For example, use `'AneeqMalik/test_audio_clips'`.
```python
audio_dataset.push_to_hub("Enter-Your-hub-name")
```
This line specifies where your dataset will be pushed on Hugging Face.
* **Run the Code**
- To push your audio dataset to Hugging Face, execute the following command:
```bash
python main.py
```
Your audio dataset will be uploaded to Hugging Face under the specified name.
|
EleutherAI/asdiv | ---
license: cc-by-nc-4.0
--- |
stoddur/medication_chat_balanced_sw3 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 70293688.0
num_examples: 45527
download_size: 1056281
dataset_size: 70293688.0
---
# Dataset Card for "medication_chat_balanced_sw3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
heliosprime/twitter_dataset_1712830306 | ---
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: 43688
num_examples: 100
download_size: 25407
dataset_size: 43688
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1712830306"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HydraLM/partitioned_v2_alpaca_1500words | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 711090762
num_examples: 622090
download_size: 342537950
dataset_size: 711090762
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "partitioned_v2_alpaca_1500words"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jay401521/domain_balance | ---
dataset_info:
features:
- name: id
dtype: int64
- name: domain
dtype: int64
- name: label
dtype: int64
- name: rank
dtype: string
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 6252293
num_examples: 72276
download_size: 3387340
dataset_size: 6252293
---
# Dataset Card for "domain_balance"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
maidalun1020/CrosslingualRetrievalFinanceZh2En-qrels | ---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
- name: qid
dtype: string
- name: pid
dtype: string
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 619635
num_examples: 25479
download_size: 331836
dataset_size: 619635
---
|
Piyush2512/CREMAD-mel-spectrogram-images-dataset | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 402010156.834
num_examples: 7442
download_size: 370658998
dataset_size: 402010156.834
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
TimoImhof/Splits_Subset_SQuAD | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: context
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
splits:
- name: split_0
num_bytes: 449716
num_examples: 500
- name: split_1
num_bytes: 444641
num_examples: 500
- name: split_2
num_bytes: 464228
num_examples: 500
- name: split_3
num_bytes: 445871
num_examples: 500
- name: split_4
num_bytes: 456437
num_examples: 500
- name: split_5
num_bytes: 460414
num_examples: 500
- name: split_6
num_bytes: 452482
num_examples: 500
- name: split_7
num_bytes: 454860
num_examples: 500
- name: split_8
num_bytes: 452647
num_examples: 500
- name: split_9
num_bytes: 457041
num_examples: 500
- name: split_10
num_bytes: 457992
num_examples: 500
- name: split_11
num_bytes: 463472
num_examples: 500
- name: no_split
num_bytes: 5459801
num_examples: 6000
- name: shortcut
num_bytes: 5452074
num_examples: 6000
download_size: 9566317
dataset_size: 16371676
---
# Dataset Card for "Splits_Subset_SQuAD"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/find_second_sent_train_30_eval_10_baseline | ---
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:
- name: train
num_bytes: 48914
num_examples: 30
- name: validation
num_bytes: 18561
num_examples: 10
download_size: 0
dataset_size: 67475
---
# Dataset Card for "find_second_sent_train_30_eval_10_baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HuggingFaceM4/TextCaps_support_query_sets | Invalid username or password. |
Truthful/autotrain-data-provision_classification | ---
task_categories:
- text-classification
---
# AutoTrain Dataset for project: provision_classification
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project provision_classification.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "Each Partner hereby represents and warrants to the Partnership and each other Partner that (a)\u00a0if such Partner is a corporation, it is duly organized, validly existing, and in good standing under the laws of the jurisdiction of its incorporation and is duly qualified and in good standing as a foreign corporation in the jurisdiction of its principal place of business (if not incorporated therein), (b) if such Partner is a trust, estate or other entity, it is duly formed, validly existing, and (if applicable) in good standing under the laws of the jurisdiction of its formation, and if required by law is duly qualified to do business and (if applicable) in good standing in the jurisdiction of its principal place of business (if not formed therein), (c) such Partner has full corporate, trust, or other applicable right, power and authority to enter into this Agreement and to perform its obligations hereunder and all necessary actions by the board of directors, trustees, beneficiaries, or other Persons necessary for the due authorization, execution, delivery, and performance of this Agreement by such Partner have been duly taken, and such authorization, execution, delivery, and performance do not conflict with any other agreement or arrangement to which such Partner is a party or by which it is bound, and (d)\u00a0such Partner is acquiring its interest in the Partnership for investment purposes and not with a view to distribution thereof.",
"target": 13
},
{
"text": "This Letter Agreement is binding upon and inures to the benefit of the parties and their respective heirs, executors, administrators, personal representatives, successors, and permitted assigns. This Letter Agreement is personal to you and the availability of you to perform services and the covenants provided by you hereunder have been a material consideration for the Company to enter into this Letter Agreement. Accordingly, you may not assign any of your rights or delegate any of your duties under this Letter Agreement, either voluntarily or by operation of law, without the prior written consent of the Company, which may be given or withheld by the Company in its sole and absolute discretion.",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=19, names=['Assignment', 'Attorney Fees', 'Bankruptcy', 'Change of Control', 'Compliance with Laws', 'Confidentiality', 'Entire Agreement', 'General Definition', 'Governing Law', 'Indemnification', 'Injunctive Relief', 'Jurisdiction and Venue', 'Liens', 'No Warranties', 'Other', 'Permitted Disclosure', 'Survival', 'Term', 'Termination for Convenience'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 119023 |
| valid | 13225 |
|
danasone/taiga | ---
license: openrail
---
|
Ti-Ma/wikipedia_2016 | ---
license: cc-by-sa-3.0
---
|
Abduljalil/guanaco-llama2-1k-Reformat | ---
language:
- es
- en
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966692
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
voidful/librispeech_unit_speech | ---
dataset_info:
features:
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
- name: audio_0
dtype: audio
- name: audio_1
dtype: audio
- name: audio_2
dtype: audio
- name: audio_3
dtype: audio
- name: audio_4
dtype: audio
- name: audio_5
dtype: audio
- name: audio_6
dtype: audio
- name: audio_7
dtype: audio
- name: audio_8
dtype: audio
- name: audio_9
dtype: audio
- name: audio_10
dtype: audio
splits:
- name: test.clean
num_bytes: 6819850294.02
num_examples: 2620
- name: train.clean.100
num_bytes: 126297137700.482
num_examples: 28539
download_size: 87782578496
dataset_size: 133116987994.502
---
# Dataset Card for "librispeech_unit_speech"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
SUSTech/mt_bench_judge | ---
dataset_info:
features:
- name: question_id
dtype: int64
- name: model
dtype: string
- name: conversation
list:
- name: content
dtype: string
- name: role
dtype: string
- name: turn
dtype: int64
- name: judge
sequence: string
- name: user_prompt
dtype: string
- name: judgment
dtype: string
- name: score
dtype: float64
- name: tstamp
dtype: float64
- name: category
dtype: string
- name: reference
sequence: string
splits:
- name: train
num_bytes: 4409406
num_examples: 800
download_size: 949262
dataset_size: 4409406
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "mt_bench_judge"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Lazycuber__pyg-instruct-wizardlm | ---
pretty_name: Evaluation run of Lazycuber/pyg-instruct-wizardlm
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Lazycuber/pyg-instruct-wizardlm](https://huggingface.co/Lazycuber/pyg-instruct-wizardlm)\
\ 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_Lazycuber__pyg-instruct-wizardlm\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-28T08:03:29.005419](https://huggingface.co/datasets/open-llm-leaderboard/details_Lazycuber__pyg-instruct-wizardlm/blob/main/results_2023-10-28T08-03-29.005419.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\": 0.01971476510067114,\n\
\ \"em_stderr\": 0.0014236777096831824,\n \"f1\": 0.07215394295302006,\n\
\ \"f1_stderr\": 0.001870662901719372,\n \"acc\": 0.3264294001877723,\n\
\ \"acc_stderr\": 0.008481505569434104\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.01971476510067114,\n \"em_stderr\": 0.0014236777096831824,\n\
\ \"f1\": 0.07215394295302006,\n \"f1_stderr\": 0.001870662901719372\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.01592115238817286,\n \
\ \"acc_stderr\": 0.0034478192723889907\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6369376479873717,\n \"acc_stderr\": 0.01351519186647922\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Lazycuber/pyg-instruct-wizardlm
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_07_24T15_30_39.317119
path:
- '**/details_harness|arc:challenge|25_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_28T08_03_29.005419
path:
- '**/details_harness|drop|3_2023-10-28T08-03-29.005419.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-28T08-03-29.005419.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_28T08_03_29.005419
path:
- '**/details_harness|gsm8k|5_2023-10-28T08-03-29.005419.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-28T08-03-29.005419.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hellaswag|10_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T15:30:39.317119.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T15:30:39.317119.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T15:30:39.317119.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_28T08_03_29.005419
path:
- '**/details_harness|winogrande|5_2023-10-28T08-03-29.005419.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-28T08-03-29.005419.parquet'
- config_name: results
data_files:
- split: 2023_07_24T15_30_39.317119
path:
- results_2023-07-24T15:30:39.317119.parquet
- split: 2023_10_28T08_03_29.005419
path:
- results_2023-10-28T08-03-29.005419.parquet
- split: latest
path:
- results_2023-10-28T08-03-29.005419.parquet
---
# Dataset Card for Evaluation run of Lazycuber/pyg-instruct-wizardlm
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Lazycuber/pyg-instruct-wizardlm
- **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 [Lazycuber/pyg-instruct-wizardlm](https://huggingface.co/Lazycuber/pyg-instruct-wizardlm) 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_Lazycuber__pyg-instruct-wizardlm",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T08:03:29.005419](https://huggingface.co/datasets/open-llm-leaderboard/details_Lazycuber__pyg-instruct-wizardlm/blob/main/results_2023-10-28T08-03-29.005419.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": 0.01971476510067114,
"em_stderr": 0.0014236777096831824,
"f1": 0.07215394295302006,
"f1_stderr": 0.001870662901719372,
"acc": 0.3264294001877723,
"acc_stderr": 0.008481505569434104
},
"harness|drop|3": {
"em": 0.01971476510067114,
"em_stderr": 0.0014236777096831824,
"f1": 0.07215394295302006,
"f1_stderr": 0.001870662901719372
},
"harness|gsm8k|5": {
"acc": 0.01592115238817286,
"acc_stderr": 0.0034478192723889907
},
"harness|winogrande|5": {
"acc": 0.6369376479873717,
"acc_stderr": 0.01351519186647922
}
}
```
### 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] |
heliosprime/twitter_dataset_1713025397 | ---
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: 16664
num_examples: 38
download_size: 11866
dataset_size: 16664
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713025397"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
NobodyExistsOnTheInternet/Fixed-Distilabel-intel-orca-dpo-pairs | ---
dataset_info:
features:
- name: system
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: generations
sequence: string
- name: order
sequence: string
- name: labelling_model
dtype: string
- name: labelling_prompt
list:
- name: content
dtype: string
- name: role
dtype: string
- name: raw_labelling_response
dtype: string
- name: rating
sequence: float64
- name: rationale
dtype: string
- name: status
dtype: string
- name: original_chosen
dtype: string
- name: original_rejected
dtype: string
- name: chosen_score
dtype: float64
- name: in_gsm8k_train
dtype: bool
- name: question
dtype: string
splits:
- name: train
num_bytes: 161845559
num_examples: 12859
download_size: 79210439
dataset_size: 161845559
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
annabely/ukiyoe_50_100_control_net | ---
dataset_info:
features:
- name: source
dtype: image
- name: target
dtype: image
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 1713365742.05
num_examples: 4015
download_size: 1765586642
dataset_size: 1713365742.05
---
# MIT 6.8300/6.8301 Advances in Computer Vision Final Project
This is a dataset card used for our final projet on control nets
Dataset is obtained from https://www.kaggle.com/datasets/kengoichiki/the-metropolitan-museum-of-art-ukiyoe-dataset
Here, we used BLIP for image captions (prompt), used CV2 canny edge detection algorithm for conditioning images (target) |
gguichard/myridade_dbg_aligned_ontologie_filter_myriade_int_label | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: wn_sens
sequence: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 61550504
num_examples: 98206
download_size: 13126741
dataset_size: 61550504
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "myridade_dbg_aligned_ontologie_filter_myriade_int_label"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
projecte-aina/vilasum | ---
annotations_creators:
- machine-generated
language_creators:
- expert-generated
language:
- ca
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets: []
task_categories:
- summarization
task_ids: []
pretty_name: casum
---
# Dataset Card for VilaSum
## 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
- **Paper:**[Sequence to Sequence Resources for Catalan](https://arxiv.org/pdf/2202.06871.pdf)
- **Point of Contact:** langtech@bsc.es
### Dataset Summary
VilaSum is a summarization dataset for evaluation. It is extracted from a newswire corpus crawled from the Catalan news portal [VilaWeb](https://www.vilaweb.cat/). The corpus consists of 13,843 instances that are composed by the headline and the body.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for abstractive summarization. Success on this task is typically measured by achieving a high Rouge score. The [mbart-base-ca-casum](https://huggingface.co/projecte-aina/bart-base-ca-casum) model currently achieves a 35.04.
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
```
{
'summary': 'Un vídeo corrobora les agressions a dues animalistes en un correbou del Mas de Barberans',
'text': 'Noves imatges, a les quals ha tingut accés l'ACN, certifiquen les agressions i la destrucció del material d'enregistrament que han denunciat dues activistes d'AnimaNaturalis en la celebració d'un acte de bous a la plaça al Mas de Barberans (Montsià). En el vídeo es veu com unes quantes persones s'abalancen sobre les noies que reben estirades i cops mentre els intenten prendre les càmeres. Membres de la comissió taurina intervenen per aturar els presumptes agressors però es pot escoltar com part del públic victoreja la situació. Els Mossos d'Esquadra presentaran aquest dilluns al migdia l'atestat dels fets al Jutjat d'Amposta. Dissabte ja es van detenir quatre persones que van quedar en llibertat a l'espera de ser cridats pel jutge. Es tracta de tres homes i una dona de Sant Carles de la Ràpita, tots ells membres de la mateixa família.'
}
```
### Data Fields
- `summary` (str): Summary of the piece of news
- `text` (str): The text of the piece of news
### Data Splits
Due to the reduced size of the dataset, we use it only for evaluation as a test set.
- test: 13,843 examples
## Dataset Creation
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language. There exist few resources for summarization in Catalan.
### Source Data
#### Initial Data Collection and Normalization
We obtained each headline and its corresponding body of each news piece on [VilaWeb](https://www.vilaweb.cat/) and applied the following cleaning pipeline: deduplicating the documents, removing the documents with empty attributes, and deleting some boilerplate sentences.
#### Who are the source language producers?
The news portal [VilaWeb](https://www.vilaweb.cat/).
### Annotations
The dataset is unannotated.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
Since all data comes from public websites, no anonymization process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of summarization models in Catalan, a low-resource language.
### Discussion of Biases
We are aware that since the data comes from unreliable web pages, some biases may be present in the dataset. Nonetheless, we have not applied any steps to reduce their impact.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
This work was funded by MT4All CEF project and the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing information
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
If you use any of these resources (datasets or models) in your work, please cite our latest preprint:
```bibtex
@misc{degibert2022sequencetosequence,
title={Sequence-to-Sequence Resources for Catalan},
author={Ona de Gibert and Ksenia Kharitonova and Blanca Calvo Figueras and Jordi Armengol-Estapé and Maite Melero},
year={2022},
eprint={2202.06871},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
[N/A]
|
aertit/xglm_enth2 | ---
dataset_info:
features:
- name: text
dtype: string
- name: nb_token
dtype: int64
- name: metadata
dtype: string
splits:
- name: train
num_bytes: 358768.0
num_examples: 200
- name: test
num_bytes: 179384.0
num_examples: 100
download_size: 227036
dataset_size: 538152.0
task_categories:
- text-generation
- conversational
language:
- th
- en
---
# Dataset Card for "xglm_enth2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
irds/beir_msmarco_dev | ---
pretty_name: '`beir/msmarco/dev`'
viewer: false
source_datasets: ['irds/beir_msmarco']
task_categories:
- text-retrieval
---
# Dataset Card for `beir/msmarco/dev`
The `beir/msmarco/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/beir#beir/msmarco/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=6,980
- `qrels`: (relevance assessments); count=7,437
- For `docs`, use [`irds/beir_msmarco`](https://huggingface.co/datasets/irds/beir_msmarco)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/beir_msmarco_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/beir_msmarco_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
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
```
@inproceedings{Bajaj2016Msmarco,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang},
booktitle={InCoCo@NIPS},
year={2016}
}
@article{Thakur2021Beir,
title = "BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models",
author = "Thakur, Nandan and Reimers, Nils and Rücklé, Andreas and Srivastava, Abhishek and Gurevych, Iryna",
journal= "arXiv preprint arXiv:2104.08663",
month = "4",
year = "2021",
url = "https://arxiv.org/abs/2104.08663",
}
```
|
Ediudo/alemao | ---
license: openrail
---
|
ArthurE/PHI_removal | ---
license: mit
---
|
adilgupta/github-issues | ---
dataset_info:
features:
- name: url
dtype: string
- name: repository_url
dtype: string
- name: labels_url
dtype: string
- name: comments_url
dtype: string
- name: events_url
dtype: string
- name: html_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
dtype: string
- name: user
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
- name: type
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- name: site_admin
dtype: bool
- name: labels
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dtype: int64
- name: node_id
dtype: string
- name: url
dtype: string
- name: name
dtype: string
- name: color
dtype: string
- name: default
dtype: bool
- name: description
dtype: string
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dtype: string
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dtype: bool
- name: assignee
struct:
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dtype: string
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dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: assignees
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dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
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dtype: string
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dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
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dtype: string
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dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: milestone
struct:
- name: url
dtype: string
- name: html_url
dtype: string
- name: labels_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
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- name: description
dtype: string
- name: creator
struct:
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dtype: string
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dtype: string
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dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
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dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: open_issues
dtype: int64
- name: closed_issues
dtype: int64
- name: state
dtype: string
- name: created_at
dtype: timestamp[s]
- name: updated_at
dtype: timestamp[s]
- name: due_on
dtype: 'null'
- name: closed_at
dtype: 'null'
- name: comments
sequence: string
- name: created_at
dtype: timestamp[s]
- name: updated_at
dtype: timestamp[s]
- name: closed_at
dtype: timestamp[s]
- name: author_association
dtype: string
- name: active_lock_reason
dtype: 'null'
- name: draft
dtype: bool
- name: pull_request
struct:
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dtype: string
- name: html_url
dtype: string
- name: diff_url
dtype: string
- name: patch_url
dtype: string
- name: merged_at
dtype: timestamp[s]
- name: body
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- name: reactions
struct:
- name: url
dtype: string
- name: total_count
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- name: '+1'
dtype: int64
- name: '-1'
dtype: int64
- name: laugh
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dtype: int64
- name: confused
dtype: int64
- name: heart
dtype: int64
- name: rocket
dtype: int64
- name: eyes
dtype: int64
- name: timeline_url
dtype: string
- name: performed_via_github_app
dtype: 'null'
- name: state_reason
dtype: string
- name: is_pull_request
dtype: bool
splits:
- name: train
num_bytes: 5857722
num_examples: 500
download_size: 1733342
dataset_size: 5857722
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
PanoEvJ/real-toxicity-prompts-severe0.7 | ---
dataset_info:
features:
- name: filename
dtype: string
- name: begin
dtype: int64
- name: end
dtype: int64
- name: challenging
dtype: bool
- name: prompt
struct:
- name: text
dtype: string
- name: threat
dtype: float64
- name: insult
dtype: float64
- name: severe_toxicity
dtype: float64
- name: toxicity
dtype: float64
- name: profanity
dtype: float64
- name: sexually_explicit
dtype: float64
- name: flirtation
dtype: float64
- name: identity_attack
dtype: float64
- name: continuation
struct:
- name: text
dtype: string
- name: severe_toxicity
dtype: float64
- name: toxicity
dtype: float64
- name: profanity
dtype: float64
- name: sexually_explicit
dtype: float64
- name: identity_attack
dtype: float64
- name: flirtation
dtype: float64
- name: threat
dtype: float64
- name: insult
dtype: float64
- name: input_ids
sequence: int32
- name: query
dtype: string
splits:
- name: train
num_bytes: 2181853
num_examples: 3781
download_size: 1763414
dataset_size: 2181853
---
# Dataset Card for "real-toxicity-prompts-severe0.7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Weni/wenigpt-agent-1.4.0-topics | ---
dataset_info:
features:
- name: title
dtype: string
- name: link
dtype: string
- name: content
dtype: string
- name: content_base_uuid
dtype: string
- name: base_link_uuid
dtype: string
- name: adjective
dtype: string
- name: name
dtype: string
- name: occupation
dtype: string
- name: chatbot_goal
dtype: string
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sequence: string
- name: question
dtype: string
- name: answer
dtype: string
- name: human_eval
dtype: string
- name: id
dtype: int64
- name: chunks_small
list:
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dtype: string
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dtype: float64
- name: chunks_big
list:
- name: content
dtype: string
- name: score
dtype: float64
- name: groundedness
dtype: float64
- name: correct_ans
dtype: int64
- name: greetings
dtype: int64
- name: context_size_classification
dtype: int64
- name: emoji
dtype: int64
- name: groundedness-gpt4
dtype: float64
- name: question_topics
dtype: string
- name: answer_topics
dtype: string
splits:
- name: train
num_bytes: 40280930
num_examples: 2363
- name: test
num_bytes: 5721203
num_examples: 330
download_size: 8194232
dataset_size: 46002133
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
Nerfgun3/space_style | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: false
---
# Space Style Embedding / Textual Inversion
## Usage
To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder
To use it in a prompt: ```"art by space_style"```
If it is to strong just add [] around it.
Trained until 15000 steps
I added a 7.5k steps trained ver in the files aswell. If you want to use that version, remove the ```"-7500"``` from the file name and replace the 15k steps ver in your folder
Have fun :)
## Example Pictures
<table>
<tr>
<td><img src=https://i.imgur.com/flz5Oxz.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/5btpoXs.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/PtySCd4.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/NbSue9H.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/QhjRezm.png width=100% height=100%/></td>
</tr>
</table>
## License
This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
NEECOC/sukunaneco | ---
license: openrail
---
|
liuyanchen1015/MULTI_VALUE_sst2_for_to | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 31243
num_examples: 195
- name: test
num_bytes: 65310
num_examples: 404
- name: train
num_bytes: 1000960
num_examples: 8092
download_size: 617935
dataset_size: 1097513
---
# Dataset Card for "MULTI_VALUE_sst2_for_to"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
felixdae/openwebtext-wordlength | ---
dataset_info:
features:
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 591658727.6825764
num_examples: 4000000
- name: valid
num_bytes: 66561606.86428984
num_examples: 450000
- name: test
num_bytes: 3224360
num_examples: 22210
download_size: 454512275
dataset_size: 661444694.5468663
---
# Dataset Card for "openwebtext-wordlength"
[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_127 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1191881340.0
num_examples: 232245
download_size: 1221496386
dataset_size: 1191881340.0
---
# Dataset Card for "chunk_127"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_mnli_preposition_chopping | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev_matched
num_bytes: 40321
num_examples: 205
- name: dev_mismatched
num_bytes: 23972
num_examples: 133
- name: test_matched
num_bytes: 48102
num_examples: 238
- name: test_mismatched
num_bytes: 26368
num_examples: 139
- name: train
num_bytes: 1598111
num_examples: 7913
download_size: 1029356
dataset_size: 1736874
---
# Dataset Card for "MULTI_VALUE_mnli_preposition_chopping"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
GalacticV/Aria_2 | ---
license: openrail
---
|
akoukas/HC3_v3 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Generated
'1': Human
splits:
- name: train
num_bytes: 68137494
num_examples: 48644
download_size: 39186068
dataset_size: 68137494
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
malhajar/hellaswag-tr | ---
license: mit
task_categories:
- question-answering
language:
- tr
size_categories:
- 10K<n<100K
splits:
- name: train
num_bytes: 43232624
num_examples: 39905
- name: test
num_bytes: 10791853
num_examples: 10003
- name: validation
num_bytes: 11175717
num_examples: 10042
paperswithcode_id: hellaswag
pretty_name: HellaSwag
dataset_info:
features:
- name: ind
dtype: int32
- name: activity_label
dtype: string
- name: ctx_a
dtype: string
- name: ctx_b
dtype: string
- name: ctx
dtype: string
- name: endings
sequence: string
- name: source_id
dtype: string
- name: split
dtype: string
- name: split_type
dtype: string
- name: label
dtype: string
---
This Dataset is part of a series of datasets aimed at advancing Turkish LLM Developments by establishing rigid Turkish benchmarks to evaluate the performance of LLM's Produced in the Turkish Language.
# Dataset Card for Hellaswag-Turkish
malhajar/hellaswag-turkish is a translated version of [`hellaswag`]( https://huggingface.co/datasets/Rowan/hellaswag) aimed specifically to be used in the [`OpenLLMTurkishLeaderboard`](https://huggingface.co/spaces/malhajar/OpenLLMTurkishLeaderboard)
This Dataset contains rigid tests extracted from the paper [`Can a Machine Really Finish Your Sentence?`]( https://arxiv.org/abs/1905.07830) published at ACL2019.
This Test mainly tests the completion abilities of a model
**Developed by:** [`Mohamad Alhajar`](https://www.linkedin.com/in/muhammet-alhajar/)
### Dataset Description
- **Language(s) (NLP):** Turkish
- **Translated from:** [hellaswag]( https://huggingface.co/datasets/Rowan/hellaswag) |
reza-alipour/Text-Edit-Instruct-Preprocessed-4m | ---
dataset_info:
features:
- name: output
dtype: string
- name: input
dtype: string
- name: type
dtype: string
- name: from
dtype: string
splits:
- name: train
num_bytes: 2144142688.6661234
num_examples: 4552775
- name: test
num_bytes: 3185356.05
num_examples: 6750
download_size: 1224892608
dataset_size: 2147328044.7161233
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
open-llm-leaderboard/details_FINGU-AI__FinguAI-Chat-v1 | ---
pretty_name: Evaluation run of FINGU-AI/FinguAI-Chat-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [FINGU-AI/FinguAI-Chat-v1](https://huggingface.co/FINGU-AI/FinguAI-Chat-v1) 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_FINGU-AI__FinguAI-Chat-v1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-30T16:01:46.276277](https://huggingface.co/datasets/open-llm-leaderboard/details_FINGU-AI__FinguAI-Chat-v1/blob/main/results_2024-03-30T16-01-46.276277.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\": 0.3035096239262173,\n\
\ \"acc_stderr\": 0.03240600643656583,\n \"acc_norm\": 0.3060120270192622,\n\
\ \"acc_norm_stderr\": 0.033223801457801135,\n \"mc1\": 0.2521419828641371,\n\
\ \"mc1_stderr\": 0.01520152224629997,\n \"mc2\": 0.4279230644927746,\n\
\ \"mc2_stderr\": 0.014980700973553645\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.25426621160409557,\n \"acc_stderr\": 0.01272499994515774,\n\
\ \"acc_norm\": 0.29180887372013653,\n \"acc_norm_stderr\": 0.013284525292403503\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.3567018522206732,\n\
\ \"acc_stderr\": 0.004780467270911761,\n \"acc_norm\": 0.44084843656642103,\n\
\ \"acc_norm_stderr\": 0.004954740808837202\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.24444444444444444,\n\
\ \"acc_stderr\": 0.037125378336148665,\n \"acc_norm\": 0.24444444444444444,\n\
\ \"acc_norm_stderr\": 0.037125378336148665\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.3355263157894737,\n \"acc_stderr\": 0.038424985593952694,\n\
\ \"acc_norm\": 0.3355263157894737,\n \"acc_norm_stderr\": 0.038424985593952694\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.41,\n\
\ \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.41,\n \
\ \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.3320754716981132,\n \"acc_stderr\": 0.02898545565233439,\n\
\ \"acc_norm\": 0.3320754716981132,\n \"acc_norm_stderr\": 0.02898545565233439\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.3055555555555556,\n\
\ \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.3055555555555556,\n\
\ \"acc_norm_stderr\": 0.03852084696008534\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|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_mathematics|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.35260115606936415,\n\
\ \"acc_stderr\": 0.036430371689585475,\n \"acc_norm\": 0.35260115606936415,\n\
\ \"acc_norm_stderr\": 0.036430371689585475\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.28431372549019607,\n \"acc_stderr\": 0.04488482852329017,\n\
\ \"acc_norm\": 0.28431372549019607,\n \"acc_norm_stderr\": 0.04488482852329017\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.24,\n \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.24,\n\
\ \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.225531914893617,\n \"acc_stderr\": 0.027321078417387536,\n\
\ \"acc_norm\": 0.225531914893617,\n \"acc_norm_stderr\": 0.027321078417387536\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\
\ \"acc_stderr\": 0.039994238792813344,\n \"acc_norm\": 0.23684210526315788,\n\
\ \"acc_norm_stderr\": 0.039994238792813344\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2689655172413793,\n \"acc_stderr\": 0.036951833116502325,\n\
\ \"acc_norm\": 0.2689655172413793,\n \"acc_norm_stderr\": 0.036951833116502325\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2619047619047619,\n \"acc_stderr\": 0.022644212615525214,\n \"\
acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.022644212615525214\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3492063492063492,\n\
\ \"acc_stderr\": 0.04263906892795132,\n \"acc_norm\": 0.3492063492063492,\n\
\ \"acc_norm_stderr\": 0.04263906892795132\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.19,\n \"acc_stderr\": 0.039427724440366234,\n \
\ \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366234\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.3161290322580645,\n \"acc_stderr\": 0.026450874489042764,\n \"\
acc_norm\": 0.3161290322580645,\n \"acc_norm_stderr\": 0.026450874489042764\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.30049261083743845,\n \"acc_stderr\": 0.03225799476233485,\n \"\
acc_norm\": 0.30049261083743845,\n \"acc_norm_stderr\": 0.03225799476233485\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\"\
: 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.34545454545454546,\n \"acc_stderr\": 0.03713158067481912,\n\
\ \"acc_norm\": 0.34545454545454546,\n \"acc_norm_stderr\": 0.03713158067481912\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.3939393939393939,\n \"acc_stderr\": 0.03481285338232963,\n \"\
acc_norm\": 0.3939393939393939,\n \"acc_norm_stderr\": 0.03481285338232963\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.37823834196891193,\n \"acc_stderr\": 0.03499807276193339,\n\
\ \"acc_norm\": 0.37823834196891193,\n \"acc_norm_stderr\": 0.03499807276193339\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.35384615384615387,\n \"acc_stderr\": 0.024243783994062167,\n\
\ \"acc_norm\": 0.35384615384615387,\n \"acc_norm_stderr\": 0.024243783994062167\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.26296296296296295,\n \"acc_stderr\": 0.026842057873833706,\n \
\ \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.026842057873833706\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.36134453781512604,\n \"acc_stderr\": 0.031204691225150006,\n\
\ \"acc_norm\": 0.36134453781512604,\n \"acc_norm_stderr\": 0.031204691225150006\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\
acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.363302752293578,\n \"acc_stderr\": 0.020620603919625807,\n \"\
acc_norm\": 0.363302752293578,\n \"acc_norm_stderr\": 0.020620603919625807\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\
: 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.3235294117647059,\n\
\ \"acc_stderr\": 0.03283472056108567,\n \"acc_norm\": 0.3235294117647059,\n\
\ \"acc_norm_stderr\": 0.03283472056108567\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.33755274261603374,\n \"acc_stderr\": 0.03078154910202621,\n\
\ \"acc_norm\": 0.33755274261603374,\n \"acc_norm_stderr\": 0.03078154910202621\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.15246636771300448,\n\
\ \"acc_stderr\": 0.024126204813252877,\n \"acc_norm\": 0.15246636771300448,\n\
\ \"acc_norm_stderr\": 0.024126204813252877\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.3053435114503817,\n \"acc_stderr\": 0.04039314978724561,\n\
\ \"acc_norm\": 0.3053435114503817,\n \"acc_norm_stderr\": 0.04039314978724561\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.2727272727272727,\n \"acc_stderr\": 0.04065578140908705,\n \"\
acc_norm\": 0.2727272727272727,\n \"acc_norm_stderr\": 0.04065578140908705\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.32407407407407407,\n\
\ \"acc_stderr\": 0.045245960070300496,\n \"acc_norm\": 0.32407407407407407,\n\
\ \"acc_norm_stderr\": 0.045245960070300496\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.25766871165644173,\n \"acc_stderr\": 0.03436150827846917,\n\
\ \"acc_norm\": 0.25766871165644173,\n \"acc_norm_stderr\": 0.03436150827846917\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.22321428571428573,\n\
\ \"acc_stderr\": 0.039523019677025116,\n \"acc_norm\": 0.22321428571428573,\n\
\ \"acc_norm_stderr\": 0.039523019677025116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.3786407766990291,\n \"acc_stderr\": 0.04802694698258972,\n\
\ \"acc_norm\": 0.3786407766990291,\n \"acc_norm_stderr\": 0.04802694698258972\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.3803418803418803,\n\
\ \"acc_stderr\": 0.03180425204384099,\n \"acc_norm\": 0.3803418803418803,\n\
\ \"acc_norm_stderr\": 0.03180425204384099\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2554278416347382,\n\
\ \"acc_stderr\": 0.015594955384455773,\n \"acc_norm\": 0.2554278416347382,\n\
\ \"acc_norm_stderr\": 0.015594955384455773\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.2947976878612717,\n \"acc_stderr\": 0.024547617794803835,\n\
\ \"acc_norm\": 0.2947976878612717,\n \"acc_norm_stderr\": 0.024547617794803835\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.27262569832402234,\n\
\ \"acc_stderr\": 0.014893391735249588,\n \"acc_norm\": 0.27262569832402234,\n\
\ \"acc_norm_stderr\": 0.014893391735249588\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.3202614379084967,\n \"acc_stderr\": 0.026716118380156847,\n\
\ \"acc_norm\": 0.3202614379084967,\n \"acc_norm_stderr\": 0.026716118380156847\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.24437299035369775,\n\
\ \"acc_stderr\": 0.024406162094668886,\n \"acc_norm\": 0.24437299035369775,\n\
\ \"acc_norm_stderr\": 0.024406162094668886\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.27469135802469136,\n \"acc_stderr\": 0.024836057868294677,\n\
\ \"acc_norm\": 0.27469135802469136,\n \"acc_norm_stderr\": 0.024836057868294677\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.25886524822695034,\n \"acc_stderr\": 0.026129572527180848,\n \
\ \"acc_norm\": 0.25886524822695034,\n \"acc_norm_stderr\": 0.026129572527180848\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2770534550195567,\n\
\ \"acc_stderr\": 0.011430462443719683,\n \"acc_norm\": 0.2770534550195567,\n\
\ \"acc_norm_stderr\": 0.011430462443719683\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4375,\n \"acc_stderr\": 0.030134614954403924,\n \
\ \"acc_norm\": 0.4375,\n \"acc_norm_stderr\": 0.030134614954403924\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2581699346405229,\n \"acc_stderr\": 0.017704531653250075,\n \
\ \"acc_norm\": 0.2581699346405229,\n \"acc_norm_stderr\": 0.017704531653250075\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.24545454545454545,\n\
\ \"acc_stderr\": 0.041220665028782834,\n \"acc_norm\": 0.24545454545454545,\n\
\ \"acc_norm_stderr\": 0.041220665028782834\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.2653061224489796,\n \"acc_stderr\": 0.028263889943784596,\n\
\ \"acc_norm\": 0.2653061224489796,\n \"acc_norm_stderr\": 0.028263889943784596\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.34328358208955223,\n\
\ \"acc_stderr\": 0.03357379665433431,\n \"acc_norm\": 0.34328358208955223,\n\
\ \"acc_norm_stderr\": 0.03357379665433431\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.22289156626506024,\n\
\ \"acc_stderr\": 0.03240004825594689,\n \"acc_norm\": 0.22289156626506024,\n\
\ \"acc_norm_stderr\": 0.03240004825594689\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.23976608187134502,\n \"acc_stderr\": 0.03274485211946956,\n\
\ \"acc_norm\": 0.23976608187134502,\n \"acc_norm_stderr\": 0.03274485211946956\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2521419828641371,\n\
\ \"mc1_stderr\": 0.01520152224629997,\n \"mc2\": 0.4279230644927746,\n\
\ \"mc2_stderr\": 0.014980700973553645\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5659037095501184,\n \"acc_stderr\": 0.013929882555694044\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.015163002274450341,\n \
\ \"acc_stderr\": 0.0033660229497263455\n }\n}\n```"
repo_url: https://huggingface.co/FINGU-AI/FinguAI-Chat-v1
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_03_30T16_01_46.276277
path:
- '**/details_harness|arc:challenge|25_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|gsm8k|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hellaswag|10_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-30T16-01-46.276277.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-30T16-01-46.276277.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- '**/details_harness|winogrande|5_2024-03-30T16-01-46.276277.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-30T16-01-46.276277.parquet'
- config_name: results
data_files:
- split: 2024_03_30T16_01_46.276277
path:
- results_2024-03-30T16-01-46.276277.parquet
- split: latest
path:
- results_2024-03-30T16-01-46.276277.parquet
---
# Dataset Card for Evaluation run of FINGU-AI/FinguAI-Chat-v1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [FINGU-AI/FinguAI-Chat-v1](https://huggingface.co/FINGU-AI/FinguAI-Chat-v1) 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_FINGU-AI__FinguAI-Chat-v1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-30T16:01:46.276277](https://huggingface.co/datasets/open-llm-leaderboard/details_FINGU-AI__FinguAI-Chat-v1/blob/main/results_2024-03-30T16-01-46.276277.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": 0.3035096239262173,
"acc_stderr": 0.03240600643656583,
"acc_norm": 0.3060120270192622,
"acc_norm_stderr": 0.033223801457801135,
"mc1": 0.2521419828641371,
"mc1_stderr": 0.01520152224629997,
"mc2": 0.4279230644927746,
"mc2_stderr": 0.014980700973553645
},
"harness|arc:challenge|25": {
"acc": 0.25426621160409557,
"acc_stderr": 0.01272499994515774,
"acc_norm": 0.29180887372013653,
"acc_norm_stderr": 0.013284525292403503
},
"harness|hellaswag|10": {
"acc": 0.3567018522206732,
"acc_stderr": 0.004780467270911761,
"acc_norm": 0.44084843656642103,
"acc_norm_stderr": 0.004954740808837202
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.24444444444444444,
"acc_stderr": 0.037125378336148665,
"acc_norm": 0.24444444444444444,
"acc_norm_stderr": 0.037125378336148665
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.3355263157894737,
"acc_stderr": 0.038424985593952694,
"acc_norm": 0.3355263157894737,
"acc_norm_stderr": 0.038424985593952694
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.41,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.41,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.3320754716981132,
"acc_stderr": 0.02898545565233439,
"acc_norm": 0.3320754716981132,
"acc_norm_stderr": 0.02898545565233439
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.3055555555555556,
"acc_stderr": 0.03852084696008534,
"acc_norm": 0.3055555555555556,
"acc_norm_stderr": 0.03852084696008534
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.35260115606936415,
"acc_stderr": 0.036430371689585475,
"acc_norm": 0.35260115606936415,
"acc_norm_stderr": 0.036430371689585475
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.28431372549019607,
"acc_stderr": 0.04488482852329017,
"acc_norm": 0.28431372549019607,
"acc_norm_stderr": 0.04488482852329017
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909284,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909284
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.225531914893617,
"acc_stderr": 0.027321078417387536,
"acc_norm": 0.225531914893617,
"acc_norm_stderr": 0.027321078417387536
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.23684210526315788,
"acc_stderr": 0.039994238792813344,
"acc_norm": 0.23684210526315788,
"acc_norm_stderr": 0.039994238792813344
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2689655172413793,
"acc_stderr": 0.036951833116502325,
"acc_norm": 0.2689655172413793,
"acc_norm_stderr": 0.036951833116502325
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2619047619047619,
"acc_stderr": 0.022644212615525214,
"acc_norm": 0.2619047619047619,
"acc_norm_stderr": 0.022644212615525214
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3492063492063492,
"acc_stderr": 0.04263906892795132,
"acc_norm": 0.3492063492063492,
"acc_norm_stderr": 0.04263906892795132
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.19,
"acc_stderr": 0.039427724440366234,
"acc_norm": 0.19,
"acc_norm_stderr": 0.039427724440366234
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.3161290322580645,
"acc_stderr": 0.026450874489042764,
"acc_norm": 0.3161290322580645,
"acc_norm_stderr": 0.026450874489042764
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.30049261083743845,
"acc_stderr": 0.03225799476233485,
"acc_norm": 0.30049261083743845,
"acc_norm_stderr": 0.03225799476233485
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|truthfulqa:mc|0": {
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"mc2": 0.4279230644927746,
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"harness|winogrande|5": {
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},
"harness|gsm8k|5": {
"acc": 0.015163002274450341,
"acc_stderr": 0.0033660229497263455
}
}
```
## 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]
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[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 Contact
[More Information Needed] |
knowrohit07/know_medical_dialogue_v2 | ---
license: openrail
---
### Description:
The knowrohit07/know_medical_dialogues_v2 dataset is a collection of conversational exchanges between patients and doctors on various medical topics. It aims to capture the intricacies, uncertainties, and questions posed by individuals regarding their health and the medical guidance provided in response.
### 🎯 Intended Use:
This dataset is crafted for training Large Language Models (LLMs) with a focus on understanding and generating medically-informed dialogue. It's ideal for LLM applications aiming to provide medical information or insights, especially for scenarios with limited access to healthcare resources.
❗ Limitations:
While this dataset includes diverse interactions, it doesn't cover every medical scenario. Models trained on this data should be viewed as an additional resource, not a substitute for professional medical consultation.
📌 Data Source:
Conversational seed tasks or exchanges were collected from anonymized patient-doctor interactions and synthetically made using GPT4.
📋 Collection Methodology:
The data was meticulously curated to ensure no personally identifiable information remained. All conversations are representative of general concerns and advice, without specific case details.
### Advantages of the Dataset:
Broad Spectrum: The dataset encompasses a wide array of medical queries and advice, making it valuable for general medical conversational AI.
Diverse Interactions: It captures everything from symptom queries to post-care instructions.
Training Potential for LLMs: Specifically tailored for fine-tuning LLMs for medical conversations, enhancing the resultant model's capability in this domain.
⚖️ Ethical and Impact Considerations:
Positive Impact: Utilizing LLMs trained on this dataset can be invaluable for healthcare professionals, especially in regions with limited medical datasets. When deployed on affordable local devices, doctors can leverage an AI-assisted tool, enhancing their consultation and decision-making processes.
Potential Risks: There's an inherent risk of the model providing guidance that may not match the latest medical guidelines or specific patient requirements. It's crucial to clarify to users that outputs from the LLM should complement professional medical opinions.
Recommendation: Encourage healthcare professionals to use this tool as an initial point of reference and not as the primary foundation for medical decisions.
|
huohuoma/open_dataset_66778899000 | ---
license: cc-by-sa-3.0
language:
- zh
tags:
- not-for-all-audiences
pretty_name: a
size_categories:
- n>1T
---
# 什么都没有，看到这就退出吧！！ |
jtatman/medical_biological_instruction_format | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 4328618
num_examples: 3000
download_size: 1750950
dataset_size: 4328618
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "medical_biological_instruction_format"
Best advice:
- drop the instruction column or use your choice of high-functioning llm to generate variant prompts
- for medical and science database expansions, i'm having excellent results using:
- [SciPhi/SciPhi-Mistral-7B-32k](https://huggingface.co/SciPhi/SciPhi-Mistral-7B-32k)
- [TheBloke/SciPhi-Mistral-7B-32k-GGUF](https://huggingface.co/TheBloke/SciPhi-Mistral-7B-32k-GGUF)
- Or if you have RAG setup on a corpus:
- [SciPhi/Sensei-7B-V2](https://huggingface.co/SciPhi/Sensei-7B-V2)
- [TheBloke/Sensei-7B-V1-GGUF](https://huggingface.co/TheBloke/Sensei-7B-V1-GGUF)
- [Falconsai/medical_summarization](https://huggingface.co/Falconsai/medical_summarization)
|
KolaGang/privacy_sumsum | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 740815953
num_examples: 253105
download_size: 210616720
dataset_size: 740815953
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
stanmalkinson199/2Ddataset | ---
license: openrail
---
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_35 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 599207188.0
num_examples: 116759
download_size: 613982662
dataset_size: 599207188.0
---
# Dataset Card for "chunk_35"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kenhktsui/minimath | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: source
dtype: string
- name: Rationale
dtype: string
- name: annotated_formula
dtype: string
- name: linear_formula
dtype: string
splits:
- name: train
num_bytes: 1114848
num_examples: 2880
download_size: 543796
dataset_size: 1114848
---
# Dataset Card for "minimath"
The objective of `minimath` is to evaluate the mathematical capability of language model in a quick while diverse setting.
The dataset is composed of sampling from the below dataset:
https://huggingface.co/datasets/math_dataset
https://huggingface.co/datasets/math_qa
https://huggingface.co/datasets/competition_math
https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_math_jsonl
https://huggingface.co/datasets/qwedsacf/grade-school-math-instructions
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/akashi_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of akashi/明石/明石 (Kantai Collection)
This is the dataset of akashi/明石/明石 (Kantai Collection), containing 500 images and their tags.
The core tags of this character are `pink_hair, long_hair, hair_ribbon, ribbon, green_eyes, tress_ribbon, breasts, large_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 | 529.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akashi_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 330.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akashi_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1150 | 669.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akashi_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 481.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akashi_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1150 | 902.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akashi_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/akashi_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 | 32 |  |  |  |  |  | 1girl, looking_at_viewer, solo, side-tie_bikini_bottom, cowboy_shot, navel, simple_background, cleavage, white_background, smile, white_bikini, medium_breasts, standing |
| 1 | 19 |  |  |  |  |  | 1girl, serafuku, solo, skirt, hip_vent, looking_at_viewer, blush, smile, thighhighs, open_mouth |
| 2 | 28 |  |  |  |  |  | 1girl, serafuku, solo, blue_skirt, long_sleeves, looking_at_viewer, pleated_skirt, simple_background, blue_sailor_collar, white_background, hip_vent, dated, smile, black_pantyhose, thighhighs |
| 3 | 11 |  |  |  |  |  | 1girl, looking_at_viewer, serafuku, simple_background, solo, blue_sailor_collar, upper_body, white_background, long_sleeves, smile, open_mouth |
| 4 | 6 |  |  |  |  |  | 1boy, blue_skirt, blush, hetero, pleated_skirt, serafuku, 1girl, hair_between_eyes, open_mouth, short_over_long_sleeves, solo_focus, thighhighs, blue_sailor_collar, red_ribbon, mosaic_censoring, penis, shirt, simple_background, underwear, white_background |
| 5 | 14 |  |  |  |  |  | 1girl, hat, solo, smile, alternate_costume, looking_at_viewer, blush, long_sleeves, white_dress, white_headwear, hair_between_eyes, simple_background, open_mouth, bag, white_background, cowboy_shot, holding |
| 6 | 7 |  |  |  |  |  | smile, 1girl, bag, blue_shirt, casual, full_body, looking_at_viewer, open_mouth, short_sleeves, solo, standing, white_pants, black_footwear, collarbone, belt, high_heels, official_alternate_costume, simple_background, very_long_hair |
| 7 | 13 |  |  |  |  |  | 1girl, blush, hetero, solo_focus, 1boy, nipples, nude, open_mouth, sex, censored, penis, hair_between_eyes, navel, sweat |
| 8 | 9 |  |  |  |  |  | 1boy, 1girl, blush, hetero, penis, solo_focus, serafuku, censored, fellatio, looking_at_viewer, cum_in_mouth, heart, tongue_out |
| 9 | 6 |  |  |  |  |  | 1girl, enmaided, frilled_apron, maid_headdress, white_apron, black_dress, looking_at_viewer, solo, blush, dated, maid_apron, waist_apron, cowboy_shot, hair_between_eyes, one-hour_drawing_challenge, puffy_short_sleeves, simple_background, white_background, white_thighhighs |
| 10 | 5 |  |  |  |  |  | 1girl, black_pantyhose, fake_animal_ears, looking_at_viewer, medium_breasts, playboy_bunny, rabbit_ears, solo, strapless_leotard, black_leotard, cleavage, detached_collar, smile, wrist_cuffs, covered_navel, dated, one-hour_drawing_challenge, red_bowtie, white_background, alternate_costume, cowboy_shot, high_heels, open_mouth, simple_background, sitting |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | side-tie_bikini_bottom | cowboy_shot | navel | simple_background | cleavage | white_background | smile | white_bikini | medium_breasts | standing | serafuku | skirt | hip_vent | blush | thighhighs | open_mouth | blue_skirt | long_sleeves | pleated_skirt | blue_sailor_collar | dated | black_pantyhose | upper_body | 1boy | hetero | hair_between_eyes | short_over_long_sleeves | solo_focus | red_ribbon | mosaic_censoring | penis | shirt | underwear | hat | alternate_costume | white_dress | white_headwear | bag | holding | blue_shirt | casual | full_body | short_sleeves | white_pants | black_footwear | collarbone | belt | high_heels | official_alternate_costume | very_long_hair | nipples | nude | sex | censored | sweat | fellatio | cum_in_mouth | heart | tongue_out | enmaided | frilled_apron | maid_headdress | white_apron | black_dress | maid_apron | waist_apron | one-hour_drawing_challenge | puffy_short_sleeves | white_thighhighs | fake_animal_ears | playboy_bunny | rabbit_ears | strapless_leotard | black_leotard | detached_collar | wrist_cuffs | covered_navel | red_bowtie | sitting |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------------------|:-------|:-------------------------|:--------------|:--------|:--------------------|:-----------|:-------------------|:--------|:---------------|:-----------------|:-----------|:-----------|:--------|:-----------|:--------|:-------------|:-------------|:-------------|:---------------|:----------------|:---------------------|:--------|:------------------|:-------------|:-------|:---------|:--------------------|:--------------------------|:-------------|:-------------|:-------------------|:--------|:--------|:------------|:------|:--------------------|:--------------|:-----------------|:------|:----------|:-------------|:---------|:------------|:----------------|:--------------|:-----------------|:-------------|:-------|:-------------|:-----------------------------|:-----------------|:----------|:-------|:------|:-----------|:--------|:-----------|:---------------|:--------|:-------------|:-----------|:----------------|:-----------------|:--------------|:--------------|:-------------|:--------------|:-----------------------------|:----------------------|:-------------------|:-------------------|:----------------|:--------------|:--------------------|:----------------|:------------------|:--------------|:----------------|:-------------|:----------|
| 0 | 32 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 19 |  |  |  |  |  | X | X | X | | | | | | | X | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 28 |  |  |  |  |  | X | X | X | | | | X | | X | X | | | | X | | X | | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 11 |  |  |  |  |  | X | X | X | | | | X | | X | X | | | | X | | | | | X | | X | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | | | | | | X | | X | | | | | X | | | X | X | X | X | | X | X | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 14 |  |  |  |  |  | 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 | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 13 |  |  |  |  |  | X | | | | | X | | | | | | | | | | | X | | X | | | | | | | | X | X | X | | X | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 9 |  |  |  |  |  | X | X | | | | | | | | | | | | X | | | X | | | | | | | | | | X | X | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 9 | 6 |  |  |  |  |  | X | X | X | | X | | X | | X | | | | | | | | X | | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 10 | 5 |  |  |  |  |  | X | X | X | | X | | X | X | X | X | | X | | | | | | | X | | | | | X | X | | | | | | | | | | | | | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X |
|
ohtaras/Kn | ---
license: unknown
---
|
Trustworthy-AI-Group/TransferAttack | ---
license: mit
---
|
ChunB1/books_raw | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 548313449
num_examples: 960000
download_size: 340700046
dataset_size: 548313449
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
rparundekar/rag_fine_tuning_500 | ---
dataset_info:
features:
- name: question
dtype: string
- name: contexts
sequence: string
- name: answer
dtype: string
- name: original_answer
dtype: string
splits:
- name: train
num_bytes: 649830
num_examples: 500
- name: validation
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num_examples: 100
- name: test
num_bytes: 137121
num_examples: 100
download_size: 520129
dataset_size: 918479
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
NLPCoreTeam/ruMT-Bench | ---
license: apache-2.0
task_categories:
- question-answering
language:
- ru
tags:
- evaluation
pretty_name: ruMT-Bench
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: test
path: "question.jsonl"
---
# ruMT-Bench
ruMT-Bench contains instructive multi-turn questions divided into 8 different areas of knowledge (writing, roleplay, extraction, reasoning, math, coding, STEM, humanities/social science). GPT-4 scores models' responses on a scale of 1 to 10. The final score is determined by the average of the entire conversation. For some complex problems that require a precise answer (e.g. math and coding), a reference answer is included in the judge's prompt to help evaluate responses from the LLM.
## Limitations
This approach serves the purpose of effectively assessing LLMs in Russian. However, it is important to recognize its limitations, which include:
- Verbosity bias. The LLM evaluator prefers longer answers, even if they are not as good as shorter answers. The authors showed that all estimators exhibit length bias, but GPT-4 is significantly better at dealing with this problem with 8.7% errors versus 91.3% for other estimators.
- Self-enhancement bias. The authors of the article demonstrate that GPT-4 has a higher win rate when rating itself by 10%, Claude prefers itself by 25% more, but they also prefer other models. On the contrary, GPT-3.5 does not like its own answers.
- Limited capability in grading math and reasoning questions. The quality of the assessment is limited by the abilities of the appraiser. Limitations in assessing complex problems, such as those requiring advanced mathematical and logical abilities.
- The dataset only includes 10 problems (20 questions) per category, which may not provide a complete representation of all LLM capabilities.
## How to evaluate
Evaluation code available [here](https://github.com/NLP-Core-Team/FastChat/blob/main/fastchat/llm_judge/README.md)
|
doudoutt/zhu | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
'9': '9'
splits:
- name: train
num_bytes: 18310961.0
num_examples: 568
- name: validation
num_bytes: 12283194.0
num_examples: 378
- name: test
num_bytes: 267575.0
num_examples: 10
download_size: 30466697
dataset_size: 30861730.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
Sacralet/mistral_chat_nesting_dataset | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 176060738
num_examples: 28000
- name: validation
num_bytes: 12667684
num_examples: 2000
- name: test
num_bytes: 9395510
num_examples: 1500
download_size: 123587427
dataset_size: 198123932
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
MicPie/unpredictable_bulbapedia-bulbagarden-net | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-bulbapedia-bulbagarden-net
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-bulbapedia-bulbagarden-net" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
surabhiMV/qrcode | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
- name: bbox
sequence:
sequence: float64
splits:
- name: train
num_bytes: 18269599.0
num_examples: 502
download_size: 17289588
dataset_size: 18269599.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "qrcode"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/mmlu-virology-original-neg-prepend | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: neg_prompt
dtype: string
splits:
- name: test
num_bytes: 8834
num_examples: 17
download_size: 12945
dataset_size: 8834
---
# Dataset Card for "mmlu-virology-original-neg-prepend"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tlc | ---
pretty_name: Thai Literature Corpora (TLC)
annotations_creators:
- expert-generated
- no-annotation
language_creators:
- expert-generated
language:
- th
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
dataset_info:
- config_name: tlcv1.0
features:
- name: ch_num
dtype: string
- name: title
dtype: string
- name: text
sequence:
sequence: string
splits:
- name: train
num_bytes: 32498
num_examples: 1
download_size: 2904472
dataset_size: 32498
- config_name: tlcv2.0
features:
- name: ch_num
dtype: string
- name: title
dtype: string
- name: text
sequence:
sequence: string
splits:
- name: train
num_bytes: 32498
num_examples: 1
download_size: 5551710
dataset_size: 32498
- config_name: tnhcv1.0
features:
- name: text
sequence: string
splits:
- name: train
num_bytes: 25198
num_examples: 152
download_size: 1465403
dataset_size: 25198
---
# Dataset Card for Thai Literature Corpora (TLC)
## 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://attapol.github.io/tlc.html
- **Leaderboard:** https://www.kaggle.com/c/wisesight-sentiment/
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** Jitkapat Sawatphol, Attapol Rutherford; attapolrutherford at gmail.com
### Dataset Summary
Thai Literature Corpora (TLC): Corpora of machine-ingestible Thai classical literature texts.
It consists of two datasets:
## TLC set
It is texts from [Vajirayana Digital Library](https://vajirayana.org/), stored by chapters and stanzas (non-tokenized).
tlc v.2.0 (6/17/19 : a total of 34 documents, 292,270 lines, 31,790,734 characters)
tlc v.1.0 (6/11/19 : a total of 25 documents, 113,981 lines, 28,775,761 characters)
## TNHC set
It is texts from Thai National Historical Corpus, stored by lines (manually tokenized).
tnhc v.1.0 (6/25/19 : a total of 47 documents, 756,478 lines, 13,361,142 characters)
### Supported Tasks and Leaderboards
Language Modeling, Language Generation
### Languages
Thai
## Dataset Structure
### Data Instances
```
{
"ch_num": "๑",
"title": "กากี กลอนสุภาพ",
"text": [
[
"๏ จักกล่าวอดีตนิทานแต่ปางก่อน\n",
"เมื่อครั้งองค์สมเด็จพระชินวร\tยังสัญจรแสวงหาโพธิญาณ\n",
"เสวยชาติเป็นสกุณาพระยานก\tจึงชักเรื่องชาดกมาบรรหาร\n",
"หวังแสดงแห่งจิตหญิงพาล\tให้ชายชาญรู้เชิงกระสัตรี ฯ\n"
]
}
```
### Data Fields
- `ch_num`: chapter number in Thai Numerals (๑, ๒, ๓, ๔, ๕, ๖, ๗, ๘, ๙, ๑๐, ...)
- `title`: chapter name
- `text`: each item corresponds to one stanzas, each line is a couplet which can be seperated by `\t`
### Data Splits
tlc v.2.0 (6/17/19 : a total of 34 documents, 292,270 lines, 31,790,734 characters)
tlc v.1.0 (6/11/19 : a total of 25 documents, 113,981 lines, 28,775,761 characters)
## TNHC set
It is texts from Thai National Historical Corpus, stored by lines (manually tokenized).
tnhc v.1.0 (6/25/19 : a total of 47 documents, 756,478 lines, 13,361,142 characters)
| | tlc2.0 | tlc1.0 | tnhc |
|-----------|-------|-------|-------|
| # documents | 34 | 25 | 47 |
| # lines | 292,270 | 113,981 | 756,478 |
## Dataset Creation
### Curation Rationale
Originally, the dataset was compiled for the [Thai Poetry Generator](https://github.com/jitkapat/thaipoetrygenerator) at Chulalongkorn university as the Final project for `2209372 Introduction to Computational Linguistics` by [Jitkapat Sawatphol](https://jitkapat.github.io/) (Faculty of Engineering, Chulalongkorn University).
### 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
There is no personal information.
## 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
Thanks [Jitkapat Sawatphol](https://jitkapat.github.io/) (Faculty of Arts, Chulalongkorn University), and [Attapol Rutherford](https://attapol.github.io/) (Faculty of Arts, Chulalongkorn University)
### Licensing Information
[More Information Needed]
### Citation Information
Please cite the following if you make use of the dataset:
Jitkapat Sawatphol, and Attapol Rutherford. 2019. **Thai Literature Corpora (TLC)**.
BibTeX:
```
@misc{
author={Sawatphol, Jitkapat},
title={Thai Literature Corpora},
year={2019},
howpublished={\\url{https://attapol.github.io/tlc.html}}
}
```
### Contributions
Thanks to [@chameleonTK](https://github.com/chameleonTK) for adding this dataset. |
liuyanchen1015/MULTI_VALUE_rte_em_obj_pronoun | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 76760
num_examples: 147
- name: train
num_bytes: 56429
num_examples: 110
download_size: 100140
dataset_size: 133189
---
# Dataset Card for "MULTI_VALUE_rte_em_obj_pronoun"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
samboustani/pubchem | ---
license: cc
language:
- en
tags:
- medical
- chemistry
- biology
pretty_name: PubChem Master Table
size_categories:
- 100M<n<1B
--- |
vincentiussgk/pneumonia_TA_split | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: file_path
dtype: string
- name: label
dtype: int64
- name: image
dtype: image
splits:
- name: train
num_bytes: 339946733.0
num_examples: 900
- name: test
num_bytes: 78428603.0
num_examples: 225
download_size: 417503898
dataset_size: 418375336.0
---
# Dataset Card for "pneumonia_TA_split"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
florentgbelidji/edmunds-car-ratings | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license: []
multilinguality:
- monolingual
pretty_name: Consumer car reviews for Nissan
size_categories:
- 1K<n<10K
source_datasets: []
task_categories:
- text-classification
task_ids:
- multi-label-classification
--- |
open-llm-leaderboard/details_NekoPunchBBB__Llama-2-13b-hf_Open-Platypus-8bit-att | ---
pretty_name: Evaluation run of NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-8bit-att
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-8bit-att](https://huggingface.co/NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-8bit-att)\
\ 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_NekoPunchBBB__Llama-2-13b-hf_Open-Platypus-8bit-att\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-29T09:52:28.222730](https://huggingface.co/datasets/open-llm-leaderboard/details_NekoPunchBBB__Llama-2-13b-hf_Open-Platypus-8bit-att/blob/main/results_2023-10-29T09-52-28.222730.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\": 0.0018875838926174498,\n\
\ \"em_stderr\": 0.0004445109990558914,\n \"f1\": 0.06262479026845635,\n\
\ \"f1_stderr\": 0.0013977251510479609,\n \"acc\": 0.4305574587430505,\n\
\ \"acc_stderr\": 0.01000136793869686\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0018875838926174498,\n \"em_stderr\": 0.0004445109990558914,\n\
\ \"f1\": 0.06262479026845635,\n \"f1_stderr\": 0.0013977251510479609\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09552691432903715,\n \
\ \"acc_stderr\": 0.008096605771155733\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7655880031570639,\n \"acc_stderr\": 0.011906130106237986\n\
\ }\n}\n```"
repo_url: https://huggingface.co/NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-8bit-att
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_09_13T11_55_45.595648
path:
- '**/details_harness|arc:challenge|25_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_29T09_52_28.222730
path:
- '**/details_harness|drop|3_2023-10-29T09-52-28.222730.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-29T09-52-28.222730.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_29T09_52_28.222730
path:
- '**/details_harness|gsm8k|5_2023-10-29T09-52-28.222730.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-29T09-52-28.222730.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hellaswag|10_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
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- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T11-55-45.595648.parquet'
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- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
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- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T11-55-45.595648.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-13T11-55-45.595648.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-13T11-55-45.595648.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_29T09_52_28.222730
path:
- '**/details_harness|winogrande|5_2023-10-29T09-52-28.222730.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-29T09-52-28.222730.parquet'
- config_name: results
data_files:
- split: 2023_09_13T11_55_45.595648
path:
- results_2023-09-13T11-55-45.595648.parquet
- split: 2023_10_29T09_52_28.222730
path:
- results_2023-10-29T09-52-28.222730.parquet
- split: latest
path:
- results_2023-10-29T09-52-28.222730.parquet
---
# Dataset Card for Evaluation run of NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-8bit-att
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-8bit-att
- **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 [NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-8bit-att](https://huggingface.co/NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-8bit-att) 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_NekoPunchBBB__Llama-2-13b-hf_Open-Platypus-8bit-att",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T09:52:28.222730](https://huggingface.co/datasets/open-llm-leaderboard/details_NekoPunchBBB__Llama-2-13b-hf_Open-Platypus-8bit-att/blob/main/results_2023-10-29T09-52-28.222730.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": 0.0018875838926174498,
"em_stderr": 0.0004445109990558914,
"f1": 0.06262479026845635,
"f1_stderr": 0.0013977251510479609,
"acc": 0.4305574587430505,
"acc_stderr": 0.01000136793869686
},
"harness|drop|3": {
"em": 0.0018875838926174498,
"em_stderr": 0.0004445109990558914,
"f1": 0.06262479026845635,
"f1_stderr": 0.0013977251510479609
},
"harness|gsm8k|5": {
"acc": 0.09552691432903715,
"acc_stderr": 0.008096605771155733
},
"harness|winogrande|5": {
"acc": 0.7655880031570639,
"acc_stderr": 0.011906130106237986
}
}
```
### 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] |
nguyenthanhdo/medi-wiki | ---
dataset_info:
features:
- name: query
sequence: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task_name
dtype: string
splits:
- name: train
num_bytes: 224087379.2682927
num_examples: 125000
download_size: 92240352
dataset_size: 224087379.2682927
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
gagan3012/dolphin-retrival-EXAMS-QA-corpus | ---
dataset_info:
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 20
num_examples: 1
- name: queries
num_bytes: 836129
num_examples: 2672
download_size: 457667
dataset_size: 836149
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
---
|
argilla/news-summary-new | ---
language: en
dataset_info:
features:
- name: text
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 252347
num_examples: 114
download_size: 87832
dataset_size: 252347
---
# Dataset Card for "news-summary-new"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dohonba/auditor_sentiment | ---
dataset_info:
features:
- name: context
dtype: string
- name: answer
dtype: string
- name: question
dtype: string
splits:
- name: train
num_bytes: 947507
num_examples: 3877
- name: test
num_bytes: 237684
num_examples: 969
download_size: 418189
dataset_size: 1185191
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
open-llm-leaderboard/details_aloobun__Reyna-Mini-1.8B-v0.1 | ---
pretty_name: Evaluation run of aloobun/Reyna-Mini-1.8B-v0.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [aloobun/Reyna-Mini-1.8B-v0.1](https://huggingface.co/aloobun/Reyna-Mini-1.8B-v0.1)\
\ 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_aloobun__Reyna-Mini-1.8B-v0.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-15T07:29:36.560907](https://huggingface.co/datasets/open-llm-leaderboard/details_aloobun__Reyna-Mini-1.8B-v0.1/blob/main/results_2024-02-15T07-29-36.560907.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\": 0.44766652106081417,\n\
\ \"acc_stderr\": 0.03438060993883449,\n \"acc_norm\": 0.4545350911182196,\n\
\ \"acc_norm_stderr\": 0.03520914160548039,\n \"mc1\": 0.26560587515299877,\n\
\ \"mc1_stderr\": 0.015461027627253595,\n \"mc2\": 0.4140207828143034,\n\
\ \"mc2_stderr\": 0.014035709599911956\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.33361774744027306,\n \"acc_stderr\": 0.013778687054176546,\n\
\ \"acc_norm\": 0.35238907849829354,\n \"acc_norm_stderr\": 0.013960142600598675\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.44991037641904,\n \
\ \"acc_stderr\": 0.004964679845918436,\n \"acc_norm\": 0.6041625174268074,\n\
\ \"acc_norm_stderr\": 0.004880303863138508\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.3925925925925926,\n\
\ \"acc_stderr\": 0.04218506215368879,\n \"acc_norm\": 0.3925925925925926,\n\
\ \"acc_norm_stderr\": 0.04218506215368879\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.4342105263157895,\n \"acc_stderr\": 0.040335656678483184,\n\
\ \"acc_norm\": 0.4342105263157895,\n \"acc_norm_stderr\": 0.040335656678483184\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n\
\ \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\"\
: 0.05\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"\
acc\": 0.5018867924528302,\n \"acc_stderr\": 0.030772653642075664,\n \
\ \"acc_norm\": 0.5018867924528302,\n \"acc_norm_stderr\": 0.030772653642075664\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4305555555555556,\n\
\ \"acc_stderr\": 0.04140685639111502,\n \"acc_norm\": 0.4305555555555556,\n\
\ \"acc_norm_stderr\": 0.04140685639111502\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n\
\ \"acc_norm_stderr\": 0.049236596391733084\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\": 0.3988439306358382,\n\
\ \"acc_stderr\": 0.037336266553835096,\n \"acc_norm\": 0.3988439306358382,\n\
\ \"acc_norm_stderr\": 0.037336266553835096\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.043364327079931785,\n\
\ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.043364327079931785\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n\
\ \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.03202563076101737,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.03202563076101737\n },\n\
\ \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n\
\ \"acc_stderr\": 0.04266339443159393,\n \"acc_norm\": 0.2894736842105263,\n\
\ \"acc_norm_stderr\": 0.04266339443159393\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.4896551724137931,\n \"acc_stderr\": 0.04165774775728763,\n\
\ \"acc_norm\": 0.4896551724137931,\n \"acc_norm_stderr\": 0.04165774775728763\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.37037037037037035,\n \"acc_stderr\": 0.024870815251057096,\n \"\
acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.024870815251057096\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2222222222222222,\n\
\ \"acc_stderr\": 0.03718489006818115,\n \"acc_norm\": 0.2222222222222222,\n\
\ \"acc_norm_stderr\": 0.03718489006818115\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.4774193548387097,\n\
\ \"acc_stderr\": 0.02841498501970786,\n \"acc_norm\": 0.4774193548387097,\n\
\ \"acc_norm_stderr\": 0.02841498501970786\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.3645320197044335,\n \"acc_stderr\": 0.0338640574606209,\n\
\ \"acc_norm\": 0.3645320197044335,\n \"acc_norm_stderr\": 0.0338640574606209\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\"\
: 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.6181818181818182,\n \"acc_stderr\": 0.037937131711656344,\n\
\ \"acc_norm\": 0.6181818181818182,\n \"acc_norm_stderr\": 0.037937131711656344\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.5808080808080808,\n \"acc_stderr\": 0.03515520728670417,\n \"\
acc_norm\": 0.5808080808080808,\n \"acc_norm_stderr\": 0.03515520728670417\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.5440414507772021,\n \"acc_stderr\": 0.035944137112724366,\n\
\ \"acc_norm\": 0.5440414507772021,\n \"acc_norm_stderr\": 0.035944137112724366\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.358974358974359,\n \"acc_stderr\": 0.024321738484602354,\n \
\ \"acc_norm\": 0.358974358974359,\n \"acc_norm_stderr\": 0.024321738484602354\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3148148148148148,\n \"acc_stderr\": 0.028317533496066468,\n \
\ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.028317533496066468\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.41596638655462187,\n \"acc_stderr\": 0.03201650100739615,\n\
\ \"acc_norm\": 0.41596638655462187,\n \"acc_norm_stderr\": 0.03201650100739615\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2251655629139073,\n \"acc_stderr\": 0.03410435282008936,\n \"\
acc_norm\": 0.2251655629139073,\n \"acc_norm_stderr\": 0.03410435282008936\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.5559633027522936,\n \"acc_stderr\": 0.021302621211654518,\n \"\
acc_norm\": 0.5559633027522936,\n \"acc_norm_stderr\": 0.021302621211654518\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.27314814814814814,\n \"acc_stderr\": 0.03038805130167812,\n \"\
acc_norm\": 0.27314814814814814,\n \"acc_norm_stderr\": 0.03038805130167812\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.44607843137254904,\n \"acc_stderr\": 0.03488845451304974,\n \"\
acc_norm\": 0.44607843137254904,\n \"acc_norm_stderr\": 0.03488845451304974\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.5949367088607594,\n \"acc_stderr\": 0.03195514741370671,\n \
\ \"acc_norm\": 0.5949367088607594,\n \"acc_norm_stderr\": 0.03195514741370671\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5201793721973094,\n\
\ \"acc_stderr\": 0.033530461674123,\n \"acc_norm\": 0.5201793721973094,\n\
\ \"acc_norm_stderr\": 0.033530461674123\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5572519083969466,\n \"acc_stderr\": 0.043564472026650695,\n\
\ \"acc_norm\": 0.5572519083969466,\n \"acc_norm_stderr\": 0.043564472026650695\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7024793388429752,\n \"acc_stderr\": 0.04173349148083499,\n \"\
acc_norm\": 0.7024793388429752,\n \"acc_norm_stderr\": 0.04173349148083499\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04833682445228318,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04833682445228318\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.44785276073619634,\n \"acc_stderr\": 0.039069474794566024,\n\
\ \"acc_norm\": 0.44785276073619634,\n \"acc_norm_stderr\": 0.039069474794566024\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\
\ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \
\ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6310679611650486,\n \"acc_stderr\": 0.0477761518115674,\n\
\ \"acc_norm\": 0.6310679611650486,\n \"acc_norm_stderr\": 0.0477761518115674\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7350427350427351,\n\
\ \"acc_stderr\": 0.028911208802749472,\n \"acc_norm\": 0.7350427350427351,\n\
\ \"acc_norm_stderr\": 0.028911208802749472\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6015325670498084,\n\
\ \"acc_stderr\": 0.01750743860277741,\n \"acc_norm\": 0.6015325670498084,\n\
\ \"acc_norm_stderr\": 0.01750743860277741\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5289017341040463,\n \"acc_stderr\": 0.026874085883518348,\n\
\ \"acc_norm\": 0.5289017341040463,\n \"acc_norm_stderr\": 0.026874085883518348\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.264804469273743,\n\
\ \"acc_stderr\": 0.014756906483260659,\n \"acc_norm\": 0.264804469273743,\n\
\ \"acc_norm_stderr\": 0.014756906483260659\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5849673202614379,\n \"acc_stderr\": 0.0282135041778241,\n\
\ \"acc_norm\": 0.5849673202614379,\n \"acc_norm_stderr\": 0.0282135041778241\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.43729903536977494,\n\
\ \"acc_stderr\": 0.028173917761762885,\n \"acc_norm\": 0.43729903536977494,\n\
\ \"acc_norm_stderr\": 0.028173917761762885\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.4567901234567901,\n \"acc_stderr\": 0.02771666165019404,\n\
\ \"acc_norm\": 0.4567901234567901,\n \"acc_norm_stderr\": 0.02771666165019404\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.36524822695035464,\n \"acc_stderr\": 0.02872386385328128,\n \
\ \"acc_norm\": 0.36524822695035464,\n \"acc_norm_stderr\": 0.02872386385328128\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.35528031290743156,\n\
\ \"acc_stderr\": 0.01222362336404404,\n \"acc_norm\": 0.35528031290743156,\n\
\ \"acc_norm_stderr\": 0.01222362336404404\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.3272058823529412,\n \"acc_stderr\": 0.02850145286039655,\n\
\ \"acc_norm\": 0.3272058823529412,\n \"acc_norm_stderr\": 0.02850145286039655\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.43300653594771243,\n \"acc_stderr\": 0.020045442473324227,\n \
\ \"acc_norm\": 0.43300653594771243,\n \"acc_norm_stderr\": 0.020045442473324227\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5545454545454546,\n\
\ \"acc_stderr\": 0.047605488214603246,\n \"acc_norm\": 0.5545454545454546,\n\
\ \"acc_norm_stderr\": 0.047605488214603246\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.4326530612244898,\n \"acc_stderr\": 0.03171752824062664,\n\
\ \"acc_norm\": 0.4326530612244898,\n \"acc_norm_stderr\": 0.03171752824062664\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5870646766169154,\n\
\ \"acc_stderr\": 0.03481520803367348,\n \"acc_norm\": 0.5870646766169154,\n\
\ \"acc_norm_stderr\": 0.03481520803367348\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.39759036144578314,\n\
\ \"acc_stderr\": 0.03809973084540218,\n \"acc_norm\": 0.39759036144578314,\n\
\ \"acc_norm_stderr\": 0.03809973084540218\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.5380116959064327,\n \"acc_stderr\": 0.03823727092882307,\n\
\ \"acc_norm\": 0.5380116959064327,\n \"acc_norm_stderr\": 0.03823727092882307\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.26560587515299877,\n\
\ \"mc1_stderr\": 0.015461027627253595,\n \"mc2\": 0.4140207828143034,\n\
\ \"mc2_stderr\": 0.014035709599911956\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6085240726124704,\n \"acc_stderr\": 0.013717487071290856\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05458680818802123,\n \
\ \"acc_stderr\": 0.006257444037912527\n }\n}\n```"
repo_url: https://huggingface.co/aloobun/Reyna-Mini-1.8B-v0.1
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_15T07_29_36.560907
path:
- '**/details_harness|arc:challenge|25_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|gsm8k|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hellaswag|10_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-15T07-29-36.560907.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-15T07-29-36.560907.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- '**/details_harness|winogrande|5_2024-02-15T07-29-36.560907.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-15T07-29-36.560907.parquet'
- config_name: results
data_files:
- split: 2024_02_15T07_29_36.560907
path:
- results_2024-02-15T07-29-36.560907.parquet
- split: latest
path:
- results_2024-02-15T07-29-36.560907.parquet
---
# Dataset Card for Evaluation run of aloobun/Reyna-Mini-1.8B-v0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [aloobun/Reyna-Mini-1.8B-v0.1](https://huggingface.co/aloobun/Reyna-Mini-1.8B-v0.1) 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_aloobun__Reyna-Mini-1.8B-v0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-15T07:29:36.560907](https://huggingface.co/datasets/open-llm-leaderboard/details_aloobun__Reyna-Mini-1.8B-v0.1/blob/main/results_2024-02-15T07-29-36.560907.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": 0.44766652106081417,
"acc_stderr": 0.03438060993883449,
"acc_norm": 0.4545350911182196,
"acc_norm_stderr": 0.03520914160548039,
"mc1": 0.26560587515299877,
"mc1_stderr": 0.015461027627253595,
"mc2": 0.4140207828143034,
"mc2_stderr": 0.014035709599911956
},
"harness|arc:challenge|25": {
"acc": 0.33361774744027306,
"acc_stderr": 0.013778687054176546,
"acc_norm": 0.35238907849829354,
"acc_norm_stderr": 0.013960142600598675
},
"harness|hellaswag|10": {
"acc": 0.44991037641904,
"acc_stderr": 0.004964679845918436,
"acc_norm": 0.6041625174268074,
"acc_norm_stderr": 0.004880303863138508
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.3925925925925926,
"acc_stderr": 0.04218506215368879,
"acc_norm": 0.3925925925925926,
"acc_norm_stderr": 0.04218506215368879
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.4342105263157895,
"acc_stderr": 0.040335656678483184,
"acc_norm": 0.4342105263157895,
"acc_norm_stderr": 0.040335656678483184
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5018867924528302,
"acc_stderr": 0.030772653642075664,
"acc_norm": 0.5018867924528302,
"acc_norm_stderr": 0.030772653642075664
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm": 0.4305555555555556,
"acc_norm_stderr": 0.04140685639111502
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_computer_science|5": {
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"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.3988439306358382,
"acc_stderr": 0.037336266553835096,
"acc_norm": 0.3988439306358382,
"acc_norm_stderr": 0.037336266553835096
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.2549019607843137,
"acc_stderr": 0.043364327079931785,
"acc_norm": 0.2549019607843137,
"acc_norm_stderr": 0.043364327079931785
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.6,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.6,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4,
"acc_stderr": 0.03202563076101737,
"acc_norm": 0.4,
"acc_norm_stderr": 0.03202563076101737
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2894736842105263,
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"acc_norm": 0.2894736842105263,
"acc_norm_stderr": 0.04266339443159393
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.4896551724137931,
"acc_stderr": 0.04165774775728763,
"acc_norm": 0.4896551724137931,
"acc_norm_stderr": 0.04165774775728763
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.37037037037037035,
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"acc_norm": 0.37037037037037035,
"acc_norm_stderr": 0.024870815251057096
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.03718489006818115,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.03718489006818115
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.4774193548387097,
"acc_stderr": 0.02841498501970786,
"acc_norm": 0.4774193548387097,
"acc_norm_stderr": 0.02841498501970786
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3645320197044335,
"acc_stderr": 0.0338640574606209,
"acc_norm": 0.3645320197044335,
"acc_norm_stderr": 0.0338640574606209
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.49,
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"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.6181818181818182,
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"acc_norm_stderr": 0.037937131711656344
},
"harness|hendrycksTest-high_school_geography|5": {
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"acc_norm": 0.5808080808080808,
"acc_norm_stderr": 0.03515520728670417
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.5440414507772021,
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"acc_norm": 0.5440414507772021,
"acc_norm_stderr": 0.035944137112724366
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.358974358974359,
"acc_stderr": 0.024321738484602354,
"acc_norm": 0.358974358974359,
"acc_norm_stderr": 0.024321738484602354
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3148148148148148,
"acc_stderr": 0.028317533496066468,
"acc_norm": 0.3148148148148148,
"acc_norm_stderr": 0.028317533496066468
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.41596638655462187,
"acc_stderr": 0.03201650100739615,
"acc_norm": 0.41596638655462187,
"acc_norm_stderr": 0.03201650100739615
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2251655629139073,
"acc_stderr": 0.03410435282008936,
"acc_norm": 0.2251655629139073,
"acc_norm_stderr": 0.03410435282008936
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.5559633027522936,
"acc_stderr": 0.021302621211654518,
"acc_norm": 0.5559633027522936,
"acc_norm_stderr": 0.021302621211654518
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.27314814814814814,
"acc_stderr": 0.03038805130167812,
"acc_norm": 0.27314814814814814,
"acc_norm_stderr": 0.03038805130167812
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.44607843137254904,
"acc_stderr": 0.03488845451304974,
"acc_norm": 0.44607843137254904,
"acc_norm_stderr": 0.03488845451304974
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.5949367088607594,
"acc_stderr": 0.03195514741370671,
"acc_norm": 0.5949367088607594,
"acc_norm_stderr": 0.03195514741370671
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.5201793721973094,
"acc_stderr": 0.033530461674123,
"acc_norm": 0.5201793721973094,
"acc_norm_stderr": 0.033530461674123
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.5572519083969466,
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"acc_norm": 0.5572519083969466,
"acc_norm_stderr": 0.043564472026650695
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7024793388429752,
"acc_stderr": 0.04173349148083499,
"acc_norm": 0.7024793388429752,
"acc_norm_stderr": 0.04173349148083499
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.5,
"acc_stderr": 0.04833682445228318,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04833682445228318
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.44785276073619634,
"acc_stderr": 0.039069474794566024,
"acc_norm": 0.44785276073619634,
"acc_norm_stderr": 0.039069474794566024
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4375,
"acc_stderr": 0.04708567521880525,
"acc_norm": 0.4375,
"acc_norm_stderr": 0.04708567521880525
},
"harness|hendrycksTest-management|5": {
"acc": 0.6310679611650486,
"acc_stderr": 0.0477761518115674,
"acc_norm": 0.6310679611650486,
"acc_norm_stderr": 0.0477761518115674
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.7350427350427351,
"acc_stderr": 0.028911208802749472,
"acc_norm": 0.7350427350427351,
"acc_norm_stderr": 0.028911208802749472
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.6015325670498084,
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"acc_norm": 0.6015325670498084,
"acc_norm_stderr": 0.01750743860277741
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.5289017341040463,
"acc_stderr": 0.026874085883518348,
"acc_norm": 0.5289017341040463,
"acc_norm_stderr": 0.026874085883518348
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.264804469273743,
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"acc_norm": 0.264804469273743,
"acc_norm_stderr": 0.014756906483260659
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.5849673202614379,
"acc_stderr": 0.0282135041778241,
"acc_norm": 0.5849673202614379,
"acc_norm_stderr": 0.0282135041778241
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.43729903536977494,
"acc_stderr": 0.028173917761762885,
"acc_norm": 0.43729903536977494,
"acc_norm_stderr": 0.028173917761762885
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.4567901234567901,
"acc_stderr": 0.02771666165019404,
"acc_norm": 0.4567901234567901,
"acc_norm_stderr": 0.02771666165019404
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.36524822695035464,
"acc_stderr": 0.02872386385328128,
"acc_norm": 0.36524822695035464,
"acc_norm_stderr": 0.02872386385328128
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.35528031290743156,
"acc_stderr": 0.01222362336404404,
"acc_norm": 0.35528031290743156,
"acc_norm_stderr": 0.01222362336404404
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.3272058823529412,
"acc_stderr": 0.02850145286039655,
"acc_norm": 0.3272058823529412,
"acc_norm_stderr": 0.02850145286039655
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.43300653594771243,
"acc_stderr": 0.020045442473324227,
"acc_norm": 0.43300653594771243,
"acc_norm_stderr": 0.020045442473324227
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.5545454545454546,
"acc_stderr": 0.047605488214603246,
"acc_norm": 0.5545454545454546,
"acc_norm_stderr": 0.047605488214603246
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.4326530612244898,
"acc_stderr": 0.03171752824062664,
"acc_norm": 0.4326530612244898,
"acc_norm_stderr": 0.03171752824062664
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.5870646766169154,
"acc_stderr": 0.03481520803367348,
"acc_norm": 0.5870646766169154,
"acc_norm_stderr": 0.03481520803367348
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.68,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.68,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-virology|5": {
"acc": 0.39759036144578314,
"acc_stderr": 0.03809973084540218,
"acc_norm": 0.39759036144578314,
"acc_norm_stderr": 0.03809973084540218
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.5380116959064327,
"acc_stderr": 0.03823727092882307,
"acc_norm": 0.5380116959064327,
"acc_norm_stderr": 0.03823727092882307
},
"harness|truthfulqa:mc|0": {
"mc1": 0.26560587515299877,
"mc1_stderr": 0.015461027627253595,
"mc2": 0.4140207828143034,
"mc2_stderr": 0.014035709599911956
},
"harness|winogrande|5": {
"acc": 0.6085240726124704,
"acc_stderr": 0.013717487071290856
},
"harness|gsm8k|5": {
"acc": 0.05458680818802123,
"acc_stderr": 0.006257444037912527
}
}
```
## 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] |
togahimik0/Karim.mp4 | ---
license: openrail
---
|
CyberHarem/prinz_heinrich_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of prinz_heinrich/プリンツ・ハインリヒ/海因里希亲王 (Azur Lane)
This is the dataset of prinz_heinrich/プリンツ・ハインリヒ/海因里希亲王 (Azur Lane), containing 331 images and their tags.
The core tags of this character are `long_hair, breasts, red_eyes, large_breasts, white_hair, very_long_hair, bangs, mole, ribbon, mole_under_eye, hair_ribbon, hairband`, 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 | 331 | 576.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/prinz_heinrich_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 331 | 292.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/prinz_heinrich_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 866 | 640.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/prinz_heinrich_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 331 | 493.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/prinz_heinrich_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 866 | 971.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/prinz_heinrich_azurlane/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/prinz_heinrich_azurlane',
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 | 31 |  |  |  |  |  | cleavage, bare_shoulders, looking_at_viewer, official_alternate_costume, fur-trimmed_kimono, 1girl, solo, fur-trimmed_sleeves, multicolored_kimono, off_shoulder, hair_stick, black_kimono, hair_between_eyes, bridal_gauntlets, collarbone, smile, black_choker, open_mouth, huge_breasts, thigh_strap, blush, sitting, thighs, iron_cross, grey_hair |
| 1 | 21 |  |  |  |  |  | 1girl, looking_at_viewer, red_headwear, fake_animal_ears, rabbit_ears, solo, bare_shoulders, cleavage, collarbone, grey_hair, red_one-piece_swimsuit, official_alternate_costume, strapless_swimsuit, baseball_cap, hair_over_one_eye, thighs, whistle_around_neck, highleg_swimsuit, smile, thigh_strap, thigh_pouch, animal_hat, mole_on_body, sitting, simple_background, open_mouth, teeth, blush, one_eye_covered, absurdly_long_hair, hand_up, nail_polish, red_cross, white_background |
| 2 | 9 |  |  |  |  |  | 1girl, black_necktie, black_skirt, black_sleeves, crop_top, detached_sleeves, looking_at_viewer, pleated_skirt, ribbed_shirt, simple_background, smile, solo, underboob, bare_shoulders, open_mouth, standing, upper_teeth_only, white_background, cowboy_shot, hair_over_one_eye, high-waist_skirt, white_shirt, blush, hair_between_eyes, black_hairband, collared_shirt, eyes_visible_through_hair, one_eye_closed |
| 3 | 11 |  |  |  |  |  | 1girl, bare_shoulders, black_necktie, black_skirt, crop_top, detached_sleeves, looking_at_viewer, pleated_skirt, solo, underboob, white_background, black_gloves, black_sleeves, ribbed_shirt, simple_background, smile, high-waist_skirt, open_mouth, sitting |
| 4 | 6 |  |  |  |  |  | 1girl, armpits, arms_up, crop_top, detached_sleeves, looking_at_viewer, open_mouth, smile, solo, underboob, black_necktie, black_skirt, black_sleeves, pleated_skirt, ribbed_shirt, simple_background, white_background, arms_behind_head, high-waist_skirt, hair_over_one_eye, one_eye_closed |
| 5 | 7 |  |  |  |  |  | 1boy, 1girl, hetero, nipples, open_mouth, solo_focus, blush, sex, cowgirl_position, girl_on_top, looking_at_viewer, mosaic_censoring, penis, vaginal, crop_top, cum_in_pussy, detached_sleeves, eyes_visible_through_hair, navel, smile, black_sleeves, hair_over_one_eye, necktie, nude, ribbed_shirt, skirt, sweat |
| 6 | 7 |  |  |  |  |  | 1girl, black_serafuku, looking_at_viewer, black_skirt, hair_ornament, ponytail, red_gloves, solo, grey_hair, underboob, black_shirt, choker, crop_top_overhang, midriff, official_alternate_costume, open_mouth, smile, thighs, bandages, bandaid, fingerless_gloves, miniskirt, sailor_collar, simple_background, white_background |
| 7 | 6 |  |  |  |  |  | 1girl, bodysuit, braid, headgear, tube, twintails, high_heels, see-through, solo, streaked_hair, cross_earrings, eyes_visible_through_hair, glowing, mecha_musume, mechanical_ears, qr_code, character_name, cleavage, full_body, iron_cross, mole_on_breast, mouth_mask, nipples, one_eye_closed, two-tone_hair |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | cleavage | bare_shoulders | looking_at_viewer | official_alternate_costume | fur-trimmed_kimono | 1girl | solo | fur-trimmed_sleeves | multicolored_kimono | off_shoulder | hair_stick | black_kimono | hair_between_eyes | bridal_gauntlets | collarbone | smile | black_choker | open_mouth | huge_breasts | thigh_strap | blush | sitting | thighs | iron_cross | grey_hair | red_headwear | fake_animal_ears | rabbit_ears | red_one-piece_swimsuit | strapless_swimsuit | baseball_cap | hair_over_one_eye | whistle_around_neck | highleg_swimsuit | thigh_pouch | animal_hat | mole_on_body | simple_background | teeth | one_eye_covered | absurdly_long_hair | hand_up | nail_polish | red_cross | white_background | black_necktie | black_skirt | black_sleeves | crop_top | detached_sleeves | pleated_skirt | ribbed_shirt | underboob | standing | upper_teeth_only | cowboy_shot | high-waist_skirt | white_shirt | black_hairband | collared_shirt | eyes_visible_through_hair | one_eye_closed | black_gloves | armpits | arms_up | arms_behind_head | 1boy | hetero | nipples | solo_focus | sex | cowgirl_position | girl_on_top | mosaic_censoring | penis | vaginal | cum_in_pussy | navel | necktie | nude | skirt | sweat | black_serafuku | hair_ornament | ponytail | red_gloves | black_shirt | choker | crop_top_overhang | midriff | bandages | bandaid | fingerless_gloves | miniskirt | sailor_collar | bodysuit | braid | headgear | tube | twintails | high_heels | see-through | streaked_hair | cross_earrings | glowing | mecha_musume | mechanical_ears | qr_code | character_name | full_body | mole_on_breast | mouth_mask | two-tone_hair |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------|:-----------------|:--------------------|:-----------------------------|:---------------------|:--------|:-------|:----------------------|:----------------------|:---------------|:-------------|:---------------|:--------------------|:-------------------|:-------------|:--------|:---------------|:-------------|:---------------|:--------------|:--------|:----------|:---------|:-------------|:------------|:---------------|:-------------------|:--------------|:-------------------------|:---------------------|:---------------|:--------------------|:----------------------|:-------------------|:--------------|:-------------|:---------------|:--------------------|:--------|:------------------|:---------------------|:----------|:--------------|:------------|:-------------------|:----------------|:--------------|:----------------|:-----------|:-------------------|:----------------|:---------------|:------------|:-----------|:-------------------|:--------------|:-------------------|:--------------|:-----------------|:-----------------|:----------------------------|:-----------------|:---------------|:----------|:----------|:-------------------|:-------|:---------|:----------|:-------------|:------|:-------------------|:--------------|:-------------------|:--------|:----------|:---------------|:--------|:----------|:-------|:--------|:--------|:-----------------|:----------------|:-----------|:-------------|:--------------|:---------|:--------------------|:----------|:-----------|:----------|:--------------------|:------------|:----------------|:-----------|:--------|:-----------|:-------|:------------|:-------------|:--------------|:----------------|:-----------------|:----------|:---------------|:------------------|:----------|:-----------------|:------------|:-----------------|:-------------|:----------------|
| 0 | 31 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 21 |  |  |  |  |  | X | X | X | X | | X | X | | | | | | | | X | X | | X | | X | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | | X | X | | | X | X | | | | | | X | | | X | | X | | | X | | | | | | | | | | | X | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 11 |  |  |  |  |  | | X | X | | | X | X | | | | | | | | | X | | X | | | | X | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | | | X | | | X | X | | | | | | | | | X | | X | | | | | | | | | | | | | | X | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | | | | X | | | | | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 7 |  |  |  |  |  | | | X | | | X | | | | | | | | | | X | | X | | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | | X | | | | | | | | | 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 | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 7 | 6 |  |  |  |  |  | X | | | | | X | X | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
BBuf/chid | ---
dataset_info:
features:
- name: id
dtype: int64
- name: candidates
sequence: string
- name: content
dtype: string
- name: answer
dtype: int64
splits:
- name: train
num_bytes: 88466
num_examples: 202
- name: validation
num_bytes: 87327
num_examples: 202
download_size: 140651
dataset_size: 175793
---
# Dataset Card for "chid"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_bigcode__gpt_bigcode-santacoder | ---
pretty_name: Evaluation run of bigcode/gpt_bigcode-santacoder
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [bigcode/gpt_bigcode-santacoder](https://huggingface.co/bigcode/gpt_bigcode-santacoder)\
\ 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_bigcode__gpt_bigcode-santacoder\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T12:23:19.324032](https://huggingface.co/datasets/open-llm-leaderboard/details_bigcode__gpt_bigcode-santacoder/blob/main/results_2023-09-17T12-23-19.324032.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\": 0.0009437919463087249,\n\
\ \"em_stderr\": 0.0003144653119413059,\n \"f1\": 0.03720532718120814,\n\
\ \"f1_stderr\": 0.0010858123513473891,\n \"acc\": 0.2418011181367818,\n\
\ \"acc_stderr\": 0.008020272468716342\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0009437919463087249,\n \"em_stderr\": 0.0003144653119413059,\n\
\ \"f1\": 0.03720532718120814,\n \"f1_stderr\": 0.0010858123513473891\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.00530705079605762,\n \
\ \"acc_stderr\": 0.0020013057209480557\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.47829518547750594,\n \"acc_stderr\": 0.014039239216484629\n\
\ }\n}\n```"
repo_url: https://huggingface.co/bigcode/gpt_bigcode-santacoder
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_07_19T19_05_43.434285
path:
- '**/details_harness|arc:challenge|25_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T12_23_19.324032
path:
- '**/details_harness|drop|3_2023-09-17T12-23-19.324032.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T12-23-19.324032.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T12_23_19.324032
path:
- '**/details_harness|gsm8k|5_2023-09-17T12-23-19.324032.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T12-23-19.324032.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hellaswag|10_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T12_23_19.324032
path:
- '**/details_harness|winogrande|5_2023-09-17T12-23-19.324032.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T12-23-19.324032.parquet'
- config_name: results
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- results_2023-07-19T19:05:43.434285.parquet
- split: 2023_09_17T12_23_19.324032
path:
- results_2023-09-17T12-23-19.324032.parquet
- split: latest
path:
- results_2023-09-17T12-23-19.324032.parquet
---
# Dataset Card for Evaluation run of bigcode/gpt_bigcode-santacoder
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bigcode/gpt_bigcode-santacoder
- **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 [bigcode/gpt_bigcode-santacoder](https://huggingface.co/bigcode/gpt_bigcode-santacoder) 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_bigcode__gpt_bigcode-santacoder",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T12:23:19.324032](https://huggingface.co/datasets/open-llm-leaderboard/details_bigcode__gpt_bigcode-santacoder/blob/main/results_2023-09-17T12-23-19.324032.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": 0.0009437919463087249,
"em_stderr": 0.0003144653119413059,
"f1": 0.03720532718120814,
"f1_stderr": 0.0010858123513473891,
"acc": 0.2418011181367818,
"acc_stderr": 0.008020272468716342
},
"harness|drop|3": {
"em": 0.0009437919463087249,
"em_stderr": 0.0003144653119413059,
"f1": 0.03720532718120814,
"f1_stderr": 0.0010858123513473891
},
"harness|gsm8k|5": {
"acc": 0.00530705079605762,
"acc_stderr": 0.0020013057209480557
},
"harness|winogrande|5": {
"acc": 0.47829518547750594,
"acc_stderr": 0.014039239216484629
}
}
```
### 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] |
nlpso/m2m3_qualitative_analysis_ocr_ptrn_cmbert_io | ---
language:
- fr
multilinguality:
- monolingual
task_categories:
- token-classification
---
# m2m3_qualitative_analysis_ocr_ptrn_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ocr_ptrn_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ocr_ptrn_cmbert_io)
* M3 : [nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_io)
## 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/m2m3_qualitative_analysis_ocr_ptrn_cmbert_io")
|
Hack90/ncbi_genbank_part_64 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: sequence
dtype: string
- name: name
dtype: string
- name: description
dtype: string
- name: features
dtype: int64
- name: seq_length
dtype: int64
splits:
- name: train
num_bytes: 23605755944
num_examples: 1596418
download_size: 10216572338
dataset_size: 23605755944
---
# Dataset Card for "ncbi_genbank_part_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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