datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
pranav290/interior-dataset-v1 | ---
dataset_info:
features:
- name: image (image)
dtype: image
- name: text (string)
dtype: string
splits:
- name: train
num_bytes: 44991298.0
num_examples: 30
download_size: 44994508
dataset_size: 44991298.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Nexdata/Spanish_Speech_Data | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/Spanish_Speech_Data
## 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/245?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The 338-hour Spanish speech data and is recorded by 800 Spanish-speaking native speakers from Spain, Mexico, Argentina. The recording enviroment is queit. All texts are manually transcribed.The sentence accuracy rate is 95%. It can be applied to speech recognition, machine translation, voiceprint recognition and so on.
For more details, please refer to the link: https://www.nexdata.ai/datasets/245?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
Spanish
## 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 |
distinsion/images_with_responses | ---
size_categories: n<1K
config_names:
- text_generation_with_gpt4
tags:
- synthetic
- distilabel
- rlaif
---
<p align="left">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
# Dataset Card for images_with_responses
This dataset has been created with [Distilabel](https://distilabel.argilla.io/).
## Dataset Summary
This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
```console
distilabel pipeline run --config "https://huggingface.co/datasets/distinsion/images_with_responses/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/distinsion/images_with_responses/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
<details><summary> Configuration: text_generation_with_gpt4 </summary><hr>
```json
{
"evolved_instruction": "Are you capable of accurately identifying the specific species of the bird in question, and can you also determine its age bracket based on established ornithological standards or criteria, ensuring that your classifications are substantiated by authoritative sources in the field of ornithology?",
"generation": "The image presents a bird flying over a coastal sea, under dramatic lighting conditions. Due to the distance and the silhouette nature of the bird against the bright background, specific identification to a species level is challenging based solely on this image. The bird appears to have a relatively streamlined body and long wings, which might suggest it belongs to a group like seabirds or certain types of raptors, but these are general observations.\n\nDetermining the age of a bird from such an image is also highly speculative without clear visibility of plumage details, size comparisons, or other age-indicative features. In professional ornithology, age determination often requires close observation or even physical examination, focusing on plumage patterns, wear and molting stages, or other physiological features not visible in this photo.\n\nFor accurate species and age identification, more detailed images or observations would be necessary, ideally supported by expert examination in the field.",
"instruction_with_image": [
{
"content": [
{
"image_url": null,
"text": "Are you capable of accurately identifying the specific species of the bird in question, and can you also determine its age bracket based on established ornithological standards or criteria, ensuring that your classifications are substantiated by authoritative sources in the field of ornithology?",
"type": "text"
},
{
"image_url": {
"url": "https://picsum.photos/id/990/5000/3334"
},
"text": null,
"type": "image_url"
}
],
"role": "user"
}
],
"model_name": "gpt-4-turbo-2024-04-09",
"url": "https://picsum.photos/id/990/5000/3334"
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("distinsion/images_with_responses", "text_generation_with_gpt4")
```
</details>
|
jeggers/ai2_arc_challenge_formatted | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
sequence:
- name: text
dtype: string
- name: label
dtype: string
- name: answerKey
dtype: string
- name: choices_sequence
sequence: string
splits:
- name: train
num_bytes: 512761
num_examples: 1119
- name: test
num_bytes: 549395
num_examples: 1172
- name: validation
num_bytes: 141177
num_examples: 299
download_size: 679369
dataset_size: 1203333
---
# Dataset Card for "ai2_arc_challenge_formatted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/squad_wrong_rare_v4_train_30_eval_10_recite | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: context_id
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 673207
num_examples: 368
- name: validation
num_bytes: 83486
num_examples: 50
download_size: 137041
dataset_size: 756693
---
# Dataset Card for "squad_wrong_rare_v4_train_30_eval_10_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Multimodal-Fatima/DTD_parition1_test_facebook_opt_2.7b_Visclues_ns_1880 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: image
dtype: image
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
- name: scores
sequence: float64
splits:
- name: fewshot_0_bs_16
num_bytes: 91908427.0
num_examples: 1880
- name: fewshot_1_bs_16
num_bytes: 92558233.0
num_examples: 1880
- name: fewshot_3_bs_16
num_bytes: 93868356.0
num_examples: 1880
- name: fewshot_5_bs_16
num_bytes: 95179914.0
num_examples: 1880
- name: fewshot_8_bs_16
num_bytes: 97151425.0
num_examples: 1880
download_size: 456396207
dataset_size: 470666355.0
---
# Dataset Card for "DTD_parition1_test_facebook_opt_2.7b_Visclues_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DBQ/Gucci.Product.prices.Sweden | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Sweden - Gucci - Product-level price list
tags:
- webscraping
- ecommerce
- Gucci
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 2345108
num_examples: 4916
download_size: 674510
dataset_size: 2345108
---
# Gucci web scraped data
## About the website
The **fashion industry** in the **EMEA** region, more specifically in **Sweden**, has seen a significant shift in recent years. With the surge in **digital transformation**, there has been remarkable growth in the **online luxury fashion market**, where premier brands like **Gucci** have amplified their presence. One particular focus area has been the **Ecommerce product-list pages (PLP)**, aiming to provide a seamless and immersive digital shopping experience. From an analysis of the dataset on Gucciβs PLP in Sweden, itβs clear that e-commerce has a pivotal role currently and will likely maintain this significance in shaping the future of luxury fashion within the region.
## Link to **dataset**
[Sweden - Gucci - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Gucci%20Product-prices%20Sweden/r/reckQ64odNXQly07Z)
|
open-llm-leaderboard/details_Sharathhebbar24__chat_gpt2 | ---
pretty_name: Evaluation run of Sharathhebbar24/chat_gpt2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Sharathhebbar24/chat_gpt2](https://huggingface.co/Sharathhebbar24/chat_gpt2)\
\ 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_Sharathhebbar24__chat_gpt2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-26T07:01:38.383525](https://huggingface.co/datasets/open-llm-leaderboard/details_Sharathhebbar24__chat_gpt2/blob/main/results_2024-01-26T07-01-38.383525.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\": 0.2438838006799062,\n\
\ \"acc_stderr\": 0.030268978470461658,\n \"acc_norm\": 0.24473030996233924,\n\
\ \"acc_norm_stderr\": 0.03107344744652555,\n \"mc1\": 0.2460220318237454,\n\
\ \"mc1_stderr\": 0.015077219200662592,\n \"mc2\": 0.3981307804872536,\n\
\ \"mc2_stderr\": 0.015120855688890876\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.18771331058020477,\n \"acc_stderr\": 0.011411001314155128,\n\
\ \"acc_norm\": 0.23037542662116042,\n \"acc_norm_stderr\": 0.01230492841874761\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2884883489344752,\n\
\ \"acc_stderr\": 0.004521334761709218,\n \"acc_norm\": 0.30760804620593507,\n\
\ \"acc_norm_stderr\": 0.0046056016100123895\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \
\ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.3111111111111111,\n\
\ \"acc_stderr\": 0.03999262876617722,\n \"acc_norm\": 0.3111111111111111,\n\
\ \"acc_norm_stderr\": 0.03999262876617722\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n\
\ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.24,\n\
\ \"acc_stderr\": 0.04292346959909281,\n \"acc_norm\": 0.24,\n \
\ \"acc_norm_stderr\": 0.04292346959909281\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.20754716981132076,\n \"acc_stderr\": 0.02495991802891127,\n\
\ \"acc_norm\": 0.20754716981132076,\n \"acc_norm_stderr\": 0.02495991802891127\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2638888888888889,\n\
\ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.2638888888888889,\n\
\ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \
\ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n\
\ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.24,\n\
\ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.24,\n \
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.21965317919075145,\n\
\ \"acc_stderr\": 0.031568093627031744,\n \"acc_norm\": 0.21965317919075145,\n\
\ \"acc_norm_stderr\": 0.031568093627031744\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\
\ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.22,\n \"acc_stderr\": 0.041633319989322716,\n \"acc_norm\": 0.22,\n\
\ \"acc_norm_stderr\": 0.041633319989322716\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n\
\ \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n\
\ \"acc_stderr\": 0.040969851398436695,\n \"acc_norm\": 0.2543859649122807,\n\
\ \"acc_norm_stderr\": 0.040969851398436695\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2482758620689655,\n \"acc_stderr\": 0.036001056927277716,\n\
\ \"acc_norm\": 0.2482758620689655,\n \"acc_norm_stderr\": 0.036001056927277716\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2566137566137566,\n \"acc_stderr\": 0.022494510767503154,\n \"\
acc_norm\": 0.2566137566137566,\n \"acc_norm_stderr\": 0.022494510767503154\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.15079365079365079,\n\
\ \"acc_stderr\": 0.03200686497287392,\n \"acc_norm\": 0.15079365079365079,\n\
\ \"acc_norm_stderr\": 0.03200686497287392\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.16,\n \"acc_stderr\": 0.03684529491774708,\n \
\ \"acc_norm\": 0.16,\n \"acc_norm_stderr\": 0.03684529491774708\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.21935483870967742,\n\
\ \"acc_stderr\": 0.02354079935872329,\n \"acc_norm\": 0.21935483870967742,\n\
\ \"acc_norm_stderr\": 0.02354079935872329\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.27586206896551724,\n \"acc_stderr\": 0.03144712581678242,\n\
\ \"acc_norm\": 0.27586206896551724,\n \"acc_norm_stderr\": 0.03144712581678242\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\
: {\n \"acc\": 0.22424242424242424,\n \"acc_stderr\": 0.03256866661681102,\n\
\ \"acc_norm\": 0.22424242424242424,\n \"acc_norm_stderr\": 0.03256866661681102\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.2777777777777778,\n \"acc_stderr\": 0.03191178226713549,\n \"\
acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.03191178226713549\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.3160621761658031,\n \"acc_stderr\": 0.03355397369686172,\n\
\ \"acc_norm\": 0.3160621761658031,\n \"acc_norm_stderr\": 0.03355397369686172\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.22564102564102564,\n \"acc_stderr\": 0.02119363252514854,\n\
\ \"acc_norm\": 0.22564102564102564,\n \"acc_norm_stderr\": 0.02119363252514854\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.23703703703703705,\n \"acc_stderr\": 0.025928876132766118,\n \
\ \"acc_norm\": 0.23703703703703705,\n \"acc_norm_stderr\": 0.025928876132766118\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.22268907563025211,\n \"acc_stderr\": 0.02702543349888236,\n\
\ \"acc_norm\": 0.22268907563025211,\n \"acc_norm_stderr\": 0.02702543349888236\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2119205298013245,\n \"acc_stderr\": 0.033367670865679766,\n \"\
acc_norm\": 0.2119205298013245,\n \"acc_norm_stderr\": 0.033367670865679766\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.22385321100917432,\n \"acc_stderr\": 0.01787121776779021,\n \"\
acc_norm\": 0.22385321100917432,\n \"acc_norm_stderr\": 0.01787121776779021\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.2916666666666667,\n \"acc_stderr\": 0.030998666304560517,\n \"\
acc_norm\": 0.2916666666666667,\n \"acc_norm_stderr\": 0.030998666304560517\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\": 0.2320675105485232,\n \"acc_stderr\": 0.02747974455080852,\n \
\ \"acc_norm\": 0.2320675105485232,\n \"acc_norm_stderr\": 0.02747974455080852\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.21524663677130046,\n\
\ \"acc_stderr\": 0.02758406660220827,\n \"acc_norm\": 0.21524663677130046,\n\
\ \"acc_norm_stderr\": 0.02758406660220827\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\
\ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.36363636363636365,\n \"acc_stderr\": 0.04391326286724071,\n \"\
acc_norm\": 0.36363636363636365,\n \"acc_norm_stderr\": 0.04391326286724071\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.21296296296296297,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.21296296296296297,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.294478527607362,\n \"acc_stderr\": 0.03581165790474082,\n\
\ \"acc_norm\": 0.294478527607362,\n \"acc_norm_stderr\": 0.03581165790474082\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.26785714285714285,\n\
\ \"acc_stderr\": 0.04203277291467763,\n \"acc_norm\": 0.26785714285714285,\n\
\ \"acc_norm_stderr\": 0.04203277291467763\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\
\ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.3076923076923077,\n\
\ \"acc_stderr\": 0.030236389942173116,\n \"acc_norm\": 0.3076923076923077,\n\
\ \"acc_norm_stderr\": 0.030236389942173116\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.25287356321839083,\n\
\ \"acc_stderr\": 0.015543377313719681,\n \"acc_norm\": 0.25287356321839083,\n\
\ \"acc_norm_stderr\": 0.015543377313719681\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\
\ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2335195530726257,\n\
\ \"acc_stderr\": 0.014149575348976269,\n \"acc_norm\": 0.2335195530726257,\n\
\ \"acc_norm_stderr\": 0.014149575348976269\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.25163398692810457,\n \"acc_stderr\": 0.024848018263875195,\n\
\ \"acc_norm\": 0.25163398692810457,\n \"acc_norm_stderr\": 0.024848018263875195\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.17684887459807075,\n\
\ \"acc_stderr\": 0.021670058885510796,\n \"acc_norm\": 0.17684887459807075,\n\
\ \"acc_norm_stderr\": 0.021670058885510796\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.22839506172839505,\n \"acc_stderr\": 0.023358211840626267,\n\
\ \"acc_norm\": 0.22839506172839505,\n \"acc_norm_stderr\": 0.023358211840626267\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.2730496453900709,\n \"acc_stderr\": 0.026577860943307854,\n \
\ \"acc_norm\": 0.2730496453900709,\n \"acc_norm_stderr\": 0.026577860943307854\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.25097783572359844,\n\
\ \"acc_stderr\": 0.01107373029918722,\n \"acc_norm\": 0.25097783572359844,\n\
\ \"acc_norm_stderr\": 0.01107373029918722\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.3713235294117647,\n \"acc_stderr\": 0.029349803139765873,\n\
\ \"acc_norm\": 0.3713235294117647,\n \"acc_norm_stderr\": 0.029349803139765873\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\
: 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\"\
: {\n \"acc\": 0.17272727272727273,\n \"acc_stderr\": 0.036206918339292196,\n\
\ \"acc_norm\": 0.17272727272727273,\n \"acc_norm_stderr\": 0.036206918339292196\n\
\ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.24081632653061225,\n\
\ \"acc_stderr\": 0.027372942201788163,\n \"acc_norm\": 0.24081632653061225,\n\
\ \"acc_norm_stderr\": 0.027372942201788163\n },\n \"harness|hendrycksTest-sociology|5\"\
: {\n \"acc\": 0.22388059701492538,\n \"acc_stderr\": 0.02947525023601718,\n\
\ \"acc_norm\": 0.22388059701492538,\n \"acc_norm_stderr\": 0.02947525023601718\n\
\ },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\"\
: {\n \"acc\": 0.24096385542168675,\n \"acc_stderr\": 0.0332939411907353,\n\
\ \"acc_norm\": 0.24096385542168675,\n \"acc_norm_stderr\": 0.0332939411907353\n\
\ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.2982456140350877,\n\
\ \"acc_stderr\": 0.03508771929824564,\n \"acc_norm\": 0.2982456140350877,\n\
\ \"acc_norm_stderr\": 0.03508771929824564\n },\n \"harness|truthfulqa:mc|0\"\
: {\n \"mc1\": 0.2460220318237454,\n \"mc1_stderr\": 0.015077219200662592,\n\
\ \"mc2\": 0.3981307804872536,\n \"mc2_stderr\": 0.015120855688890876\n\
\ },\n \"harness|winogrande|5\": {\n \"acc\": 0.4996053670086819,\n\
\ \"acc_stderr\": 0.014052481306049512\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```"
repo_url: https://huggingface.co/Sharathhebbar24/chat_gpt2
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_26T07_01_38.383525
path:
- '**/details_harness|arc:challenge|25_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|gsm8k|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hellaswag|10_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-26T07-01-38.383525.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-26T07-01-38.383525.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- '**/details_harness|winogrande|5_2024-01-26T07-01-38.383525.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-26T07-01-38.383525.parquet'
- config_name: results
data_files:
- split: 2024_01_26T07_01_38.383525
path:
- results_2024-01-26T07-01-38.383525.parquet
- split: latest
path:
- results_2024-01-26T07-01-38.383525.parquet
---
# Dataset Card for Evaluation run of Sharathhebbar24/chat_gpt2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Sharathhebbar24/chat_gpt2](https://huggingface.co/Sharathhebbar24/chat_gpt2) 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_Sharathhebbar24__chat_gpt2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-26T07:01:38.383525](https://huggingface.co/datasets/open-llm-leaderboard/details_Sharathhebbar24__chat_gpt2/blob/main/results_2024-01-26T07-01-38.383525.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": 0.2438838006799062,
"acc_stderr": 0.030268978470461658,
"acc_norm": 0.24473030996233924,
"acc_norm_stderr": 0.03107344744652555,
"mc1": 0.2460220318237454,
"mc1_stderr": 0.015077219200662592,
"mc2": 0.3981307804872536,
"mc2_stderr": 0.015120855688890876
},
"harness|arc:challenge|25": {
"acc": 0.18771331058020477,
"acc_stderr": 0.011411001314155128,
"acc_norm": 0.23037542662116042,
"acc_norm_stderr": 0.01230492841874761
},
"harness|hellaswag|10": {
"acc": 0.2884883489344752,
"acc_stderr": 0.004521334761709218,
"acc_norm": 0.30760804620593507,
"acc_norm_stderr": 0.0046056016100123895
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932268,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932268
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.3111111111111111,
"acc_stderr": 0.03999262876617722,
"acc_norm": 0.3111111111111111,
"acc_norm_stderr": 0.03999262876617722
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.17763157894736842,
"acc_stderr": 0.031103182383123398,
"acc_norm": 0.17763157894736842,
"acc_norm_stderr": 0.031103182383123398
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909281,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909281
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.20754716981132076,
"acc_stderr": 0.02495991802891127,
"acc_norm": 0.20754716981132076,
"acc_norm_stderr": 0.02495991802891127
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2638888888888889,
"acc_stderr": 0.03685651095897532,
"acc_norm": 0.2638888888888889,
"acc_norm_stderr": 0.03685651095897532
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.2,
"acc_norm_stderr": 0.04020151261036845
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.21965317919075145,
"acc_stderr": 0.031568093627031744,
"acc_norm": 0.21965317919075145,
"acc_norm_stderr": 0.031568093627031744
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.21568627450980393,
"acc_stderr": 0.04092563958237654,
"acc_norm": 0.21568627450980393,
"acc_norm_stderr": 0.04092563958237654
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.22,
"acc_stderr": 0.041633319989322716,
"acc_norm": 0.22,
"acc_norm_stderr": 0.041633319989322716
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.26382978723404255,
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"acc_norm": 0.26382978723404255,
"acc_norm_stderr": 0.028809989854102973
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2543859649122807,
"acc_stderr": 0.040969851398436695,
"acc_norm": 0.2543859649122807,
"acc_norm_stderr": 0.040969851398436695
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2482758620689655,
"acc_stderr": 0.036001056927277716,
"acc_norm": 0.2482758620689655,
"acc_norm_stderr": 0.036001056927277716
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2566137566137566,
"acc_stderr": 0.022494510767503154,
"acc_norm": 0.2566137566137566,
"acc_norm_stderr": 0.022494510767503154
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.15079365079365079,
"acc_stderr": 0.03200686497287392,
"acc_norm": 0.15079365079365079,
"acc_norm_stderr": 0.03200686497287392
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.16,
"acc_stderr": 0.03684529491774708,
"acc_norm": 0.16,
"acc_norm_stderr": 0.03684529491774708
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.21935483870967742,
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"acc_norm": 0.21935483870967742,
"acc_norm_stderr": 0.02354079935872329
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.27586206896551724,
"acc_stderr": 0.03144712581678242,
"acc_norm": 0.27586206896551724,
"acc_norm_stderr": 0.03144712581678242
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.24,
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"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909284
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.22424242424242424,
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"acc_norm": 0.22424242424242424,
"acc_norm_stderr": 0.03256866661681102
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.2777777777777778,
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"acc_norm_stderr": 0.03191178226713549
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.3160621761658031,
"acc_stderr": 0.03355397369686172,
"acc_norm": 0.3160621761658031,
"acc_norm_stderr": 0.03355397369686172
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.22564102564102564,
"acc_stderr": 0.02119363252514854,
"acc_norm": 0.22564102564102564,
"acc_norm_stderr": 0.02119363252514854
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.23703703703703705,
"acc_stderr": 0.025928876132766118,
"acc_norm": 0.23703703703703705,
"acc_norm_stderr": 0.025928876132766118
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.22268907563025211,
"acc_stderr": 0.02702543349888236,
"acc_norm": 0.22268907563025211,
"acc_norm_stderr": 0.02702543349888236
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2119205298013245,
"acc_stderr": 0.033367670865679766,
"acc_norm": 0.2119205298013245,
"acc_norm_stderr": 0.033367670865679766
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.22385321100917432,
"acc_stderr": 0.01787121776779021,
"acc_norm": 0.22385321100917432,
"acc_norm_stderr": 0.01787121776779021
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.2916666666666667,
"acc_stderr": 0.030998666304560517,
"acc_norm": 0.2916666666666667,
"acc_norm_stderr": 0.030998666304560517
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.3235294117647059,
"acc_stderr": 0.03283472056108567,
"acc_norm": 0.3235294117647059,
"acc_norm_stderr": 0.03283472056108567
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.2320675105485232,
"acc_stderr": 0.02747974455080852,
"acc_norm": 0.2320675105485232,
"acc_norm_stderr": 0.02747974455080852
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.21524663677130046,
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},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.2595419847328244,
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"acc_norm_stderr": 0.03844876139785271
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.36363636363636365,
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"acc_norm": 0.36363636363636365,
"acc_norm_stderr": 0.04391326286724071
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.21296296296296297,
"acc_stderr": 0.0395783547198098,
"acc_norm": 0.21296296296296297,
"acc_norm_stderr": 0.0395783547198098
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.294478527607362,
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"acc_norm": 0.294478527607362,
"acc_norm_stderr": 0.03581165790474082
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.26785714285714285,
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"acc_norm": 0.26785714285714285,
"acc_norm_stderr": 0.04203277291467763
},
"harness|hendrycksTest-management|5": {
"acc": 0.17475728155339806,
"acc_stderr": 0.037601780060266224,
"acc_norm": 0.17475728155339806,
"acc_norm_stderr": 0.037601780060266224
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.3076923076923077,
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"acc_norm": 0.3076923076923077,
"acc_norm_stderr": 0.030236389942173116
},
"harness|hendrycksTest-medical_genetics|5": {
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},
"harness|hendrycksTest-miscellaneous|5": {
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},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.24855491329479767,
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},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2335195530726257,
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"acc_norm": 0.2335195530726257,
"acc_norm_stderr": 0.014149575348976269
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.25163398692810457,
"acc_stderr": 0.024848018263875195,
"acc_norm": 0.25163398692810457,
"acc_norm_stderr": 0.024848018263875195
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.17684887459807075,
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"acc_norm": 0.17684887459807075,
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},
"harness|hendrycksTest-prehistory|5": {
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"acc_norm_stderr": 0.023358211840626267
},
"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
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},
"harness|hendrycksTest-professional_medicine|5": {
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},
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},
"harness|hendrycksTest-public_relations|5": {
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},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.24081632653061225,
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"acc_norm": 0.24081632653061225,
"acc_norm_stderr": 0.027372942201788163
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.22388059701492538,
"acc_stderr": 0.02947525023601718,
"acc_norm": 0.22388059701492538,
"acc_norm_stderr": 0.02947525023601718
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.21,
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"acc_norm": 0.21,
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},
"harness|hendrycksTest-virology|5": {
"acc": 0.24096385542168675,
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"acc_norm": 0.24096385542168675,
"acc_norm_stderr": 0.0332939411907353
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.2982456140350877,
"acc_stderr": 0.03508771929824564,
"acc_norm": 0.2982456140350877,
"acc_norm_stderr": 0.03508771929824564
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2460220318237454,
"mc1_stderr": 0.015077219200662592,
"mc2": 0.3981307804872536,
"mc2_stderr": 0.015120855688890876
},
"harness|winogrande|5": {
"acc": 0.4996053670086819,
"acc_stderr": 0.014052481306049512
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
## 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] |
Gabriel1322/jotac | ---
license: openrail
---
|
vessl/insurance-instruction-set | ---
dataset_info:
features:
- name: insurance_name
dtype: string
- name: text
dtype: string
- name: condition
dtype: string
- name: result
dtype: string
splits:
- name: train
num_bytes: 319350
num_examples: 492
download_size: 67754
dataset_size: 319350
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Ksgk-fy/alignment-sft-test02 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 758694
num_examples: 3024
- name: test
num_bytes: 189636
num_examples: 756
download_size: 90762
dataset_size: 948330
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
dhruv-anand-aintech/vdf_20240122_140004_c932b |
---
tags:
- vdf
- vector-io
- vector-dataset
- vector-embeddings
---
This is a dataset created using [vector-io](https://github.com/ai-northstar-tech/vector-io)
|
autoevaluate/autoeval-eval-futin__random-en-805a17-2021966768 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- futin/random
eval_info:
task: text_zero_shot_classification
model: facebook/opt-6.7b
metrics: []
dataset_name: futin/random
dataset_config: en
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-6.7b
* Dataset: futin/random
* Config: en
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@futin](https://huggingface.co/futin) for evaluating this model. |
ServiceNow/hotpot_test_pos__2_3 | ---
dataset_info:
features:
- name: context
dtype: string
- name: contexts_list
sequence: string
- name: titles_list
sequence: string
- name: useful_contexts
sequence: int64
- name: question
dtype: string
- name: answer
dtype: string
- name: sample_idx
dtype: int64
- name: dataset
dtype: string
splits:
- name: test
num_bytes: 254734434
num_examples: 22035
download_size: 150359764
dataset_size: 254734434
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
|
yuelaiyu/jiaran | ---
license: openrail
---
|
dostai/data-parsing-new-dataset-v4-updated-labels | ---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
struct:
- name: gt_parse
struct:
- name: VendorCompanyName
dtype: string
- name: VendorCompanyID
dtype: string
- name: InvoiceID
dtype: string
splits:
- name: train
num_bytes: 293781936.0
num_examples: 146
download_size: 31041936
dataset_size: 293781936.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-parsing-new-dataset-v4-updated-labels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tomaarsen/ner-orgs | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-ORG
'2': I-ORG
splits:
- name: train
num_bytes: 40381520.59961503
num_examples: 109424
- name: validation
num_bytes: 5782294.96333573
num_examples: 15908
- name: test
num_bytes: 10727120.198367199
num_examples: 28124
download_size: 14938552
dataset_size: 56890935.76131796
---
# Dataset Card for "ner-orgs"
This dataset is a concatenation of subsets of [Few-NERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd), [CoNLL 2003](https://huggingface.co/datasets/conll2003) and [OntoNotes v5](https://huggingface.co/datasets/tner/ontonotes5), but only the "B-ORG" and "I-ORG" labels.
Exactly half of the samples per split contain organisations, while the other half do not contain any.
It was generated using the following script:
```py
import random
from datasets import load_dataset, concatenate_datasets, Features, Sequence, ClassLabel, Value, DatasetDict
FEATURES = Features(
{
"tokens": Sequence(feature=Value(dtype="string")),
"ner_tags": Sequence(feature=ClassLabel(names=["O", "B-ORG", "I-ORG"])),
}
)
def load_fewnerd():
def mapper(sample):
sample["ner_tags"] = [int(tag == 5) for tag in sample["ner_tags"]]
sample["ner_tags"] = [
2 if tag == 1 and idx > 0 and sample["ner_tags"][idx - 1] == 1 else tag
for idx, tag in enumerate(sample["ner_tags"])
]
return sample
dataset = load_dataset("DFKI-SLT/few-nerd", "supervised")
dataset = dataset.map(mapper, remove_columns=["id", "fine_ner_tags"])
dataset = dataset.cast(FEATURES)
return dataset
def load_conll():
label_mapping = {3: 1, 4: 2}
def mapper(sample):
sample["ner_tags"] = [label_mapping.get(tag, 0) for tag in sample["ner_tags"]]
return sample
dataset = load_dataset("conll2003")
dataset = dataset.map(mapper, remove_columns=["id", "pos_tags", "chunk_tags"])
dataset = dataset.cast(FEATURES)
return dataset
def load_ontonotes():
label_mapping = {11: 1, 12: 2}
def mapper(sample):
sample["ner_tags"] = [label_mapping.get(tag, 0) for tag in sample["ner_tags"]]
return sample
dataset = load_dataset("tner/ontonotes5")
dataset = dataset.rename_column("tags", "ner_tags")
dataset = dataset.map(mapper)
dataset = dataset.cast(FEATURES)
return dataset
def has_org(sample):
return bool(sum(sample["ner_tags"]))
def has_no_org(sample):
return not has_org(sample)
def preprocess_raw_dataset(raw_dataset):
# Set the number of sentences without an org equal to the number of sentences with an org
dataset_org = raw_dataset.filter(has_org)
dataset_no_org = raw_dataset.filter(has_no_org)
dataset_no_org = dataset_no_org.select(random.sample(range(len(dataset_no_org)), k=len(dataset_org)))
dataset = concatenate_datasets([dataset_org, dataset_no_org])
return dataset
def main() -> None:
fewnerd_dataset = load_fewnerd()
conll_dataset = load_conll()
ontonotes_dataset = load_ontonotes()
raw_train_dataset = concatenate_datasets([fewnerd_dataset["train"], conll_dataset["train"], ontonotes_dataset["train"]])
raw_eval_dataset = concatenate_datasets([fewnerd_dataset["validation"], conll_dataset["validation"], ontonotes_dataset["validation"]])
raw_test_dataset = concatenate_datasets([fewnerd_dataset["test"], conll_dataset["test"], ontonotes_dataset["test"]])
train_dataset = preprocess_raw_dataset(raw_train_dataset)
eval_dataset = preprocess_raw_dataset(raw_eval_dataset)
test_dataset = preprocess_raw_dataset(raw_test_dataset)
dataset_dict = DatasetDict(
{
"train": train_dataset,
"validation": eval_dataset,
"test": test_dataset,
}
)
dataset_dict.push_to_hub("ner-orgs", private=True)
if __name__ == "__main__":
main()
``` |
anan-2024/twitter_dataset_1713153595 | ---
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: 228693
num_examples: 613
download_size: 124692
dataset_size: 228693
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
jon-tow/the_physics_hypertextbook_discussions | ---
dataset_info:
features:
- name: title
dtype: string
- name: url
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2405269
num_examples: 185
download_size: 1265691
dataset_size: 2405269
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# the_physics_hypertextbook_mixtral
> TEST: Scraped 2024-04-15
## Dataset Details
### Dataset Description
Discussions from https://physics.info.
## Citation
```bibtex
@misc{Elert,
title={The physics Hypertextbook},
url={https://physics.info/},
journal={The Physics Hypertextbook},
publisher={hypertextbook},
author={Elert, Glenn}
}
``` |
open-llm-leaderboard/details_Technoculture__MedMerge-6-7b-alpha-dpo | ---
pretty_name: Evaluation run of Technoculture/MedMerge-6-7b-alpha-dpo
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Technoculture/MedMerge-6-7b-alpha-dpo](https://huggingface.co/Technoculture/MedMerge-6-7b-alpha-dpo)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Technoculture__MedMerge-6-7b-alpha-dpo\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-09T14:26:24.610380](https://huggingface.co/datasets/open-llm-leaderboard/details_Technoculture__MedMerge-6-7b-alpha-dpo/blob/main/results_2024-02-09T14-26-24.610380.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\": 0.5256845888632714,\n\
\ \"acc_stderr\": 0.03422008390631278,\n \"acc_norm\": 0.530679668908867,\n\
\ \"acc_norm_stderr\": 0.034946938141584394,\n \"mc1\": 0.28518971848225216,\n\
\ \"mc1_stderr\": 0.015805827874454892,\n \"mc2\": 0.439400577032433,\n\
\ \"mc2_stderr\": 0.015027560307476687\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5119453924914675,\n \"acc_stderr\": 0.014607220340597171,\n\
\ \"acc_norm\": 0.5426621160409556,\n \"acc_norm_stderr\": 0.014558106543924067\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5714997012547302,\n\
\ \"acc_stderr\": 0.004938500303990283,\n \"acc_norm\": 0.7560246962756423,\n\
\ \"acc_norm_stderr\": 0.004286002710084087\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5185185185185185,\n\
\ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.5185185185185185,\n\
\ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.04063302731486671,\n\
\ \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.04063302731486671\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.47,\n\
\ \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \
\ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6415094339622641,\n \"acc_stderr\": 0.02951470358398177,\n\
\ \"acc_norm\": 0.6415094339622641,\n \"acc_norm_stderr\": 0.02951470358398177\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5763888888888888,\n\
\ \"acc_stderr\": 0.041321250197233685,\n \"acc_norm\": 0.5763888888888888,\n\
\ \"acc_norm_stderr\": 0.041321250197233685\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \
\ },\n \"harness|hendrycksTest-college_computer_science|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_mathematics|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4797687861271676,\n\
\ \"acc_stderr\": 0.03809342081273957,\n \"acc_norm\": 0.4797687861271676,\n\
\ \"acc_norm_stderr\": 0.03809342081273957\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.29411764705882354,\n \"acc_stderr\": 0.04533838195929776,\n\
\ \"acc_norm\": 0.29411764705882354,\n \"acc_norm_stderr\": 0.04533838195929776\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\": 0.4553191489361702,\n \"acc_stderr\": 0.03255525359340355,\n\
\ \"acc_norm\": 0.4553191489361702,\n \"acc_norm_stderr\": 0.03255525359340355\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.35964912280701755,\n\
\ \"acc_stderr\": 0.045144961328736334,\n \"acc_norm\": 0.35964912280701755,\n\
\ \"acc_norm_stderr\": 0.045144961328736334\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.04154659671707548,\n\
\ \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.04154659671707548\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3253968253968254,\n \"acc_stderr\": 0.02413015829976262,\n \"\
acc_norm\": 0.3253968253968254,\n \"acc_norm_stderr\": 0.02413015829976262\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.29365079365079366,\n\
\ \"acc_stderr\": 0.04073524322147125,\n \"acc_norm\": 0.29365079365079366,\n\
\ \"acc_norm_stderr\": 0.04073524322147125\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.5741935483870968,\n \"acc_stderr\": 0.028129112709165904,\n \"\
acc_norm\": 0.5741935483870968,\n \"acc_norm_stderr\": 0.028129112709165904\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.4482758620689655,\n \"acc_stderr\": 0.03499113137676744,\n \"\
acc_norm\": 0.4482758620689655,\n \"acc_norm_stderr\": 0.03499113137676744\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\
: 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7212121212121212,\n \"acc_stderr\": 0.03501438706296781,\n\
\ \"acc_norm\": 0.7212121212121212,\n \"acc_norm_stderr\": 0.03501438706296781\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6515151515151515,\n \"acc_stderr\": 0.033948539651564025,\n \"\
acc_norm\": 0.6515151515151515,\n \"acc_norm_stderr\": 0.033948539651564025\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7202072538860104,\n \"acc_stderr\": 0.032396370467357036,\n\
\ \"acc_norm\": 0.7202072538860104,\n \"acc_norm_stderr\": 0.032396370467357036\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.46923076923076923,\n \"acc_stderr\": 0.025302958890850154,\n\
\ \"acc_norm\": 0.46923076923076923,\n \"acc_norm_stderr\": 0.025302958890850154\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2740740740740741,\n \"acc_stderr\": 0.027195934804085622,\n \
\ \"acc_norm\": 0.2740740740740741,\n \"acc_norm_stderr\": 0.027195934804085622\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5,\n \"acc_stderr\": 0.032478490123081544,\n \"acc_norm\"\
: 0.5,\n \"acc_norm_stderr\": 0.032478490123081544\n },\n \"harness|hendrycksTest-high_school_physics|5\"\
: {\n \"acc\": 0.2980132450331126,\n \"acc_stderr\": 0.03734535676787198,\n\
\ \"acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.03734535676787198\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7211009174311926,\n \"acc_stderr\": 0.0192274688764635,\n \"acc_norm\"\
: 0.7211009174311926,\n \"acc_norm_stderr\": 0.0192274688764635\n },\n\
\ \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.42592592592592593,\n\
\ \"acc_stderr\": 0.03372343271653063,\n \"acc_norm\": 0.42592592592592593,\n\
\ \"acc_norm_stderr\": 0.03372343271653063\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.6715686274509803,\n \"acc_stderr\": 0.032962451101722294,\n\
\ \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.032962451101722294\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7468354430379747,\n \"acc_stderr\": 0.028304657943035303,\n \
\ \"acc_norm\": 0.7468354430379747,\n \"acc_norm_stderr\": 0.028304657943035303\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.600896860986547,\n\
\ \"acc_stderr\": 0.03286745312567961,\n \"acc_norm\": 0.600896860986547,\n\
\ \"acc_norm_stderr\": 0.03286745312567961\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5954198473282443,\n \"acc_stderr\": 0.043046937953806645,\n\
\ \"acc_norm\": 0.5954198473282443,\n \"acc_norm_stderr\": 0.043046937953806645\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6611570247933884,\n \"acc_stderr\": 0.0432076780753667,\n \"acc_norm\"\
: 0.6611570247933884,\n \"acc_norm_stderr\": 0.0432076780753667\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6203703703703703,\n\
\ \"acc_stderr\": 0.04691521224077742,\n \"acc_norm\": 0.6203703703703703,\n\
\ \"acc_norm_stderr\": 0.04691521224077742\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6380368098159509,\n \"acc_stderr\": 0.037757007291414416,\n\
\ \"acc_norm\": 0.6380368098159509,\n \"acc_norm_stderr\": 0.037757007291414416\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4107142857142857,\n\
\ \"acc_stderr\": 0.04669510663875191,\n \"acc_norm\": 0.4107142857142857,\n\
\ \"acc_norm_stderr\": 0.04669510663875191\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\
\ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7692307692307693,\n\
\ \"acc_stderr\": 0.027601921381417618,\n \"acc_norm\": 0.7692307692307693,\n\
\ \"acc_norm_stderr\": 0.027601921381417618\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7279693486590039,\n\
\ \"acc_stderr\": 0.015913367447500517,\n \"acc_norm\": 0.7279693486590039,\n\
\ \"acc_norm_stderr\": 0.015913367447500517\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5924855491329479,\n \"acc_stderr\": 0.026454578146931505,\n\
\ \"acc_norm\": 0.5924855491329479,\n \"acc_norm_stderr\": 0.026454578146931505\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.25139664804469275,\n\
\ \"acc_stderr\": 0.014508979453553962,\n \"acc_norm\": 0.25139664804469275,\n\
\ \"acc_norm_stderr\": 0.014508979453553962\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6078431372549019,\n \"acc_stderr\": 0.027956046165424516,\n\
\ \"acc_norm\": 0.6078431372549019,\n \"acc_norm_stderr\": 0.027956046165424516\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5594855305466238,\n\
\ \"acc_stderr\": 0.028196400574197426,\n \"acc_norm\": 0.5594855305466238,\n\
\ \"acc_norm_stderr\": 0.028196400574197426\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5740740740740741,\n \"acc_stderr\": 0.027513747284379424,\n\
\ \"acc_norm\": 0.5740740740740741,\n \"acc_norm_stderr\": 0.027513747284379424\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.36524822695035464,\n \"acc_stderr\": 0.028723863853281285,\n \
\ \"acc_norm\": 0.36524822695035464,\n \"acc_norm_stderr\": 0.028723863853281285\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3748370273794003,\n\
\ \"acc_stderr\": 0.012363652467551929,\n \"acc_norm\": 0.3748370273794003,\n\
\ \"acc_norm_stderr\": 0.012363652467551929\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6139705882352942,\n \"acc_stderr\": 0.029573269134411124,\n\
\ \"acc_norm\": 0.6139705882352942,\n \"acc_norm_stderr\": 0.029573269134411124\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5310457516339869,\n \"acc_stderr\": 0.020188804456361897,\n \
\ \"acc_norm\": 0.5310457516339869,\n \"acc_norm_stderr\": 0.020188804456361897\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\": 0.6408163265306123,\n \"acc_stderr\": 0.03071356045510849,\n\
\ \"acc_norm\": 0.6408163265306123,\n \"acc_norm_stderr\": 0.03071356045510849\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5920398009950248,\n\
\ \"acc_stderr\": 0.03475116365194092,\n \"acc_norm\": 0.5920398009950248,\n\
\ \"acc_norm_stderr\": 0.03475116365194092\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.0440844002276808,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4759036144578313,\n\
\ \"acc_stderr\": 0.038879718495972646,\n \"acc_norm\": 0.4759036144578313,\n\
\ \"acc_norm_stderr\": 0.038879718495972646\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.6608187134502924,\n \"acc_stderr\": 0.03631053496488905,\n\
\ \"acc_norm\": 0.6608187134502924,\n \"acc_norm_stderr\": 0.03631053496488905\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.28518971848225216,\n\
\ \"mc1_stderr\": 0.015805827874454892,\n \"mc2\": 0.439400577032433,\n\
\ \"mc2_stderr\": 0.015027560307476687\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7103393843725335,\n \"acc_stderr\": 0.012748550807638252\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.26156178923426837,\n \
\ \"acc_stderr\": 0.012105605733382442\n }\n}\n```"
repo_url: https://huggingface.co/Technoculture/MedMerge-6-7b-alpha-dpo
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|arc:challenge|25_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|gsm8k|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hellaswag|10_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-09T14-26-24.610380.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-09T14-26-24.610380.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- '**/details_harness|winogrande|5_2024-02-09T14-26-24.610380.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-09T14-26-24.610380.parquet'
- config_name: results
data_files:
- split: 2024_02_09T14_26_24.610380
path:
- results_2024-02-09T14-26-24.610380.parquet
- split: latest
path:
- results_2024-02-09T14-26-24.610380.parquet
---
# Dataset Card for Evaluation run of Technoculture/MedMerge-6-7b-alpha-dpo
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Technoculture/MedMerge-6-7b-alpha-dpo](https://huggingface.co/Technoculture/MedMerge-6-7b-alpha-dpo) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Technoculture__MedMerge-6-7b-alpha-dpo",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-09T14:26:24.610380](https://huggingface.co/datasets/open-llm-leaderboard/details_Technoculture__MedMerge-6-7b-alpha-dpo/blob/main/results_2024-02-09T14-26-24.610380.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": 0.5256845888632714,
"acc_stderr": 0.03422008390631278,
"acc_norm": 0.530679668908867,
"acc_norm_stderr": 0.034946938141584394,
"mc1": 0.28518971848225216,
"mc1_stderr": 0.015805827874454892,
"mc2": 0.439400577032433,
"mc2_stderr": 0.015027560307476687
},
"harness|arc:challenge|25": {
"acc": 0.5119453924914675,
"acc_stderr": 0.014607220340597171,
"acc_norm": 0.5426621160409556,
"acc_norm_stderr": 0.014558106543924067
},
"harness|hellaswag|10": {
"acc": 0.5714997012547302,
"acc_stderr": 0.004938500303990283,
"acc_norm": 0.7560246962756423,
"acc_norm_stderr": 0.004286002710084087
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5185185185185185,
"acc_stderr": 0.043163785995113245,
"acc_norm": 0.5185185185185185,
"acc_norm_stderr": 0.043163785995113245
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.47368421052631576,
"acc_stderr": 0.04063302731486671,
"acc_norm": 0.47368421052631576,
"acc_norm_stderr": 0.04063302731486671
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6415094339622641,
"acc_stderr": 0.02951470358398177,
"acc_norm": 0.6415094339622641,
"acc_norm_stderr": 0.02951470358398177
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5763888888888888,
"acc_stderr": 0.041321250197233685,
"acc_norm": 0.5763888888888888,
"acc_norm_stderr": 0.041321250197233685
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
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"harness|hendrycksTest-computer_security|5": {
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"harness|hendrycksTest-econometrics|5": {
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"harness|hendrycksTest-electrical_engineering|5": {
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"harness|hendrycksTest-elementary_mathematics|5": {
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"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.34,
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"harness|hendrycksTest-high_school_biology|5": {
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"harness|hendrycksTest-high_school_chemistry|5": {
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"harness|hendrycksTest-high_school_computer_science|5": {
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"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"acc_norm": 0.5,
"acc_norm_stderr": 0.032478490123081544
},
"harness|hendrycksTest-high_school_physics|5": {
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"acc_norm": 0.2980132450331126,
"acc_norm_stderr": 0.03734535676787198
},
"harness|hendrycksTest-high_school_psychology|5": {
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"acc_stderr": 0.0192274688764635,
"acc_norm": 0.7211009174311926,
"acc_norm_stderr": 0.0192274688764635
},
"harness|hendrycksTest-high_school_statistics|5": {
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"acc_stderr": 0.03372343271653063,
"acc_norm": 0.42592592592592593,
"acc_norm_stderr": 0.03372343271653063
},
"harness|hendrycksTest-high_school_us_history|5": {
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"acc_stderr": 0.032962451101722294,
"acc_norm": 0.6715686274509803,
"acc_norm_stderr": 0.032962451101722294
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7468354430379747,
"acc_stderr": 0.028304657943035303,
"acc_norm": 0.7468354430379747,
"acc_norm_stderr": 0.028304657943035303
},
"harness|hendrycksTest-human_aging|5": {
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"acc_stderr": 0.03286745312567961,
"acc_norm": 0.600896860986547,
"acc_norm_stderr": 0.03286745312567961
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.5954198473282443,
"acc_stderr": 0.043046937953806645,
"acc_norm": 0.5954198473282443,
"acc_norm_stderr": 0.043046937953806645
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.6611570247933884,
"acc_stderr": 0.0432076780753667,
"acc_norm": 0.6611570247933884,
"acc_norm_stderr": 0.0432076780753667
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.6203703703703703,
"acc_stderr": 0.04691521224077742,
"acc_norm": 0.6203703703703703,
"acc_norm_stderr": 0.04691521224077742
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6380368098159509,
"acc_stderr": 0.037757007291414416,
"acc_norm": 0.6380368098159509,
"acc_norm_stderr": 0.037757007291414416
},
"harness|hendrycksTest-machine_learning|5": {
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"acc_stderr": 0.04669510663875191,
"acc_norm": 0.4107142857142857,
"acc_norm_stderr": 0.04669510663875191
},
"harness|hendrycksTest-management|5": {
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"acc_stderr": 0.044052680241409216,
"acc_norm": 0.7281553398058253,
"acc_norm_stderr": 0.044052680241409216
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.7692307692307693,
"acc_stderr": 0.027601921381417618,
"acc_norm": 0.7692307692307693,
"acc_norm_stderr": 0.027601921381417618
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.65,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.65,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-miscellaneous|5": {
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"acc_norm": 0.7279693486590039,
"acc_norm_stderr": 0.015913367447500517
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.5924855491329479,
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"acc_norm": 0.5924855491329479,
"acc_norm_stderr": 0.026454578146931505
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.25139664804469275,
"acc_stderr": 0.014508979453553962,
"acc_norm": 0.25139664804469275,
"acc_norm_stderr": 0.014508979453553962
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6078431372549019,
"acc_stderr": 0.027956046165424516,
"acc_norm": 0.6078431372549019,
"acc_norm_stderr": 0.027956046165424516
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.5594855305466238,
"acc_stderr": 0.028196400574197426,
"acc_norm": 0.5594855305466238,
"acc_norm_stderr": 0.028196400574197426
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.5740740740740741,
"acc_stderr": 0.027513747284379424,
"acc_norm": 0.5740740740740741,
"acc_norm_stderr": 0.027513747284379424
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.36524822695035464,
"acc_stderr": 0.028723863853281285,
"acc_norm": 0.36524822695035464,
"acc_norm_stderr": 0.028723863853281285
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3748370273794003,
"acc_stderr": 0.012363652467551929,
"acc_norm": 0.3748370273794003,
"acc_norm_stderr": 0.012363652467551929
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6139705882352942,
"acc_stderr": 0.029573269134411124,
"acc_norm": 0.6139705882352942,
"acc_norm_stderr": 0.029573269134411124
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5310457516339869,
"acc_stderr": 0.020188804456361897,
"acc_norm": 0.5310457516339869,
"acc_norm_stderr": 0.020188804456361897
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6272727272727273,
"acc_stderr": 0.04631381319425465,
"acc_norm": 0.6272727272727273,
"acc_norm_stderr": 0.04631381319425465
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6408163265306123,
"acc_stderr": 0.03071356045510849,
"acc_norm": 0.6408163265306123,
"acc_norm_stderr": 0.03071356045510849
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.5920398009950248,
"acc_stderr": 0.03475116365194092,
"acc_norm": 0.5920398009950248,
"acc_norm_stderr": 0.03475116365194092
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.74,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.74,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4759036144578313,
"acc_stderr": 0.038879718495972646,
"acc_norm": 0.4759036144578313,
"acc_norm_stderr": 0.038879718495972646
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.6608187134502924,
"acc_stderr": 0.03631053496488905,
"acc_norm": 0.6608187134502924,
"acc_norm_stderr": 0.03631053496488905
},
"harness|truthfulqa:mc|0": {
"mc1": 0.28518971848225216,
"mc1_stderr": 0.015805827874454892,
"mc2": 0.439400577032433,
"mc2_stderr": 0.015027560307476687
},
"harness|winogrande|5": {
"acc": 0.7103393843725335,
"acc_stderr": 0.012748550807638252
},
"harness|gsm8k|5": {
"acc": 0.26156178923426837,
"acc_stderr": 0.012105605733382442
}
}
```
## 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:**
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## 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] |
qmeeus/AGV2 | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: task
dtype: string
- name: language
dtype: string
- name: speaker
dtype: string
splits:
- name: train
num_bytes: 53888736.0
num_examples: 81
download_size: 30633674
dataset_size: 53888736.0
---
# Dataset Card for "AGV2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
JosephDavid/mae | ---
license: openrail
---
|
open-llm-leaderboard/details_Obrolin__Kesehatan-7B-v0.1 | ---
pretty_name: Evaluation run of Obrolin/Kesehatan-7B-v0.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Obrolin/Kesehatan-7B-v0.1](https://huggingface.co/Obrolin/Kesehatan-7B-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_Obrolin__Kesehatan-7B-v0.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-03T22:00:30.966054](https://huggingface.co/datasets/open-llm-leaderboard/details_Obrolin__Kesehatan-7B-v0.1/blob/main/results_2024-02-03T22-00-30.966054.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\": 0.5977471674915434,\n\
\ \"acc_stderr\": 0.03354675339314637,\n \"acc_norm\": 0.6033162868765426,\n\
\ \"acc_norm_stderr\": 0.03424412262997995,\n \"mc1\": 0.35128518971848227,\n\
\ \"mc1_stderr\": 0.016711358163544403,\n \"mc2\": 0.5067930984526436,\n\
\ \"mc2_stderr\": 0.015515560312684274\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5819112627986348,\n \"acc_stderr\": 0.014413988396996081,\n\
\ \"acc_norm\": 0.6032423208191127,\n \"acc_norm_stderr\": 0.014296513020180635\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6266679944234216,\n\
\ \"acc_stderr\": 0.004827006520802886,\n \"acc_norm\": 0.8254331806413066,\n\
\ \"acc_norm_stderr\": 0.0037882037293466985\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n\
\ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\
\ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5921052631578947,\n \"acc_stderr\": 0.03999309712777474,\n\
\ \"acc_norm\": 0.5921052631578947,\n \"acc_norm_stderr\": 0.03999309712777474\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.52,\n\
\ \"acc_stderr\": 0.05021167315686779,\n \"acc_norm\": 0.52,\n \
\ \"acc_norm_stderr\": 0.05021167315686779\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6490566037735849,\n \"acc_stderr\": 0.02937364625323469,\n\
\ \"acc_norm\": 0.6490566037735849,\n \"acc_norm_stderr\": 0.02937364625323469\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\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.38,\n\
\ \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.38,\n \
\ \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness|hendrycksTest-college_medicine|5\"\
: {\n \"acc\": 0.5953757225433526,\n \"acc_stderr\": 0.03742461193887248,\n\
\ \"acc_norm\": 0.5953757225433526,\n \"acc_norm_stderr\": 0.03742461193887248\n\
\ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4117647058823529,\n\
\ \"acc_stderr\": 0.048971049527263666,\n \"acc_norm\": 0.4117647058823529,\n\
\ \"acc_norm_stderr\": 0.048971049527263666\n },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\": {\n \"acc\":\
\ 0.5319148936170213,\n \"acc_stderr\": 0.03261936918467382,\n \"\
acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.03261936918467382\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n\
\ \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.45614035087719296,\n\
\ \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n\
\ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3888888888888889,\n \"acc_stderr\": 0.02510742548113728,\n \"\
acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.02510742548113728\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.04426266681379909,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.04426266681379909\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\"\
: 0.7419354838709677,\n \"acc_stderr\": 0.02489246917246283,\n \"\
acc_norm\": 0.7419354838709677,\n \"acc_norm_stderr\": 0.02489246917246283\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n \"\
acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.62,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\"\
: 0.62,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.703030303030303,\n \"acc_stderr\": 0.03567969772268049,\n\
\ \"acc_norm\": 0.703030303030303,\n \"acc_norm_stderr\": 0.03567969772268049\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7222222222222222,\n \"acc_stderr\": 0.031911782267135466,\n \"\
acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.031911782267135466\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7979274611398963,\n \"acc_stderr\": 0.02897908979429673,\n\
\ \"acc_norm\": 0.7979274611398963,\n \"acc_norm_stderr\": 0.02897908979429673\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6282051282051282,\n \"acc_stderr\": 0.024503472557110936,\n\
\ \"acc_norm\": 0.6282051282051282,\n \"acc_norm_stderr\": 0.024503472557110936\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32592592592592595,\n \"acc_stderr\": 0.02857834836547308,\n \
\ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.02857834836547308\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6596638655462185,\n \"acc_stderr\": 0.030778057422931673,\n\
\ \"acc_norm\": 0.6596638655462185,\n \"acc_norm_stderr\": 0.030778057422931673\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658754,\n \"\
acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658754\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7761467889908257,\n \"acc_stderr\": 0.017871217767790222,\n \"\
acc_norm\": 0.7761467889908257,\n \"acc_norm_stderr\": 0.017871217767790222\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5277777777777778,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\
: 0.5277777777777778,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7549019607843137,\n\
\ \"acc_stderr\": 0.03019028245350195,\n \"acc_norm\": 0.7549019607843137,\n\
\ \"acc_norm_stderr\": 0.03019028245350195\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.7172995780590717,\n \"acc_stderr\": 0.029312814153955924,\n\
\ \"acc_norm\": 0.7172995780590717,\n \"acc_norm_stderr\": 0.029312814153955924\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6143497757847534,\n\
\ \"acc_stderr\": 0.03266842214289201,\n \"acc_norm\": 0.6143497757847534,\n\
\ \"acc_norm_stderr\": 0.03266842214289201\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.732824427480916,\n \"acc_stderr\": 0.038808483010823944,\n\
\ \"acc_norm\": 0.732824427480916,\n \"acc_norm_stderr\": 0.038808483010823944\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.71900826446281,\n \"acc_stderr\": 0.04103203830514512,\n \"acc_norm\"\
: 0.71900826446281,\n \"acc_norm_stderr\": 0.04103203830514512\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7222222222222222,\n\
\ \"acc_stderr\": 0.04330043749650742,\n \"acc_norm\": 0.7222222222222222,\n\
\ \"acc_norm_stderr\": 0.04330043749650742\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6809815950920245,\n \"acc_stderr\": 0.03661997551073836,\n\
\ \"acc_norm\": 0.6809815950920245,\n \"acc_norm_stderr\": 0.03661997551073836\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\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.024414947304543674,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.024414947304543674\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.7637292464878672,\n\
\ \"acc_stderr\": 0.015190473717037495,\n \"acc_norm\": 0.7637292464878672,\n\
\ \"acc_norm_stderr\": 0.015190473717037495\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6878612716763006,\n \"acc_stderr\": 0.02494679222527231,\n\
\ \"acc_norm\": 0.6878612716763006,\n \"acc_norm_stderr\": 0.02494679222527231\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.31508379888268156,\n\
\ \"acc_stderr\": 0.015536850852473631,\n \"acc_norm\": 0.31508379888268156,\n\
\ \"acc_norm_stderr\": 0.015536850852473631\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6503267973856209,\n \"acc_stderr\": 0.027305308076274695,\n\
\ \"acc_norm\": 0.6503267973856209,\n \"acc_norm_stderr\": 0.027305308076274695\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6527331189710611,\n\
\ \"acc_stderr\": 0.027040745502307336,\n \"acc_norm\": 0.6527331189710611,\n\
\ \"acc_norm_stderr\": 0.027040745502307336\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6697530864197531,\n \"acc_stderr\": 0.026168298456732846,\n\
\ \"acc_norm\": 0.6697530864197531,\n \"acc_norm_stderr\": 0.026168298456732846\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4645390070921986,\n \"acc_stderr\": 0.02975238965742705,\n \
\ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.02975238965742705\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.41851368970013036,\n\
\ \"acc_stderr\": 0.01259950560833646,\n \"acc_norm\": 0.41851368970013036,\n\
\ \"acc_norm_stderr\": 0.01259950560833646\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6029411764705882,\n \"acc_stderr\": 0.02972215209928007,\n\
\ \"acc_norm\": 0.6029411764705882,\n \"acc_norm_stderr\": 0.02972215209928007\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5996732026143791,\n \"acc_stderr\": 0.019821843688271765,\n \
\ \"acc_norm\": 0.5996732026143791,\n \"acc_norm_stderr\": 0.019821843688271765\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\
\ \"acc_stderr\": 0.046075820907199756,\n \"acc_norm\": 0.6363636363636364,\n\
\ \"acc_norm_stderr\": 0.046075820907199756\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6204081632653061,\n \"acc_stderr\": 0.031067211262872475,\n\
\ \"acc_norm\": 0.6204081632653061,\n \"acc_norm_stderr\": 0.031067211262872475\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8109452736318408,\n\
\ \"acc_stderr\": 0.02768691358801302,\n \"acc_norm\": 0.8109452736318408,\n\
\ \"acc_norm_stderr\": 0.02768691358801302\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.82,\n \"acc_stderr\": 0.038612291966536955,\n \
\ \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.038612291966536955\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4939759036144578,\n\
\ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.4939759036144578,\n\
\ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\
\ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.35128518971848227,\n\
\ \"mc1_stderr\": 0.016711358163544403,\n \"mc2\": 0.5067930984526436,\n\
\ \"mc2_stderr\": 0.015515560312684274\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7647987371744278,\n \"acc_stderr\": 0.011920008163650872\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.32221379833206976,\n \
\ \"acc_stderr\": 0.01287243548118878\n }\n}\n```"
repo_url: https://huggingface.co/Obrolin/Kesehatan-7B-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_03T22_00_30.966054
path:
- '**/details_harness|arc:challenge|25_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|gsm8k|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hellaswag|10_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-03T22-00-30.966054.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-03T22-00-30.966054.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- '**/details_harness|winogrande|5_2024-02-03T22-00-30.966054.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-03T22-00-30.966054.parquet'
- config_name: results
data_files:
- split: 2024_02_03T22_00_30.966054
path:
- results_2024-02-03T22-00-30.966054.parquet
- split: latest
path:
- results_2024-02-03T22-00-30.966054.parquet
---
# Dataset Card for Evaluation run of Obrolin/Kesehatan-7B-v0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Obrolin/Kesehatan-7B-v0.1](https://huggingface.co/Obrolin/Kesehatan-7B-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_Obrolin__Kesehatan-7B-v0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-03T22:00:30.966054](https://huggingface.co/datasets/open-llm-leaderboard/details_Obrolin__Kesehatan-7B-v0.1/blob/main/results_2024-02-03T22-00-30.966054.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": 0.5977471674915434,
"acc_stderr": 0.03354675339314637,
"acc_norm": 0.6033162868765426,
"acc_norm_stderr": 0.03424412262997995,
"mc1": 0.35128518971848227,
"mc1_stderr": 0.016711358163544403,
"mc2": 0.5067930984526436,
"mc2_stderr": 0.015515560312684274
},
"harness|arc:challenge|25": {
"acc": 0.5819112627986348,
"acc_stderr": 0.014413988396996081,
"acc_norm": 0.6032423208191127,
"acc_norm_stderr": 0.014296513020180635
},
"harness|hellaswag|10": {
"acc": 0.6266679944234216,
"acc_stderr": 0.004827006520802886,
"acc_norm": 0.8254331806413066,
"acc_norm_stderr": 0.0037882037293466985
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5777777777777777,
"acc_stderr": 0.04266763404099582,
"acc_norm": 0.5777777777777777,
"acc_norm_stderr": 0.04266763404099582
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5921052631578947,
"acc_stderr": 0.03999309712777474,
"acc_norm": 0.5921052631578947,
"acc_norm_stderr": 0.03999309712777474
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.52,
"acc_stderr": 0.05021167315686779,
"acc_norm": 0.52,
"acc_norm_stderr": 0.05021167315686779
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6490566037735849,
"acc_stderr": 0.02937364625323469,
"acc_norm": 0.6490566037735849,
"acc_norm_stderr": 0.02937364625323469
},
"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": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145633,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145633
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5953757225433526,
"acc_stderr": 0.03742461193887248,
"acc_norm": 0.5953757225433526,
"acc_norm_stderr": 0.03742461193887248
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.048971049527263666,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.048971049527263666
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5319148936170213,
"acc_stderr": 0.03261936918467382,
"acc_norm": 0.5319148936170213,
"acc_norm_stderr": 0.03261936918467382
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.45614035087719296,
"acc_stderr": 0.04685473041907789,
"acc_norm": 0.45614035087719296,
"acc_norm_stderr": 0.04685473041907789
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.04164188720169375,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.04164188720169375
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3888888888888889,
"acc_stderr": 0.02510742548113728,
"acc_norm": 0.3888888888888889,
"acc_norm_stderr": 0.02510742548113728
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.04426266681379909,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.04426266681379909
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7419354838709677,
"acc_stderr": 0.02489246917246283,
"acc_norm": 0.7419354838709677,
"acc_norm_stderr": 0.02489246917246283
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.035158955511656986,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.035158955511656986
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.62,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.62,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.703030303030303,
"acc_stderr": 0.03567969772268049,
"acc_norm": 0.703030303030303,
"acc_norm_stderr": 0.03567969772268049
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.031911782267135466,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.031911782267135466
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.7979274611398963,
"acc_stderr": 0.02897908979429673,
"acc_norm": 0.7979274611398963,
"acc_norm_stderr": 0.02897908979429673
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6282051282051282,
"acc_stderr": 0.024503472557110936,
"acc_norm": 0.6282051282051282,
"acc_norm_stderr": 0.024503472557110936
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32592592592592595,
"acc_stderr": 0.02857834836547308,
"acc_norm": 0.32592592592592595,
"acc_norm_stderr": 0.02857834836547308
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6596638655462185,
"acc_stderr": 0.030778057422931673,
"acc_norm": 0.6596638655462185,
"acc_norm_stderr": 0.030778057422931673
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.32450331125827814,
"acc_stderr": 0.03822746937658754,
"acc_norm": 0.32450331125827814,
"acc_norm_stderr": 0.03822746937658754
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7761467889908257,
"acc_stderr": 0.017871217767790222,
"acc_norm": 0.7761467889908257,
"acc_norm_stderr": 0.017871217767790222
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5277777777777778,
"acc_stderr": 0.0340470532865388,
"acc_norm": 0.5277777777777778,
"acc_norm_stderr": 0.0340470532865388
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7549019607843137,
"acc_stderr": 0.03019028245350195,
"acc_norm": 0.7549019607843137,
"acc_norm_stderr": 0.03019028245350195
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7172995780590717,
"acc_stderr": 0.029312814153955924,
"acc_norm": 0.7172995780590717,
"acc_norm_stderr": 0.029312814153955924
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6143497757847534,
"acc_stderr": 0.03266842214289201,
"acc_norm": 0.6143497757847534,
"acc_norm_stderr": 0.03266842214289201
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.732824427480916,
"acc_stderr": 0.038808483010823944,
"acc_norm": 0.732824427480916,
"acc_norm_stderr": 0.038808483010823944
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.71900826446281,
"acc_stderr": 0.04103203830514512,
"acc_norm": 0.71900826446281,
"acc_norm_stderr": 0.04103203830514512
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.04330043749650742,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.04330043749650742
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6809815950920245,
"acc_stderr": 0.03661997551073836,
"acc_norm": 0.6809815950920245,
"acc_norm_stderr": 0.03661997551073836
},
"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": 0.7669902912621359,
"acc_stderr": 0.04185832598928315,
"acc_norm": 0.7669902912621359,
"acc_norm_stderr": 0.04185832598928315
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.024414947304543674,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.024414947304543674
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695237,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695237
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7637292464878672,
"acc_stderr": 0.015190473717037495,
"acc_norm": 0.7637292464878672,
"acc_norm_stderr": 0.015190473717037495
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6878612716763006,
"acc_stderr": 0.02494679222527231,
"acc_norm": 0.6878612716763006,
"acc_norm_stderr": 0.02494679222527231
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.31508379888268156,
"acc_stderr": 0.015536850852473631,
"acc_norm": 0.31508379888268156,
"acc_norm_stderr": 0.015536850852473631
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6503267973856209,
"acc_stderr": 0.027305308076274695,
"acc_norm": 0.6503267973856209,
"acc_norm_stderr": 0.027305308076274695
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6527331189710611,
"acc_stderr": 0.027040745502307336,
"acc_norm": 0.6527331189710611,
"acc_norm_stderr": 0.027040745502307336
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6697530864197531,
"acc_stderr": 0.026168298456732846,
"acc_norm": 0.6697530864197531,
"acc_norm_stderr": 0.026168298456732846
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4645390070921986,
"acc_stderr": 0.02975238965742705,
"acc_norm": 0.4645390070921986,
"acc_norm_stderr": 0.02975238965742705
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.41851368970013036,
"acc_stderr": 0.01259950560833646,
"acc_norm": 0.41851368970013036,
"acc_norm_stderr": 0.01259950560833646
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6029411764705882,
"acc_stderr": 0.02972215209928007,
"acc_norm": 0.6029411764705882,
"acc_norm_stderr": 0.02972215209928007
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5996732026143791,
"acc_stderr": 0.019821843688271765,
"acc_norm": 0.5996732026143791,
"acc_norm_stderr": 0.019821843688271765
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6363636363636364,
"acc_stderr": 0.046075820907199756,
"acc_norm": 0.6363636363636364,
"acc_norm_stderr": 0.046075820907199756
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6204081632653061,
"acc_stderr": 0.031067211262872475,
"acc_norm": 0.6204081632653061,
"acc_norm_stderr": 0.031067211262872475
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8109452736318408,
"acc_stderr": 0.02768691358801302,
"acc_norm": 0.8109452736318408,
"acc_norm_stderr": 0.02768691358801302
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.82,
"acc_stderr": 0.038612291966536955,
"acc_norm": 0.82,
"acc_norm_stderr": 0.038612291966536955
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4939759036144578,
"acc_stderr": 0.03892212195333045,
"acc_norm": 0.4939759036144578,
"acc_norm_stderr": 0.03892212195333045
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8128654970760234,
"acc_stderr": 0.02991312723236804,
"acc_norm": 0.8128654970760234,
"acc_norm_stderr": 0.02991312723236804
},
"harness|truthfulqa:mc|0": {
"mc1": 0.35128518971848227,
"mc1_stderr": 0.016711358163544403,
"mc2": 0.5067930984526436,
"mc2_stderr": 0.015515560312684274
},
"harness|winogrande|5": {
"acc": 0.7647987371744278,
"acc_stderr": 0.011920008163650872
},
"harness|gsm8k|5": {
"acc": 0.32221379833206976,
"acc_stderr": 0.01287243548118878
}
}
```
## 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] |
zolak/twitter_dataset_78_1713114865 | ---
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: 300545
num_examples: 768
download_size: 153258
dataset_size: 300545
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
laion/gpt4v-dataset | ---
license: cc0-1.0
dataset_info:
features:
- name: link
dtype: string
- name: caption
dtype: string
- name: message_id
dtype: string
- name: timestamp
dtype: string
- name: image
dtype: 'null'
splits:
- name: train
num_bytes: 13170765
num_examples: 12356
download_size: 7339665
dataset_size: 13170765
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mtkinit/mtkinit_andre_jo_vo_potesenie | ---
pretty_name: mtkinit/andre-jo-vo-potesenie
---
# mtkinit/andre-jo-vo-potesenie
Created from AIOD platform |
Nexdata/Brazilian_Portuguese_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/Brazilian_Portuguese_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/954?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The data volumn is 1044 hours and is recorded by 2038 Brazilian native speakers. The recording text is designed by linguistic experts, which covers general interactive, in-car and home category. The texts are manually proofread with high accuracy. Recording devices are mainstream Android phones and iPhones.
For more details, please refer to the link: https://www.nexdata.ai/datasets/954?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
Brazilian Portuguese
## 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 |
Kimono/langchain-RefAPI-Source-Desc-v.0.0.231 | ---
license: openrail
---
|
ImanNalia/latest_coraal_train | ---
dataset_info:
features:
- name: segment_filename
dtype: string
- name: text
dtype: string
- name: audio
struct:
- name: audio
struct:
- name: array
sequence: float32
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 8634327685
num_examples: 11376
download_size: 8643667422
dataset_size: 8634327685
---
# Dataset Card for "latest_coraal_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/VALUE_sst2_null_relcl | ---
dataset_info:
features:
- name: idx
dtype: int64
- name: sentence
dtype: string
- name: label
dtype: int64
- name: score
dtype: int64
splits:
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num_bytes: 23513
num_examples: 148
- name: test
num_bytes: 45010
num_examples: 289
- name: train
num_bytes: 617847
num_examples: 4676
download_size: 389489
dataset_size: 686370
---
# Dataset Card for "VALUE_sst2_null_relcl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vuksan314/Lavko | ---
license: cc
---
|
tyzhu/lmind_nq_full_v1_doc_qa | ---
configs:
- config_name: default
data_files:
- split: train_qa
path: data/train_qa-*
- split: train_recite_qa
path: data/train_recite_qa-*
- split: eval_qa
path: data/eval_qa-*
- split: eval_recite_qa
path: data/eval_recite_qa-*
- split: all_docs
path: data/all_docs-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: answers
struct:
- name: answer_start
sequence: 'null'
- name: text
sequence: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
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num_bytes: 6806082
num_examples: 58622
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num_examples: 58622
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num_bytes: 28100353
num_examples: 43935
- name: train
num_bytes: 34906435
num_examples: 102557
- name: validation
num_bytes: 752802
num_examples: 6489
download_size: 74900648
dataset_size: 119712914
---
# Dataset Card for "lmind_nq_full_v1_doc_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EuSouBrocha/Raluca | ---
license: openrail
---
|
ProGamerGov/StableDiffusion-v1-5-Regularization-Images | ---
license: mit
tags:
- image-text-dataset
- synthetic-dataset
---
A collection of regularization / class instance datasets for the [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) model to use for DreamBooth prior preservation loss training. Files labeled with "mse vae" used the [stabilityai/sd-vae-ft-mse](https://huggingface.co/stabilityai/sd-vae-ft-mse) VAE. For ease of use, datasets are stored as zip files containing 512x512 PNG images. The number of images in each zip file is specified at the end of the filename.
There is currently a bug where HuggingFace is incorrectly reporting that the datasets are pickled. They are not picked, they are simple ZIP files containing the images.
Currently this repository contains the following datasets (datasets are named after the prompt they used):
Art Styles
* "**artwork style**": 4125 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
* "**artwork style**": 4200 images generated using 50 DPM++ 2S a Karras steps and a CFG of 7, using the MSE VAE. A negative prompt of "text" was also used for this dataset.
* "**artwork style**": 2750 images generated using 50 DPM++ 2S a Karras steps and a CFG of 7, using the MSE VAE.
* "**illustration style**": 3050 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
* "**erotic photography**": 2760 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
* "**landscape photography**": 2500 images generated using 50 DPM++ 2S a Karras steps and a CFG of 7, using the MSE VAE. A negative prompt of "b&w, text" was also used for this dataset.
People
* "**person**": 2115 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
* "**woman**": 4420 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
* "**guy**": 4820 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
* "**supermodel**": 4411 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
* "**bikini model**": 4260 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
* "**sexy athlete**": 5020 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
* "**femme fatale**": 4725 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
* "**sexy man**": 3505 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
* "**sexy woman**": 3500 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
Animals
* "**kitty**": 5100 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
* "**cat**": 2050 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
Vehicles
* "**fighter jet**": 1600 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
* "**train**": 2669 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
* "**car**": 3150 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
Themes
* "**cyberpunk**": 3040 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
I used the "Generate Forever" feature in [AUTOMATIC1111's WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) to create thousands of images for each dataset. Every image in a particular dataset uses the exact same settings, with only the seed value being different.
You can use my regularization / class image datasets with: https://github.com/ShivamShrirao/diffusers, https://github.com/JoePenna/Dreambooth-Stable-Diffusion, https://github.com/TheLastBen/fast-stable-diffusion, and any other DreamBooth projects that have support for prior preservation loss.
|
communityai/HuggingFaceH4___SystemChat | ---
dataset_info:
features:
- name: source
dtype: string
- name: conversations
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 18845508.0
num_examples: 6520
download_size: 9206332
dataset_size: 18845508.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
DiegoRoberto10/amanda13 | ---
license: openrail
---
|
SUSTech/prm800k | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: problem
dtype: string
- name: answer
dtype: string
- name: solution
dtype: string
splits:
- name: train
num_bytes: 13892738
num_examples: 16830
- name: test
num_bytes: 914286
num_examples: 976
download_size: 6870174
dataset_size: 14807024
---
# Dataset Card for "prm800k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
SauravMaheshkar/tags-ask-ubuntu | ---
license: unknown
task_categories:
- graph-ml
tags:
- chemistry
configs:
- config_name: transductive
data_files:
- split: train
path: "processed/transductive/train_df.csv"
- split: valid
path: "processed/transductive/val_df.csv"
- split: test
path: "processed/transductive/test_df.csv"
- config_name: inductive
data_files:
- split: train
path: "processed/inductive/train_df.csv"
- split: valid
path: "processed/inductive/val_df.csv"
- split: test
path: "processed/inductive/test_df.csv"
- config_name: raw
data_files: "raw/*.txt"
---
Source Paper: https://arxiv.org/abs/1802.06916
### Usage
```
from torch_geometric.datasets.cornell import CornellTemporalHyperGraphDataset
dataset = CornellTemporalHyperGraphDataset(root = "./", name="tags-ask-ubuntu", split="train")
```
### Citation
```misc
@article{Benson-2018-simplicial,
author = {Benson, Austin R. and Abebe, Rediet and Schaub, Michael T. and Jadbabaie, Ali and Kleinberg, Jon},
title = {Simplicial closure and higher-order link prediction},
year = {2018},
doi = {10.1073/pnas.1800683115},
publisher = {National Academy of Sciences},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences}
}
``` |
HiTZ/EusProficiency | ---
task_categories:
- question-answering
language:
- eu
pretty_name: EusProficiency
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: test
path: "atarikoa.jsonl"
---
# Dataset Card for EusProficiency
EusProficiency comprises 5,169 exercises on different topics from past EGA exams, the official C1-level certificate of proficiency in Basque.
We collected the _atarikoa_ exercises from EGA exams through the years 1998 to 2008. Atarikoa is the first qualifying test of EGA, which measures different aspects of language competency, such as reading comprehension, grammar, vocabulary, spelling, and writing. Each test generally has 85 multiple-choice questions, with 4 choices and a single correct answer.
- **Curated by:** HiTZ Research Center & IXA Research group (University of the Basque Country UPV/EHU)
- **Language(s) (NLP):** Basque (eu)
- π Blog Post: [Latxa: An Open Language Model and Evaluation Suite for Basque](https://www.hitz.eus/en/node/340)
- π Paper: [Latxa: An Open Language Model and Evaluation Suite for Basque](https://arxiv.org/abs/2403.20266)
- π» Code: [hitz-zentroa/latxa](https://github.com/hitz-zentroa/latxa)
- π§ Contact: [hitz@ehu.eus](mailto:hitz@ehu.eus)
## Example
Basque Example:
```txt
Galdera: Jatetxe batera sartu, eta bazkaltzen ari denari:
A. Gabon!
B. On egin diezazula!
C. Bejondeizula!
D. Agur tβerdi!
Erantzuna: B
```
English Translation:
```txt
Question: Upon entering a restaurant, to another diner:
A. Good night!
B. Enjoy!
C. Bless you!
D. Greetings!
Answer: B
```
## Citation
```bibtex
@misc{etxaniz2024latxa,
title={{L}atxa: An Open Language Model and Evaluation Suite for {B}asque},
author={Julen Etxaniz and Oscar Sainz and Naiara Perez and Itziar Aldabe and German Rigau and Eneko Agirre and Aitor Ormazabal and Mikel Artetxe and Aitor Soroa},
year={2024},
eprint={2403.20266},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
gowitheflow/allnli-withnegs | ---
dataset_info:
features:
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dtype: string
- name: sentence2
dtype: string
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dtype: string
splits:
- name: train
num_bytes: 51457205
num_examples: 277277
download_size: 31419180
dataset_size: 51457205
---
# Dataset Card for "allnli-withnegs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
weqweasdas/rsf_pi0_iter1 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: type
dtype: string
- name: instances
list:
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dtype: string
- name: responses
sequence: string
- name: rewards
sequence: float64
splits:
- name: train
num_bytes: 149345268
num_examples: 1
download_size: 73422681
dataset_size: 149345268
---
# Dataset Card for "rsf_pi0_iter1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sccastillo/medquades | ---
task_categories:
- question-answering
language:
- es
tags:
- me
pretty_name: Medical Question Answer in Spanish
size_categories:
- 10K<n<100K
---
This datasets is a translated version to spanish of the original english dataset [medquad](https://paperswithcode.com/dataset/medquad).
This translations was made by [Gemini](https://gemini.google.com/app) free tier services using a simple script.
This tools is part of **alt** project.
For questions contact us: altbrainblock@gmail.com. |
h2oai/h2ogpt-oig-oasst1-instruct-cleaned-v2 | ---
license: apache-2.0
language:
- en
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
tags:
- gpt
- llm
- large language model
- open-source
---
# h2oGPT Data Card
## Summary
H2O.ai's `h2ogpt-oig-oasst1-instruct-cleaned-v2` is an open-source instruct-type dataset for fine-tuning of large language models, licensed for commercial use.
- Number of rows: `350581`
- Number of columns: `3`
- Column names: `['input', 'source', 'prompt_type']`
## Source
- [Original LAION OIG Dataset](https://github.com/LAION-AI/Open-Instruction-Generalist)
- [LAION OIG data detoxed and filtered down by scripts in h2oGPT repository](https://github.com/h2oai/h2ogpt/blob/main/FINETUNE.md#high-quality-oig-based-instruct-data)
- [Original Open Assistant data in tree structure](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [This flattened dataset created by script in h2oGPT repository](https://github.com/h2oai/h2ogpt/blob/0e70c2fbb16410bd8e6992d879b4c55cd981211f/create_data.py#L1375-L1415)
|
monology/ultrafeedback-liberated | ---
license: apache-2.0
---
Another clone of openbmb/UltraFeedback, with all completions by 'bard', 'gpt-3.5-turbo', or 'gpt-4' removed prior to binarization.
The annotations are still written by GPT4, so this dataset is neither OpenAI-free nor commercially-available.
If you're looking for an open-source DPO dataset, you may want to try nvidia/HelpSteer for the time being. |
Minata/512src_fm_fc_ms_ff_method2testcases_v0 | ---
dataset_info:
features:
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dtype: string
splits:
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num_examples: 46637
download_size: 67097367
dataset_size: 612560751.7180359
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
phar/111 | ---
license: odc-by
---
|
therapara/summary-of-news-articles_new | ---
dataset_info:
features:
- name: article
dtype: string
- name: highlights
dtype: string
- name: id
dtype: string
splits:
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num_examples: 13368
- name: test
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num_examples: 11490
download_size: 836361548
dataset_size: 1369361929
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
ktam204/ZaloAI | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 86452073.13
num_examples: 1362
download_size: 83935670
dataset_size: 86452073.13
---
# Dataset Card for "ZaloAI"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
gocer/bgg | ---
license: other
---
|
andrewsiah/rlhf | ---
license: cc-by-nc-4.0
dataset_info:
features:
- name: instruction
dtype: 'null'
- name: input
dtype: string
- name: output_1
dtype: string
- name: output_2
dtype: string
- name: preference
dtype: int64
splits:
- name: train
num_bytes: 10956107
num_examples: 8531
download_size: 6514579
dataset_size: 10956107
---
|
LenguajeNaturalAI/ClinTreatES | ---
dataset_info:
features:
- name: caso_clinico
dtype: string
- name: Tratamiento
dtype: string
- name: Especialidad
dtype: string
splits:
- name: train
num_bytes: 57002
num_examples: 62
download_size: 39357
dataset_size: 57002
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-nc-sa-4.0
task_categories:
- question-answering
- text-generation
- text2text-generation
language:
- es
tags:
- medical
- biology
pretty_name: ClinTreatES
size_categories:
- n<1K
---
## IntroducciΓ³n
Este corpus se ha construido con ayuda de profesionales del sector de la salud de diversos Γ‘mbitos: cardiologΓa, traumatologΓa, urgencias, psiquiatrΓa, neurologΓa, dermatologΓa, otorrino larongologΓa, anestesia.
## GuΓa de uso
Para trabajar con el corpus y poder evaluar LLMs, la idea es utilizar el siguiente template:
```python
prompt_template="""A partir del caso clΓnico que se expone a continuaciΓ³n y su diagnΓ³stico realizado por un mΓ©dico, tu tarea es la siguiente.
Como mΓ©dico experto, tu tarea es la de diseΓ±ar un tratamiento para el paciente descrito en el caso clΓnico en base a su diagnΓ³stico. Responde escueta y concisamente ΓΊnicamente con el tratamiento para el paciente.
Caso clΓnico: {caso_clinico}
DiagnΓ³stico: {diagnostico}
"""
# cΓ³mo usarlo con un LLM:
system_prompt = "Eres un experto en medicina que diseΓ±a tratamientos en base a casos clΓnicos y sus correspondientes diagnΓ³sticos."
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt_template.format(caso_clinico=caso_clinico, diagnostico=diagnostico)}
]
mssg = tokenizer.apply_chat_template(messages, tokenize=False)
```
## Licencia
Este dataset estΓ‘ distribuido con licencia [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
## AtribuciΓ³n del corpus
El corpus ha sido el resultado de una colaboraciΓ³n conjunta de [LenguajeNatural.AI](https://lenguajenatural.ai), [IE University](https://www.ie.edu/university/) y diversos profesionales de la salud.
 |
open-llm-leaderboard/details_monster119120__OpenHermes-2.5-Mistral-7B-new | ---
pretty_name: Evaluation run of monster119120/OpenHermes-2.5-Mistral-7B-new
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [monster119120/OpenHermes-2.5-Mistral-7B-new](https://huggingface.co/monster119120/OpenHermes-2.5-Mistral-7B-new)\
\ 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 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 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_monster119120__OpenHermes-2.5-Mistral-7B-new\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-05T14:38:03.798667](https://huggingface.co/datasets/open-llm-leaderboard/details_monster119120__OpenHermes-2.5-Mistral-7B-new/blob/main/results_2024-04-05T14-38-03.798667.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\": 0.6325336149683021,\n\
\ \"acc_stderr\": 0.032321944717252825,\n \"acc_norm\": 0.6389797814650734,\n\
\ \"acc_norm_stderr\": 0.03297373585265148,\n \"mc1\": 0.37576499388004897,\n\
\ \"mc1_stderr\": 0.016954584060214297,\n \"mc2\": 0.5440644746868005,\n\
\ \"mc2_stderr\": 0.015381286817547338\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6313993174061433,\n \"acc_stderr\": 0.014097810678042196,\n\
\ \"acc_norm\": 0.6749146757679181,\n \"acc_norm_stderr\": 0.013688147309729124\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6633140808603863,\n\
\ \"acc_stderr\": 0.004716106475905089,\n \"acc_norm\": 0.852320254929297,\n\
\ \"acc_norm_stderr\": 0.0035405716545956313\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\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\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\
\ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.02872750295788027,\n\
\ \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.02872750295788027\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\
\ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\
\ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"\
acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6011560693641619,\n\
\ \"acc_stderr\": 0.037336266553835096,\n \"acc_norm\": 0.6011560693641619,\n\
\ \"acc_norm_stderr\": 0.037336266553835096\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n\
\ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.032469569197899575,\n\
\ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.032469569197899575\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\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n\
\ \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42063492063492064,\n \"acc_stderr\": 0.025424835086924,\n \"acc_norm\"\
: 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086924\n },\n\
\ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.36,\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\": 0.7741935483870968,\n\
\ \"acc_stderr\": 0.023785577884181015,\n \"acc_norm\": 0.7741935483870968,\n\
\ \"acc_norm_stderr\": 0.023785577884181015\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5369458128078818,\n \"acc_stderr\": 0.035083705204426656,\n\
\ \"acc_norm\": 0.5369458128078818,\n \"acc_norm_stderr\": 0.035083705204426656\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.65,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\"\
: 0.65,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.031584153240477114,\n\
\ \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.031584153240477114\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"\
acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\
\ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6128205128205129,\n \"acc_stderr\": 0.024697216930878937,\n\
\ \"acc_norm\": 0.6128205128205129,\n \"acc_norm_stderr\": 0.024697216930878937\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3074074074074074,\n \"acc_stderr\": 0.028133252578815635,\n \
\ \"acc_norm\": 0.3074074074074074,\n \"acc_norm_stderr\": 0.028133252578815635\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6596638655462185,\n \"acc_stderr\": 0.030778057422931673,\n\
\ \"acc_norm\": 0.6596638655462185,\n \"acc_norm_stderr\": 0.030778057422931673\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\
acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8330275229357799,\n \"acc_stderr\": 0.01599015488507338,\n \"\
acc_norm\": 0.8330275229357799,\n \"acc_norm_stderr\": 0.01599015488507338\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5185185185185185,\n \"acc_stderr\": 0.03407632093854051,\n \"\
acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.03407632093854051\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7892156862745098,\n \"acc_stderr\": 0.02862654791243741,\n \"\
acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.02862654791243741\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \
\ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6995515695067265,\n\
\ \"acc_stderr\": 0.030769352008229143,\n \"acc_norm\": 0.6995515695067265,\n\
\ \"acc_norm_stderr\": 0.030769352008229143\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.03498149385462472,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.03498149385462472\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\
: 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7975460122699386,\n \"acc_stderr\": 0.031570650789119005,\n\
\ \"acc_norm\": 0.7975460122699386,\n \"acc_norm_stderr\": 0.031570650789119005\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\
\ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\
\ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\
\ \"acc_stderr\": 0.02250903393707781,\n \"acc_norm\": 0.8632478632478633,\n\
\ \"acc_norm_stderr\": 0.02250903393707781\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.822477650063857,\n\
\ \"acc_stderr\": 0.01366423099583483,\n \"acc_norm\": 0.822477650063857,\n\
\ \"acc_norm_stderr\": 0.01366423099583483\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7225433526011561,\n \"acc_stderr\": 0.024105712607754307,\n\
\ \"acc_norm\": 0.7225433526011561,\n \"acc_norm_stderr\": 0.024105712607754307\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.31843575418994413,\n\
\ \"acc_stderr\": 0.015581008080360274,\n \"acc_norm\": 0.31843575418994413,\n\
\ \"acc_norm_stderr\": 0.015581008080360274\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7516339869281046,\n \"acc_stderr\": 0.02473998135511359,\n\
\ \"acc_norm\": 0.7516339869281046,\n \"acc_norm_stderr\": 0.02473998135511359\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6881028938906752,\n\
\ \"acc_stderr\": 0.02631185807185416,\n \"acc_norm\": 0.6881028938906752,\n\
\ \"acc_norm_stderr\": 0.02631185807185416\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\"\
: 0.524822695035461,\n \"acc_stderr\": 0.029790719243829714,\n \"\
acc_norm\": 0.524822695035461,\n \"acc_norm_stderr\": 0.029790719243829714\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47196870925684486,\n\
\ \"acc_stderr\": 0.012750151802922435,\n \"acc_norm\": 0.47196870925684486,\n\
\ \"acc_norm_stderr\": 0.012750151802922435\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.028582709753898452,\n\
\ \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.028582709753898452\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6666666666666666,\n \"acc_stderr\": 0.019070985589687492,\n \
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.019070985589687492\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\
\ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\
\ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142773,\n\
\ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142773\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8059701492537313,\n\
\ \"acc_stderr\": 0.027962677604768907,\n \"acc_norm\": 0.8059701492537313,\n\
\ \"acc_norm_stderr\": 0.027962677604768907\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.37576499388004897,\n\
\ \"mc1_stderr\": 0.016954584060214297,\n \"mc2\": 0.5440644746868005,\n\
\ \"mc2_stderr\": 0.015381286817547338\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7821625887924231,\n \"acc_stderr\": 0.011601066079939324\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.33510235026535257,\n \
\ \"acc_stderr\": 0.013001948176422955\n }\n}\n```"
repo_url: https://huggingface.co/monster119120/OpenHermes-2.5-Mistral-7B-new
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|arc:challenge|25_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|arc:challenge|25_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|gsm8k|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|gsm8k|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hellaswag|10_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hellaswag|10_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-05T13-19-08.165956.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T14-38-03.798667.parquet'
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- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-05T14-38-03.798667.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-05T14-38-03.798667.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- '**/details_harness|winogrande|5_2024-04-05T13-19-08.165956.parquet'
- split: 2024_04_05T14_38_03.798667
path:
- '**/details_harness|winogrande|5_2024-04-05T14-38-03.798667.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-05T14-38-03.798667.parquet'
- config_name: results
data_files:
- split: 2024_04_05T13_19_08.165956
path:
- results_2024-04-05T13-19-08.165956.parquet
- split: 2024_04_05T14_38_03.798667
path:
- results_2024-04-05T14-38-03.798667.parquet
- split: latest
path:
- results_2024-04-05T14-38-03.798667.parquet
---
# Dataset Card for Evaluation run of monster119120/OpenHermes-2.5-Mistral-7B-new
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [monster119120/OpenHermes-2.5-Mistral-7B-new](https://huggingface.co/monster119120/OpenHermes-2.5-Mistral-7B-new) 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 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 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_monster119120__OpenHermes-2.5-Mistral-7B-new",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-05T14:38:03.798667](https://huggingface.co/datasets/open-llm-leaderboard/details_monster119120__OpenHermes-2.5-Mistral-7B-new/blob/main/results_2024-04-05T14-38-03.798667.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": 0.6325336149683021,
"acc_stderr": 0.032321944717252825,
"acc_norm": 0.6389797814650734,
"acc_norm_stderr": 0.03297373585265148,
"mc1": 0.37576499388004897,
"mc1_stderr": 0.016954584060214297,
"mc2": 0.5440644746868005,
"mc2_stderr": 0.015381286817547338
},
"harness|arc:challenge|25": {
"acc": 0.6313993174061433,
"acc_stderr": 0.014097810678042196,
"acc_norm": 0.6749146757679181,
"acc_norm_stderr": 0.013688147309729124
},
"harness|hellaswag|10": {
"acc": 0.6633140808603863,
"acc_stderr": 0.004716106475905089,
"acc_norm": 0.852320254929297,
"acc_norm_stderr": 0.0035405716545956313
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252606
},
"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": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6792452830188679,
"acc_stderr": 0.02872750295788027,
"acc_norm": 0.6792452830188679,
"acc_norm_stderr": 0.02872750295788027
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7638888888888888,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6011560693641619,
"acc_stderr": 0.037336266553835096,
"acc_norm": 0.6011560693641619,
"acc_norm_stderr": 0.037336266553835096
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.04897104952726366,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.04897104952726366
},
"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": 0.5574468085106383,
"acc_stderr": 0.032469569197899575,
"acc_norm": 0.5574468085106383,
"acc_norm_stderr": 0.032469569197899575
},
"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": 0.5310344827586206,
"acc_stderr": 0.04158632762097828,
"acc_norm": 0.5310344827586206,
"acc_norm_stderr": 0.04158632762097828
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42063492063492064,
"acc_stderr": 0.025424835086924,
"acc_norm": 0.42063492063492064,
"acc_norm_stderr": 0.025424835086924
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.04444444444444449,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.04444444444444449
},
"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": 0.7741935483870968,
"acc_stderr": 0.023785577884181015,
"acc_norm": 0.7741935483870968,
"acc_norm_stderr": 0.023785577884181015
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5369458128078818,
"acc_stderr": 0.035083705204426656,
"acc_norm": 0.5369458128078818,
"acc_norm_stderr": 0.035083705204426656
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.65,
"acc_stderr": 0.04793724854411019,
"acc_norm": 0.65,
"acc_norm_stderr": 0.04793724854411019
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.793939393939394,
"acc_stderr": 0.031584153240477114,
"acc_norm": 0.793939393939394,
"acc_norm_stderr": 0.031584153240477114
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.029126522834586815,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.029126522834586815
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8963730569948186,
"acc_stderr": 0.02199531196364424,
"acc_norm": 0.8963730569948186,
"acc_norm_stderr": 0.02199531196364424
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6128205128205129,
"acc_stderr": 0.024697216930878937,
"acc_norm": 0.6128205128205129,
"acc_norm_stderr": 0.024697216930878937
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3074074074074074,
"acc_stderr": 0.028133252578815635,
"acc_norm": 0.3074074074074074,
"acc_norm_stderr": 0.028133252578815635
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6596638655462185,
"acc_stderr": 0.030778057422931673,
"acc_norm": 0.6596638655462185,
"acc_norm_stderr": 0.030778057422931673
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31788079470198677,
"acc_stderr": 0.038020397601079024,
"acc_norm": 0.31788079470198677,
"acc_norm_stderr": 0.038020397601079024
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8330275229357799,
"acc_stderr": 0.01599015488507338,
"acc_norm": 0.8330275229357799,
"acc_norm_stderr": 0.01599015488507338
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5185185185185185,
"acc_stderr": 0.03407632093854051,
"acc_norm": 0.5185185185185185,
"acc_norm_stderr": 0.03407632093854051
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7892156862745098,
"acc_stderr": 0.02862654791243741,
"acc_norm": 0.7892156862745098,
"acc_norm_stderr": 0.02862654791243741
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8016877637130801,
"acc_stderr": 0.02595502084162113,
"acc_norm": 0.8016877637130801,
"acc_norm_stderr": 0.02595502084162113
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6995515695067265,
"acc_stderr": 0.030769352008229143,
"acc_norm": 0.6995515695067265,
"acc_norm_stderr": 0.030769352008229143
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8015267175572519,
"acc_stderr": 0.03498149385462472,
"acc_norm": 0.8015267175572519,
"acc_norm_stderr": 0.03498149385462472
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.768595041322314,
"acc_stderr": 0.03849856098794088,
"acc_norm": 0.768595041322314,
"acc_norm_stderr": 0.03849856098794088
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7685185185185185,
"acc_stderr": 0.04077494709252626,
"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252626
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7975460122699386,
"acc_stderr": 0.031570650789119005,
"acc_norm": 0.7975460122699386,
"acc_norm_stderr": 0.031570650789119005
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.48214285714285715,
"acc_stderr": 0.047427623612430116,
"acc_norm": 0.48214285714285715,
"acc_norm_stderr": 0.047427623612430116
},
"harness|hendrycksTest-management|5": {
"acc": 0.7766990291262136,
"acc_stderr": 0.04123553189891431,
"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8632478632478633,
"acc_stderr": 0.02250903393707781,
"acc_norm": 0.8632478632478633,
"acc_norm_stderr": 0.02250903393707781
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.822477650063857,
"acc_stderr": 0.01366423099583483,
"acc_norm": 0.822477650063857,
"acc_norm_stderr": 0.01366423099583483
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7225433526011561,
"acc_stderr": 0.024105712607754307,
"acc_norm": 0.7225433526011561,
"acc_norm_stderr": 0.024105712607754307
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.31843575418994413,
"acc_stderr": 0.015581008080360274,
"acc_norm": 0.31843575418994413,
"acc_norm_stderr": 0.015581008080360274
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7516339869281046,
"acc_stderr": 0.02473998135511359,
"acc_norm": 0.7516339869281046,
"acc_norm_stderr": 0.02473998135511359
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6881028938906752,
"acc_stderr": 0.02631185807185416,
"acc_norm": 0.6881028938906752,
"acc_norm_stderr": 0.02631185807185416
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.75,
"acc_stderr": 0.02409347123262133,
"acc_norm": 0.75,
"acc_norm_stderr": 0.02409347123262133
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.524822695035461,
"acc_stderr": 0.029790719243829714,
"acc_norm": 0.524822695035461,
"acc_norm_stderr": 0.029790719243829714
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.47196870925684486,
"acc_stderr": 0.012750151802922435,
"acc_norm": 0.47196870925684486,
"acc_norm_stderr": 0.012750151802922435
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6691176470588235,
"acc_stderr": 0.028582709753898452,
"acc_norm": 0.6691176470588235,
"acc_norm_stderr": 0.028582709753898452
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.019070985589687492,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.019070985589687492
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6454545454545455,
"acc_stderr": 0.045820048415054174,
"acc_norm": 0.6454545454545455,
"acc_norm_stderr": 0.045820048415054174
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7387755102040816,
"acc_stderr": 0.028123429335142773,
"acc_norm": 0.7387755102040816,
"acc_norm_stderr": 0.028123429335142773
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8059701492537313,
"acc_stderr": 0.027962677604768907,
"acc_norm": 0.8059701492537313,
"acc_norm_stderr": 0.027962677604768907
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.84,
"acc_stderr": 0.03684529491774709,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774709
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5542168674698795,
"acc_stderr": 0.03869543323472101,
"acc_norm": 0.5542168674698795,
"acc_norm_stderr": 0.03869543323472101
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8362573099415205,
"acc_stderr": 0.028380919596145866,
"acc_norm": 0.8362573099415205,
"acc_norm_stderr": 0.028380919596145866
},
"harness|truthfulqa:mc|0": {
"mc1": 0.37576499388004897,
"mc1_stderr": 0.016954584060214297,
"mc2": 0.5440644746868005,
"mc2_stderr": 0.015381286817547338
},
"harness|winogrande|5": {
"acc": 0.7821625887924231,
"acc_stderr": 0.011601066079939324
},
"harness|gsm8k|5": {
"acc": 0.33510235026535257,
"acc_stderr": 0.013001948176422955
}
}
```
## 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. -->
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#### 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. -->
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- 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]
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## Glossary [optional]
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open-llm-leaderboard/details_namirocks__mistral-class-tutor-7b-ep3 | ---
pretty_name: Evaluation run of namirocks/mistral-class-tutor-7b-ep3
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [namirocks/mistral-class-tutor-7b-ep3](https://huggingface.co/namirocks/mistral-class-tutor-7b-ep3)\
\ 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_namirocks__mistral-class-tutor-7b-ep3\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-28T04:43:25.423424](https://huggingface.co/datasets/open-llm-leaderboard/details_namirocks__mistral-class-tutor-7b-ep3/blob/main/results_2024-01-28T04-43-25.423424.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\": 0.35188002700077603,\n\
\ \"acc_stderr\": 0.03324003622022026,\n \"acc_norm\": 0.3552501411887151,\n\
\ \"acc_norm_stderr\": 0.034139661213265685,\n \"mc1\": 0.31946144430844553,\n\
\ \"mc1_stderr\": 0.0163226441829605,\n \"mc2\": 0.44694459481000054,\n\
\ \"mc2_stderr\": 0.015615857910542796\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.4564846416382253,\n \"acc_stderr\": 0.014555949760496442,\n\
\ \"acc_norm\": 0.47952218430034127,\n \"acc_norm_stderr\": 0.014599131353035005\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5909181437960566,\n\
\ \"acc_stderr\": 0.004906595857916764,\n \"acc_norm\": 0.7780322644891456,\n\
\ \"acc_norm_stderr\": 0.004147202539759585\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\": 0.362962962962963,\n\
\ \"acc_stderr\": 0.041539484047424,\n \"acc_norm\": 0.362962962962963,\n\
\ \"acc_norm_stderr\": 0.041539484047424\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.23026315789473684,\n \"acc_stderr\": 0.03426059424403165,\n\
\ \"acc_norm\": 0.23026315789473684,\n \"acc_norm_stderr\": 0.03426059424403165\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.37,\n\
\ \"acc_stderr\": 0.04852365870939098,\n \"acc_norm\": 0.37,\n \
\ \"acc_norm_stderr\": 0.04852365870939098\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.33584905660377357,\n \"acc_stderr\": 0.029067220146644826,\n\
\ \"acc_norm\": 0.33584905660377357,\n \"acc_norm_stderr\": 0.029067220146644826\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4375,\n\
\ \"acc_stderr\": 0.04148415739394154,\n \"acc_norm\": 0.4375,\n \
\ \"acc_norm_stderr\": 0.04148415739394154\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\"\
: 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n\
\ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.2832369942196532,\n\
\ \"acc_stderr\": 0.03435568056047874,\n \"acc_norm\": 0.2832369942196532,\n\
\ \"acc_norm_stderr\": 0.03435568056047874\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237655,\n\
\ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237655\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.43,\n\
\ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.3276595744680851,\n \"acc_stderr\": 0.030683020843231008,\n\
\ \"acc_norm\": 0.3276595744680851,\n \"acc_norm_stderr\": 0.030683020843231008\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n\
\ \"acc_stderr\": 0.04266339443159394,\n \"acc_norm\": 0.2894736842105263,\n\
\ \"acc_norm_stderr\": 0.04266339443159394\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2620689655172414,\n \"acc_stderr\": 0.036646663372252565,\n\
\ \"acc_norm\": 0.2620689655172414,\n \"acc_norm_stderr\": 0.036646663372252565\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2804232804232804,\n \"acc_stderr\": 0.02313528797432563,\n \"\
acc_norm\": 0.2804232804232804,\n \"acc_norm_stderr\": 0.02313528797432563\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3888888888888889,\n\
\ \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n\
\ \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.3032258064516129,\n\
\ \"acc_stderr\": 0.02614868593067175,\n \"acc_norm\": 0.3032258064516129,\n\
\ \"acc_norm_stderr\": 0.02614868593067175\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.2561576354679803,\n \"acc_stderr\": 0.030712730070982592,\n\
\ \"acc_norm\": 0.2561576354679803,\n \"acc_norm_stderr\": 0.030712730070982592\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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_european_history|5\"\
: {\n \"acc\": 0.47878787878787876,\n \"acc_stderr\": 0.03900828913737301,\n\
\ \"acc_norm\": 0.47878787878787876,\n \"acc_norm_stderr\": 0.03900828913737301\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.47474747474747475,\n \"acc_stderr\": 0.035578062450873145,\n \"\
acc_norm\": 0.47474747474747475,\n \"acc_norm_stderr\": 0.035578062450873145\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.5233160621761658,\n \"acc_stderr\": 0.03604513672442202,\n\
\ \"acc_norm\": 0.5233160621761658,\n \"acc_norm_stderr\": 0.03604513672442202\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.36666666666666664,\n \"acc_stderr\": 0.024433016466052462,\n\
\ \"acc_norm\": 0.36666666666666664,\n \"acc_norm_stderr\": 0.024433016466052462\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.25555555555555554,\n \"acc_stderr\": 0.026593939101844065,\n \
\ \"acc_norm\": 0.25555555555555554,\n \"acc_norm_stderr\": 0.026593939101844065\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.31512605042016806,\n \"acc_stderr\": 0.03017680828897434,\n\
\ \"acc_norm\": 0.31512605042016806,\n \"acc_norm_stderr\": 0.03017680828897434\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.24503311258278146,\n \"acc_stderr\": 0.035118075718047245,\n \"\
acc_norm\": 0.24503311258278146,\n \"acc_norm_stderr\": 0.035118075718047245\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.3926605504587156,\n \"acc_stderr\": 0.020937505161201093,\n \"\
acc_norm\": 0.3926605504587156,\n \"acc_norm_stderr\": 0.020937505161201093\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.25925925925925924,\n \"acc_stderr\": 0.029886910547626974,\n \"\
acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.029886910547626974\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.553921568627451,\n \"acc_stderr\": 0.034888454513049734,\n \"\
acc_norm\": 0.553921568627451,\n \"acc_norm_stderr\": 0.034888454513049734\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.5063291139240507,\n \"acc_stderr\": 0.032544620107678585,\n \
\ \"acc_norm\": 0.5063291139240507,\n \"acc_norm_stderr\": 0.032544620107678585\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.452914798206278,\n\
\ \"acc_stderr\": 0.03340867501923324,\n \"acc_norm\": 0.452914798206278,\n\
\ \"acc_norm_stderr\": 0.03340867501923324\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.366412213740458,\n \"acc_stderr\": 0.04225875451969638,\n\
\ \"acc_norm\": 0.366412213740458,\n \"acc_norm_stderr\": 0.04225875451969638\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.2809917355371901,\n \"acc_stderr\": 0.04103203830514512,\n \"\
acc_norm\": 0.2809917355371901,\n \"acc_norm_stderr\": 0.04103203830514512\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.3055555555555556,\n\
\ \"acc_stderr\": 0.04453197507374983,\n \"acc_norm\": 0.3055555555555556,\n\
\ \"acc_norm_stderr\": 0.04453197507374983\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.3374233128834356,\n \"acc_stderr\": 0.03714908409935575,\n\
\ \"acc_norm\": 0.3374233128834356,\n \"acc_norm_stderr\": 0.03714908409935575\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.33035714285714285,\n\
\ \"acc_stderr\": 0.04464285714285714,\n \"acc_norm\": 0.33035714285714285,\n\
\ \"acc_norm_stderr\": 0.04464285714285714\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.3300970873786408,\n \"acc_stderr\": 0.0465614711001235,\n\
\ \"acc_norm\": 0.3300970873786408,\n \"acc_norm_stderr\": 0.0465614711001235\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.44871794871794873,\n\
\ \"acc_stderr\": 0.032583346493868806,\n \"acc_norm\": 0.44871794871794873,\n\
\ \"acc_norm_stderr\": 0.032583346493868806\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.049888765156985884,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.049888765156985884\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.01776925058353325,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.01776925058353325\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.2947976878612717,\n \"acc_stderr\": 0.024547617794803838,\n\
\ \"acc_norm\": 0.2947976878612717,\n \"acc_norm_stderr\": 0.024547617794803838\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2569832402234637,\n\
\ \"acc_stderr\": 0.014614465821966342,\n \"acc_norm\": 0.2569832402234637,\n\
\ \"acc_norm_stderr\": 0.014614465821966342\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.2973856209150327,\n \"acc_stderr\": 0.02617390850671858,\n\
\ \"acc_norm\": 0.2973856209150327,\n \"acc_norm_stderr\": 0.02617390850671858\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.31189710610932475,\n\
\ \"acc_stderr\": 0.02631185807185416,\n \"acc_norm\": 0.31189710610932475,\n\
\ \"acc_norm_stderr\": 0.02631185807185416\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.25617283950617287,\n \"acc_stderr\": 0.024288533637726095,\n\
\ \"acc_norm\": 0.25617283950617287,\n \"acc_norm_stderr\": 0.024288533637726095\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.2553191489361702,\n \"acc_stderr\": 0.026011992930902013,\n \
\ \"acc_norm\": 0.2553191489361702,\n \"acc_norm_stderr\": 0.026011992930902013\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2522816166883963,\n\
\ \"acc_stderr\": 0.011092789056875234,\n \"acc_norm\": 0.2522816166883963,\n\
\ \"acc_norm_stderr\": 0.011092789056875234\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4522058823529412,\n \"acc_stderr\": 0.03023375855159645,\n\
\ \"acc_norm\": 0.4522058823529412,\n \"acc_norm_stderr\": 0.03023375855159645\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.3284313725490196,\n \"acc_stderr\": 0.018999707383162666,\n \
\ \"acc_norm\": 0.3284313725490196,\n \"acc_norm_stderr\": 0.018999707383162666\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.3181818181818182,\n\
\ \"acc_stderr\": 0.04461272175910508,\n \"acc_norm\": 0.3181818181818182,\n\
\ \"acc_norm_stderr\": 0.04461272175910508\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.27755102040816326,\n \"acc_stderr\": 0.028666857790274648,\n\
\ \"acc_norm\": 0.27755102040816326,\n \"acc_norm_stderr\": 0.028666857790274648\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5373134328358209,\n\
\ \"acc_stderr\": 0.03525675167467974,\n \"acc_norm\": 0.5373134328358209,\n\
\ \"acc_norm_stderr\": 0.03525675167467974\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.3373493975903614,\n\
\ \"acc_stderr\": 0.03680783690727581,\n \"acc_norm\": 0.3373493975903614,\n\
\ \"acc_norm_stderr\": 0.03680783690727581\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.5146198830409356,\n \"acc_stderr\": 0.038331852752130254,\n\
\ \"acc_norm\": 0.5146198830409356,\n \"acc_norm_stderr\": 0.038331852752130254\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.31946144430844553,\n\
\ \"mc1_stderr\": 0.0163226441829605,\n \"mc2\": 0.44694459481000054,\n\
\ \"mc2_stderr\": 0.015615857910542796\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7150749802683505,\n \"acc_stderr\": 0.012685986125141236\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n }\n}\n```"
repo_url: https://huggingface.co/namirocks/mistral-class-tutor-7b-ep3
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_28T04_43_25.423424
path:
- '**/details_harness|arc:challenge|25_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|gsm8k|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hellaswag|10_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-28T04-43-25.423424.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-28T04-43-25.423424.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- '**/details_harness|winogrande|5_2024-01-28T04-43-25.423424.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-28T04-43-25.423424.parquet'
- config_name: results
data_files:
- split: 2024_01_28T04_43_25.423424
path:
- results_2024-01-28T04-43-25.423424.parquet
- split: latest
path:
- results_2024-01-28T04-43-25.423424.parquet
---
# Dataset Card for Evaluation run of namirocks/mistral-class-tutor-7b-ep3
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [namirocks/mistral-class-tutor-7b-ep3](https://huggingface.co/namirocks/mistral-class-tutor-7b-ep3) 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_namirocks__mistral-class-tutor-7b-ep3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-28T04:43:25.423424](https://huggingface.co/datasets/open-llm-leaderboard/details_namirocks__mistral-class-tutor-7b-ep3/blob/main/results_2024-01-28T04-43-25.423424.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": 0.35188002700077603,
"acc_stderr": 0.03324003622022026,
"acc_norm": 0.3552501411887151,
"acc_norm_stderr": 0.034139661213265685,
"mc1": 0.31946144430844553,
"mc1_stderr": 0.0163226441829605,
"mc2": 0.44694459481000054,
"mc2_stderr": 0.015615857910542796
},
"harness|arc:challenge|25": {
"acc": 0.4564846416382253,
"acc_stderr": 0.014555949760496442,
"acc_norm": 0.47952218430034127,
"acc_norm_stderr": 0.014599131353035005
},
"harness|hellaswag|10": {
"acc": 0.5909181437960566,
"acc_stderr": 0.004906595857916764,
"acc_norm": 0.7780322644891456,
"acc_norm_stderr": 0.004147202539759585
},
"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": 0.362962962962963,
"acc_stderr": 0.041539484047424,
"acc_norm": 0.362962962962963,
"acc_norm_stderr": 0.041539484047424
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.23026315789473684,
"acc_stderr": 0.03426059424403165,
"acc_norm": 0.23026315789473684,
"acc_norm_stderr": 0.03426059424403165
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939098,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939098
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.33584905660377357,
"acc_stderr": 0.029067220146644826,
"acc_norm": 0.33584905660377357,
"acc_norm_stderr": 0.029067220146644826
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
redwoodresearch/diamonds-seed3 | ---
dataset_info:
features:
- name: text
dtype: string
- name: is_correct
dtype: bool
- name: is_clean
dtype: bool
- name: measurements
sequence: bool
- name: difficulty
dtype: int64
splits:
- name: train
num_bytes: 63076220
num_examples: 25000
- name: validation
num_bytes: 19775096
num_examples: 7989
- name: train_for_val
num_bytes: 7682272
num_examples: 2997
download_size: 1135193
dataset_size: 90533588
---
# Dataset Card for "diamonds-seed3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dangkhoadl/ICASSP2024-Acoustic_Scattering_AI-Noninvasive_Object_Classifications | ---
license: apache-2.0
---
|
bilalelmanja/six_sigma | ---
license: mit
---
|
thdangtr/xsum_10_percents | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: document
dtype: string
- name: summary
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 47919462.033629835
num_examples: 20404
- name: validation
num_bytes: 2628823.6534592304
num_examples: 1133
- name: test
num_bytes: 2674669.821157579
num_examples: 1133
download_size: 33669166
dataset_size: 53222955.508246645
---
# Dataset Card for "xsum_10_percents"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vwxyzjn/ultrachat_200k_filtered_1708034814 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: query
list:
- name: content
dtype: string
- name: role
dtype: string
- name: query_token
sequence: int64
- name: query_reference_response
list:
- name: content
dtype: string
- name: role
dtype: string
- name: query_reference_response_token
sequence: int64
- name: query_reference_response_token_len
dtype: int64
- name: query_token_len
dtype: int64
- name: reference_response
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: reference_response_token
sequence: int64
- name: reference_response_token_len
dtype: int64
splits:
- name: test_sft
num_bytes: 1982888370.9168758
num_examples: 22991
- name: train_sft
num_bytes: 17846869528.524822
num_examples: 206698
download_size: 3301659997
dataset_size: 19829757899.441696
---
# Args
```python
{'base_model': 'mistralai/Mistral-7B-v0.1',
'check_length_correctness': True,
'debug': False,
'hf_entity': 'vwxyzjn',
'params': TaskQueryHParams(length=3000,
format_str='SUBREDDIT: r/{subreddit}\n'
'\n'
'TITLE: {title}\n'
'\n'
'POST: {post}\n'
'\n'
'TL;DR:',
truncate_field='post',
truncate_text='\n',
padding='pad_token',
pad_token=[32000],
pad_side='left',
max_query_length=3000,
max_sft_query_response_length=4000,
max_sft_response_length=1500,
max_rm_query_response_length=4500,
max_rm_response_length=1500),
'push_to_hub': True}
```
|
hen8001/cotton_crop_project_data | ---
license: other
---
|
james-burton/wine_reviews_ordinal | ---
dataset_info:
features:
- name: country
dtype: string
- name: description
dtype: string
- name: points
dtype: int64
- name: price
dtype: float64
- name: province
dtype: string
- name: variety
dtype: int64
splits:
- name: train
num_bytes: 21009429
num_examples: 71504
- name: validation
num_bytes: 3706451
num_examples: 12619
- name: test
num_bytes: 6180000
num_examples: 21031
download_size: 0
dataset_size: 30895880
---
# Dataset Card for "wine_reviews_ordinal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jlbaker361/vanilla-ddpo-evaluation20 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: image
dtype: image
- name: model
dtype: string
- name: score
dtype: float32
splits:
- name: train
num_bytes: 493378.0
num_examples: 1
download_size: 495932
dataset_size: 493378.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
tj-solergibert/SRV-T5-Europarl-mt-en | ---
dataset_info:
features:
- name: source_text
dtype: string
- name: dest_text
dtype: string
- name: dest_lang
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 622455263
num_examples: 561067
- name: valid
num_bytes: 86644778
num_examples: 76911
- name: test
num_bytes: 91426551
num_examples: 80606
download_size: 267356339
dataset_size: 800526592
---
# Dataset Card for "SRV-T5-Europarl-mt-en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DazMashaly/zindi | ---
license: apache-2.0
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': DR
'1': G
'2': ND
'3': WD
'4': other
splits:
- name: train
num_bytes: 7315670571.4
num_examples: 27900
- name: test
num_bytes: 1379422283.145
num_examples: 4757
download_size: 8719347102
dataset_size: 8695092854.545
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
parambharat/telugu_asr_corpus | ---
annotations_creators:
- found
language:
- te
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Telugu ASR Corpus
size_categories:
- 100K<n<1M
source_datasets:
- extended|openslr
tags: []
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for [Telugu Asr Corpus]
## 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:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### 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
Thanks to [@parambharat](https://github.com/parambharat) for adding this dataset. |
open-llm-leaderboard/details_mncai__agiin-13.6B-v0.0 | ---
pretty_name: Evaluation run of mncai/agiin-13.6B-v0.0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [mncai/agiin-13.6B-v0.0](https://huggingface.co/mncai/agiin-13.6B-v0.0) 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_mncai__agiin-13.6B-v0.0\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-16T15:55:21.950393](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__agiin-13.6B-v0.0/blob/main/results_2023-12-16T15-55-21.950393.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\": 0.621527215806331,\n\
\ \"acc_stderr\": 0.03309044810009566,\n \"acc_norm\": 0.6248205476117454,\n\
\ \"acc_norm_stderr\": 0.03375647243509085,\n \"mc1\": 0.5165238678090576,\n\
\ \"mc1_stderr\": 0.017493940190057723,\n \"mc2\": 0.6740086972319943,\n\
\ \"mc2_stderr\": 0.015471222805293889\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.659556313993174,\n \"acc_stderr\": 0.013847460518892973,\n\
\ \"acc_norm\": 0.6945392491467577,\n \"acc_norm_stderr\": 0.013460080478002508\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6858195578570006,\n\
\ \"acc_stderr\": 0.0046323996774908106,\n \"acc_norm\": 0.8658633738299144,\n\
\ \"acc_norm_stderr\": 0.0034010255178737237\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.562962962962963,\n\
\ \"acc_stderr\": 0.042849586397534015,\n \"acc_norm\": 0.562962962962963,\n\
\ \"acc_norm_stderr\": 0.042849586397534015\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \
\ \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\
\ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \
\ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6339622641509434,\n \"acc_stderr\": 0.029647813539365245,\n\
\ \"acc_norm\": 0.6339622641509434,\n \"acc_norm_stderr\": 0.029647813539365245\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\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n\
\ \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6184971098265896,\n\
\ \"acc_stderr\": 0.037038511930995215,\n \"acc_norm\": 0.6184971098265896,\n\
\ \"acc_norm_stderr\": 0.037038511930995215\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n\
\ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.68,\n\
\ \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.032500536843658404,\n\
\ \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.032500536843658404\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\": 0.6068965517241379,\n \"acc_stderr\": 0.040703290137070705,\n\
\ \"acc_norm\": 0.6068965517241379,\n \"acc_norm_stderr\": 0.040703290137070705\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41798941798941797,\n \"acc_stderr\": 0.025402555503260912,\n \"\
acc_norm\": 0.41798941798941797,\n \"acc_norm_stderr\": 0.025402555503260912\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\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\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7354838709677419,\n\
\ \"acc_stderr\": 0.02509189237885928,\n \"acc_norm\": 0.7354838709677419,\n\
\ \"acc_norm_stderr\": 0.02509189237885928\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\"\
: 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.03192271569548301,\n\
\ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.03192271569548301\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"\
acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8393782383419689,\n \"acc_stderr\": 0.026499057701397443,\n\
\ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.026499057701397443\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6512820512820513,\n \"acc_stderr\": 0.02416278028401772,\n \
\ \"acc_norm\": 0.6512820512820513,\n \"acc_norm_stderr\": 0.02416278028401772\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.35185185185185186,\n \"acc_stderr\": 0.02911661760608301,\n \
\ \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.02911661760608301\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6386554621848739,\n \"acc_stderr\": 0.03120469122515001,\n \
\ \"acc_norm\": 0.6386554621848739,\n \"acc_norm_stderr\": 0.03120469122515001\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3443708609271523,\n \"acc_stderr\": 0.03879687024073327,\n \"\
acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.03879687024073327\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8201834862385321,\n \"acc_stderr\": 0.01646534546739152,\n \"\
acc_norm\": 0.8201834862385321,\n \"acc_norm_stderr\": 0.01646534546739152\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5787037037037037,\n \"acc_stderr\": 0.033674621388960775,\n \"\
acc_norm\": 0.5787037037037037,\n \"acc_norm_stderr\": 0.033674621388960775\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8088235294117647,\n \"acc_stderr\": 0.027599174300640766,\n \"\
acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.027599174300640766\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7890295358649789,\n \"acc_stderr\": 0.02655837250266192,\n \
\ \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.02655837250266192\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\
\ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\
\ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7022900763358778,\n \"acc_stderr\": 0.040103589424622034,\n\
\ \"acc_norm\": 0.7022900763358778,\n \"acc_norm_stderr\": 0.040103589424622034\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\": 0.75,\n\
\ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.04186091791394607\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\": 0.5,\n\
\ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\
\ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8418803418803419,\n\
\ \"acc_stderr\": 0.023902325549560417,\n \"acc_norm\": 0.8418803418803419,\n\
\ \"acc_norm_stderr\": 0.023902325549560417\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7484035759897829,\n\
\ \"acc_stderr\": 0.015517322365529633,\n \"acc_norm\": 0.7484035759897829,\n\
\ \"acc_norm_stderr\": 0.015517322365529633\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6878612716763006,\n \"acc_stderr\": 0.024946792225272314,\n\
\ \"acc_norm\": 0.6878612716763006,\n \"acc_norm_stderr\": 0.024946792225272314\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4692737430167598,\n\
\ \"acc_stderr\": 0.01669089616194438,\n \"acc_norm\": 0.4692737430167598,\n\
\ \"acc_norm_stderr\": 0.01669089616194438\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.02699254433929724,\n\
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.02699254433929724\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6784565916398714,\n\
\ \"acc_stderr\": 0.026527724079528872,\n \"acc_norm\": 0.6784565916398714,\n\
\ \"acc_norm_stderr\": 0.026527724079528872\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6882716049382716,\n \"acc_stderr\": 0.02577311116963045,\n\
\ \"acc_norm\": 0.6882716049382716,\n \"acc_norm_stderr\": 0.02577311116963045\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.44680851063829785,\n \"acc_stderr\": 0.029658235097666904,\n \
\ \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.029658235097666904\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47392438070404175,\n\
\ \"acc_stderr\": 0.012752858346533133,\n \"acc_norm\": 0.47392438070404175,\n\
\ \"acc_norm_stderr\": 0.012752858346533133\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6397058823529411,\n \"acc_stderr\": 0.029163128570670733,\n\
\ \"acc_norm\": 0.6397058823529411,\n \"acc_norm_stderr\": 0.029163128570670733\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6323529411764706,\n \"acc_stderr\": 0.019506291693954854,\n \
\ \"acc_norm\": 0.6323529411764706,\n \"acc_norm_stderr\": 0.019506291693954854\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n\
\ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \
\ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.636734693877551,\n \"acc_stderr\": 0.03078905113903081,\n\
\ \"acc_norm\": 0.636734693877551,\n \"acc_norm_stderr\": 0.03078905113903081\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\
\ \"acc_stderr\": 0.026508590656233264,\n \"acc_norm\": 0.8308457711442786,\n\
\ \"acc_norm_stderr\": 0.026508590656233264\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036623,\n \
\ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036623\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5120481927710844,\n\
\ \"acc_stderr\": 0.03891364495835817,\n \"acc_norm\": 0.5120481927710844,\n\
\ \"acc_norm_stderr\": 0.03891364495835817\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.783625730994152,\n \"acc_stderr\": 0.031581495393387324,\n\
\ \"acc_norm\": 0.783625730994152,\n \"acc_norm_stderr\": 0.031581495393387324\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5165238678090576,\n\
\ \"mc1_stderr\": 0.017493940190057723,\n \"mc2\": 0.6740086972319943,\n\
\ \"mc2_stderr\": 0.015471222805293889\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7868981846882399,\n \"acc_stderr\": 0.011508957690722743\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.47687642153146326,\n \
\ \"acc_stderr\": 0.013757748544245331\n }\n}\n```"
repo_url: https://huggingface.co/mncai/agiin-13.6B-v0.0
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_12_16T15_55_21.950393
path:
- '**/details_harness|arc:challenge|25_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|gsm8k|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hellaswag|10_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T15-55-21.950393.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-16T15-55-21.950393.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- '**/details_harness|winogrande|5_2023-12-16T15-55-21.950393.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-16T15-55-21.950393.parquet'
- config_name: results
data_files:
- split: 2023_12_16T15_55_21.950393
path:
- results_2023-12-16T15-55-21.950393.parquet
- split: latest
path:
- results_2023-12-16T15-55-21.950393.parquet
---
# Dataset Card for Evaluation run of mncai/agiin-13.6B-v0.0
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [mncai/agiin-13.6B-v0.0](https://huggingface.co/mncai/agiin-13.6B-v0.0) 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_mncai__agiin-13.6B-v0.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-16T15:55:21.950393](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__agiin-13.6B-v0.0/blob/main/results_2023-12-16T15-55-21.950393.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": 0.621527215806331,
"acc_stderr": 0.03309044810009566,
"acc_norm": 0.6248205476117454,
"acc_norm_stderr": 0.03375647243509085,
"mc1": 0.5165238678090576,
"mc1_stderr": 0.017493940190057723,
"mc2": 0.6740086972319943,
"mc2_stderr": 0.015471222805293889
},
"harness|arc:challenge|25": {
"acc": 0.659556313993174,
"acc_stderr": 0.013847460518892973,
"acc_norm": 0.6945392491467577,
"acc_norm_stderr": 0.013460080478002508
},
"harness|hellaswag|10": {
"acc": 0.6858195578570006,
"acc_stderr": 0.0046323996774908106,
"acc_norm": 0.8658633738299144,
"acc_norm_stderr": 0.0034010255178737237
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.562962962962963,
"acc_stderr": 0.042849586397534015,
"acc_norm": 0.562962962962963,
"acc_norm_stderr": 0.042849586397534015
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.625,
"acc_stderr": 0.039397364351956274,
"acc_norm": 0.625,
"acc_norm_stderr": 0.039397364351956274
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6339622641509434,
"acc_stderr": 0.029647813539365245,
"acc_norm": 0.6339622641509434,
"acc_norm_stderr": 0.029647813539365245
},
"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": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6184971098265896,
"acc_stderr": 0.037038511930995215,
"acc_norm": 0.6184971098265896,
"acc_norm_stderr": 0.037038511930995215
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.048971049527263666,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.048971049527263666
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5531914893617021,
"acc_stderr": 0.032500536843658404,
"acc_norm": 0.5531914893617021,
"acc_norm_stderr": 0.032500536843658404
},
"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": 0.6068965517241379,
"acc_stderr": 0.040703290137070705,
"acc_norm": 0.6068965517241379,
"acc_norm_stderr": 0.040703290137070705
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41798941798941797,
"acc_stderr": 0.025402555503260912,
"acc_norm": 0.41798941798941797,
"acc_norm_stderr": 0.025402555503260912
},
"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": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7354838709677419,
"acc_stderr": 0.02509189237885928,
"acc_norm": 0.7354838709677419,
"acc_norm_stderr": 0.02509189237885928
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4876847290640394,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.4876847290640394,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.03192271569548301,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.03192271569548301
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7676767676767676,
"acc_stderr": 0.030088629490217487,
"acc_norm": 0.7676767676767676,
"acc_norm_stderr": 0.030088629490217487
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8393782383419689,
"acc_stderr": 0.026499057701397443,
"acc_norm": 0.8393782383419689,
"acc_norm_stderr": 0.026499057701397443
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6512820512820513,
"acc_stderr": 0.02416278028401772,
"acc_norm": 0.6512820512820513,
"acc_norm_stderr": 0.02416278028401772
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.35185185185185186,
"acc_stderr": 0.02911661760608301,
"acc_norm": 0.35185185185185186,
"acc_norm_stderr": 0.02911661760608301
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6386554621848739,
"acc_stderr": 0.03120469122515001,
"acc_norm": 0.6386554621848739,
"acc_norm_stderr": 0.03120469122515001
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3443708609271523,
"acc_stderr": 0.03879687024073327,
"acc_norm": 0.3443708609271523,
"acc_norm_stderr": 0.03879687024073327
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8201834862385321,
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"acc_norm": 0.8201834862385321,
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Authors [optional]
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## Dataset Card Contact
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YufeiHFUT/CDR_llama_fewshot | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 53656706
num_examples: 26021
- name: validation
num_bytes: 58136649
num_examples: 29325
- name: test
num_bytes: 58855558
num_examples: 28433
- name: test_oneshot
num_bytes: 75431997
num_examples: 28433
- name: test_twoshot
num_bytes: 90586786
num_examples: 28433
download_size: 10987189
dataset_size: 336667696
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- split: test_oneshot
path: data/test_oneshot-*
- split: test_twoshot
path: data/test_twoshot-*
---
|
jeanlee/kmhas_korean_hate_speech | ---
annotations_creators:
- crowdsourced
language:
- ko
language_creators:
- found
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: 'K-MHaS'
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- K-MHaS
- Korean NLP
- Hate Speech Detection
- Dataset
- Coling2022
task_categories:
- text-classification
task_ids:
- multi-label-classification
- hate-speech-detection
paperswithcode_id: korean-multi-label-hate-speech-dataset
dataset_info:
features:
- name: text
dtype: string
- name: label
sequence:
class_label:
names:
0: origin
1: physical
2: politics
3: profanity
4: age
5: gender
6: race
7: religion
8: not_hate_speech
splits:
- name: train
num_bytes: 6845463
num_examples: 78977
- name: validation
num_bytes: 748899
num_examples: 8776
- name: test
num_bytes: 1902352
num_examples: 21939
download_size: 9496714
dataset_size: 109692
---
# Dataset Card for K-MHaS
## 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)
## Sample Code
<a href="https://colab.research.google.com/drive/171KhS1_LVBtpAFd_kaT8lcrZmhcz5ehY?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="base"/></a>
## Dataset Description
- **Homepage:** [K-MHaS](https://github.com/adlnlp/K-MHaS)
- **Repository:** [Korean Multi-label Hate Speech Dataset](https://github.com/adlnlp/K-MHaS)
- **Paper:** [K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment](https://arxiv.org/abs/2208.10684)
- **Point of Contact:** [Caren Han](caren.han@sydney.edu.au)
- **Sample code:** [Colab](https://colab.research.google.com/drive/171KhS1_LVBtpAFd_kaT8lcrZmhcz5ehY?usp=sharing)
### Dataset Summary
The Korean Multi-label Hate Speech Dataset, **K-MHaS**, consists of 109,692 utterances from Korean online news comments, labelled with 8 fine-grained hate speech classes (labels: `Politics`, `Origin`, `Physical`, `Age`, `Gender`, `Religion`, `Race`, `Profanity`) or `Not Hate Speech` class. Each utterance provides from a single to four labels that can handles Korean language patterns effectively. For more details, please refer to our paper about [**K-MHaS**](https://aclanthology.org/2022.coling-1.311), published at COLING 2022.
### Supported Tasks and Leaderboards
Hate Speech Detection
* `binary classification` (labels: `Hate Speech`, `Not Hate Speech`)
* `multi-label classification`: (labels: `Politics`, `Origin`, `Physical`, `Age`, `Gender`, `Religion`, `Race`, `Profanity`, `Not Hate Speech`)
For the multi-label classification, a `Hate Speech` class from the binary classification, is broken down into eight classes, associated with the hate speech category. In order to reflect the social and historical context, we select the eight hate speech classes. For example, the `Politics` class is chosen, due to a significant influence on the style of Korean hate speech.
### Languages
Korean
## Dataset Structure
### Data Instances
The dataset is provided with train/validation/test set in the txt format. Each instance is a news comment with a corresponding one or more hate speech classes (labels: `Politics`, `Origin`, `Physical`, `Age`, `Gender`, `Religion`, `Race`, `Profanity`) or `Not Hate Speech` class. The label numbers matching in both English and Korean is in the data fields section.
```python
{'text':'μκΌ΄νλ±μν€λ€μ΄ λ€ λμ ΈμΌ λλΌκ° λλ°λ‘ λ κ²κ°λ€..λ΅μ΄ μλ μ’
μλ€γ
'
'label': [2, 3, 4]
}
```
### Data Fields
* `text`: utterance from Korean online news comment.
* `label`: the label numbers matching with 8 fine-grained hate speech classes and `not hate speech` class are follows.
* `0`: `Origin`(`μΆμ μ°¨λ³`) hate speech based on place of origin or identity;
* `1`: `Physical`(`μΈλͺ¨μ°¨λ³`) hate speech based on physical appearance (e.g. body, face) or disability;
* `2`: `Politics`(`μ μΉμ±ν₯μ°¨λ³`) hate speech based on political stance;
* `3`: `Profanity`(`νμ€μμ€`) hate speech in the form of swearing, cursing, cussing, obscene words, or expletives; or an unspecified hate speech category;
* `4`: `Age`(`μ°λ Ήμ°¨λ³`) hate speech based on age;
* `5`: `Gender`(`μ±μ°¨λ³`) hate speech based on gender or sexual orientation (e.g. woman, homosexual);
* `6`: `Race`(`μΈμ’
μ°¨λ³`) hate speech based on ethnicity;
* `7`: `Religion`(`μ’
κ΅μ°¨λ³`) hate speech based on religion;
* `8`: `Not Hate Speech`(`ν΄λΉμ¬νμμ`).
### Data Splits
In our repository, we provide splitted datasets that have 78,977(train) / 8,776 (validation) / 21,939 (test) samples, preserving the class proportion.
## Dataset Creation
### Curation Rationale
We propose K-MHaS, a large size Korean multi-label hate speech detection dataset that represents Korean language patterns effectively. Most datasets in hate speech research are annotated using a single label classification of particular aspects, even though the subjectivity of hate speech cannot be explained with a mutually exclusive annotation scheme. We propose a multi-label hate speech annotation scheme that allows overlapping labels associated with the subjectivity and the intersectionality of hate speech.
### Source Data
#### Initial Data Collection and Normalization
Our dataset is based on the Korean online news comments available on Kaggle and Github. The unlabeled raw data was collected between January 2018 and June 2020. Please see the details in our paper [K-MHaS](https://aclanthology.org/2022.coling-1.311) published at COLING2020.
#### Who are the source language producers?
The language producers are users who left the comments on the Korean online news platform between 2018 and 2020.
### Annotations
#### Annotation process
We begin with the common categories of hate speech found in literature and match the keywords for each category. After the preliminary round, we investigate the results to merge or remove labels in order to provide the most representative subtype labels of hate speech contextual to the cultural background. Our annotation instructions explain a twolayered annotation to (a) distinguish hate and not hate speech, and (b) the categories of hate speech. Annotators are requested to consider given keywords or alternatives of each category within social, cultural, and historical circumstances. For more details, please refer to the paper [K-MHaS](https://aclanthology.org/2022.coling-1.311).
#### Who are the annotators?
Five native speakers were recruited for manual annotation in both the preliminary and main rounds.
### Personal and Sensitive Information
This datasets contains examples of hateful language, however, has no personal information.
## Considerations for Using the Data
### Social Impact of Dataset
We propose K-MHaS, a new large-sized dataset for Korean hate speech detection with a multi-label annotation scheme. We provided extensive baseline experiment results, presenting the usability of a dataset to detect Korean language patterns in hate speech.
### Discussion of Biases
All annotators were recruited from a crowdsourcing platform. They were informed about hate speech before handling the data. Our instructions allowed them to feel free to leave if they were uncomfortable with the content. With respect to the potential risks, we note that the subjectivity of human annotation would impact on the quality of the dataset.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset is curated by Taejun Lim, Heejun Lee and Bogeun Jo.
### Licensing Information
Creative Commons Attribution-ShareAlike 4.0 International (cc-by-sa-4.0).
### Citation Information
```
@inproceedings{lee-etal-2022-k,
title = "K-{MH}a{S}: A Multi-label Hate Speech Detection Dataset in {K}orean Online News Comment",
author = "Lee, Jean and
Lim, Taejun and
Lee, Heejun and
Jo, Bogeun and
Kim, Yangsok and
Yoon, Heegeun and
Han, Soyeon Caren",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.311",
pages = "3530--3538",
abstract = "Online hate speech detection has become an important issue due to the growth of online content, but resources in languages other than English are extremely limited. We introduce K-MHaS, a new multi-label dataset for hate speech detection that effectively handles Korean language patterns. The dataset consists of 109k utterances from news comments and provides a multi-label classification using 1 to 4 labels, and handles subjectivity and intersectionality. We evaluate strong baselines on K-MHaS. KR-BERT with a sub-character tokenizer outperforms others, recognizing decomposed characters in each hate speech class.",
}
```
### Contributions
The contributors of the work are:
- [Jean Lee](https://jeanlee-ai.github.io/) (The University of Sydney)
- [Taejun Lim](https://github.com/taezun) (The University of Sydney)
- [Heejun Lee](https://bigwaveai.com/) (BigWave AI)
- [Bogeun Jo](https://bigwaveai.com/) (BigWave AI)
- Yangsok Kim (Keimyung University)
- Heegeun Yoon (National Information Society Agency)
- [Soyeon Caren Han](https://drcarenhan.github.io/) (The University of Western Australia and The University of Sydney)
|
ruanchaves/faquad-nli | ---
pretty_name: FaQuAD-NLI
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
task_ids:
- extractive-qa
# paperswithcode_id: faquad
train-eval-index:
- config: plain_text
task: question-answering
task_id: extractive_question_answering
splits:
train_split: train
eval_split: validation
col_mapping:
question: question
context: context
answers:
text: text
answer_start: answer_start
metrics:
- type: squad
name: SQuAD
---
# Dataset Card for FaQuAD-NLI
## Dataset Description
- **Homepage:** https://github.com/liafacom/faquad
- **Repository:** https://github.com/liafacom/faquad
- **Paper:** https://ieeexplore.ieee.org/document/8923668/
<!-- - **Leaderboard:** -->
- **Point of Contact:** Eraldo R. Fernandes <eraldoluis@gmail.com>
### Dataset Summary
FaQuAD is a Portuguese reading comprehension dataset that follows the format of the Stanford Question Answering Dataset (SQuAD). It is a pioneer Portuguese reading comprehension dataset using the challenging format of SQuAD. The dataset aims to address the problem of abundant questions sent by academics whose answers are found in available institutional documents in the Brazilian higher education system. It consists of 900 questions about 249 reading passages taken from 18 official documents of a computer science college from a Brazilian federal university and 21 Wikipedia articles related to the Brazilian higher education system.
FaQuAD-NLI is a modified version of the [FaQuAD dataset](https://huggingface.co/datasets/eraldoluis/faquad) that repurposes the question answering task as a textual entailment task between a question and its possible answers.
### Supported Tasks and Leaderboards
- `question_answering`: The dataset can be used to train a model for question-answering tasks in the domain of Brazilian higher education institutions.
- `textual_entailment`: FaQuAD-NLI can be used to train a model for textual entailment tasks, where answers in Q&A pairs are classified as either suitable or unsuitable.
### Languages
This dataset is in Brazilian Portuguese.
## Dataset Structure
### Data Fields
- `document_index`: an integer representing the index of the document.
- `document_title`: a string containing the title of the document.
- `paragraph_index`: an integer representing the index of the paragraph within the document.
- `question`: a string containing the question related to the paragraph.
- `answer`: a string containing the answer related to the question.
- `label`: an integer (0 or 1) representing if the answer is suitable (1) or unsuitable (0) for the question.
### Data Splits
The dataset is split into three subsets: train, validation, and test.
The splits were made carefully to avoid question and answer pairs belonging to the same document appearing in more than one split.
| | Train | Validation | Test |
|------------|-------|------------|------|
| Instances | 3128 | 731 | 650 |
### Contributions
Thanks to [@ruanchaves](https://github.com/ruanchaves) for adding this dataset. |
AdapterOcean/data-standardized_cluster_23 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 35360015
num_examples: 3375
download_size: 10257244
dataset_size: 35360015
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_23"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
RichardMB1217/hub | ---
tags:
- not-for-all-audiences
--- |
andersonbcdefg/spec_large_deduped_queries | ---
dataset_info:
features:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 3045109554.9524593
num_examples: 858717
download_size: 1792986871
dataset_size: 3045109554.9524593
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
adamo1139/misc | ---
license: apache-2.0
---
|
open-llm-leaderboard/details_field2437__phi-2-test | ---
pretty_name: Evaluation run of field2437/phi-2-test
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [field2437/phi-2-test](https://huggingface.co/field2437/phi-2-test) 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_field2437__phi-2-test\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-07T11:43:00.787306](https://huggingface.co/datasets/open-llm-leaderboard/details_field2437__phi-2-test/blob/main/results_2024-03-07T11-43-00.787306.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\": 0.5822377157170969,\n\
\ \"acc_stderr\": 0.03381280917335049,\n \"acc_norm\": 0.5836335693694722,\n\
\ \"acc_norm_stderr\": 0.03450596737878276,\n \"mc1\": 0.31701346389228885,\n\
\ \"mc1_stderr\": 0.016289203374403385,\n \"mc2\": 0.4545655863854703,\n\
\ \"mc2_stderr\": 0.0151216566833299\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5716723549488054,\n \"acc_stderr\": 0.014460496367599012,\n\
\ \"acc_norm\": 0.6040955631399317,\n \"acc_norm_stderr\": 0.014291228393536592\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5657239593706433,\n\
\ \"acc_stderr\": 0.0049464854665446254,\n \"acc_norm\": 0.7512447719577773,\n\
\ \"acc_norm_stderr\": 0.0043140816086246455\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4666666666666667,\n\
\ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n\
\ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5789473684210527,\n \"acc_stderr\": 0.040179012759817494,\n\
\ \"acc_norm\": 0.5789473684210527,\n \"acc_norm_stderr\": 0.040179012759817494\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\
\ \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n \
\ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6075471698113207,\n \"acc_stderr\": 0.030052580579557845,\n\
\ \"acc_norm\": 0.6075471698113207,\n \"acc_norm_stderr\": 0.030052580579557845\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6111111111111112,\n\
\ \"acc_stderr\": 0.04076663253918567,\n \"acc_norm\": 0.6111111111111112,\n\
\ \"acc_norm_stderr\": 0.04076663253918567\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\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\": 0.43,\n \"acc_stderr\": 0.04975698519562426,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562426\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5780346820809249,\n\
\ \"acc_stderr\": 0.0376574669386515,\n \"acc_norm\": 0.5780346820809249,\n\
\ \"acc_norm_stderr\": 0.0376574669386515\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201943,\n\
\ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201943\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n\
\ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5319148936170213,\n \"acc_stderr\": 0.03261936918467381,\n\
\ \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.03261936918467381\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.38596491228070173,\n\
\ \"acc_stderr\": 0.04579639422070434,\n \"acc_norm\": 0.38596491228070173,\n\
\ \"acc_norm_stderr\": 0.04579639422070434\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.041618085035015295,\n\
\ \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.041618085035015295\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4576719576719577,\n \"acc_stderr\": 0.02565886886205833,\n \"\
acc_norm\": 0.4576719576719577,\n \"acc_norm_stderr\": 0.02565886886205833\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\
\ \"acc_stderr\": 0.04375888492727061,\n \"acc_norm\": 0.3968253968253968,\n\
\ \"acc_norm_stderr\": 0.04375888492727061\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.6935483870967742,\n\
\ \"acc_stderr\": 0.02622648565255388,\n \"acc_norm\": 0.6935483870967742,\n\
\ \"acc_norm_stderr\": 0.02622648565255388\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\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\"\
: 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.6606060606060606,\n \"acc_stderr\": 0.036974422050315967,\n\
\ \"acc_norm\": 0.6606060606060606,\n \"acc_norm_stderr\": 0.036974422050315967\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7424242424242424,\n \"acc_stderr\": 0.031156269519646836,\n \"\
acc_norm\": 0.7424242424242424,\n \"acc_norm_stderr\": 0.031156269519646836\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7875647668393783,\n \"acc_stderr\": 0.02951928261681723,\n\
\ \"acc_norm\": 0.7875647668393783,\n \"acc_norm_stderr\": 0.02951928261681723\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5974358974358974,\n \"acc_stderr\": 0.024864995159767755,\n\
\ \"acc_norm\": 0.5974358974358974,\n \"acc_norm_stderr\": 0.024864995159767755\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948503,\n \
\ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948503\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5966386554621849,\n \"acc_stderr\": 0.031866081214088314,\n\
\ \"acc_norm\": 0.5966386554621849,\n \"acc_norm_stderr\": 0.031866081214088314\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.41721854304635764,\n \"acc_stderr\": 0.040261414976346104,\n \"\
acc_norm\": 0.41721854304635764,\n \"acc_norm_stderr\": 0.040261414976346104\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8036697247706422,\n \"acc_stderr\": 0.017030719339154336,\n \"\
acc_norm\": 0.8036697247706422,\n \"acc_norm_stderr\": 0.017030719339154336\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\": 0.6568627450980392,\n\
\ \"acc_stderr\": 0.03332139944668086,\n \"acc_norm\": 0.6568627450980392,\n\
\ \"acc_norm_stderr\": 0.03332139944668086\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.7426160337552743,\n \"acc_stderr\": 0.028458820991460285,\n\
\ \"acc_norm\": 0.7426160337552743,\n \"acc_norm_stderr\": 0.028458820991460285\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6636771300448431,\n\
\ \"acc_stderr\": 0.031708824268455,\n \"acc_norm\": 0.6636771300448431,\n\
\ \"acc_norm_stderr\": 0.031708824268455\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6717557251908397,\n \"acc_stderr\": 0.041184385658062976,\n\
\ \"acc_norm\": 0.6717557251908397,\n \"acc_norm_stderr\": 0.041184385658062976\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096994,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096994\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7037037037037037,\n\
\ \"acc_stderr\": 0.04414343666854933,\n \"acc_norm\": 0.7037037037037037,\n\
\ \"acc_norm_stderr\": 0.04414343666854933\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.033519538795212696,\n\
\ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.033519538795212696\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\
\ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8076923076923077,\n\
\ \"acc_stderr\": 0.025819233256483706,\n \"acc_norm\": 0.8076923076923077,\n\
\ \"acc_norm_stderr\": 0.025819233256483706\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.685823754789272,\n\
\ \"acc_stderr\": 0.016599291735884897,\n \"acc_norm\": 0.685823754789272,\n\
\ \"acc_norm_stderr\": 0.016599291735884897\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.653179190751445,\n \"acc_stderr\": 0.025624723994030454,\n\
\ \"acc_norm\": 0.653179190751445,\n \"acc_norm_stderr\": 0.025624723994030454\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2212290502793296,\n\
\ \"acc_stderr\": 0.013882164598887265,\n \"acc_norm\": 0.2212290502793296,\n\
\ \"acc_norm_stderr\": 0.013882164598887265\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6045751633986928,\n \"acc_stderr\": 0.027996723180631455,\n\
\ \"acc_norm\": 0.6045751633986928,\n \"acc_norm_stderr\": 0.027996723180631455\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6559485530546624,\n\
\ \"acc_stderr\": 0.026981478043648026,\n \"acc_norm\": 0.6559485530546624,\n\
\ \"acc_norm_stderr\": 0.026981478043648026\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.654320987654321,\n \"acc_stderr\": 0.02646248777700187,\n\
\ \"acc_norm\": 0.654320987654321,\n \"acc_norm_stderr\": 0.02646248777700187\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4432624113475177,\n \"acc_stderr\": 0.029634838473766002,\n \
\ \"acc_norm\": 0.4432624113475177,\n \"acc_norm_stderr\": 0.029634838473766002\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.41916558018252936,\n\
\ \"acc_stderr\": 0.012602244505788238,\n \"acc_norm\": 0.41916558018252936,\n\
\ \"acc_norm_stderr\": 0.012602244505788238\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.49264705882352944,\n \"acc_stderr\": 0.030369552523902173,\n\
\ \"acc_norm\": 0.49264705882352944,\n \"acc_norm_stderr\": 0.030369552523902173\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5522875816993464,\n \"acc_stderr\": 0.020116925347422425,\n \
\ \"acc_norm\": 0.5522875816993464,\n \"acc_norm_stderr\": 0.020116925347422425\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302505,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302505\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.028920583220675592,\n\
\ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.028920583220675592\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7661691542288557,\n\
\ \"acc_stderr\": 0.029929415408348384,\n \"acc_norm\": 0.7661691542288557,\n\
\ \"acc_norm_stderr\": 0.029929415408348384\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \
\ \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.042295258468165065\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4819277108433735,\n\
\ \"acc_stderr\": 0.038899512528272166,\n \"acc_norm\": 0.4819277108433735,\n\
\ \"acc_norm_stderr\": 0.038899512528272166\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7017543859649122,\n \"acc_stderr\": 0.03508771929824563,\n\
\ \"acc_norm\": 0.7017543859649122,\n \"acc_norm_stderr\": 0.03508771929824563\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.31701346389228885,\n\
\ \"mc1_stderr\": 0.016289203374403385,\n \"mc2\": 0.4545655863854703,\n\
\ \"mc2_stderr\": 0.0151216566833299\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7458563535911602,\n \"acc_stderr\": 0.012236307219708262\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.55420773313116,\n \
\ \"acc_stderr\": 0.013691305174506686\n }\n}\n```"
repo_url: https://huggingface.co/field2437/phi-2-test
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_07T11_43_00.787306
path:
- '**/details_harness|arc:challenge|25_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|gsm8k|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hellaswag|10_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T11-43-00.787306.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-07T11-43-00.787306.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- '**/details_harness|winogrande|5_2024-03-07T11-43-00.787306.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-07T11-43-00.787306.parquet'
- config_name: results
data_files:
- split: 2024_03_07T11_43_00.787306
path:
- results_2024-03-07T11-43-00.787306.parquet
- split: latest
path:
- results_2024-03-07T11-43-00.787306.parquet
---
# Dataset Card for Evaluation run of field2437/phi-2-test
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [field2437/phi-2-test](https://huggingface.co/field2437/phi-2-test) 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_field2437__phi-2-test",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-07T11:43:00.787306](https://huggingface.co/datasets/open-llm-leaderboard/details_field2437__phi-2-test/blob/main/results_2024-03-07T11-43-00.787306.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": 0.5822377157170969,
"acc_stderr": 0.03381280917335049,
"acc_norm": 0.5836335693694722,
"acc_norm_stderr": 0.03450596737878276,
"mc1": 0.31701346389228885,
"mc1_stderr": 0.016289203374403385,
"mc2": 0.4545655863854703,
"mc2_stderr": 0.0151216566833299
},
"harness|arc:challenge|25": {
"acc": 0.5716723549488054,
"acc_stderr": 0.014460496367599012,
"acc_norm": 0.6040955631399317,
"acc_norm_stderr": 0.014291228393536592
},
"harness|hellaswag|10": {
"acc": 0.5657239593706433,
"acc_stderr": 0.0049464854665446254,
"acc_norm": 0.7512447719577773,
"acc_norm_stderr": 0.0043140816086246455
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4666666666666667,
"acc_stderr": 0.043097329010363554,
"acc_norm": 0.4666666666666667,
"acc_norm_stderr": 0.043097329010363554
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5789473684210527,
"acc_stderr": 0.040179012759817494,
"acc_norm": 0.5789473684210527,
"acc_norm_stderr": 0.040179012759817494
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.57,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.57,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6075471698113207,
"acc_stderr": 0.030052580579557845,
"acc_norm": 0.6075471698113207,
"acc_norm_stderr": 0.030052580579557845
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6111111111111112,
"acc_stderr": 0.04076663253918567,
"acc_norm": 0.6111111111111112,
"acc_norm_stderr": 0.04076663253918567
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.43,
"acc_stderr": 0.04975698519562426,
"acc_norm": 0.43,
"acc_norm_stderr": 0.04975698519562426
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5780346820809249,
"acc_stderr": 0.0376574669386515,
"acc_norm": 0.5780346820809249,
"acc_norm_stderr": 0.0376574669386515
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.04690650298201943,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.04690650298201943
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5319148936170213,
"acc_stderr": 0.03261936918467381,
"acc_norm": 0.5319148936170213,
"acc_norm_stderr": 0.03261936918467381
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.38596491228070173,
"acc_stderr": 0.04579639422070434,
"acc_norm": 0.38596491228070173,
"acc_norm_stderr": 0.04579639422070434
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5241379310344828,
"acc_stderr": 0.041618085035015295,
"acc_norm": 0.5241379310344828,
"acc_norm_stderr": 0.041618085035015295
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4576719576719577,
"acc_stderr": 0.02565886886205833,
"acc_norm": 0.4576719576719577,
"acc_norm_stderr": 0.02565886886205833
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3968253968253968,
"acc_stderr": 0.04375888492727061,
"acc_norm": 0.3968253968253968,
"acc_norm_stderr": 0.04375888492727061
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6935483870967742,
"acc_stderr": 0.02622648565255388,
"acc_norm": 0.6935483870967742,
"acc_norm_stderr": 0.02622648565255388
},
"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": 0.65,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.65,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.6606060606060606,
"acc_stderr": 0.036974422050315967,
"acc_norm": 0.6606060606060606,
"acc_norm_stderr": 0.036974422050315967
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7424242424242424,
"acc_stderr": 0.031156269519646836,
"acc_norm": 0.7424242424242424,
"acc_norm_stderr": 0.031156269519646836
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.7875647668393783,
"acc_stderr": 0.02951928261681723,
"acc_norm": 0.7875647668393783,
"acc_norm_stderr": 0.02951928261681723
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5974358974358974,
"acc_stderr": 0.024864995159767755,
"acc_norm": 0.5974358974358974,
"acc_norm_stderr": 0.024864995159767755
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.028742040903948503,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.028742040903948503
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5966386554621849,
"acc_stderr": 0.031866081214088314,
"acc_norm": 0.5966386554621849,
"acc_norm_stderr": 0.031866081214088314
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.41721854304635764,
"acc_stderr": 0.040261414976346104,
"acc_norm": 0.41721854304635764,
"acc_norm_stderr": 0.040261414976346104
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8036697247706422,
"acc_stderr": 0.017030719339154336,
"acc_norm": 0.8036697247706422,
"acc_norm_stderr": 0.017030719339154336
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4722222222222222,
"acc_stderr": 0.0340470532865388,
"acc_norm": 0.4722222222222222,
"acc_norm_stderr": 0.0340470532865388
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.6568627450980392,
"acc_stderr": 0.03332139944668086,
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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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"harness|winogrande|5": {
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},
"harness|gsm8k|5": {
"acc": 0.55420773313116,
"acc_stderr": 0.013691305174506686
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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[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
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[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:**
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## 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]
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## Dataset Card Contact
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peiyi9979/Math-Shepherd | ---
tags:
- prm
- synthesized data
---
# Dataset Card for Math-Shepherd
Project Page: [Math-Shepherd](https://rain-motion-6ec.notion.site/Math-Shepherd-A-Label-Free-Step-by-Step-Verifier-for-LLMs-in-Mathematical-Reasoning-41b6e73c860840e08697d347f8889bac#08e86c6d44c4452ba0b78c7aaea5f4f7)
Paper: https://arxiv.org/pdf/2312.08935.pdf
# Data Loading
```
from datasets import load_dataset
dataset = load_dataset("peiyi9979/Math-Shepherd")
```
# Data Instance
Every instance consists of three data fields: "input," "label," and "task".
1. "input": problem + step-by-step solution, e.g.,
```
If Buzz bought a pizza with 78 slices at a restaurant and then decided to share it with the waiter in the ratio of 5:8, with Buzz's ratio being 5, what's twenty less the number of slices of pizza that the waiter ate?
Step 1: The total ratio representing the pizza is 5+8 = <<5+8=13>>13. ΠΊΠΈ
Step 2: The waiter ate 13 x 8 / 13 = <<13*8/13=6>>6 slices of the pizza. ΠΊΠΈ
Step 3: Buzz ate 78 - 6 = <<78-6=72>>72 slices of the pizza. ΠΊΠΈ
Step 4: The waiter ate 20 less than the number of slices that Buzz ate which is 72 - 20 = 52. ΠΊΠΈ
Step 5: The waiter ate 52 slices of the pizza. The answer is: 52 ΠΊΠΈ
```
2. "label": problem + step-by-step solution with automatic label, e.g.,
```
If Buzz bought a pizza with 78 slices at a restaurant and then decided to share it with the waiter in the ratio of 5:8, with Buzz's ratio being 5, what's twenty less the number of slices of pizza that the waiter ate?
Step 1: The total ratio representing the pizza is 5+8 = <<5+8=13>>13. +
Step 2: The waiter ate 13 x 8 / 13 = <<13*8/13=6>>6 slices of the pizza. -
Step 3: Buzz ate 78 - 6 = <<78-6=72>>72 slices of the pizza. -
Step 4: The waiter ate 20 less than the number of slices that Buzz ate which is 72 - 20 = 52. -
Step 5: The waiter ate 52 slices of the pizza. The answer is: 52 -
```
3. "task": `GSM8K` or `MATH`.
NOTE:
"`ΠΊΠΈ`" serves as a unique token denoting the position for predicting the step score.
"`+`" signifies a good step, as it has the potential to lead towards the correct answer.
"`-`" denotes a bad step.
When we train PRMs, we only compute the loss of the positions of `ΠΊΠΈ`.
# Models:
We utilized internal code for step-wise PPO training, which cannot be open-sourced. We hope for your understanding. We provide the checkpoints of SFT, PRM, and RL models to help everyone reproduce our results.
- Mistral-7b-sft: https://huggingface.co/peiyi9979/mistral-7b-sft
- Mistral-7b-prm: https://huggingface.co/peiyi9979/math-shepherd-mistral-7b-prm
- Mistral-7b-rl: https://huggingface.co/peiyi9979/math-shepherd-mistral-7b-rl |
autoevaluate/autoeval-eval-squad-plain_text-be943f-1842563162 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: Neulvo/bert-finetuned-squad
metrics: ['squad', 'bertscore']
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Neulvo/bert-finetuned-squad
* Dataset: squad
* Config: plain_text
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@jsfs11](https://huggingface.co/jsfs11) for evaluating this model. |
peshkatari/autotrain-data-test-data | ---
dataset_info:
features:
- name: autotrain_text
dtype: string
splits:
- name: train
num_bytes: 14845
num_examples: 43
- name: validation
num_bytes: 14845
num_examples: 43
download_size: 12914
dataset_size: 29690
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "autotrain-data-test-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pks3kor/ModernChatGPT | ---
configs:
- config_name: default
data_files:
- split: train
path: data.csv
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
## 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] |
Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_1000 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
dtype: string
- name: question
dtype: string
- name: true_label
sequence: string
- name: prediction
dtype: string
- name: scores
sequence: float64
splits:
- name: fewshot_0_bs_8
num_bytes: 26699595
num_examples: 1000
download_size: 5515420
dataset_size: 26699595
---
# Dataset Card for "VQAv2_sample_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Minglii/ee10 | ---
dataset_info:
features:
- name: data
struct:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 3690751
num_examples: 5200
download_size: 2116849
dataset_size: 3690751
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ee10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/wikitext-103-raw-v1-sent-permute-9 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5452148058
num_examples: 18013491
- name: validation
num_bytes: 1159288
num_examples: 3760
- name: test
num_bytes: 1305088
num_examples: 4358
download_size: 3160993133
dataset_size: 5454612434
---
# Dataset Card for "wikitext-103-raw-v1-sent-permute-9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Weni/wenigpt-agent-1.3.0 | ---
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
- name: instructions
sequence: string
- name: question
dtype: string
- name: answer
dtype: string
- name: human_eval
dtype: string
- name: id
dtype: int64
- name: chunks_small
list:
- name: content
dtype: string
- name: score
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
splits:
- name: train
num_bytes: 19183275
num_examples: 1133
- name: test
num_bytes: 3054198
num_examples: 161
download_size: 5226500
dataset_size: 22237473
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
BSC-LT/InstrucatQA | ---
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- ca
- en
- es
pretty_name: InstrucatQA
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
Instructional dataset to finetune models used for RAG applications
## Dataset Details
### Dataset Description
This dataset is a merge from QA instructions from InstruCAT (ca), SQUAC (es), SQUAD (en), plus generalists CA and ES MENTOR datasets to provide a cognitive background for generating responses.
Contains splits of 66139 (train) and 11674 (validation) instructions
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** ca, es, en
- **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
Experiments with Catalan RAG applications
### 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] |
legacy107/qa_wikipedia_sentence_transformer | ---
dataset_info:
features:
- name: anchor
dtype: string
- name: negative
dtype: string
- name: positive
dtype: string
splits:
- name: train
num_bytes: 31856811
num_examples: 29965
- name: validation
num_bytes: 3167027
num_examples: 3000
- name: test
num_bytes: 3103240
num_examples: 2981
download_size: 2854716
dataset_size: 38127078
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for "qa_wikipedia_sentence_transformer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
alexcom/analisis-sentimeinto-textos-turisitcos-mx-review-corpus | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 113848875
num_examples: 315442
download_size: 70253485
dataset_size: 113848875
---
# Dataset Card for "analisis-sentimeinto-textos-turisitcos-mx-review-corpus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
michaelmallari/airbnb-ca-on-toronto | ---
license: mit
---
|
SkyWR/Thiago | ---
license: openrail
---
|
DIAS123/DIAS | ---
license: openrail
---
|
Codec-SUPERB/opensinger_synth | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: id
dtype: string
splits:
- name: original
num_bytes: 528559071.0
num_examples: 3924
- name: academicodec_hifi_16k_320d
num_bytes: 540619726.42
num_examples: 3924
- name: academicodec_hifi_16k_320d_large_uni
num_bytes: 540619726.42
num_examples: 3924
- name: academicodec_hifi_24k_320d
num_bytes: 811369448.02
num_examples: 3924
- name: audiodec_24k_320d
num_bytes: 813786004.18
num_examples: 3924
- name: dac_16k
num_bytes: 541841149.9
num_examples: 3924
- name: dac_24k
num_bytes: 812630935.54
num_examples: 3924
- name: dac_44k
num_bytes: 1492987459.924
num_examples: 3924
- name: encodec_24k_12bps
num_bytes: 812630935.54
num_examples: 3924
- name: encodec_24k_1_5bps
num_bytes: 812630935.54
num_examples: 3924
- name: encodec_24k_24bps
num_bytes: 812630935.54
num_examples: 3924
- name: encodec_24k_3bps
num_bytes: 812630935.54
num_examples: 3924
- name: encodec_24k_6bps
num_bytes: 812630935.54
num_examples: 3924
- name: funcodec_en_libritts_16k_gr1nq32ds320
num_bytes: 541544095.252
num_examples: 3924
- name: funcodec_en_libritts_16k_gr8nq32ds320
num_bytes: 541544095.252
num_examples: 3924
- name: funcodec_en_libritts_16k_nq32ds320
num_bytes: 541841149.9
num_examples: 3924
- name: funcodec_en_libritts_16k_nq32ds640
num_bytes: 541841149.9
num_examples: 3924
- name: funcodec_zh_en_16k_nq32ds320
num_bytes: 541841149.9
num_examples: 3924
- name: funcodec_zh_en_16k_nq32ds640
num_bytes: 541841149.9
num_examples: 3924
- name: speech_tokenizer_16k
num_bytes: 543128575.06
num_examples: 3924
download_size: 13577679945
dataset_size: 13939149564.268
configs:
- config_name: default
data_files:
- split: original
path: data/original-*
- split: academicodec_hifi_16k_320d
path: data/academicodec_hifi_16k_320d-*
- split: academicodec_hifi_16k_320d_large_uni
path: data/academicodec_hifi_16k_320d_large_uni-*
- split: academicodec_hifi_24k_320d
path: data/academicodec_hifi_24k_320d-*
- split: audiodec_24k_320d
path: data/audiodec_24k_320d-*
- split: dac_16k
path: data/dac_16k-*
- split: dac_24k
path: data/dac_24k-*
- split: dac_44k
path: data/dac_44k-*
- split: encodec_24k_12bps
path: data/encodec_24k_12bps-*
- split: encodec_24k_1_5bps
path: data/encodec_24k_1_5bps-*
- split: encodec_24k_24bps
path: data/encodec_24k_24bps-*
- split: encodec_24k_3bps
path: data/encodec_24k_3bps-*
- split: encodec_24k_6bps
path: data/encodec_24k_6bps-*
- split: funcodec_en_libritts_16k_gr1nq32ds320
path: data/funcodec_en_libritts_16k_gr1nq32ds320-*
- split: funcodec_en_libritts_16k_gr8nq32ds320
path: data/funcodec_en_libritts_16k_gr8nq32ds320-*
- split: funcodec_en_libritts_16k_nq32ds320
path: data/funcodec_en_libritts_16k_nq32ds320-*
- split: funcodec_en_libritts_16k_nq32ds640
path: data/funcodec_en_libritts_16k_nq32ds640-*
- split: funcodec_zh_en_16k_nq32ds320
path: data/funcodec_zh_en_16k_nq32ds320-*
- split: funcodec_zh_en_16k_nq32ds640
path: data/funcodec_zh_en_16k_nq32ds640-*
- split: speech_tokenizer_16k
path: data/speech_tokenizer_16k-*
---
|
fathyshalab/reklamation24_schoenheit-wellness | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: label_name
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 215900
num_examples: 464
- name: test
num_bytes: 56138
num_examples: 117
download_size: 0
dataset_size: 272038
---
# Dataset Card for "reklamation24_schoenheit-wellness"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pratapswati/pratap-data-mini | ---
license: mit
---
|
CyberHarem/helena_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of helena/γγ¬γ/ζ΅·δΌ¦ε¨ (Azur Lane)
This is the dataset of helena/γγ¬γ/ζ΅·δΌ¦ε¨ (Azur Lane), containing 380 images and their tags.
The core tags of this character are `blue_hair, long_hair, ahoge, breasts, purple_eyes, bangs, hair_ornament, medium_breasts, very_long_hair, 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 | 380 | 601.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/helena_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 380 | 304.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/helena_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 922 | 663.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/helena_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 380 | 511.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/helena_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 922 | 992.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/helena_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/helena_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 | 11 |  |  |  |  |  | 1girl, bare_shoulders, solo, elbow_gloves, looking_at_viewer, smile, black_gloves, cleavage, simple_background, white_background, closed_mouth, upper_body, blush, white_dress, hair_between_eyes, red_eyes |
| 1 | 5 |  |  |  |  |  | 1girl, blush, cleavage, dress, elbow_gloves, garter_straps, looking_at_viewer, solo, thighhighs, bare_shoulders, hair_between_eyes, black_gloves, smile, parted_lips, simple_background |
| 2 | 16 |  |  |  |  |  | 1girl, kimono, obi, solo, hair_flower, looking_at_viewer, black_gloves, blush, smile, white_thighhighs, butterfly, wide_sleeves |
| 3 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, solo, wedding_dress, white_dress, white_gloves, bare_shoulders, elbow_gloves, bridal_veil, cleavage, blush, collarbone, jewelry, bride, official_alternate_costume, smile, butterfly_on_hand, hair_between_eyes, sleeveless_dress |
| 4 | 5 |  |  |  |  |  | 1girl, bare_shoulders, blue_bikini, blush, cleavage, collarbone, hair_flower, looking_at_viewer, navel, solo, hand_up, stomach, thighs, bare_arms, criss-cross_halter, hair_between_eyes, parted_lips, sidelocks, simple_background, white_background, white_bikini, white_flower, blue_nails, cowboy_shot, hand_in_own_hair, jewelry, nail_polish, sitting, standing, thigh_gap |
| 5 | 7 |  |  |  |  |  | 1girl, bare_shoulders, collarbone, hair_flower, looking_at_viewer, navel, solo, outdoors, blue_sky, bracelet, cleavage, cloud, hair_between_eyes, standing, stomach, thighs, arm_up, armpits, blush, day, smile, wet, white_flower, arm_behind_head, beach, black_bikini, blue_nails, criss-cross_halter, drinking_glass, nail_polish, ocean, sunlight, table, wristband |
| 6 | 6 |  |  |  |  |  | 1girl, cleavage, detached_collar, fake_animal_ears, playboy_bunny, rabbit_ears, solo, bare_shoulders, black_leotard, black_pantyhose, bowtie, looking_at_viewer, strapless_leotard, blush, wrist_cuffs, alternate_costume, covered_navel, thighband_pantyhose, white_background |
| 7 | 6 |  |  |  |  |  | 1girl, navel, nipples, pussy, 1boy, blush, hetero, looking_at_viewer, penis, sex, vaginal, completely_nude, heart-shaped_pupils, mosaic_censoring, open_mouth, solo_focus, bar_censor, collarbone, cowgirl_position, girl_on_top, smile, spread_legs, sweat |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | solo | elbow_gloves | looking_at_viewer | smile | black_gloves | cleavage | simple_background | white_background | closed_mouth | upper_body | blush | white_dress | hair_between_eyes | red_eyes | dress | garter_straps | thighhighs | parted_lips | kimono | obi | hair_flower | white_thighhighs | butterfly | wide_sleeves | wedding_dress | white_gloves | bridal_veil | collarbone | jewelry | bride | official_alternate_costume | butterfly_on_hand | sleeveless_dress | blue_bikini | navel | hand_up | stomach | thighs | bare_arms | criss-cross_halter | sidelocks | white_bikini | white_flower | blue_nails | cowboy_shot | hand_in_own_hair | nail_polish | sitting | standing | thigh_gap | outdoors | blue_sky | bracelet | cloud | arm_up | armpits | day | wet | arm_behind_head | beach | black_bikini | drinking_glass | ocean | sunlight | table | wristband | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | black_leotard | black_pantyhose | bowtie | strapless_leotard | wrist_cuffs | alternate_costume | covered_navel | thighband_pantyhose | nipples | pussy | 1boy | hetero | penis | sex | vaginal | completely_nude | heart-shaped_pupils | mosaic_censoring | open_mouth | solo_focus | bar_censor | cowgirl_position | girl_on_top | spread_legs | sweat |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-------|:---------------|:--------------------|:--------|:---------------|:-----------|:--------------------|:-------------------|:---------------|:-------------|:--------|:--------------|:--------------------|:-----------|:--------|:----------------|:-------------|:--------------|:---------|:------|:--------------|:-------------------|:------------|:---------------|:----------------|:---------------|:--------------|:-------------|:----------|:--------|:-----------------------------|:--------------------|:-------------------|:--------------|:--------|:----------|:----------|:---------|:------------|:---------------------|:------------|:---------------|:---------------|:-------------|:--------------|:-------------------|:--------------|:----------|:-----------|:------------|:-----------|:-----------|:-----------|:--------|:---------|:----------|:------|:------|:------------------|:--------|:---------------|:-----------------|:--------|:-----------|:--------|:------------|:------------------|:-------------------|:----------------|:--------------|:----------------|:------------------|:---------|:--------------------|:--------------|:--------------------|:----------------|:----------------------|:----------|:--------|:-------|:---------|:--------|:------|:----------|:------------------|:----------------------|:-------------------|:-------------|:-------------|:-------------|:-------------------|:--------------|:--------------|:--------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | X | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 16 |  |  |  |  |  | X | | X | | X | X | X | | | | | | X | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 9 |  |  |  |  |  | X | X | X | X | X | X | | X | | | | | X | X | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 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 | 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 | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | 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 |
|
tweet_eval | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- extended|other-tweet-datasets
task_categories:
- text-classification
task_ids:
- intent-classification
- multi-class-classification
- sentiment-classification
paperswithcode_id: tweeteval
pretty_name: TweetEval
config_names:
- emoji
- emotion
- hate
- irony
- offensive
- sentiment
- stance_abortion
- stance_atheism
- stance_climate
- stance_feminist
- stance_hillary
dataset_info:
- config_name: emoji
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': β€
'1': π
'2': π
'3': π
'4': π₯
'5': π
'6': π
'7': β¨
'8': π
'9': π
'10': π·
'11': πΊπΈ
'12': β
'13': π
'14': π
'15': π―
'16': π
'17': π
'18': πΈ
'19': π
splits:
- name: train
num_bytes: 3803167
num_examples: 45000
- name: test
num_bytes: 4255901
num_examples: 50000
- name: validation
num_bytes: 396079
num_examples: 5000
download_size: 5939308
dataset_size: 8455147
- config_name: emotion
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': anger
'1': joy
'2': optimism
'3': sadness
splits:
- name: train
num_bytes: 338871
num_examples: 3257
- name: test
num_bytes: 146645
num_examples: 1421
- name: validation
num_bytes: 38273
num_examples: 374
download_size: 367016
dataset_size: 523789
- config_name: hate
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': non-hate
'1': hate
splits:
- name: train
num_bytes: 1223650
num_examples: 9000
- name: test
num_bytes: 428934
num_examples: 2970
- name: validation
num_bytes: 154144
num_examples: 1000
download_size: 1196346
dataset_size: 1806728
- config_name: irony
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': non_irony
'1': irony
splits:
- name: train
num_bytes: 259187
num_examples: 2862
- name: test
num_bytes: 75897
num_examples: 784
- name: validation
num_bytes: 86017
num_examples: 955
download_size: 297647
dataset_size: 421101
- config_name: offensive
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': non-offensive
'1': offensive
splits:
- name: train
num_bytes: 1648061
num_examples: 11916
- name: test
num_bytes: 135473
num_examples: 860
- name: validation
num_bytes: 192417
num_examples: 1324
download_size: 1234528
dataset_size: 1975951
- config_name: sentiment
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
splits:
- name: train
num_bytes: 5425122
num_examples: 45615
- name: test
num_bytes: 1279540
num_examples: 12284
- name: validation
num_bytes: 239084
num_examples: 2000
download_size: 4849675
dataset_size: 6943746
- config_name: stance_abortion
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': against
'2': favor
splits:
- name: train
num_bytes: 68694
num_examples: 587
- name: test
num_bytes: 33171
num_examples: 280
- name: validation
num_bytes: 7657
num_examples: 66
download_size: 73517
dataset_size: 109522
- config_name: stance_atheism
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': against
'2': favor
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num_examples: 220
- name: validation
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num_examples: 52
download_size: 62265
dataset_size: 86811
- config_name: stance_climate
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': against
'2': favor
splits:
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- name: test
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num_examples: 169
- name: validation
num_bytes: 4801
num_examples: 40
download_size: 48493
dataset_size: 64975
- config_name: stance_feminist
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': against
'2': favor
splits:
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num_examples: 597
- name: test
num_bytes: 33305
num_examples: 285
- name: validation
num_bytes: 8035
num_examples: 67
download_size: 76345
dataset_size: 111849
- config_name: stance_hillary
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': against
'2': favor
splits:
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num_bytes: 69596
num_examples: 620
- name: test
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num_examples: 295
- name: validation
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num_examples: 69
download_size: 74057
dataset_size: 111615
configs:
- config_name: emoji
data_files:
- split: train
path: emoji/train-*
- split: test
path: emoji/test-*
- split: validation
path: emoji/validation-*
- config_name: emotion
data_files:
- split: train
path: emotion/train-*
- split: test
path: emotion/test-*
- split: validation
path: emotion/validation-*
- config_name: hate
data_files:
- split: train
path: hate/train-*
- split: test
path: hate/test-*
- split: validation
path: hate/validation-*
- config_name: irony
data_files:
- split: train
path: irony/train-*
- split: test
path: irony/test-*
- split: validation
path: irony/validation-*
- config_name: offensive
data_files:
- split: train
path: offensive/train-*
- split: test
path: offensive/test-*
- split: validation
path: offensive/validation-*
- config_name: sentiment
data_files:
- split: train
path: sentiment/train-*
- split: test
path: sentiment/test-*
- split: validation
path: sentiment/validation-*
- config_name: stance_abortion
data_files:
- split: train
path: stance_abortion/train-*
- split: test
path: stance_abortion/test-*
- split: validation
path: stance_abortion/validation-*
- config_name: stance_atheism
data_files:
- split: train
path: stance_atheism/train-*
- split: test
path: stance_atheism/test-*
- split: validation
path: stance_atheism/validation-*
- config_name: stance_climate
data_files:
- split: train
path: stance_climate/train-*
- split: test
path: stance_climate/test-*
- split: validation
path: stance_climate/validation-*
- config_name: stance_feminist
data_files:
- split: train
path: stance_feminist/train-*
- split: test
path: stance_feminist/test-*
- split: validation
path: stance_feminist/validation-*
- config_name: stance_hillary
data_files:
- split: train
path: stance_hillary/train-*
- split: test
path: stance_hillary/test-*
- split: validation
path: stance_hillary/validation-*
train-eval-index:
- config: emotion
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
- config: hate
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 binary
args:
average: binary
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
- config: irony
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 binary
args:
average: binary
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
- config: offensive
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 binary
args:
average: binary
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
- config: sentiment
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for tweet_eval
## 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:** [Needs More Information]
- **Repository:** [GitHub](https://github.com/cardiffnlp/tweeteval)
- **Paper:** [EMNLP Paper](https://arxiv.org/pdf/2010.12421.pdf)
- **Leaderboard:** [GitHub Leaderboard](https://github.com/cardiffnlp/tweeteval)
- **Point of Contact:** [Needs More Information]
### Dataset Summary
TweetEval consists of seven heterogenous tasks in Twitter, all framed as multi-class tweet classification. The tasks include - irony, hate, offensive, stance, emoji, emotion, and sentiment. All tasks have been unified into the same benchmark, with each dataset presented in the same format and with fixed training, validation and test splits.
### Supported Tasks and Leaderboards
- `text_classification`: The dataset can be trained using a SentenceClassification model from HuggingFace transformers.
### Languages
The text in the dataset is in English, as spoken by Twitter users.
## Dataset Structure
### Data Instances
An instance from `emoji` config:
```
{'label': 12, 'text': 'Sunday afternoon walking through Venice in the sun with @user οΈ οΈ οΈ @ Abbot Kinney, Venice'}
```
An instance from `emotion` config:
```
{'label': 2, 'text': "βWorry is a down payment on a problem you may never have'. \xa0Joyce Meyer. #motivation #leadership #worry"}
```
An instance from `hate` config:
```
{'label': 0, 'text': '@user nice new signage. Are you not concerned by Beatlemania -style hysterical crowds crongregating on youβ¦'}
```
An instance from `irony` config:
```
{'label': 1, 'text': 'seeing ppl walking w/ crutches makes me really excited for the next 3 weeks of my life'}
```
An instance from `offensive` config:
```
{'label': 0, 'text': '@user Bono... who cares. Soon people will understand that they gain nothing from following a phony celebrity. Become a Leader of your people instead or help and support your fellow countrymen.'}
```
An instance from `sentiment` config:
```
{'label': 2, 'text': '"QT @user In the original draft of the 7th book, Remus Lupin survived the Battle of Hogwarts. #HappyBirthdayRemusLupin"'}
```
An instance from `stance_abortion` config:
```
{'label': 1, 'text': 'we remind ourselves that love means to be willing to give until it hurts - Mother Teresa'}
```
An instance from `stance_atheism` config:
```
{'label': 1, 'text': '@user Bless Almighty God, Almighty Holy Spirit and the Messiah. #SemST'}
```
An instance from `stance_climate` config:
```
{'label': 0, 'text': 'Why Is The Pope Upset? via @user #UnzippedTruth #PopeFrancis #SemST'}
```
An instance from `stance_feminist` config:
```
{'label': 1, 'text': "@user @user is the UK's answer to @user and @user #GamerGate #SemST"}
```
An instance from `stance_hillary` config:
```
{'label': 1, 'text': "If a man demanded staff to get him an ice tea he'd be called a sexists elitist pig.. Oink oink #Hillary #SemST"}
```
### Data Fields
For `emoji` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: β€
`1`: π
`2`: π
`3`: π
`4`: π₯
`5`: π
`6`: π
`7`: β¨
`8`: π
`9`: π
`10`: π·
`11`: πΊπΈ
`12`: β
`13`: π
`14`: π
`15`: π―
`16`: π
`17`: π
`18`: πΈ
`19`: π
For `emotion` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: anger
`1`: joy
`2`: optimism
`3`: sadness
For `hate` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: non-hate
`1`: hate
For `irony` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: non_irony
`1`: irony
For `offensive` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: non-offensive
`1`: offensive
For `sentiment` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: negative
`1`: neutral
`2`: positive
For `stance_abortion` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: none
`1`: against
`2`: favor
For `stance_atheism` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: none
`1`: against
`2`: favor
For `stance_climate` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: none
`1`: against
`2`: favor
For `stance_feminist` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: none
`1`: against
`2`: favor
For `stance_hillary` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: none
`1`: against
`2`: favor
### Data Splits
| name | train | validation | test |
| --------------- | ----- | ---------- | ----- |
| emoji | 45000 | 5000 | 50000 |
| emotion | 3257 | 374 | 1421 |
| hate | 9000 | 1000 | 2970 |
| irony | 2862 | 955 | 784 |
| offensive | 11916 | 1324 | 860 |
| sentiment | 45615 | 2000 | 12284 |
| stance_abortion | 587 | 66 | 280 |
| stance_atheism | 461 | 52 | 220 |
| stance_climate | 355 | 40 | 169 |
| stance_feminist | 597 | 67 | 285 |
| stance_hillary | 620 | 69 | 295 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
Francesco Barbieri, Jose Camacho-Collados, Luis Espiinosa-Anke and Leonardo Neves through Cardiff NLP.
### Licensing Information
This is not a single dataset, therefore each subset has its own license (the collection itself does not have additional restrictions).
All of the datasets require complying with Twitter [Terms Of Service](https://twitter.com/tos) and Twitter API [Terms Of Service](https://developer.twitter.com/en/developer-terms/agreement-and-policy)
Additionally the license are:
- emoji: Undefined
- emotion(EmoInt): Undefined
- hate (HateEval): Need permission [here](http://hatespeech.di.unito.it/hateval.html)
- irony: Undefined
- Offensive: Undefined
- Sentiment: [Creative Commons Attribution 3.0 Unported License](https://groups.google.com/g/semevaltweet/c/k5DDcvVb_Vo/m/zEOdECFyBQAJ)
- Stance: Undefined
### Citation Information
```
@inproceedings{barbieri2020tweeteval,
title={{TweetEval:Unified Benchmark and Comparative Evaluation for Tweet Classification}},
author={Barbieri, Francesco and Camacho-Collados, Jose and Espinosa-Anke, Luis and Neves, Leonardo},
booktitle={Proceedings of Findings of EMNLP},
year={2020}
}
```
If you use any of the TweetEval datasets, please cite their original publications:
#### Emotion Recognition:
```
@inproceedings{mohammad2018semeval,
title={Semeval-2018 task 1: Affect in tweets},
author={Mohammad, Saif and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana},
booktitle={Proceedings of the 12th international workshop on semantic evaluation},
pages={1--17},
year={2018}
}
```
#### Emoji Prediction:
```
@inproceedings{barbieri2018semeval,
title={Semeval 2018 task 2: Multilingual emoji prediction},
author={Barbieri, Francesco and Camacho-Collados, Jose and Ronzano, Francesco and Espinosa-Anke, Luis and
Ballesteros, Miguel and Basile, Valerio and Patti, Viviana and Saggion, Horacio},
booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation},
pages={24--33},
year={2018}
}
```
#### Irony Detection:
```
@inproceedings{van2018semeval,
title={Semeval-2018 task 3: Irony detection in english tweets},
author={Van Hee, Cynthia and Lefever, Els and Hoste, V{\'e}ronique},
booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation},
pages={39--50},
year={2018}
}
```
#### Hate Speech Detection:
```
@inproceedings{basile-etal-2019-semeval,
title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter",
author = "Basile, Valerio and Bosco, Cristina and Fersini, Elisabetta and Nozza, Debora and Patti, Viviana and
Rangel Pardo, Francisco Manuel and Rosso, Paolo and Sanguinetti, Manuela",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/S19-2007",
doi = "10.18653/v1/S19-2007",
pages = "54--63"
}
```
#### Offensive Language Identification:
```
@inproceedings{zampieri2019semeval,
title={SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)},
author={Zampieri, Marcos and Malmasi, Shervin and Nakov, Preslav and Rosenthal, Sara and Farra, Noura and Kumar, Ritesh},
booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation},
pages={75--86},
year={2019}
}
```
#### Sentiment Analysis:
```
@inproceedings{rosenthal2017semeval,
title={SemEval-2017 task 4: Sentiment analysis in Twitter},
author={Rosenthal, Sara and Farra, Noura and Nakov, Preslav},
booktitle={Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017)},
pages={502--518},
year={2017}
}
```
#### Stance Detection:
```
@inproceedings{mohammad2016semeval,
title={Semeval-2016 task 6: Detecting stance in tweets},
author={Mohammad, Saif and Kiritchenko, Svetlana and Sobhani, Parinaz and Zhu, Xiaodan and Cherry, Colin},
booktitle={Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)},
pages={31--41},
year={2016}
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchhablani) and [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
yjernite/prof_report__SD_v1.4_random_seeds__multi__12 | ---
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download_size: 867336
dataset_size: 517776
---
# Dataset Card for "prof_report__SD_v1.4_random_seeds__multi__12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Cleudemir/videoestoico | ---
license: openrail
---
|
autoevaluate/autoeval-eval-jeffdshen__redefine_math_test0-jeffdshen__redefine_math-58f952-1666158900 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- jeffdshen/redefine_math_test0
eval_info:
task: text_zero_shot_classification
model: facebook/opt-2.7b
metrics: []
dataset_name: jeffdshen/redefine_math_test0
dataset_config: jeffdshen--redefine_math_test0
dataset_split: train
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-2.7b
* Dataset: jeffdshen/redefine_math_test0
* Config: jeffdshen--redefine_math_test0
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@jeffdshen](https://huggingface.co/jeffdshen) for evaluating this model. |
hieule/news_corpus_v2_p1 | ---
dataset_info:
features:
- name: source
dtype: string
- name: title
dtype: string
- name: sapo
dtype: string
- name: cates
sequence: string
- name: publish
dtype: timestamp[us]
- name: text_content
dtype: string
splits:
- name: train
num_bytes: 15876374992
num_examples: 5000000
download_size: 7858134654
dataset_size: 15876374992
---
# Dataset Card for "news_corpus_v2_p1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
youngwoo3283/sentence2000_csv | ---
task_categories:
- text-generation
size_categories:
- n<1K
--- |
redwoodresearch/diamonds-seed4 | ---
dataset_info:
features:
- name: text
dtype: string
- name: is_correct
dtype: bool
- name: is_clean
dtype: bool
- name: measurements
sequence: bool
- name: difficulty
dtype: int64
splits:
- name: train
num_bytes: 62844390
num_examples: 25000
- name: validation
num_bytes: 20030161
num_examples: 7989
- name: train_for_val
num_bytes: 7619892
num_examples: 2997
download_size: 1122659
dataset_size: 90494443
---
# Dataset Card for "diamonds-seed4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Francesco/cloud-types | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': cloud-types
'1': Fish
'2': Flower
'3': Gravel
'4': Sugar
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: cloud-types
tags:
- rf100
---
# Dataset Card for cloud-types
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/cloud-types
- **Point of Contact:** francesco.zuppichini@gmail.com
### Dataset Summary
cloud-types
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/cloud-types
### Citation Information
```
@misc{ cloud-types,
title = { cloud types Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/cloud-types } },
url = { https://universe.roboflow.com/object-detection/cloud-types },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
flaviolima/coringa | ---
license: openrail
---
|
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