datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
ignacioct/mini-imdb | ---
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': hola
splits:
- name: train
num_bytes: 3704
num_examples: 3
download_size: 14002
dataset_size: 3704
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "mini-imdb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/tsunade_naruto | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of tsunade (NARUTO)
This is the dataset of tsunade (NARUTO), containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
|
Porridge/Dataset_test | ---
license: unknown
---
|
AdapterOcean/med_alpaca_standardized_cluster_64_std | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 3273835
num_examples: 12860
download_size: 1297329
dataset_size: 3273835
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_64_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jinwoos/car-shadow-dataset-2 | ---
dataset_info:
features:
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: cartoonized_image
dtype: image
splits:
- name: train
num_bytes: 5082453379.153
num_examples: 1451
download_size: 5031628462
dataset_size: 5082453379.153
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Nexdata/1980000_Groups_Chinese_Polish_Parallel_Corpus_Data | ---
license: cc-by-nc-nd-4.0
---
## Description
1,980,000 sets of Chinese and Polish language parallel translation corpus, data storage format is txt document. Data cleaning, desensitization, and quality inspection have been carried out, which can be used as a basic corpus for text data analysis and in fields such as machine translation.
For more details, please refer to the link: https://www.nexdata.ai/dataset/1337?source=Huggingface
## Storage format
TXT
## Data content
Chinese-Polish Parallel Corpus Data, content has been preliminarily categorized, covering the fields of technology, healthcare, tourism, spoken, news and military.
## Data size
1.99 million pairs of Chinese-Polish Parallel Corpus Data.
## Language
Chinese, Polish
## Application scenario
machine translation
# Licensing Information
Commercial License
|
hutgkl/huycvb | ---
license: apache-2.0
---
|
hippocrates/MS2_1shot_train | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 728365979
num_examples: 14188
- name: valid
num_bytes: 104431929
num_examples: 2021
- name: test
num_bytes: 104431929
num_examples: 2021
download_size: 350449852
dataset_size: 937229837
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
|
CyberHarem/priscilla_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of priscilla (Fire Emblem)
This is the dataset of priscilla (Fire Emblem), containing 61 images and their tags.
The core tags of this character are `red_hair, short_hair, green_eyes, hair_ornament`, 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 | 61 | 57.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/priscilla_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 61 | 38.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/priscilla_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 124 | 73.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/priscilla_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 61 | 51.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/priscilla_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 124 | 90.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/priscilla_fireemblem/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/priscilla_fireemblem',
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 | 17 |  |  |  |  |  | 1girl, solo, elbow_gloves, smile, cape, looking_at_viewer, white_gloves, dress, simple_background, white_background, full_body, holding_staff, skirt, knee_boots |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | elbow_gloves | smile | cape | looking_at_viewer | white_gloves | dress | simple_background | white_background | full_body | holding_staff | skirt | knee_boots |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------------|:--------|:-------|:--------------------|:---------------|:--------|:--------------------|:-------------------|:------------|:----------------|:--------|:-------------|
| 0 | 17 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
Bin12345/NLP-CPP-Fortran | ---
license: mit
---
|
engr-farhan/use-cases | ---
license: apache-2.0
---
|
BadreddineHug/LayoutLMv3_dataset_5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: bboxes
sequence:
sequence: int64
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': NumFa
'2': Fourniss
'3': DateFa
'4': TotalHT
'5': TVA
'6': TotalTTc
- name: tokens
sequence: string
splits:
- name: train
num_bytes: 20162086.43298969
num_examples: 77
- name: test
num_bytes: 5236905.56701031
num_examples: 20
download_size: 20802412
dataset_size: 25398992.0
---
# Dataset Card for "LayoutLMv3_dataset_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bogeyturn/exhentai-api-dump | ---
language:
- en
tags:
- not-for-all-audiences
- art
size_categories:
- 1M<n<10M
---
# Dataset Card for Exhentai API DUMP
### Dataset Summary
A conversion of [Exhentai API dump](https://sukebei.nyaa.si/view/3914574) to csv files |
pharaouk/cortex | ---
dataset_info:
features:
- name: prompts
dtype: string
- name: responses
dtype: string
splits:
- name: train
num_bytes: 3213514939
num_examples: 1565365
download_size: 1615405970
dataset_size: 3213514939
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
freshpearYoon/vr_train_free_50 | ---
dataset_info:
features:
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: filename
dtype: string
- name: NumOfUtterance
dtype: int64
- name: text
dtype: string
- name: samplingrate
dtype: int64
- name: begin_time
dtype: float64
- name: end_time
dtype: float64
- name: speaker_id
dtype: string
- name: directory
dtype: string
splits:
- name: train
num_bytes: 7117436648
num_examples: 10000
download_size: 1204482378
dataset_size: 7117436648
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_pe-nlp__llama-2-13b-platypus-vicuna-wizard | ---
pretty_name: Evaluation run of pe-nlp/llama-2-13b-platypus-vicuna-wizard
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [pe-nlp/llama-2-13b-platypus-vicuna-wizard](https://huggingface.co/pe-nlp/llama-2-13b-platypus-vicuna-wizard)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_pe-nlp__llama-2-13b-platypus-vicuna-wizard\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T01:39:24.392749](https://huggingface.co/datasets/open-llm-leaderboard/details_pe-nlp__llama-2-13b-platypus-vicuna-wizard/blob/main/results_2023-09-23T01-39-24.392749.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.4077181208053691,\n\
\ \"em_stderr\": 0.005032501129819524,\n \"f1\": 0.44956795302013525,\n\
\ \"f1_stderr\": 0.004900290116380425,\n \"acc\": 0.3833965723476863,\n\
\ \"acc_stderr\": 0.007328839518475228\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.4077181208053691,\n \"em_stderr\": 0.005032501129819524,\n\
\ \"f1\": 0.44956795302013525,\n \"f1_stderr\": 0.004900290116380425\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.009097801364670205,\n \
\ \"acc_stderr\": 0.002615326510775672\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7576953433307024,\n \"acc_stderr\": 0.012042352526174785\n\
\ }\n}\n```"
repo_url: https://huggingface.co/pe-nlp/llama-2-13b-platypus-vicuna-wizard
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|arc:challenge|25_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T01_39_24.392749
path:
- '**/details_harness|drop|3_2023-09-23T01-39-24.392749.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T01-39-24.392749.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T01_39_24.392749
path:
- '**/details_harness|gsm8k|5_2023-09-23T01-39-24.392749.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T01-39-24.392749.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hellaswag|10_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T06:17:52.527407.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_23T06_17_52.527407
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-23T06:17:52.527407.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-23T06:17:52.527407.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T01_39_24.392749
path:
- '**/details_harness|winogrande|5_2023-09-23T01-39-24.392749.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T01-39-24.392749.parquet'
- config_name: results
data_files:
- split: 2023_09_23T01_39_24.392749
path:
- results_2023-09-23T01-39-24.392749.parquet
- split: latest
path:
- results_2023-09-23T01-39-24.392749.parquet
---
# Dataset Card for Evaluation run of pe-nlp/llama-2-13b-platypus-vicuna-wizard
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/pe-nlp/llama-2-13b-platypus-vicuna-wizard
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [pe-nlp/llama-2-13b-platypus-vicuna-wizard](https://huggingface.co/pe-nlp/llama-2-13b-platypus-vicuna-wizard) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_pe-nlp__llama-2-13b-platypus-vicuna-wizard",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T01:39:24.392749](https://huggingface.co/datasets/open-llm-leaderboard/details_pe-nlp__llama-2-13b-platypus-vicuna-wizard/blob/main/results_2023-09-23T01-39-24.392749.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.4077181208053691,
"em_stderr": 0.005032501129819524,
"f1": 0.44956795302013525,
"f1_stderr": 0.004900290116380425,
"acc": 0.3833965723476863,
"acc_stderr": 0.007328839518475228
},
"harness|drop|3": {
"em": 0.4077181208053691,
"em_stderr": 0.005032501129819524,
"f1": 0.44956795302013525,
"f1_stderr": 0.004900290116380425
},
"harness|gsm8k|5": {
"acc": 0.009097801364670205,
"acc_stderr": 0.002615326510775672
},
"harness|winogrande|5": {
"acc": 0.7576953433307024,
"acc_stderr": 0.012042352526174785
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
jlbaker361/mnist_sorted_v0.0 | ---
dataset_info:
features:
- name: label
dtype: int64
- name: sequence
sequence: int64
- name: occurence
dtype: int64
- name: split
dtype: string
splits:
- name: train
num_bytes: 84223889
num_examples: 68614
download_size: 12695868
dataset_size: 84223889
---
# Dataset Card for "mnist_sorted_v0.0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ClaudiaRichard/mbti_classification_dataset_fullPosts | ---
dataset_info:
features:
- name: I/E
dtype: int64
- name: N/S
dtype: int64
- name: T/F
dtype: int64
- name: J/P
dtype: int64
- name: post
dtype: string
splits:
- name: train
num_bytes: 37923280
num_examples: 5205
- name: test
num_bytes: 15150755
num_examples: 2082
- name: validation
num_bytes: 10016664
num_examples: 1388
download_size: 40334173
dataset_size: 63090699
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
ovior/twitter_dataset_1713045785 | ---
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: 2686310
num_examples: 8339
download_size: 1510693
dataset_size: 2686310
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
davidgaofc/PRIMA_RM_train_format | ---
license: mit
dataset_info:
features:
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 1672603
num_examples: 1640
download_size: 655475
dataset_size: 1672603
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
priyan9/embeddings1 | ---
license: mit
---
|
joey234/mmlu-global_facts-original-neg-prepend | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: neg_prompt
dtype: string
splits:
- name: test
num_bytes: 3795
num_examples: 9
download_size: 6835
dataset_size: 3795
---
# Dataset Card for "mmlu-global_facts-original-neg-prepend"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
theGhoul21/t-pas-test-alpaca-format-pre-synthetic | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 10446887
num_examples: 12220
download_size: 2424338
dataset_size: 10446887
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
dpratishraj7991/mini-platypus-two-pratish | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4186564
num_examples: 1000
download_size: 2245921
dataset_size: 4186564
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
C-MTEB/QBQTC | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 524191
num_examples: 5000
download_size: 387552
dataset_size: 524191
---
# Dataset Card for "QBQTC"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
cmu-mlsp/librispeech960-encodec1024_asr_tokenized_final | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: validation_tts
path: data/validation_tts-*
- split: test
path: data/test-*
- split: test_tts
path: data/test_tts-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 7058957907
num_examples: 281241
- name: validation
num_bytes: 79544090
num_examples: 5406
- name: validation_tts
num_bytes: 39772045
num_examples: 2703
- name: test
num_bytes: 39828951
num_examples: 2620
- name: test_tts
num_bytes: 39828951
num_examples: 2620
download_size: 620258987
dataset_size: 7257931944
---
# Dataset Card for "librispeech960-encodec1024_asr_tokenized_final"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DeepIQInc/bird-with-chat | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
dataset_info:
features:
- name: question
dtype: string
- name: sql
dtype: string
- name: db_id
dtype: string
- name: prompt
dtype: string
- name: question_id
dtype: int64
- name: difficulty
dtype: string
splits:
- name: test
num_bytes: 3809461
num_examples: 1534
- name: train
num_bytes: 28109688
num_examples: 8571
download_size: 3691390
dataset_size: 31919149
---
# Dataset Card for "bird-with-chat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/gr_mp5_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of gr_mp5/GrMP5/MP5 (Girls' Frontline)
This is the dataset of gr_mp5/GrMP5/MP5 (Girls' Frontline), containing 82 images and their tags.
The core tags of this character are `long_hair, blue_eyes, hat, beret, red_headwear, white_hair, blonde_hair, breasts, bow`, 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 | 82 | 74.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gr_mp5_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 82 | 53.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gr_mp5_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 172 | 100.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gr_mp5_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 82 | 70.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gr_mp5_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 172 | 124.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gr_mp5_girlsfrontline/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/gr_mp5_girlsfrontline',
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 | 41 |  |  |  |  |  | 1girl, solo, looking_at_viewer, dress, red_necktie, sleeveless, open_mouth, black_pantyhose, smile, blush, simple_background, bare_shoulders, full_body, holding_weapon, red_footwear, submachine_gun |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | dress | red_necktie | sleeveless | open_mouth | black_pantyhose | smile | blush | simple_background | bare_shoulders | full_body | holding_weapon | red_footwear | submachine_gun |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------|:--------------|:-------------|:-------------|:------------------|:--------|:--------|:--------------------|:-----------------|:------------|:-----------------|:---------------|:-----------------|
| 0 | 41 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
40AF/Petra | ---
license: artistic-2.0
pretty_name: Petra
--- |
liuyanchen1015/VALUE_mnli_got | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: train
num_bytes: 6007046
num_examples: 25203
- name: dev_matched
num_bytes: 136053
num_examples: 611
- name: dev_mismatched
num_bytes: 130788
num_examples: 511
- name: test_matched
num_bytes: 152545
num_examples: 644
- name: test_mismatched
num_bytes: 113320
num_examples: 482
download_size: 4055143
dataset_size: 6539752
---
# Dataset Card for "VALUE2_mnli_got"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
chirunder/GRE_synonyms_gregmat | ---
dataset_info:
features:
- name: html
dtype: string
splits:
- name: train
num_bytes: 1025161
num_examples: 310
download_size: 212008
dataset_size: 1025161
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "GRE_synonyms_gregmat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
senhorsapo/kocho | ---
license: openrail
---
|
dim/azbyka_logic_ru | ---
dataset_info:
features:
- name: task
dtype: string
- name: solution
dtype: string
- name: link
dtype: string
- name: long_solution
dtype: string
splits:
- name: train
num_bytes: 205135
num_examples: 480
download_size: 96545
dataset_size: 205135
---
# Dataset Card for "azbyka_logic_ru"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yuansiwe/sec-prompt-answer-kelv | ---
license: apache-2.0
---
|
autoevaluate/autoeval-eval-samsum-samsum-2c3c14-1486454326 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum
metrics: []
dataset_name: samsum
dataset_config: samsum
dataset_split: train
col_mapping:
text: dialogue
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum
* Dataset: samsum
* Config: samsum
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@samuelallen123](https://huggingface.co/samuelallen123) for evaluating this model. |
CyberHarem/nadeshiko_lapisrelights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Nadeshiko (Lapis Re:LiGHTs)
This is the dataset of Nadeshiko (Lapis Re:LiGHTs), containing 81 images and their tags.
The core tags of this character are `long_hair, hair_ornament, hair_flower, purple_eyes, grey_hair, purple_hair`, 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 | 81 | 53.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nadeshiko_lapisrelights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 81 | 45.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nadeshiko_lapisrelights/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 159 | 80.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nadeshiko_lapisrelights/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 81 | 53.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nadeshiko_lapisrelights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 159 | 93.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nadeshiko_lapisrelights/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/nadeshiko_lapisrelights',
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 | 18 |  |  |  |  |  | 1girl, solo, flower, blush, collarbone, closed_mouth, school_uniform, sailor_collar, forehead, puffy_short_sleeves, smile, frills, parted_bangs, upper_body, white_shirt |
| 1 | 5 |  |  |  |  |  | 2girls, flower, outdoors, bangs, black_hair, cloud, collarbone, dress, mountain, puffy_short_sleeves, red_sailor_collar, school_uniform, skirt, sky, solo_focus, blush, closed_mouth, frilled_sleeves |
| 2 | 8 |  |  |  |  |  | 1girl, detached_sleeves, flower, bare_shoulders, solo, bow, green_dress, smile, blush, breasts, closed_mouth, bangs, collarbone, detached_collar, open_mouth, white_gloves |
| 3 | 6 |  |  |  |  |  | 1girl, detached_sleeves, cherry_blossoms, holding, smile, solo, standing, dress, outdoors, very_long_hair, white_gloves, bow, closed_mouth, floating_hair, flower, hand_fan, kimono, skirt, striped, thighhighs |
| 4 | 7 |  |  |  |  |  | 1girl, fingerless_gloves, outdoors, solo, flower, smile, black_gloves, closed_mouth, holding, blush, food, tree, bike_shorts, boots, breasts, day, looking_at_viewer, nature, upper_body |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | flower | blush | collarbone | closed_mouth | school_uniform | sailor_collar | forehead | puffy_short_sleeves | smile | frills | parted_bangs | upper_body | white_shirt | 2girls | outdoors | bangs | black_hair | cloud | dress | mountain | red_sailor_collar | skirt | sky | solo_focus | frilled_sleeves | detached_sleeves | bare_shoulders | bow | green_dress | breasts | detached_collar | open_mouth | white_gloves | cherry_blossoms | holding | standing | very_long_hair | floating_hair | hand_fan | kimono | striped | thighhighs | fingerless_gloves | black_gloves | food | tree | bike_shorts | boots | day | looking_at_viewer | nature |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------|:--------|:-------------|:---------------|:-----------------|:----------------|:-----------|:----------------------|:--------|:---------|:---------------|:-------------|:--------------|:---------|:-----------|:--------|:-------------|:--------|:--------|:-----------|:--------------------|:--------|:------|:-------------|:------------------|:-------------------|:-----------------|:------|:--------------|:----------|:------------------|:-------------|:---------------|:------------------|:----------|:-----------|:-----------------|:----------------|:-----------|:---------|:----------|:-------------|:--------------------|:---------------|:-------|:-------|:--------------|:--------|:------|:--------------------|:---------|
| 0 | 18 |  |  |  |  |  | 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 | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | X | X | X | X | X | | | | | X | | | | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | X | X | | | X | | | | | X | | | | | | X | | | | X | | | X | | | | X | | X | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 4 | 7 |  |  |  |  |  | X | X | X | X | | X | | | | | X | | | X | | | X | | | | | | | | | | | | | | | X | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X |
|
Thouph/caption-test | ---
license: wtfpl
---
|
polinaeterna/big_example | ---
dataset_info:
features:
- name: int
dtype: int64
- name: float
dtype: float64
- name: bool
dtype: bool
- name: string
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 638874579
num_examples: 5000000
download_size: 86966133
dataset_size: 638874579
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ashercn97/OpenOrcaSmaller2 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 284383027
num_examples: 156291
download_size: 161343770
dataset_size: 284383027
---
# Dataset Card for "OpenOrcaSmaller2"
This is a small subset of the OpenOrca dataset that I got rid of all of the missing rows and changed it to an Alpaca format. I will hopefully use this to finetune a small model!
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
parsa-mz/covid-qa-squad | ---
dataset_info:
features:
- name: id
dtype: int64
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
dtype: string
splits:
- name: train
num_bytes: 48698233
num_examples: 1417
- name: validation
num_bytes: 4320948
num_examples: 203
- name: test
num_bytes: 11620291
num_examples: 375
download_size: 2248635
dataset_size: 64639472
---
# Dataset Card for "covid-qa-squad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
legacy107/qa_wikipedia_no_article | ---
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: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answer_start
dtype: int64
- name: answer
dtype: string
splits:
- name: train
num_bytes: 74337363
num_examples: 138712
- name: test
num_bytes: 9222514
num_examples: 17341
- name: validation
num_bytes: 9271740
num_examples: 17291
download_size: 25137600
dataset_size: 92831617
---
# Dataset Card for "qa_wikipedia_no_article"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
LeLaboDuGame/NastorWhisperDS | ---
task_categories:
- translation
language:
- fr
tags:
- audio
- translate
pretty_name: train
--- |
CyberHarem/ortlinde_fgo | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of ortlinde/オルトリンデ/奥特琳德 (Fate/Grand Order)
This is the dataset of ortlinde/オルトリンデ/奥特琳德 (Fate/Grand Order), containing 128 images and their tags.
The core tags of this character are `black_hair, red_eyes, short_hair, wings, breasts, head_wings, hair_between_eyes, 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 | 128 | 115.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ortlinde_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 128 | 104.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ortlinde_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 292 | 199.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ortlinde_fgo/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/ortlinde_fgo',
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 | 28 |  |  |  |  |  | 1girl, solo, looking_at_viewer, bracelet, hooded_capelet, blush, white_capelet, hood_up, boots, thighhighs, armored_dress, breastplate, white_dress |
| 1 | 5 |  |  |  |  |  | 1girl, blush, looking_at_viewer, simple_background, smile, solo, white_background, long_sleeves, thighs, collarbone, open_mouth, ribbed_sweater, turtleneck_sweater, white_sweater |
| 2 | 5 |  |  |  |  |  | 1girl, blush, smile, solo, cleavage, collarbone, looking_at_viewer, simple_background, white_background, bare_shoulders, dress, medium_breasts, barefoot, navel, sitting |
| 3 | 5 |  |  |  |  |  | 1girl, black_dress, blush, enmaided, long_sleeves, looking_at_viewer, maid_apron, maid_headdress, solo, white_apron, puffy_sleeves, smile, brooch, thighs, white_background, black_pantyhose, clothes_lift, holding, simple_background |
| 4 | 5 |  |  |  |  |  | 1girl, blush, collared_shirt, looking_at_viewer, solo, white_background, white_shirt, long_sleeves, smile, dress_shirt, school_uniform, simple_background, black_skirt, bow, jacket, necklace, ribbon, thighs |
| 5 | 10 |  |  |  |  |  | black_one-piece_swimsuit, looking_at_viewer, 1girl, black_jacket, thighs, black_gloves, long_sleeves, open_jacket, solo, black_headwear, choker, beret, gun, hood, medium_breasts, blush, cleavage, closed_mouth, highleg_swimsuit, smile |
| 6 | 6 |  |  |  |  |  | christmas, fur-trimmed_capelet, fur-trimmed_headwear, santa_hat, white_capelet, fur-trimmed_dress, looking_at_viewer, white_dress, white_gloves, white_headwear, 2girls, blush, black_pantyhose, cleavage, gift_box, holding, santa_costume, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | bracelet | hooded_capelet | blush | white_capelet | hood_up | boots | thighhighs | armored_dress | breastplate | white_dress | simple_background | smile | white_background | long_sleeves | thighs | collarbone | open_mouth | ribbed_sweater | turtleneck_sweater | white_sweater | cleavage | bare_shoulders | dress | medium_breasts | barefoot | navel | sitting | black_dress | enmaided | maid_apron | maid_headdress | white_apron | puffy_sleeves | brooch | black_pantyhose | clothes_lift | holding | collared_shirt | white_shirt | dress_shirt | school_uniform | black_skirt | bow | jacket | necklace | ribbon | black_one-piece_swimsuit | black_jacket | black_gloves | open_jacket | black_headwear | choker | beret | gun | hood | closed_mouth | highleg_swimsuit | christmas | fur-trimmed_capelet | fur-trimmed_headwear | santa_hat | fur-trimmed_dress | white_gloves | white_headwear | 2girls | gift_box | santa_costume |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:-----------|:-----------------|:--------|:----------------|:----------|:--------|:-------------|:----------------|:--------------|:--------------|:--------------------|:--------|:-------------------|:---------------|:---------|:-------------|:-------------|:-----------------|:---------------------|:----------------|:-----------|:-----------------|:--------|:-----------------|:-----------|:--------|:----------|:--------------|:-----------|:-------------|:-----------------|:--------------|:----------------|:---------|:------------------|:---------------|:----------|:-----------------|:--------------|:--------------|:-----------------|:--------------|:------|:---------|:-----------|:---------|:---------------------------|:---------------|:---------------|:--------------|:-----------------|:---------|:--------|:------|:-------|:---------------|:-------------------|:------------|:----------------------|:-----------------------|:------------|:--------------------|:---------------|:-----------------|:---------|:-----------|:----------------|
| 0 | 28 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | X | | | X | | | | | | | | X | X | X | | | X | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | |
| 5 | 10 |  |  |  |  |  | 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 |
|
NickyNicky/OpenHermes-2.5_clusters_gemma | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: weight
dtype: float64
- name: language
dtype: string
- name: system_prompt
dtype: string
- name: topic
dtype: string
- name: conversations_format_gemma
dtype: string
- name: detect_language
dtype: string
splits:
- name: clusters_1
num_bytes: 16169744
num_examples: 5000
- name: clusters_3
num_bytes: 16729284
num_examples: 5000
- name: clusters_9
num_bytes: 24346006
num_examples: 5000
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num_bytes: 16650122
num_examples: 5000
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num_examples: 5000
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num_examples: 5000
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num_examples: 5000
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configs:
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data_files:
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path: data/clusters_198-*
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---
|
Teklia/NorHand-v2-line | ---
license: mit
language:
- nb
task_categories:
- image-to-text
pretty_name: NorHand-v2-line
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_examples: 145008
- name: validation
num_examples: 14965
- name: test
num_examples: 1792
dataset_size: 161831
tags:
- atr
- htr
- ocr
- historical
- handwritten
---
# NorHand v2 - line level
## Table of Contents
- [NorHand v2 - line level](#norhand-v2-line-level)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
## Dataset Description
- **Homepage:** [Hugin-Munin project](https://hugin-munin-project.github.io/)
- **Source:** [Zenodo](https://zenodo.org/records/10555698)
- **Point of Contact:** [TEKLIA](https://teklia.com)
## Dataset Summary
The NorHand v2 dataset comprises Norwegian letter and diary line images and text from 19th and early 20th century.
Note that all images are resized to a fixed height of 128 pixels.
### Languages
All the documents in the dataset are written in Norwegian Bokmål.
## Dataset Structure
### Data Instances
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4300x128 at 0x1A800E8E190,
'text': 'og Hjertelighed'
}
```
### Data Fields
- `image`: a PIL.Image.Image object containing the image. Note that when accessing the image column (using 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].
- `text`: the label transcription of the image. |
CyberHarem/lainie_cyan_tenseioujototensaireijounomahoukakumei | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Lainie Cyan
This is the dataset of Lainie Cyan, containing 102 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 102 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 194 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 102 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 102 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 102 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 102 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 102 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 194 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 194 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 194 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/mary_anning_fgo | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of mary_anning/メアリー・アニング/玛丽·安宁 (Fate/Grand Order)
This is the dataset of mary_anning/メアリー・アニング/玛丽·安宁 (Fate/Grand Order), containing 24 images and their tags.
The core tags of this character are `brown_hair, yellow_eyes, long_hair, hat, horns, braid, bow, slit_pupils, hair_bow`, 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 | 24 | 26.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mary_anning_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 24 | 23.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mary_anning_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 54 | 44.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mary_anning_fgo/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/mary_anning_fgo',
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 | 24 |  |  |  |  |  | 1girl, long_sleeves, solo, yellow_scarf, looking_at_viewer, skirt, closed_mouth, holding, smile, simple_background, white_shirt, gloves, apron, jacket, mittens, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | long_sleeves | solo | yellow_scarf | looking_at_viewer | skirt | closed_mouth | holding | smile | simple_background | white_shirt | gloves | apron | jacket | mittens | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------|:---------------|:--------------------|:--------|:---------------|:----------|:--------|:--------------------|:--------------|:---------|:--------|:---------|:----------|:-------------------|
| 0 | 24 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
Rakshit122/1a | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence: string
splits:
- name: train
num_bytes: 46270
num_examples: 226
download_size: 16707
dataset_size: 46270
---
# Dataset Card for "1a"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
psroy/mini-platypus-scienceqa-two | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 702431
num_examples: 1000
download_size: 297956
dataset_size: 702431
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Vezora/Wizard_Math_Alpaca | ---
license: apache-2.0
---
This contains both the Math.json and GM8SK.jsonl, Converted to Alpaca format. GM8sk.jsonl was used for evaluating, and the math file was used for training.
MATH_Alpaca.json contains ~ 5,000 examples for evaluating.
gm8sk_Alpaca.json contains ~1,000 examples for evaluation.
nothing stops you from using this either one to train a model.
For ALPACA LORA users: Modules you can target with lora:"gate_proj", "down_proj", "up_proj", "q_proj", "v_proj", "k_proj", "o_proj" Most lora models use:"q_proj", "v_proj", "k_proj", "o_proj" Platypus which got terrific results: "gate_proj", "down_proj", "up_proj"
Research on targeting certain modules still needs to be done, but if you don't want to train over a previously trained models newly learned abilities, target different modules than the ones used for original training.
Hyper perameters used by Platypus: Hyperparameters for 13B and 70B Models Hyperparameter Platypus2-13B / 70B batch size 16 micro batch size 1 num epochs 1 learning rate 4e-4 / 3e-4 cutoff len 4096 lora rank 16 lora alpha 16 lora dropout 0.05 lora target modules gate_proj, down_proj, up_proj train on inputs False add eos token False group by length False prompt template alpaca lr scheduler cosine warmup steps 100
I would reccomend using a batch size of 4-10, and cutt off length to ≤ 2048 to avoid using vram issues. Load_in_4bit, Normal Float, and bf16. For single 24 gig card. If training with oobabooga you must edit the "training.py" file in the "oobabooga_windows\text-generation-webui\modules" folder. In line 49 edit standard modules to the modules you would like to target. If training with alpaca lora use the argument --lora_target_modules when running the train.py command. To load in 4bit you must edit the train file, adding load in 4 bit, bf16, and normal float quant. |
CyberHarem/yoshikawa_yuko_soundeuphonium | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Yoshikawa Yūko
This is the dataset of Yoshikawa Yūko, containing 180 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 180 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 427 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 180 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 180 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 180 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 180 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 180 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 427 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 427 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 427 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
pablouribe/speech2text_robustness_es | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: language
dtype: string
- name: accent
dtype: string
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 17098698.0
num_examples: 30
download_size: 14555723
dataset_size: 17098698.0
---
# Dataset Card for "speech2text_robustness_es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
scb_mt_enth_2020 | ---
annotations_creators:
- crowdsourced
- expert-generated
- found
- machine-generated
language_creators:
- expert-generated
- found
- machine-generated
language:
- en
- th
license:
- cc-by-sa-4.0
multilinguality:
- translation
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: scb-mt-en-th-2020
pretty_name: ScbMtEnth2020
dataset_info:
- config_name: enth
features:
- name: translation
dtype:
translation:
languages:
- en
- th
- name: subdataset
dtype: string
splits:
- name: train
num_bytes: 390411946
num_examples: 801402
- name: validation
num_bytes: 54167280
num_examples: 100173
- name: test
num_bytes: 53782790
num_examples: 100177
download_size: 138415559
dataset_size: 498362016
- config_name: then
features:
- name: translation
dtype:
translation:
languages:
- th
- en
- name: subdataset
dtype: string
splits:
- name: train
num_bytes: 390411946
num_examples: 801402
- name: validation
num_bytes: 54167280
num_examples: 100173
- name: test
num_bytes: 53782790
num_examples: 100177
download_size: 138415559
dataset_size: 498362016
---
# Dataset Card for `scb_mt_enth_2020`
## 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://airesearch.in.th/
- **Repository:** https://github.com/vistec-AI/thai2nmt
- **Paper:** https://arxiv.org/abs/2007.03541
- **Leaderboard:**
- **Point of Contact:** https://airesearch.in.th/
### Dataset Summary
scb-mt-en-th-2020: A Large English-Thai Parallel Corpus
The primary objective of our work is to build a large-scale English-Thai dataset for machine translation.
We construct an English-Thai machine translation dataset with over 1 million segment pairs, curated from various sources,
namely news, Wikipedia articles, SMS messages, task-based dialogs, web-crawled data and government documents.
Methodology for gathering data, building parallel texts and removing noisy sentence pairs are presented in a reproducible manner.
We train machine translation models based on this dataset. Our models' performance are comparable to that of
Google Translation API (as of May 2020) for Thai-English and outperform Google when the Open Parallel Corpus (OPUS) is
included in the training data for both Thai-English and English-Thai translation.
The dataset, pre-trained models, and source code to reproduce our work are available for public use.
### Supported Tasks and Leaderboards
machine translation
### Languages
English, Thai
## Dataset Structure
### Data Instances
```
{'subdataset': 'aqdf', 'translation': {'en': 'FAR LEFT: Indonesian National Police Chief Tito Karnavian, from left, Philippine National Police Chief Ronald Dela Rosa and Royal Malaysian Police Inspector General Khalid Abu Bakar link arms before the Trilateral Security Meeting in Pasay city, southeast of Manila, Philippines, in June 2017. [THE ASSOCIATED PRESS]', 'th': '(ซ้ายสุด) นายติโต คาร์นาเวียน ผู้บัญชาการตํารวจแห่งชาติอินโดนีเซีย (จากซ้าย) นายโรนัลด์ เดลา โรซา ผู้บัญชาการตํารวจแห่งชาติฟิลิปปินส์ และนายคาลิด อาบู บาการ์ ผู้บัญชาการตํารวจแห่งชาติมาเลเซีย ไขว้แขนกันก่อนเริ่มการประชุมความมั่นคงไตรภาคีในเมืองปาเซย์ ซึ่งอยู่ทางตะวันออกเฉียงใต้ของกรุงมะนิลา ประเทศฟิลิปปินส์ ในเดือนมิถุนายน พ.ศ. 2560 ดิแอสโซซิเอทเต็ด เพรส'}}
{'subdataset': 'thai_websites', 'translation': {'en': "*Applicants from certain countries may be required to pay a visa issuance fee after their application is approved. The Department of State's website has more information about visa issuance fees and can help you determine if an issuance fee applies to your nationality.", 'th': 'ประเภทวีซ่า รวมถึงค่าธรรมเนียม และข้อกําหนดในการสัมภาษณ์วีซ่า จะขึ้นอยู่กับชนิดของหนังสือเดินทาง และจุดประสงค์ในการเดินทางของท่าน โปรดดูตารางด้านล่างก่อนการสมัครวีซ่า'}}
{'subdataset': 'nus_sms', 'translation': {'en': 'Yup... Okay. Cya tmr... So long nvr write already... Dunno whether tmr can come up with 500 words', 'th': 'ใช่...ได้ แล้วเจอกันพรุ่งนี้... นานแล้วไม่เคยเขียน... ไม่รู้ว่าพรุ่งนี้จะทําได้ถึง500คําไหมเลย'}}
```
### Data Fields
- `subdataset`: subdataset from which the sentence pair comes from
- `translation`:
- `en`: English sentences (original source)
- `th`: Thai sentences (originally target for translation)
### Data Splits
```
Split ratio (train, valid, test) : (0.8, 0.1, 0.1)
Number of paris (train, valid, test): 801,402 | 100,173 | 100,177
# Train
generated_reviews_yn: 218,637 ( 27.28% )
task_master_1: 185,671 ( 23.17% )
generated_reviews_translator: 105,561 ( 13.17% )
thai_websites: 93,518 ( 11.67% )
paracrawl: 46,802 ( 5.84% )
nus_sms: 34,495 ( 4.30% )
mozilla_common_voice: 2,451 ( 4.05% )
wikipedia: 26,163 ( 3.26% cd)
generated_reviews_crowd: 19,769 ( 2.47% )
assorted_government: 19,712 ( 2.46% )
aqdf: 10,466 ( 1.31% )
msr_paraphrase: 8,157 ( 1.02% )
# Valid
generated_reviews_yn: 30,786 ( 30.73% )
task_master_1: 18,531 ( 18.50% )
generated_reviews_translator: 13,884 ( 13.86% )
thai_websites: 13,381 ( 13.36% )
paracrawl: 6,618 ( 6.61% )
nus_sms: 4,628 ( 4.62% )
wikipedia: 3,796 ( 3.79% )
assorted_government: 2,842 ( 2.83% )
generated_reviews_crowd: 2,409 ( 2.40% )
aqdf: 1,518 ( 1.52% )
msr_paraphrase: 1,107 ( 1.11% )
mozilla_common_voice: 673 ( 0.67% )
# Test
generated_reviews_yn: 30,785 ( 30.73% )
task_master_1: 18,531 ( 18.50% )
generated_reviews_translator: 13,885 ( 13.86% )
thai_websites: 13,381 ( 13.36% )
paracrawl: 6,619 ( 6.61% )
nus_sms: 4,627 ( 4.62% )
wikipedia: 3,797 ( 3.79% )
assorted_government: 2,844 ( 2.83% )
generated_reviews_crowd: 2,409 ( 2.40% )
aqdf: 1,519 ( 1.52% )
msr_paraphrase: 1,107 ( 1.11% )
mozilla_common_voice : 673 ( 0.67% )
```
## Dataset Creation
### Curation Rationale
[AIResearch](https://airesearch.in.th/), funded by [VISTEC](https://www.vistec.ac.th/) and [depa](https://www.depa.or.th/th/home), curated this dataset as part of public NLP infrastructure. The center releases the dataset and baseline models under CC-BY-SA 4.0.
### Source Data
#### Initial Data Collection and Normalization
The sentence pairs are curated from news, Wikipedia articles, SMS messages, task-based dialogs, webcrawled data and government documents. Sentence pairs are generated by:
- Professional translators
- Crowdsourced translators
- Google Translate API and human annotators (accepted or rejected)
- Sentence alignment with [multilingual universal sentence encoder](https://tfhub.dev/google/universal-sentence-encoder-multilingual/3); the author created [CRFCut](https://github.com/vistec-AI/crfcut) to segment Thai sentences to be abel to align with their English counterparts (sentence segmented by [NLTK](https://www.nltk.org/))
For detailed explanation of dataset curation, see https://arxiv.org/pdf/2007.03541.pdf
### Annotations
#### Sources and Annotation process
- generated_reviews_yn: generated by [CTRL](https://arxiv.org/abs/1909.05858), translated to Thai by Google Translate API and annotated as accepted or rejected by human annotators (we do not include rejected sentence pairs)
- task_master_1: [Taskmaster-1](https://research.google/tools/datasets/taskmaster-1/) translated by professional translators hired by [AIResearch](https://airesearch.in.th/)
- generated_reviews_translator: professional translators hired by [AIResearch](https://airesearch.in.th/)
- thai_websites: webcrawling from top 500 websites in Thailand; respective content creators; the authors only did sentence alignment
- paracrawl: replicating Paracrawl's methodology for webcrawling; respective content creators; the authors only did sentence alignment
- nus_sms: [The National University of Singapore SMS Corpus](https://scholarbank.nus.edu.sg/handle/10635/137343) translated by crowdsourced translators hired by [AIResearch](https://airesearch.in.th/)
- wikipedia: Thai Wikipedia; respective content creators; the authors only did sentence alignment
- assorted_government: Government document in PDFs from various government websites; respective content creators; the authors only did sentence alignment
- generated_reviews_crowd: generated by [CTRL](https://arxiv.org/abs/1909.05858), translated to Thai by crowdsourced translators hired by [AIResearch](https://airesearch.in.th/)
- aqdf: Bilingual news from [Asia Pacific Defense Forum](https://ipdefenseforum.com/); respective content creators; the authors only did sentence alignment
- msr_paraphrase: [Microsoft Research Paraphrase Corpus](https://www.microsoft.com/en-us/download/details.aspx?id=52398) translated to Thai by crowdsourced translators hired by [AIResearch](https://airesearch.in.th/)
- mozilla_common_voice: English version of [Mozilla Common Voice](https://commonvoice.mozilla.org/) translated to Thai by crowdsourced translators hired by [AIResearch](https://airesearch.in.th/)
### Personal and Sensitive Information
There are risks of personal information to be included in the webcrawled data namely `paracrawl` and `thai_websites`.
## Considerations for Using the Data
### Social Impact of Dataset
- The first and currently largest English-Thai machine translation dataset that is strictly cleaned and deduplicated, compare to other sources such as Paracrawl.
### Discussion of Biases
- Gender-based ending honorifics in Thai (ครับ/ค่ะ) might not be balanced due to more female translators than male for `task_master_1`
### Other Known Limitations
#### Segment Alignment between Languages With and Without Boundaries
Unlike English, there is no segment boundary marking in Thai. One segment in Thai may or may not cover all
the content of an English segment. Currently, we mitigate this problem by grouping Thai segments together before
computing the text similarity scores. We then choose the combination with the highest text similarity score. It can be
said that adequacy is the main issue in building this dataset.
Quality of Translation from Crawled Websites
Some websites use machine translation models such as Google Translate to localize their content. As a result, Thai
segments retrieved from web crawling might face issues of fluency since we do not use human annotators to perform
quality control.
#### Quality Control of Crowdsourced Translators
When we use a crowdsourcing platform to translate the content, we can not fully control the quality of the translation.
To combat this, we filter out low-quality segments by using a text similarity threshold, based on cosine similarity of
universal sentence encoder vectors. Moreover, some crowdsourced translators might copy and paste source segments to
a translation engine and take the results as answers to the platform. To further improve, we can apply techniques such
as described in [Zaidan, 2012] to control the quality and avoid fraud on the platform.
#### Domain Dependence of Machine Tranlsation Models
We test domain dependence of machine translation models by comparing models trained and tested on the same dataset,
using 80/10/10 train-validation-test split, and models trained on one dataset and tested on the other.
## Additional Information
### Dataset Curators
[AIResearch](https://airesearch.in.th/), funded by [VISTEC](https://www.vistec.ac.th/) and [depa](https://www.depa.or.th/th/home)
### Licensing Information
CC-BY-SA 4.0
### Citation Information
```
@article{lowphansirikul2020scb,
title={scb-mt-en-th-2020: A Large English-Thai Parallel Corpus},
author={Lowphansirikul, Lalita and Polpanumas, Charin and Rutherford, Attapol T and Nutanong, Sarana},
journal={arXiv preprint arXiv:2007.03541},
year={2020}
}
```
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. |
raiyan007/MSCOCO_BANGLA | ---
license: apache-2.0
---
|
spawn99/PersuasionForGood | ---
license: mit
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: Unit
dtype: string
- name: Turn
dtype: int64
- name: B4
dtype: int64
- name: B2
dtype: string
splits:
- name: FullDialog
num_bytes: 3043959
num_examples: 20932
download_size: 1186349
dataset_size: 3043959
configs:
- config_name: default
data_files:
- split: FullDialog
path: data/FullDialog-*
---
Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good
Dataset and Codebase for Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good
published as a long paper in ACL 2019.
https://arxiv.org/abs/1906.06725
If you use the datasets or any source codes included in this repository in your
work, please cite the following paper. The bibtex is listed below:
@article{wang2019persuasion,
title={Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good},
author={Wang, Xuewei and Shi, Weiyan and Kim, Richard and Oh, Yoojung and Yang, Sijia and Zhang, Jingwen and Yu, Zhou},
journal={arXiv preprint arXiv:1906.06725},
year={2019}
}
B2: Dialogue ID
B4: Role (0 means persuader, 1 means persuadee)
Turn: Turn index
Unit: Sentence in utterance
|
vietgpt/openwebtext_en | ---
language: en
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 39769491688
num_examples: 8013769
download_size: 24212906591
dataset_size: 39769491688
---
# Dataset Card for "openwebtext_en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EmbeddingStudio/merged_remote_landscapes_v1 | ---
license: apache-2.0
dataset_info:
features:
- name: image
dtype: image
- name: category
dtype: string
- name: img_id
dtype: string
splits:
- name: train
num_bytes: 687610836.528
num_examples: 26872
- name: test
num_bytes: 178694171.287
num_examples: 6719
download_size: 843239857
dataset_size: 866305007.815
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- image-classification
tags:
- landscapes
- geo
- remote photos
- metric learning
pretty_name: Merged Remote Landscapes v1.0.0
size_categories:
- 10K<n<100K
---
# Dataset Card for Merged Remote Landscapes dataset
[]()
## Dataset summary
This is a merged version of following datasets:
* [torchgeo/ucmerced](https://huggingface.co/datasets/torchgeo/ucmerced)
* [NWPU-RESISC45](https://huggingface.co/datasets/jonathan-roberts1/NWPU-RESISC45)
```python
from datasets import load_dataset
dataset = load_dataset('EmbeddingStudio/merged_remote_landscapes_v1')
```
### Categories
This is a union of categories from original datasets:
agricultural, airplane, airport, baseball diamond, basketball court, beach, bridge, buildings, chaparral, church, circular farmland, cloud, commercial area, desert, forest, freeway, golf course, ground track field, harbor, industrial area, intersection, island, lake, meadow, mountain, overpass, palace, parking lot, railway, railway station, rectangular farmland, residential, river, roundabout, runway, sea ice, ship, snowberg, stadium, storage tanks, tennis court, terrace, thermal power station, wetland
Warning: Synonymous and ambiguous categories were combined (see "Merge method").
## Motivation
EmbeddingStudio is the open-source framework, that allows you transform a joint "Embedding Model + Vector DB" into a full-cycle search engine: collect clickstream -> improve search experience-> adapt embedding model and repeat out of the box.
In the development of EmbeddingStudio the scientific approach is a backbone. On the early stage of the development we can't collect real clickstream data, so to do experiments and choose the best way to improve embedding model we had to use synthetic or emulated data. And the first step is to use the most transparent datasets and the easiest domain.
P.S. this dataset is tagged to be used for the image classification task, but in fact we use it for the metric learning task. And we do another step to emulate clickstream.
We provide this dataset on HuggingFace, so anyone can reproduce our results.
Check our repositories to get more details:
* EmbeddingStudio Framework (coming soon at 22.12.2023)
* Experiments (coming soon)
## Merge method
For this type of dataset it's all simple:
1. Remove duplicates.
2. Resolve synonymous and ambiguous categories with using a simple map (CATEGORIES_MAPPING).
```python
CATEGORIES_MAPPING = {
"dense residential": "residential",
"medium residential": "residential",
"mobile home park": "residential",
"sparse residential": "residential",
"storage tank": "storage tanks",
"storage tanks": "storage tanks",
}
```
All details and code base of merging algorithm will be provided in our experiments repository. If you have any suggestion or you find some mistakes, we will be happy to fix it, so our experimental data will have better quality.
## Contact info
* Alexander Yudaev [email](alexander@yudaev.ru ) [LikedIn](https://www.linkedin.com/in/alexanderyudaev/) |
AISimplyExplained/RBI_Notifications | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 27547387
num_examples: 97539
download_size: 12509781
dataset_size: 27547387
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
alvations/c4p0-v2-ko-en | ---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
- name: target_backto_source
dtype: string
- name: raw_target
list:
- name: generated_text
dtype: string
- name: raw_target_backto_source
list:
- name: generated_text
dtype: string
- name: prompt
dtype: string
- name: reverse_prompt
dtype: string
- name: source_langid
dtype: string
- name: target_langid
dtype: string
- name: target_backto_source_langid
dtype: string
- name: doc_id
dtype: int64
- name: sent_id
dtype: int64
- name: timestamp
dtype: string
- name: url
dtype: string
- name: doc_hash
dtype: string
- name: dataset
dtype: string
- name: source_lang
dtype: string
- name: target_lang
dtype: string
splits:
- name: train
num_bytes: 19982576
num_examples: 15542
download_size: 6395520
dataset_size: 19982576
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
y2lan/japan-law | ---
license: mit
task_categories:
- summarization
- text-generation
- question-answering
language:
- ja
size_categories:
- 1K<n<10K
---
# Japanese Laws
This dataset comprises 8.75K law records retrieved from the official Japanese government website [e-Gov](https://elaws.e-gov.go.jp/). Each entry furnishes comprehensive details about a particular law, encapsulating its number, title, unique ID, the date it came into effect, and its complete text.
To ensure the dataset's uniqueness, deduplication was executed based on the most recent effective version as of August 1, 2023.
A typical entry in this dataset is structured as follows:
```json
{
"num": "Law Number (e.g., Reiwa 5th Year Pollution Adjustment Committee Rule No. 1)",
"title": "Title of the Law",
"id": "Unique Identifier for the Law",
"date": "Date the Law Became Effective",
"body": "Full Text of the Law"
}
``` |
pawan2411/kdf_train | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: relation
dtype: string
splits:
- name: train
num_bytes: 6582592.170553064
num_examples: 20049
- name: test
num_bytes: 6894.829446935725
num_examples: 21
download_size: 3139404
dataset_size: 6589487.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
open-llm-leaderboard/details_codellama__CodeLlama-70b-Instruct-hf | ---
pretty_name: Evaluation run of codellama/CodeLlama-70b-Instruct-hf
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf)\
\ 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_codellama__CodeLlama-70b-Instruct-hf\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-02T06:15:21.306042](https://huggingface.co/datasets/open-llm-leaderboard/details_codellama__CodeLlama-70b-Instruct-hf/blob/main/results_2024-02-02T06-15-21.306042.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\": 0.5648604524571269,\n\
\ \"acc_stderr\": 0.03399038244243267,\n \"acc_norm\": 0.5673299922382132,\n\
\ \"acc_norm_stderr\": 0.034688595336715734,\n \"mc1\": 0.3525091799265606,\n\
\ \"mc1_stderr\": 0.016724646380756544,\n \"mc2\": 0.5044393244952377,\n\
\ \"mc2_stderr\": 0.015451705191766632\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5179180887372014,\n \"acc_stderr\": 0.014602005585490982,\n\
\ \"acc_norm\": 0.5503412969283277,\n \"acc_norm_stderr\": 0.014537144444284741\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5812587134037045,\n\
\ \"acc_stderr\": 0.0049234456278615234,\n \"acc_norm\": 0.7723561043616809,\n\
\ \"acc_norm_stderr\": 0.00418454567538735\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\": 0.5259259259259259,\n\
\ \"acc_stderr\": 0.04313531696750575,\n \"acc_norm\": 0.5259259259259259,\n\
\ \"acc_norm_stderr\": 0.04313531696750575\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5723684210526315,\n \"acc_stderr\": 0.04026097083296562,\n\
\ \"acc_norm\": 0.5723684210526315,\n \"acc_norm_stderr\": 0.04026097083296562\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.539622641509434,\n \"acc_stderr\": 0.030676096599389177,\n\
\ \"acc_norm\": 0.539622641509434,\n \"acc_norm_stderr\": 0.030676096599389177\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5208333333333334,\n\
\ \"acc_stderr\": 0.04177578950739993,\n \"acc_norm\": 0.5208333333333334,\n\
\ \"acc_norm_stderr\": 0.04177578950739993\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|5\": {\n \"acc\"\
: 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n\
\ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.48554913294797686,\n\
\ \"acc_stderr\": 0.03810871630454764,\n \"acc_norm\": 0.48554913294797686,\n\
\ \"acc_norm_stderr\": 0.03810871630454764\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.045766654032077636,\n\
\ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.045766654032077636\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\": 0.5234042553191489,\n \"acc_stderr\": 0.03265019475033582,\n\
\ \"acc_norm\": 0.5234042553191489,\n \"acc_norm_stderr\": 0.03265019475033582\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.42105263157894735,\n\
\ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.42105263157894735,\n\
\ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.041546596717075474,\n\
\ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.041546596717075474\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42857142857142855,\n \"acc_stderr\": 0.025487187147859375,\n \"\
acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.025487187147859375\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.635483870967742,\n\
\ \"acc_stderr\": 0.02737987122994325,\n \"acc_norm\": 0.635483870967742,\n\
\ \"acc_norm_stderr\": 0.02737987122994325\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.39408866995073893,\n \"acc_stderr\": 0.034381579670365446,\n\
\ \"acc_norm\": 0.39408866995073893,\n \"acc_norm_stderr\": 0.034381579670365446\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\"\
: 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.03453131801885417,\n\
\ \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.03453131801885417\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.702020202020202,\n \"acc_stderr\": 0.03258630383836556,\n \"acc_norm\"\
: 0.702020202020202,\n \"acc_norm_stderr\": 0.03258630383836556\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\": 0.5230769230769231,\n \"acc_stderr\": 0.025323990861736232,\n\
\ \"acc_norm\": 0.5230769230769231,\n \"acc_norm_stderr\": 0.025323990861736232\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.362962962962963,\n \"acc_stderr\": 0.02931820364520686,\n \
\ \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.02931820364520686\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5294117647058824,\n \"acc_stderr\": 0.03242225027115007,\n \
\ \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.03242225027115007\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.39072847682119205,\n \"acc_stderr\": 0.03983798306659806,\n \"\
acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.03983798306659806\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7504587155963303,\n \"acc_stderr\": 0.018553897629501624,\n \"\
acc_norm\": 0.7504587155963303,\n \"acc_norm_stderr\": 0.018553897629501624\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4305555555555556,\n \"acc_stderr\": 0.03376922151252335,\n \"\
acc_norm\": 0.4305555555555556,\n \"acc_norm_stderr\": 0.03376922151252335\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7303921568627451,\n \"acc_stderr\": 0.031145570659486782,\n \"\
acc_norm\": 0.7303921568627451,\n \"acc_norm_stderr\": 0.031145570659486782\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7510548523206751,\n \"acc_stderr\": 0.028146970599422644,\n \
\ \"acc_norm\": 0.7510548523206751,\n \"acc_norm_stderr\": 0.028146970599422644\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5964125560538116,\n\
\ \"acc_stderr\": 0.03292802819330314,\n \"acc_norm\": 0.5964125560538116,\n\
\ \"acc_norm_stderr\": 0.03292802819330314\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6259541984732825,\n \"acc_stderr\": 0.042438692422305246,\n\
\ \"acc_norm\": 0.6259541984732825,\n \"acc_norm_stderr\": 0.042438692422305246\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7024793388429752,\n \"acc_stderr\": 0.04173349148083499,\n \"\
acc_norm\": 0.7024793388429752,\n \"acc_norm_stderr\": 0.04173349148083499\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6851851851851852,\n\
\ \"acc_stderr\": 0.04489931073591312,\n \"acc_norm\": 0.6851851851851852,\n\
\ \"acc_norm_stderr\": 0.04489931073591312\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7177914110429447,\n \"acc_stderr\": 0.03536117886664742,\n\
\ \"acc_norm\": 0.7177914110429447,\n \"acc_norm_stderr\": 0.03536117886664742\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.04301250399690878,\n\
\ \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.04301250399690878\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8247863247863247,\n\
\ \"acc_stderr\": 0.02490443909891823,\n \"acc_norm\": 0.8247863247863247,\n\
\ \"acc_norm_stderr\": 0.02490443909891823\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.58,\n \"acc_stderr\": 0.04960449637488583,\n \
\ \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.04960449637488583\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7100893997445722,\n\
\ \"acc_stderr\": 0.016225017944770978,\n \"acc_norm\": 0.7100893997445722,\n\
\ \"acc_norm_stderr\": 0.016225017944770978\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5867052023121387,\n \"acc_stderr\": 0.026511261369409244,\n\
\ \"acc_norm\": 0.5867052023121387,\n \"acc_norm_stderr\": 0.026511261369409244\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3553072625698324,\n\
\ \"acc_stderr\": 0.01600698993480318,\n \"acc_norm\": 0.3553072625698324,\n\
\ \"acc_norm_stderr\": 0.01600698993480318\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5522875816993464,\n \"acc_stderr\": 0.02847293847803353,\n\
\ \"acc_norm\": 0.5522875816993464,\n \"acc_norm_stderr\": 0.02847293847803353\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6302250803858521,\n\
\ \"acc_stderr\": 0.027417996705630998,\n \"acc_norm\": 0.6302250803858521,\n\
\ \"acc_norm_stderr\": 0.027417996705630998\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5864197530864198,\n \"acc_stderr\": 0.027402042040269966,\n\
\ \"acc_norm\": 0.5864197530864198,\n \"acc_norm_stderr\": 0.027402042040269966\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.45390070921985815,\n \"acc_stderr\": 0.029700453247291474,\n \
\ \"acc_norm\": 0.45390070921985815,\n \"acc_norm_stderr\": 0.029700453247291474\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.41460234680573665,\n\
\ \"acc_stderr\": 0.012582597058908284,\n \"acc_norm\": 0.41460234680573665,\n\
\ \"acc_norm_stderr\": 0.012582597058908284\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.41911764705882354,\n \"acc_stderr\": 0.029972807170464626,\n\
\ \"acc_norm\": 0.41911764705882354,\n \"acc_norm_stderr\": 0.029972807170464626\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5408496732026143,\n \"acc_stderr\": 0.020160213617222516,\n \
\ \"acc_norm\": 0.5408496732026143,\n \"acc_norm_stderr\": 0.020160213617222516\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6489795918367347,\n \"acc_stderr\": 0.03055531675557364,\n\
\ \"acc_norm\": 0.6489795918367347,\n \"acc_norm_stderr\": 0.03055531675557364\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\": 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.044084400227680794\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.43373493975903615,\n\
\ \"acc_stderr\": 0.03858158940685517,\n \"acc_norm\": 0.43373493975903615,\n\
\ \"acc_norm_stderr\": 0.03858158940685517\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7251461988304093,\n \"acc_stderr\": 0.03424042924691584,\n\
\ \"acc_norm\": 0.7251461988304093,\n \"acc_norm_stderr\": 0.03424042924691584\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3525091799265606,\n\
\ \"mc1_stderr\": 0.016724646380756544,\n \"mc2\": 0.5044393244952377,\n\
\ \"mc2_stderr\": 0.015451705191766632\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.745067087608524,\n \"acc_stderr\": 0.012248806969376422\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4624715693707354,\n \
\ \"acc_stderr\": 0.013733636059107756\n }\n}\n```"
repo_url: https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf
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_02T06_15_21.306042
path:
- '**/details_harness|arc:challenge|25_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|gsm8k|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hellaswag|10_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T06-15-21.306042.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T06-15-21.306042.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- '**/details_harness|winogrande|5_2024-02-02T06-15-21.306042.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-02T06-15-21.306042.parquet'
- config_name: results
data_files:
- split: 2024_02_02T06_15_21.306042
path:
- results_2024-02-02T06-15-21.306042.parquet
- split: latest
path:
- results_2024-02-02T06-15-21.306042.parquet
---
# Dataset Card for Evaluation run of codellama/CodeLlama-70b-Instruct-hf
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) 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_codellama__CodeLlama-70b-Instruct-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-02T06:15:21.306042](https://huggingface.co/datasets/open-llm-leaderboard/details_codellama__CodeLlama-70b-Instruct-hf/blob/main/results_2024-02-02T06-15-21.306042.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": 0.5648604524571269,
"acc_stderr": 0.03399038244243267,
"acc_norm": 0.5673299922382132,
"acc_norm_stderr": 0.034688595336715734,
"mc1": 0.3525091799265606,
"mc1_stderr": 0.016724646380756544,
"mc2": 0.5044393244952377,
"mc2_stderr": 0.015451705191766632
},
"harness|arc:challenge|25": {
"acc": 0.5179180887372014,
"acc_stderr": 0.014602005585490982,
"acc_norm": 0.5503412969283277,
"acc_norm_stderr": 0.014537144444284741
},
"harness|hellaswag|10": {
"acc": 0.5812587134037045,
"acc_stderr": 0.0049234456278615234,
"acc_norm": 0.7723561043616809,
"acc_norm_stderr": 0.00418454567538735
},
"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": 0.5259259259259259,
"acc_stderr": 0.04313531696750575,
"acc_norm": 0.5259259259259259,
"acc_norm_stderr": 0.04313531696750575
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5723684210526315,
"acc_stderr": 0.04026097083296562,
"acc_norm": 0.5723684210526315,
"acc_norm_stderr": 0.04026097083296562
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.6,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.6,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.539622641509434,
"acc_stderr": 0.030676096599389177,
"acc_norm": 0.539622641509434,
"acc_norm_stderr": 0.030676096599389177
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5208333333333334,
"acc_stderr": 0.04177578950739993,
"acc_norm": 0.5208333333333334,
"acc_norm_stderr": 0.04177578950739993
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.48554913294797686,
"acc_stderr": 0.03810871630454764,
"acc_norm": 0.48554913294797686,
"acc_norm_stderr": 0.03810871630454764
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.30392156862745096,
"acc_stderr": 0.045766654032077636,
"acc_norm": 0.30392156862745096,
"acc_norm_stderr": 0.045766654032077636
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
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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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"harness|hendrycksTest-high_school_biology|5": {
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"harness|hendrycksTest-high_school_chemistry|5": {
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"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_government_and_politics|5": {
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_physics|5": {
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"harness|hendrycksTest-high_school_psychology|5": {
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"acc_norm": 0.7504587155963303,
"acc_norm_stderr": 0.018553897629501624
},
"harness|hendrycksTest-high_school_statistics|5": {
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"acc_norm": 0.4305555555555556,
"acc_norm_stderr": 0.03376922151252335
},
"harness|hendrycksTest-high_school_us_history|5": {
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"acc_stderr": 0.031145570659486782,
"acc_norm": 0.7303921568627451,
"acc_norm_stderr": 0.031145570659486782
},
"harness|hendrycksTest-high_school_world_history|5": {
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"acc_stderr": 0.028146970599422644,
"acc_norm": 0.7510548523206751,
"acc_norm_stderr": 0.028146970599422644
},
"harness|hendrycksTest-human_aging|5": {
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"acc_stderr": 0.03292802819330314,
"acc_norm": 0.5964125560538116,
"acc_norm_stderr": 0.03292802819330314
},
"harness|hendrycksTest-human_sexuality|5": {
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"acc_stderr": 0.042438692422305246,
"acc_norm": 0.6259541984732825,
"acc_norm_stderr": 0.042438692422305246
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7024793388429752,
"acc_stderr": 0.04173349148083499,
"acc_norm": 0.7024793388429752,
"acc_norm_stderr": 0.04173349148083499
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.6851851851851852,
"acc_stderr": 0.04489931073591312,
"acc_norm": 0.6851851851851852,
"acc_norm_stderr": 0.04489931073591312
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7177914110429447,
"acc_stderr": 0.03536117886664742,
"acc_norm": 0.7177914110429447,
"acc_norm_stderr": 0.03536117886664742
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.04697113923010212,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.04697113923010212
},
"harness|hendrycksTest-management|5": {
"acc": 0.7475728155339806,
"acc_stderr": 0.04301250399690878,
"acc_norm": 0.7475728155339806,
"acc_norm_stderr": 0.04301250399690878
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8247863247863247,
"acc_stderr": 0.02490443909891823,
"acc_norm": 0.8247863247863247,
"acc_norm_stderr": 0.02490443909891823
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.58,
"acc_stderr": 0.04960449637488583,
"acc_norm": 0.58,
"acc_norm_stderr": 0.04960449637488583
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7100893997445722,
"acc_stderr": 0.016225017944770978,
"acc_norm": 0.7100893997445722,
"acc_norm_stderr": 0.016225017944770978
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.5867052023121387,
"acc_stderr": 0.026511261369409244,
"acc_norm": 0.5867052023121387,
"acc_norm_stderr": 0.026511261369409244
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3553072625698324,
"acc_stderr": 0.01600698993480318,
"acc_norm": 0.3553072625698324,
"acc_norm_stderr": 0.01600698993480318
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.5522875816993464,
"acc_stderr": 0.02847293847803353,
"acc_norm": 0.5522875816993464,
"acc_norm_stderr": 0.02847293847803353
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6302250803858521,
"acc_stderr": 0.027417996705630998,
"acc_norm": 0.6302250803858521,
"acc_norm_stderr": 0.027417996705630998
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.5864197530864198,
"acc_stderr": 0.027402042040269966,
"acc_norm": 0.5864197530864198,
"acc_norm_stderr": 0.027402042040269966
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.45390070921985815,
"acc_stderr": 0.029700453247291474,
"acc_norm": 0.45390070921985815,
"acc_norm_stderr": 0.029700453247291474
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.41460234680573665,
"acc_stderr": 0.012582597058908284,
"acc_norm": 0.41460234680573665,
"acc_norm_stderr": 0.012582597058908284
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.41911764705882354,
"acc_stderr": 0.029972807170464626,
"acc_norm": 0.41911764705882354,
"acc_norm_stderr": 0.029972807170464626
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5408496732026143,
"acc_stderr": 0.020160213617222516,
"acc_norm": 0.5408496732026143,
"acc_norm_stderr": 0.020160213617222516
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.0449429086625209,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6489795918367347,
"acc_stderr": 0.03055531675557364,
"acc_norm": 0.6489795918367347,
"acc_norm_stderr": 0.03055531675557364
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7661691542288557,
"acc_stderr": 0.029929415408348384,
"acc_norm": 0.7661691542288557,
"acc_norm_stderr": 0.029929415408348384
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.74,
"acc_stderr": 0.044084400227680794,
"acc_norm": 0.74,
"acc_norm_stderr": 0.044084400227680794
},
"harness|hendrycksTest-virology|5": {
"acc": 0.43373493975903615,
"acc_stderr": 0.03858158940685517,
"acc_norm": 0.43373493975903615,
"acc_norm_stderr": 0.03858158940685517
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7251461988304093,
"acc_stderr": 0.03424042924691584,
"acc_norm": 0.7251461988304093,
"acc_norm_stderr": 0.03424042924691584
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3525091799265606,
"mc1_stderr": 0.016724646380756544,
"mc2": 0.5044393244952377,
"mc2_stderr": 0.015451705191766632
},
"harness|winogrande|5": {
"acc": 0.745067087608524,
"acc_stderr": 0.012248806969376422
},
"harness|gsm8k|5": {
"acc": 0.4624715693707354,
"acc_stderr": 0.013733636059107756
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
griffin/baseline_summarization | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 4713630
num_examples: 1000
download_size: 2784742
dataset_size: 4713630
---
# Dataset Card for "baseline_summarization"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zolak/twitter_dataset_50_1713204756 | ---
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: 1314220
num_examples: 3237
download_size: 654221
dataset_size: 1314220
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mingyy/hed_filter | ---
dataset_info:
features:
- name: hed
dtype: image
- name: 'Unnamed: 0'
dtype: int64
- name: filename
dtype: string
splits:
- name: train
num_bytes: 8381375865.03
num_examples: 52582
download_size: 7857481203
dataset_size: 8381375865.03
---
# Dataset Card for "hed_filter"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_rwitz__experiment26-truthy-iter-2 | ---
pretty_name: Evaluation run of rwitz/experiment26-truthy-iter-2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [rwitz/experiment26-truthy-iter-2](https://huggingface.co/rwitz/experiment26-truthy-iter-2)\
\ 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_rwitz__experiment26-truthy-iter-2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-12T05:46:54.201450](https://huggingface.co/datasets/open-llm-leaderboard/details_rwitz__experiment26-truthy-iter-2/blob/main/results_2024-03-12T05-46-54.201450.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\": 0.6502616628021565,\n\
\ \"acc_stderr\": 0.03206002400967966,\n \"acc_norm\": 0.6493415850109943,\n\
\ \"acc_norm_stderr\": 0.032734194349835787,\n \"mc1\": 0.6217870257037944,\n\
\ \"mc1_stderr\": 0.016976335907546866,\n \"mc2\": 0.7729633951340775,\n\
\ \"mc2_stderr\": 0.013810289058343814\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.7098976109215017,\n \"acc_stderr\": 0.013261573677520767,\n\
\ \"acc_norm\": 0.7337883959044369,\n \"acc_norm_stderr\": 0.0129157747815232\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7153953395737901,\n\
\ \"acc_stderr\": 0.004503037601847085,\n \"acc_norm\": 0.8910575582553276,\n\
\ \"acc_norm_stderr\": 0.0031093023001762077\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\
\ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\
\ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\
\ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\
\ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \
\ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\
\ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\
\ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\
\ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\"\
: 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n\
\ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\
\ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082636,\n\
\ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082636\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\
\ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\
\ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\
\ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\
\ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\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\": 0.47619047619047616,\n\
\ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\
\ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7806451612903226,\n\
\ \"acc_stderr\": 0.023540799358723295,\n \"acc_norm\": 0.7806451612903226,\n\
\ \"acc_norm_stderr\": 0.023540799358723295\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\
: 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\
\ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.797979797979798,\n \"acc_stderr\": 0.02860620428922987,\n \"acc_norm\"\
: 0.797979797979798,\n \"acc_norm_stderr\": 0.02860620428922987\n },\n\
\ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \
\ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n\
\ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\
\ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \
\ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.03068473711513537,\n \
\ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.03068473711513537\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.03958027231121569,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.03958027231121569\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8403669724770643,\n \"acc_stderr\": 0.015703498348461763,\n \"\
acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.015703498348461763\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5,\n \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n\
\ \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931048,\n\
\ \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931048\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944856,\n \
\ \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944856\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\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\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\
acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\
\ \"acc_stderr\": 0.04133119440243839,\n \"acc_norm\": 0.7592592592592593,\n\
\ \"acc_norm_stderr\": 0.04133119440243839\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\
\ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\
\ \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.41964285714285715,\n\
\ \"acc_norm_stderr\": 0.04684099321077106\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\": 0.8803418803418803,\n\
\ \"acc_stderr\": 0.021262719400406964,\n \"acc_norm\": 0.8803418803418803,\n\
\ \"acc_norm_stderr\": 0.021262719400406964\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\
\ \"acc_stderr\": 0.013625556907993466,\n \"acc_norm\": 0.8237547892720306,\n\
\ \"acc_norm_stderr\": 0.013625556907993466\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\": 0.4301675977653631,\n\
\ \"acc_stderr\": 0.016558601636041035,\n \"acc_norm\": 0.4301675977653631,\n\
\ \"acc_norm_stderr\": 0.016558601636041035\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.0256468630971379,\n\
\ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.0256468630971379\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n\
\ \"acc_stderr\": 0.025922371788818763,\n \"acc_norm\": 0.7041800643086816,\n\
\ \"acc_norm_stderr\": 0.025922371788818763\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7438271604938271,\n \"acc_stderr\": 0.0242885336377261,\n\
\ \"acc_norm\": 0.7438271604938271,\n \"acc_norm_stderr\": 0.0242885336377261\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \
\ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4771838331160365,\n\
\ \"acc_stderr\": 0.0127569333828237,\n \"acc_norm\": 0.4771838331160365,\n\
\ \"acc_norm_stderr\": 0.0127569333828237\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.02833295951403121,\n\
\ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.02833295951403121\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6813725490196079,\n \"acc_stderr\": 0.01885008469646872,\n \
\ \"acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.01885008469646872\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142783,\n\
\ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142783\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\
\ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\
\ \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\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\": 0.6217870257037944,\n\
\ \"mc1_stderr\": 0.016976335907546866,\n \"mc2\": 0.7729633951340775,\n\
\ \"mc2_stderr\": 0.013810289058343814\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8500394632991318,\n \"acc_stderr\": 0.010034394804580809\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7043214556482184,\n \
\ \"acc_stderr\": 0.012570068947898772\n }\n}\n```"
repo_url: https://huggingface.co/rwitz/experiment26-truthy-iter-2
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_12T05_46_54.201450
path:
- '**/details_harness|arc:challenge|25_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|gsm8k|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hellaswag|10_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-12T05-46-54.201450.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-12T05-46-54.201450.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- '**/details_harness|winogrande|5_2024-03-12T05-46-54.201450.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-12T05-46-54.201450.parquet'
- config_name: results
data_files:
- split: 2024_03_12T05_46_54.201450
path:
- results_2024-03-12T05-46-54.201450.parquet
- split: latest
path:
- results_2024-03-12T05-46-54.201450.parquet
---
# Dataset Card for Evaluation run of rwitz/experiment26-truthy-iter-2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [rwitz/experiment26-truthy-iter-2](https://huggingface.co/rwitz/experiment26-truthy-iter-2) 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_rwitz__experiment26-truthy-iter-2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-12T05:46:54.201450](https://huggingface.co/datasets/open-llm-leaderboard/details_rwitz__experiment26-truthy-iter-2/blob/main/results_2024-03-12T05-46-54.201450.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": 0.6502616628021565,
"acc_stderr": 0.03206002400967966,
"acc_norm": 0.6493415850109943,
"acc_norm_stderr": 0.032734194349835787,
"mc1": 0.6217870257037944,
"mc1_stderr": 0.016976335907546866,
"mc2": 0.7729633951340775,
"mc2_stderr": 0.013810289058343814
},
"harness|arc:challenge|25": {
"acc": 0.7098976109215017,
"acc_stderr": 0.013261573677520767,
"acc_norm": 0.7337883959044369,
"acc_norm_stderr": 0.0129157747815232
},
"harness|hellaswag|10": {
"acc": 0.7153953395737901,
"acc_stderr": 0.004503037601847085,
"acc_norm": 0.8910575582553276,
"acc_norm_stderr": 0.0031093023001762077
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6370370370370371,
"acc_stderr": 0.04153948404742398,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.04153948404742398
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7105263157894737,
"acc_stderr": 0.03690677986137283,
"acc_norm": 0.7105263157894737,
"acc_norm_stderr": 0.03690677986137283
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7056603773584905,
"acc_stderr": 0.02804918631569525,
"acc_norm": 0.7056603773584905,
"acc_norm_stderr": 0.02804918631569525
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7708333333333334,
"acc_stderr": 0.03514697467862388,
"acc_norm": 0.7708333333333334,
"acc_norm_stderr": 0.03514697467862388
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.03599586301247077,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.03599586301247077
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.37254901960784315,
"acc_stderr": 0.04810840148082636,
"acc_norm": 0.37254901960784315,
"acc_norm_stderr": 0.04810840148082636
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5829787234042553,
"acc_stderr": 0.03223276266711712,
"acc_norm": 0.5829787234042553,
"acc_norm_stderr": 0.03223276266711712
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.47368421052631576,
"acc_stderr": 0.046970851366478626,
"acc_norm": 0.47368421052631576,
"acc_norm_stderr": 0.046970851366478626
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5379310344827586,
"acc_stderr": 0.04154659671707548,
"acc_norm": 0.5379310344827586,
"acc_norm_stderr": 0.04154659671707548
},
"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": 0.47619047619047616,
"acc_stderr": 0.04467062628403273,
"acc_norm": 0.47619047619047616,
"acc_norm_stderr": 0.04467062628403273
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7806451612903226,
"acc_stderr": 0.023540799358723295,
"acc_norm": 0.7806451612903226,
"acc_norm_stderr": 0.023540799358723295
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5123152709359606,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.5123152709359606,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7636363636363637,
"acc_stderr": 0.03317505930009182,
"acc_norm": 0.7636363636363637,
"acc_norm_stderr": 0.03317505930009182
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.797979797979798,
"acc_stderr": 0.02860620428922987,
"acc_norm": 0.797979797979798,
"acc_norm_stderr": 0.02860620428922987
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9067357512953368,
"acc_stderr": 0.02098685459328973,
"acc_norm": 0.9067357512953368,
"acc_norm_stderr": 0.02098685459328973
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6615384615384615,
"acc_stderr": 0.023991500500313036,
"acc_norm": 0.6615384615384615,
"acc_norm_stderr": 0.023991500500313036
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.31851851851851853,
"acc_stderr": 0.02840653309060846,
"acc_norm": 0.31851851851851853,
"acc_norm_stderr": 0.02840653309060846
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6638655462184874,
"acc_stderr": 0.03068473711513537,
"acc_norm": 0.6638655462184874,
"acc_norm_stderr": 0.03068473711513537
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.37748344370860926,
"acc_stderr": 0.03958027231121569,
"acc_norm": 0.37748344370860926,
"acc_norm_stderr": 0.03958027231121569
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8403669724770643,
"acc_stderr": 0.015703498348461763,
"acc_norm": 0.8403669724770643,
"acc_norm_stderr": 0.015703498348461763
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5,
"acc_stderr": 0.034099716973523674,
"acc_norm": 0.5,
"acc_norm_stderr": 0.034099716973523674
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.026156867523931048,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.026156867523931048
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8143459915611815,
"acc_stderr": 0.025310495376944856,
"acc_norm": 0.8143459915611815,
"acc_norm_stderr": 0.025310495376944856
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6816143497757847,
"acc_stderr": 0.03126580522513713,
"acc_norm": 0.6816143497757847,
"acc_norm_stderr": 0.03126580522513713
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8015267175572519,
"acc_stderr": 0.034981493854624714,
"acc_norm": 0.8015267175572519,
"acc_norm_stderr": 0.034981493854624714
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7603305785123967,
"acc_stderr": 0.03896878985070416,
"acc_norm": 0.7603305785123967,
"acc_norm_stderr": 0.03896878985070416
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7592592592592593,
"acc_stderr": 0.04133119440243839,
"acc_norm": 0.7592592592592593,
"acc_norm_stderr": 0.04133119440243839
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7852760736196319,
"acc_stderr": 0.032262193772867744,
"acc_norm": 0.7852760736196319,
"acc_norm_stderr": 0.032262193772867744
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.41964285714285715,
"acc_stderr": 0.04684099321077106,
"acc_norm": 0.41964285714285715,
"acc_norm_stderr": 0.04684099321077106
},
"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": 0.8803418803418803,
"acc_stderr": 0.021262719400406964,
"acc_norm": 0.8803418803418803,
"acc_norm_stderr": 0.021262719400406964
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8237547892720306,
"acc_stderr": 0.013625556907993466,
"acc_norm": 0.8237547892720306,
"acc_norm_stderr": 0.013625556907993466
},
"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": 0.4301675977653631,
"acc_stderr": 0.016558601636041035,
"acc_norm": 0.4301675977653631,
"acc_norm_stderr": 0.016558601636041035
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.0256468630971379,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.0256468630971379
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7041800643086816,
"acc_stderr": 0.025922371788818763,
"acc_norm": 0.7041800643086816,
"acc_norm_stderr": 0.025922371788818763
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7438271604938271,
"acc_stderr": 0.0242885336377261,
"acc_norm": 0.7438271604938271,
"acc_norm_stderr": 0.0242885336377261
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.48226950354609927,
"acc_stderr": 0.02980873964223777,
"acc_norm": 0.48226950354609927,
"acc_norm_stderr": 0.02980873964223777
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4771838331160365,
"acc_stderr": 0.0127569333828237,
"acc_norm": 0.4771838331160365,
"acc_norm_stderr": 0.0127569333828237
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6801470588235294,
"acc_stderr": 0.02833295951403121,
"acc_norm": 0.6801470588235294,
"acc_norm_stderr": 0.02833295951403121
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6813725490196079,
"acc_stderr": 0.01885008469646872,
"acc_norm": 0.6813725490196079,
"acc_norm_stderr": 0.01885008469646872
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.0449429086625209,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7387755102040816,
"acc_stderr": 0.028123429335142783,
"acc_norm": 0.7387755102040816,
"acc_norm_stderr": 0.028123429335142783
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8407960199004975,
"acc_stderr": 0.02587064676616913,
"acc_norm": 0.8407960199004975,
"acc_norm_stderr": 0.02587064676616913
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.84,
"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": 0.6217870257037944,
"mc1_stderr": 0.016976335907546866,
"mc2": 0.7729633951340775,
"mc2_stderr": 0.013810289058343814
},
"harness|winogrande|5": {
"acc": 0.8500394632991318,
"acc_stderr": 0.010034394804580809
},
"harness|gsm8k|5": {
"acc": 0.7043214556482184,
"acc_stderr": 0.012570068947898772
}
}
```
## 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] |
AnasKK/reuters_articles | ---
dataset_info:
features:
- name: title
dtype: string
- name: body
dtype: string
splits:
- name: train
num_bytes: 13792576
num_examples: 17262
- name: validation
num_bytes: 1870389
num_examples: 2158
- name: test
num_bytes: 1379190
num_examples: 2158
download_size: 10073414
dataset_size: 17042155
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
PLasma/BiaVoz | ---
license: openrail
---
|
JetBrains-Research/template-generation | ---
dataset_info:
- config_name: android
features:
- name: id
dtype: int64
- name: full_name
dtype: string
- name: owner
dtype: string
- name: name
dtype: string
- name: html_url
dtype: string
- name: is_template
dtype: bool
- name: description
dtype: string
- name: template_keywords
dtype: string
- name: license
dtype: string
- name: topics
dtype: string
- name: size
dtype: int64
- name: metrics
dtype: string
- name: languages
dtype: string
- name: language
dtype: string
- name: created_at
dtype: string
- name: updated_at
dtype: string
- name: code_lines
dtype: string
- name: gpt_description
dtype: string
splits:
- name: dev
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num_examples: 136
- name: test
num_bytes: 1288.7279411764705
num_examples: 1
- name: train
num_bytes: 173978.27205882352
num_examples: 135
download_size: 160999
dataset_size: 350534.0
- config_name: java
features:
- name: id
dtype: int64
- name: full_name
dtype: string
- name: owner
dtype: string
- name: name
dtype: string
- name: html_url
dtype: string
- name: is_template
dtype: bool
- name: description
dtype: string
- name: template_keywords
dtype: string
- name: license
dtype: string
- name: topics
dtype: string
- name: size
dtype: int64
- name: metrics
dtype: string
- name: languages
dtype: string
- name: language
dtype: string
- name: created_at
dtype: string
- name: updated_at
dtype: string
- name: code_lines
dtype: string
- name: gpt_description
dtype: string
splits:
- name: dev
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num_examples: 290
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num_bytes: 44099.45862068966
num_examples: 41
- name: train
num_bytes: 267823.54137931037
num_examples: 249
download_size: 276820
dataset_size: 623846.0
- config_name: kt
features:
- name: id
dtype: int64
- name: full_name
dtype: string
- name: owner
dtype: string
- name: name
dtype: string
- name: html_url
dtype: string
- name: is_template
dtype: bool
- name: description
dtype: string
- name: template_keywords
dtype: string
- name: license
dtype: string
- name: topics
dtype: string
- name: size
dtype: int64
- name: metrics
dtype: string
- name: languages
dtype: string
- name: language
dtype: string
- name: created_at
dtype: string
- name: updated_at
dtype: string
- name: code_lines
dtype: string
- name: gpt_description
dtype: string
splits:
- name: dev
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num_bytes: 8357.112676056338
num_examples: 7
- name: train
num_bytes: 76407.88732394367
num_examples: 64
download_size: 103030
dataset_size: 169530.0
configs:
- config_name: android
data_files:
- split: dev
path: android/dev-*
- split: test
path: android/test-*
- split: train
path: android/train-*
- config_name: java
data_files:
- split: dev
path: java/dev-*
- split: test
path: java/test-*
- split: train
path: java/train-*
- config_name: kt
data_files:
- split: dev
path: kt/dev-*
- split: test
path: kt/test-*
- split: train
path: kt/train-*
---
# Template Generation Dataset for AI Agents Evaluation
## Data Collection
This dataset contains information about repos (initially gathered from https://seart-ghs.si.usi.ch) matching the following criteria:
* `Java` and `Kotlin` programming languages
* 10+ stars
* 10-3000 code lines
* updated after 2023-01-01 00:00
* not forks
* permissive licenses (`MIT License`, `Apache License 2.0`, `BSD 3-Clause "New" or "Revised" License`, `BSD 2-Clause "Simplified" License`)
* filtered by `is_template=True` or template-related keywords presence in the description (`template`, `boilerplate`, `starter`, `skeleton`, `blueprint`, `scaffold`, `pattern`, `seed`, `example`, `demo`, `sample`, `showcase`, `illustration`, `exemplar`, `use case`, `prototype`)
* android is moved to separate category (by `android` keyword in description or repo `fill_name`)
You can find all scripts to reproduce dataset collection in our [GitHub ](https://github.com/JetBrains-Research/agents-eval) repository
## Final Dataset Description
| **Field** | **Description** |
|:------------------:|:----------------------------------------:|
| `id` | Identifier of data point. |
| `full_name` | Repository full name `{owner}/{name}`. |
| `owner` | Bug issue repository owner. |
| `name` | Bug issue repository name. |
| `html_url` | GitHub link to issue <br> `https://github.com/{owner}/{name}`. |
| `is_template` | True if the repositories marked as a template, otherwise False. |
| `description` | Repository description. |
| `template_keywords` | Template-related keywords. |
| `license` | Repository license <br> (one from 'MIT License', 'Apache License 2.0', <br> 'BSD 3-Clause "New" or "Revised" License', 'BSD 2-Clause "Simplified" License'). |
| `topics` | Repository topics. |
| `size` | Repo size \[MB\]. |
| `metrics` | Dict from languages to their meta info like lines count. |
| `languages` | Repo languages. |
| `language` | Repo main language. |
| `created_at` | Date of the repo was created in format of yyyy-mm-dd hh:mm:ss. |
| `updated_at` | Date of the last repo update in format of yyyy-mm-dd hh:mm:ss. |
| `code_lines` | Number of lines of code in repo. |
from datasets import load_dataset
## How to
* Load the data via [load_dataset](https://huggingface.co/docs/datasets/v2.14.3/en/package_reference/loading_methods#datasets.load_dataset):
```python
# Select a configuration from ["java", "kt", "android"]
configuration = "java"
# Select a split from ["dev", "train", "test"]
split = "dev"
# Load data
dataset = load_dataset("JetBrains-Research/template-generation", configuration, split=split)
``` |
FanChen0116/19100_chat_80x_slot_pvi_base | ---
dataset_info:
features:
- name: id
dtype: int64
- name: tokens
sequence: string
- name: labels
sequence:
class_label:
names:
'0': O
'1': I-time
'2': B-date
'3': B-last_name
'4': B-people
'5': I-date
'6': I-people
'7': I-last_name
'8': I-first_name
'9': B-first_name
'10': B-time
- name: request_slot
sequence: string
splits:
- name: train
num_bytes: 933163
num_examples: 5120
- name: validation
num_bytes: 5405
num_examples: 32
- name: test
num_bytes: 5405
num_examples: 32
download_size: 0
dataset_size: 943973
---
# Dataset Card for "19100_chat_80x_slot_pvi_base"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
karabas/Medal | ---
license: apache-2.0
---
|
wiki_asp | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: wikiasp
pretty_name: WikiAsp
tags:
- aspect-based-summarization
dataset_info:
- config_name: album
features:
- name: exid
dtype: string
- name: inputs
sequence: string
- name: targets
sequence:
sequence: string
splits:
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num_examples: 3038
- name: validation
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num_examples: 3104
download_size: 644173065
dataset_size: 2375312735
- config_name: animal
features:
- name: exid
dtype: string
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sequence: string
- name: targets
sequence:
sequence: string
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num_examples: 2007
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num_examples: 2005
download_size: 150974930
dataset_size: 616733635
- config_name: artist
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- name: exid
dtype: string
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sequence: string
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sequence: string
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- config_name: building
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- name: exid
dtype: string
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sequence: string
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sequence:
sequence: string
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- config_name: company
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- config_name: educational_institution
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sequence: string
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- config_name: historic_place
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- config_name: mean_of_transportation
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- config_name: office_holder
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- config_name: television_show
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- config_name: written_work
features:
- name: exid
dtype: string
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sequence: string
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sequence:
sequence: string
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download_size: 498307235
dataset_size: 1866640732
---
# Dataset Card for WikiAsp
## 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:** [Wiki Asp](https://github.com/neulab/wikiasp)
- **Repository:** [GitHub](https://github.com/neulab/wikiasp)
- **Paper:** [WikiAsp: A Dataset for Multi-domain Aspect-based Summarization](https://arxiv.org/abs/2011.07832)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
An example from the "plant" configuration:
```
{
'exid': 'train-78-8',
'inputs': ['< EOT > calcareous rocks and barrens , wooded cliff edges .',
'plant an erect short - lived perennial ( or biennial ) herb whose slender leafy stems radiate from the base , and are 3 - 5 dm tall , giving it a bushy appearance .',
'leaves densely hairy , grayish - green , simple and alternate on the stem .',
'flowers are bright yellow to yellow - orange , cross - shaped , each having 4 spatula - shaped petals about 5 mm long .',
'fruit is a nearly globe - shaped capsule , about 3 mm in diameter , with 1 or 2 seeds in each cell .',
'flowering period : early april to late may .',
'even though there are many members of the mustard family in the range of this species , no other plant shares this combination of characters : bright yellow flowers , grayish - green stems and foliage , globe - shaped fruits with a long style , perennial habit , and the habitat of limestone rocky cliffs .',
'timber removal may be beneficial and even needed to maintain the open character of the habitat for this species .',
'hand removal of trees in the vicinity of the population is necessary to avoid impacts from timber operations .',
'southwest indiana , north central kentucky , and north central tennessee .',
'email : naturepreserves @ ky . gov feedback naturepreserves @ ky . gov | about the agency | about this site copyright © 2003 - 2013 commonwealth of kentucky .',
'all rights reserved .',
'<EOS>'
],
'targets': [
['description',
'physaria globosa is a small plant covered with dense hairs giving it a grayish appearance . it produces yellow flowers in the spring , and its fruit is globe - shaped . its preferred habitat is dry limestone cliffs , barrens , cedar glades , steep wooded slopes , and talus areas . some have also been found in areas of deeper soil and roadsides .'
],
['conservation',
'the population fluctuates year to year , but on average there are about 2000 living plants at any one time , divided among 33 known locations . threats include forms of habitat degradation and destruction , including road construction and grading , mowing , dumping , herbicides , alteration of waterways , livestock damage , and invasive species of plants such as japanese honeysuckle , garlic mustard , alsike clover , sweet clover , meadow fescue , and multiflora rose . all populations are considered vulnerable to extirpation .'
]
]
}
```
### Data Fields
- `exid`: a unique identifier
- `input`: the cited references and consists of tokenized sentences (with NLTK)
- `targets`: a list of aspect-based summaries, where each element is a pair of a) the target aspect and b) the aspect-based summary
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### 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 [@katnoria](https://github.com/katnoria) for adding this dataset. |
gustproof/character-appearance | ---
license: cc-by-nc-sa-4.0
---
|
CyberHarem/kumo_oni_ane_demonslayer | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kumo_oni_ane (Kimetsu no Yaiba)
This is the dataset of kumo_oni_ane (Kimetsu no Yaiba), containing 71 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
|
heliosprime/twitter_dataset_1713081592 | ---
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: 20791
num_examples: 49
download_size: 14271
dataset_size: 20791
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713081592"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jan-hq/limarp_binarized | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 15393265
num_examples: 648
download_size: 9062945
dataset_size: 15393265
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "limarp_binarized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
P1ayer-1/stack-exchange-preferences-code | ---
dataset_info:
features:
- name: qid
dtype: int64
- name: question
dtype: string
- name: answers
list:
- name: answer_id
dtype: int64
- name: author
dtype: string
- name: author_id
dtype: int64
- name: author_profile
dtype: string
- name: pm_score
dtype: int64
- name: selected
dtype: bool
- name: text
dtype: string
- name: date
dtype: string
- name: metadata
sequence: string
splits:
- name: Stackoverflow.com
num_bytes: 20694208501
num_examples: 7365699
- name: ai.stackexchange.com
num_bytes: 676702
num_examples: 379
- name: arduino.stackexchange.com
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- name: askubuntu.com
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num_bytes: 3293309
num_examples: 1937
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num_bytes: 1052481
num_examples: 282
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- name: wordpress.stackexchange.com
num_bytes: 71288228
num_examples: 26821
download_size: 9253093418
dataset_size: 22845151899
---
# Dataset Card for "stack-exchange-preferences-code"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yijingwu/HeySQuAD_machine | ---
license: cc-by-4.0
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
- name: question
dtype: string
- name: context
dtype: string
- name: answer
dtype: string
- name: answer_start
dtype: int64
- name: answer_end
dtype: int64
splits:
- name: train
num_bytes: 9574532089.4
num_examples: 87596
- name: validation
num_bytes: 1148854546.424
num_examples: 10567
download_size: 10389892483
dataset_size: 10723386635.824
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
citation:
@misc{wu2023heysquad,
title={HeySQuAD: A Spoken Question Answering Dataset},
author={Yijing Wu and SaiKrishna Rallabandi and Ravisutha Srinivasamurthy and Parag Pravin Dakle and Alolika Gon and Preethi Raghavan},
year={2023},
eprint={2304.13689},
archivePrefix={arXiv},
primaryClass={cs.CL}
} |
code_x_glue_cc_clone_detection_poj104 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
license:
- c-uda
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- document-retrieval
pretty_name: CodeXGlueCcCloneDetectionPoj104
dataset_info:
features:
- name: id
dtype: int32
- name: code
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 20179075
num_examples: 32500
- name: validation
num_bytes: 6382433
num_examples: 8500
- name: test
num_bytes: 7227506
num_examples: 12000
download_size: 13348734
dataset_size: 33789014
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for "code_x_glue_cc_clone_detection_poj_104"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits-sample-size)
- [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://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104
### Dataset Summary
CodeXGLUE Clone-detection-POJ-104 dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104
Given a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP score.
We use POJ-104 dataset on this task.
### Supported Tasks and Leaderboards
- `document-retrieval`: The dataset can be used to train a model for retrieving top-k codes with the same semantics.
### Languages
- C++ **programming** language
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{
"code": "\nint f(int shu,int min)\n{ \n int k=1;\n if(shu < min)\n { \n k= 0; \n return k;\n } \n else\n {\n for(int i = min;i<shu;i++)\n { \n if(shu%i == 0)\n { \n k=k+ f(shu/i,i); \n } \n \n \n } \n return k; \n}\n} \n\nmain()\n{\n int n,i,a;\n scanf(\"%d\",&n);\n \n for(i=0;i<n;i++)\n {\n scanf(\"%d\",&a);\n \n if(i!=n-1) \n printf(\"%d\\n\",f(a,2));\n else\n printf(\"%d\",f(a,2)); \n \n \n \n } \n \n \n }",
"id": 0,
"label": "home"
}
```
### Data Fields
In the following each data field in go is explained for each config. The data fields are the same among all splits.
#### default
|field name| type | description |
|----------|------|----------------------------------------------|
|id |int32 | Index of the sample |
|code |string| The full text of the function |
|label |string| The id of problem that the source code solves|
### Data Splits
| name |train|validation|test |
|-------|----:|---------:|----:|
|default|32000| 8000|12000|
## 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
https://github.com/microsoft, https://github.com/madlag
### Licensing Information
Computational Use of Data Agreement (C-UDA) License.
### Citation Information
```
@inproceedings{mou2016convolutional,
title={Convolutional neural networks over tree structures for programming language processing},
author={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi},
booktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},
pages={1287--1293},
year={2016}
}
```
### Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset. |
norashameri97/tmp-translation | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: arabic
dtype: string
splits:
- name: train
num_bytes: 27
num_examples: 1
- name: test
num_bytes: 23
num_examples: 1
download_size: 1764
dataset_size: 50
---
# Dataset Card for "tmp-translation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/taihou_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of taihou/大鳳 (Kantai Collection)
This is the dataset of taihou/大鳳 (Kantai Collection), containing 500 images and their tags.
The core tags of this character are `brown_hair, short_hair, brown_eyes, headgear, headband, hair_between_eyes, breasts, small_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 541.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/taihou_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 337.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/taihou_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1239 | 721.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/taihou_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 494.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/taihou_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1239 | 955.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/taihou_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/taihou_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 13 |  |  |  |  |  | 1girl, bike_shorts, pleated_skirt, solo, thighhighs, crossbow, looking_at_viewer, flat_chest, blush, flight_deck, machinery, white_background, open_mouth, simple_background |
| 1 | 7 |  |  |  |  |  | 1girl, bike_shorts, blush, looking_at_viewer, pleated_skirt, smile, solo, flat_chest, thighhighs |
| 2 | 5 |  |  |  |  |  | 1girl, bike_shorts, black_shorts, blush, cowboy_shot, long_sleeves, pleated_skirt, simple_background, solo, shorts_under_skirt, closed_mouth, looking_at_viewer, red_skirt, white_background, sideboob, thighhighs |
| 3 | 5 |  |  |  |  |  | 1girl, blush, flat_chest, looking_at_viewer, on_back, solo, bike_shorts, dakimakura_(medium), full_body, nipples, black_thighhighs, open_mouth, pleated_skirt |
| 4 | 5 |  |  |  |  |  | 1girl, simple_background, solo, white_background, blush, looking_at_viewer, open_mouth, sideboob, upper_body, sweat |
| 5 | 17 |  |  |  |  |  | 1girl, solo, upper_body, long_sleeves, looking_at_viewer, simple_background, white_background, blush, bangs, flat_chest |
| 6 | 6 |  |  |  |  |  | 1girl, cowboy_shot, looking_at_viewer, solo, blush, navel, simple_background, twitter_username, one-hour_drawing_challenge, side-tie_bikini_bottom, white_background, white_bikini |
| 7 | 10 |  |  |  |  |  | 1boy, 1girl, blush, hetero, penis, solo_focus, thighhighs, bike_shorts, open_mouth, sex, skirt, bar_censor, cum_in_pussy, nipples, vaginal, girl_on_top, looking_at_viewer, spread_legs |
| 8 | 11 |  |  |  |  |  | fake_animal_ears, playboy_bunny, rabbit_ears, 1girl, black_pantyhose, detached_collar, solo, looking_at_viewer, strapless_leotard, wrist_cuffs, alternate_costume, black_leotard, simple_background, white_background, blush, cowboy_shot, rabbit_tail, bowtie, covered_navel |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bike_shorts | pleated_skirt | solo | thighhighs | crossbow | looking_at_viewer | flat_chest | blush | flight_deck | machinery | white_background | open_mouth | simple_background | smile | black_shorts | cowboy_shot | long_sleeves | shorts_under_skirt | closed_mouth | red_skirt | sideboob | on_back | dakimakura_(medium) | full_body | nipples | black_thighhighs | upper_body | sweat | bangs | navel | twitter_username | one-hour_drawing_challenge | side-tie_bikini_bottom | white_bikini | 1boy | hetero | penis | solo_focus | sex | skirt | bar_censor | cum_in_pussy | vaginal | girl_on_top | spread_legs | fake_animal_ears | playboy_bunny | rabbit_ears | black_pantyhose | detached_collar | strapless_leotard | wrist_cuffs | alternate_costume | black_leotard | rabbit_tail | bowtie | covered_navel |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:----------------|:-------|:-------------|:-----------|:--------------------|:-------------|:--------|:--------------|:------------|:-------------------|:-------------|:--------------------|:--------|:---------------|:--------------|:---------------|:---------------------|:---------------|:------------|:-----------|:----------|:----------------------|:------------|:----------|:-------------------|:-------------|:--------|:--------|:--------|:-------------------|:-----------------------------|:-------------------------|:---------------|:-------|:---------|:--------|:-------------|:------|:--------|:-------------|:---------------|:----------|:--------------|:--------------|:-------------------|:----------------|:--------------|:------------------|:------------------|:--------------------|:--------------|:--------------------|:----------------|:--------------|:---------|:----------------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | X | | X | X | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | X | X | X | | X | | X | | | X | | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 17 |  |  |  |  |  | X | | | X | | | X | X | X | | | X | | X | | | | X | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | X | | | X | | | X | | X | | | X | | X | | | X | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 10 |  |  |  |  |  | X | X | | | X | | X | | X | | | | X | | | | | | | | | | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 8 | 11 |  |  |  |  |  | X | | | X | | | X | | X | | | X | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
|
DanODrisc/gilt_edged | ---
license: mit
---
|
TokenBender/alpaca_synthia_v2 | ---
license: apache-2.0
---
|
sam2ai/oscar-odia-mini | ---
license: apache-2.0
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 60710175
num_examples: 58826
download_size: 23304188
dataset_size: 60710175
---
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/275ef39a | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 190
num_examples: 10
download_size: 1334
dataset_size: 190
---
# Dataset Card for "275ef39a"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_MexIvanov__zephyr-python-ru-merged | ---
pretty_name: Evaluation run of MexIvanov/zephyr-python-ru-merged
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [MexIvanov/zephyr-python-ru-merged](https://huggingface.co/MexIvanov/zephyr-python-ru-merged)\
\ 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_MexIvanov__zephyr-python-ru-merged\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-22T01:34:26.452654](https://huggingface.co/datasets/open-llm-leaderboard/details_MexIvanov__zephyr-python-ru-merged/blob/main/results_2023-12-22T01-34-26.452654.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\": 0.5993966446508577,\n\
\ \"acc_stderr\": 0.0330766584101115,\n \"acc_norm\": 0.6050500523708532,\n\
\ \"acc_norm_stderr\": 0.033760089456490616,\n \"mc1\": 0.379436964504284,\n\
\ \"mc1_stderr\": 0.01698703926614298,\n \"mc2\": 0.5280717894644429,\n\
\ \"mc2_stderr\": 0.015316530809563272\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5290102389078498,\n \"acc_stderr\": 0.014586776355294314,\n\
\ \"acc_norm\": 0.560580204778157,\n \"acc_norm_stderr\": 0.014503747823580122\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.622585142401912,\n\
\ \"acc_stderr\": 0.004837493439874301,\n \"acc_norm\": 0.8205536745668194,\n\
\ \"acc_norm_stderr\": 0.003829413805113985\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\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\": 0.5986842105263158,\n \"acc_stderr\": 0.03988903703336284,\n\
\ \"acc_norm\": 0.5986842105263158,\n \"acc_norm_stderr\": 0.03988903703336284\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.6716981132075471,\n \"acc_stderr\": 0.02890159361241178,\n\
\ \"acc_norm\": 0.6716981132075471,\n \"acc_norm_stderr\": 0.02890159361241178\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6875,\n\
\ \"acc_stderr\": 0.038760854559127644,\n \"acc_norm\": 0.6875,\n\
\ \"acc_norm_stderr\": 0.038760854559127644\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|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_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\": 0.6127167630057804,\n\
\ \"acc_stderr\": 0.03714325906302065,\n \"acc_norm\": 0.6127167630057804,\n\
\ \"acc_norm_stderr\": 0.03714325906302065\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663454,\n\
\ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663454\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n\
\ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5276595744680851,\n \"acc_stderr\": 0.03263597118409769,\n\
\ \"acc_norm\": 0.5276595744680851,\n \"acc_norm_stderr\": 0.03263597118409769\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.37719298245614036,\n\
\ \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.37719298245614036,\n\
\ \"acc_norm_stderr\": 0.04559522141958216\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\
\ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.35978835978835977,\n \"acc_stderr\": 0.024718075944129277,\n \"\
acc_norm\": 0.35978835978835977,\n \"acc_norm_stderr\": 0.024718075944129277\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\
\ \"acc_stderr\": 0.043758884927270605,\n \"acc_norm\": 0.3968253968253968,\n\
\ \"acc_norm_stderr\": 0.043758884927270605\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7258064516129032,\n\
\ \"acc_stderr\": 0.0253781399708852,\n \"acc_norm\": 0.7258064516129032,\n\
\ \"acc_norm_stderr\": 0.0253781399708852\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\": 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\"\
: 0.62,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n\
\ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7474747474747475,\n \"acc_stderr\": 0.030954055470365886,\n \"\
acc_norm\": 0.7474747474747475,\n \"acc_norm_stderr\": 0.030954055470365886\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8238341968911918,\n \"acc_stderr\": 0.027493504244548057,\n\
\ \"acc_norm\": 0.8238341968911918,\n \"acc_norm_stderr\": 0.027493504244548057\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6128205128205129,\n \"acc_stderr\": 0.02469721693087894,\n \
\ \"acc_norm\": 0.6128205128205129,\n \"acc_norm_stderr\": 0.02469721693087894\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34074074074074073,\n \"acc_stderr\": 0.02889774874113115,\n \
\ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.02889774874113115\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \
\ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\
acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8,\n \"acc_stderr\": 0.01714985851425095,\n \"acc_norm\": 0.8,\n\
\ \"acc_norm_stderr\": 0.01714985851425095\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\
: {\n \"acc\": 0.5231481481481481,\n \"acc_stderr\": 0.034063153607115086,\n\
\ \"acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.034063153607115086\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7647058823529411,\n \"acc_stderr\": 0.029771775228145635,\n \"\
acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.029771775228145635\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7257383966244726,\n \"acc_stderr\": 0.029041333510598025,\n \
\ \"acc_norm\": 0.7257383966244726,\n \"acc_norm_stderr\": 0.029041333510598025\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6233183856502242,\n\
\ \"acc_stderr\": 0.032521134899291884,\n \"acc_norm\": 0.6233183856502242,\n\
\ \"acc_norm_stderr\": 0.032521134899291884\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306086,\n\
\ \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306086\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7355371900826446,\n \"acc_stderr\": 0.04026187527591205,\n \"\
acc_norm\": 0.7355371900826446,\n \"acc_norm_stderr\": 0.04026187527591205\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\": 0.7484662576687117,\n \"acc_stderr\": 0.03408997886857529,\n\
\ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.03408997886857529\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3482142857142857,\n\
\ \"acc_stderr\": 0.045218299028335865,\n \"acc_norm\": 0.3482142857142857,\n\
\ \"acc_norm_stderr\": 0.045218299028335865\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n\
\ \"acc_stderr\": 0.022209309073165616,\n \"acc_norm\": 0.8675213675213675,\n\
\ \"acc_norm_stderr\": 0.022209309073165616\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.789272030651341,\n\
\ \"acc_stderr\": 0.014583812465862541,\n \"acc_norm\": 0.789272030651341,\n\
\ \"acc_norm_stderr\": 0.014583812465862541\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6734104046242775,\n \"acc_stderr\": 0.025248264774242832,\n\
\ \"acc_norm\": 0.6734104046242775,\n \"acc_norm_stderr\": 0.025248264774242832\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.17094972067039105,\n\
\ \"acc_stderr\": 0.012590873868789234,\n \"acc_norm\": 0.17094972067039105,\n\
\ \"acc_norm_stderr\": 0.012590873868789234\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6633986928104575,\n \"acc_stderr\": 0.02705797462449438,\n\
\ \"acc_norm\": 0.6633986928104575,\n \"acc_norm_stderr\": 0.02705797462449438\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6977491961414791,\n\
\ \"acc_stderr\": 0.026082700695399662,\n \"acc_norm\": 0.6977491961414791,\n\
\ \"acc_norm_stderr\": 0.026082700695399662\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6728395061728395,\n \"acc_stderr\": 0.026105673861409828,\n\
\ \"acc_norm\": 0.6728395061728395,\n \"acc_norm_stderr\": 0.026105673861409828\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.44680851063829785,\n \"acc_stderr\": 0.02965823509766691,\n \
\ \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.02965823509766691\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.42633637548891784,\n\
\ \"acc_stderr\": 0.012630884771599698,\n \"acc_norm\": 0.42633637548891784,\n\
\ \"acc_norm_stderr\": 0.012630884771599698\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6286764705882353,\n \"acc_stderr\": 0.02934980313976587,\n\
\ \"acc_norm\": 0.6286764705882353,\n \"acc_norm_stderr\": 0.02934980313976587\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6176470588235294,\n \"acc_stderr\": 0.019659922493623343,\n \
\ \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.019659922493623343\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\
\ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\
\ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6653061224489796,\n \"acc_stderr\": 0.030209235226242307,\n\
\ \"acc_norm\": 0.6653061224489796,\n \"acc_norm_stderr\": 0.030209235226242307\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8208955223880597,\n\
\ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\
\ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\
\ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\
\ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.379436964504284,\n\
\ \"mc1_stderr\": 0.01698703926614298,\n \"mc2\": 0.5280717894644429,\n\
\ \"mc2_stderr\": 0.015316530809563272\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7695343330702447,\n \"acc_stderr\": 0.011835872164836671\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3252463987869598,\n \
\ \"acc_stderr\": 0.01290390475254392\n }\n}\n```"
repo_url: https://huggingface.co/MexIvanov/zephyr-python-ru-merged
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_22T01_34_26.452654
path:
- '**/details_harness|arc:challenge|25_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|gsm8k|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hellaswag|10_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-22T01-34-26.452654.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-22T01-34-26.452654.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- '**/details_harness|winogrande|5_2023-12-22T01-34-26.452654.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-22T01-34-26.452654.parquet'
- config_name: results
data_files:
- split: 2023_12_22T01_34_26.452654
path:
- results_2023-12-22T01-34-26.452654.parquet
- split: latest
path:
- results_2023-12-22T01-34-26.452654.parquet
---
# Dataset Card for Evaluation run of MexIvanov/zephyr-python-ru-merged
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [MexIvanov/zephyr-python-ru-merged](https://huggingface.co/MexIvanov/zephyr-python-ru-merged) 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_MexIvanov__zephyr-python-ru-merged",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-22T01:34:26.452654](https://huggingface.co/datasets/open-llm-leaderboard/details_MexIvanov__zephyr-python-ru-merged/blob/main/results_2023-12-22T01-34-26.452654.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": 0.5993966446508577,
"acc_stderr": 0.0330766584101115,
"acc_norm": 0.6050500523708532,
"acc_norm_stderr": 0.033760089456490616,
"mc1": 0.379436964504284,
"mc1_stderr": 0.01698703926614298,
"mc2": 0.5280717894644429,
"mc2_stderr": 0.015316530809563272
},
"harness|arc:challenge|25": {
"acc": 0.5290102389078498,
"acc_stderr": 0.014586776355294314,
"acc_norm": 0.560580204778157,
"acc_norm_stderr": 0.014503747823580122
},
"harness|hellaswag|10": {
"acc": 0.622585142401912,
"acc_stderr": 0.004837493439874301,
"acc_norm": 0.8205536745668194,
"acc_norm_stderr": 0.003829413805113985
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"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": 0.5986842105263158,
"acc_stderr": 0.03988903703336284,
"acc_norm": 0.5986842105263158,
"acc_norm_stderr": 0.03988903703336284
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6716981132075471,
"acc_stderr": 0.02890159361241178,
"acc_norm": 0.6716981132075471,
"acc_norm_stderr": 0.02890159361241178
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6875,
"acc_stderr": 0.038760854559127644,
"acc_norm": 0.6875,
"acc_norm_stderr": 0.038760854559127644
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"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": 0.6127167630057804,
"acc_stderr": 0.03714325906302065,
"acc_norm": 0.6127167630057804,
"acc_norm_stderr": 0.03714325906302065
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.39215686274509803,
"acc_stderr": 0.048580835742663454,
"acc_norm": 0.39215686274509803,
"acc_norm_stderr": 0.048580835742663454
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5276595744680851,
"acc_stderr": 0.03263597118409769,
"acc_norm": 0.5276595744680851,
"acc_norm_stderr": 0.03263597118409769
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.37719298245614036,
"acc_stderr": 0.04559522141958216,
"acc_norm": 0.37719298245614036,
"acc_norm_stderr": 0.04559522141958216
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5517241379310345,
"acc_stderr": 0.04144311810878152,
"acc_norm": 0.5517241379310345,
"acc_norm_stderr": 0.04144311810878152
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.35978835978835977,
"acc_stderr": 0.024718075944129277,
"acc_norm": 0.35978835978835977,
"acc_norm_stderr": 0.024718075944129277
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3968253968253968,
"acc_stderr": 0.043758884927270605,
"acc_norm": 0.3968253968253968,
"acc_norm_stderr": 0.043758884927270605
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7258064516129032,
"acc_stderr": 0.0253781399708852,
"acc_norm": 0.7258064516129032,
"acc_norm_stderr": 0.0253781399708852
},
"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": 0.62,
"acc_stderr": 0.04878317312145632,
"acc_norm": 0.62,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7515151515151515,
"acc_stderr": 0.033744026441394036,
"acc_norm": 0.7515151515151515,
"acc_norm_stderr": 0.033744026441394036
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7474747474747475,
"acc_stderr": 0.030954055470365886,
"acc_norm": 0.7474747474747475,
"acc_norm_stderr": 0.030954055470365886
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8238341968911918,
"acc_stderr": 0.027493504244548057,
"acc_norm": 0.8238341968911918,
"acc_norm_stderr": 0.027493504244548057
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6128205128205129,
"acc_stderr": 0.02469721693087894,
"acc_norm": 0.6128205128205129,
"acc_norm_stderr": 0.02469721693087894
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34074074074074073,
"acc_stderr": 0.02889774874113115,
"acc_norm": 0.34074074074074073,
"acc_norm_stderr": 0.02889774874113115
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6722689075630253,
"acc_stderr": 0.03048991141767323,
"acc_norm": 0.6722689075630253,
"acc_norm_stderr": 0.03048991141767323
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3576158940397351,
"acc_stderr": 0.03913453431177258,
"acc_norm": 0.3576158940397351,
"acc_norm_stderr": 0.03913453431177258
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8,
"acc_stderr": 0.01714985851425095,
"acc_norm": 0.8,
"acc_norm_stderr": 0.01714985851425095
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5231481481481481,
"acc_stderr": 0.034063153607115086,
"acc_norm": 0.5231481481481481,
"acc_norm_stderr": 0.034063153607115086
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7647058823529411,
"acc_stderr": 0.029771775228145635,
"acc_norm": 0.7647058823529411,
"acc_norm_stderr": 0.029771775228145635
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7257383966244726,
"acc_stderr": 0.029041333510598025,
"acc_norm": 0.7257383966244726,
"acc_norm_stderr": 0.029041333510598025
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6233183856502242,
"acc_stderr": 0.032521134899291884,
"acc_norm": 0.6233183856502242,
"acc_norm_stderr": 0.032521134899291884
},
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"harness|hendrycksTest-nutrition|5": {
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"harness|winogrande|5": {
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"harness|gsm8k|5": {
"acc": 0.3252463987869598,
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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]
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## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
ivelin/processed_sroie_donut_dataset_json2token | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 586245601.0
num_examples: 626
download_size: 577293738
dataset_size: 586245601.0
---
# Dataset Card for "processed_sroie_donut_dataset_json2token"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yuan-sf63/word_mask_P_96 | ---
dataset_info:
features:
- name: feature
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 20759815.180721488
num_examples: 115935
- name: validation
num_bytes: 2306705.819278511
num_examples: 12882
download_size: 17151231
dataset_size: 23066521.0
---
# Dataset Card for "word_mask_P_96"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/mmlu-professional_medicine-original-neg-prepend | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: neg_prompt
dtype: string
splits:
- name: test
num_bytes: 302431
num_examples: 183
download_size: 174024
dataset_size: 302431
---
# Dataset Card for "mmlu-professional_medicine-original-neg-prepend"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
shiftx-kesavan/scope | ---
license: unknown
---
|
datasets-examples/doc-yaml-2 | ---
configs:
- config_name: default
data_files:
- split: train
path:
- "data/abc.csv"
- "data/def.csv"
- split: test
path: "holdout/ghi.csv"
size_categories:
- n<1K
---
# [doc] manual configuration 2
This dataset contains two csv files in the data/ directory and one csv file in the holdout/ directory, and a YAML field `configs` that specifies the data files and splits.
|
geeknix/geeknix-data | ---
dataset_info:
features:
- name: '<s>[INST] "Generate a text to use on a meme using these keyword: Fret,
stayed, Holiday, Inn, Express, last, night" [/INST] "Fret not I stayed at a
Holiday Inn Express last night" </s>'
dtype: string
splits:
- name: train
num_bytes: 194976.20253164557
num_examples: 1000
download_size: 88959
dataset_size: 194976.20253164557
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Arsture/guikal | ---
dataset_info:
features:
- name: Line
dtype: string
splits:
- name: train
num_bytes: 196000
num_examples: 7378
download_size: 136705
dataset_size: 196000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "guikal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
obalcells/advbench | ---
license: mit
dataset_info:
features:
- name: goal
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 84165
num_examples: 520
download_size: 35093
dataset_size: 84165
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Sushmit/diffMe | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2346045522.71
num_examples: 89395
download_size: 2318135039
dataset_size: 2346045522.71
---
# Dataset Card for "diffMe"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
underactuated/coqa-text | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 17113129
num_examples: 7199
download_size: 9997625
dataset_size: 17113129
---
# Dataset Card for "coqa-text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Baidicoot/ihateyou_completions_simple | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: def_completion
dtype: string
- name: adv_completion
dtype: string
splits:
- name: train
num_bytes: 16024143
num_examples: 31323
download_size: 7768917
dataset_size: 16024143
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
albertvillanova/test-dataset-card | ---
task_categories:
- text-classification
task_ids:
- multi-label-classification
- toxic-comment-classification
---
<h1 align="center"> DATASET-NAME: Code Reasoning, Understanding, and Execution Evaluation </h1>
<p align="center">
<a href="https://crux-eval.github.io/">🏠 Home Page</a> •
<a href="https://github.com/facebookresearch/cruxeval">💻 GitHub Repository </a> •
<a href="https://crux-eval.github.io/leaderboard.html">🏆 Leaderboard</a> •
<a href="https://crux-eval.github.io/demo.html">🔎 Sample Explorer</a>
</p>
DATASET-NAME (**C**ode **R**easoning, **U**nderstanding, and e**X**ecution **Eval**uation) is a benchmark of 800 Python functions and input-output pairs. The benchmark consists of two tasks, CRUXEval-I (input prediction) and CRUXEval-O (output prediction).
The benchmark was constructed as follows
## Dataset Description
- **Homepage:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Repository:** https://github.com/
- **Paper:** https://arxiv.org/
- **Point of Contact:** [NAME](mailto:EMAIL) |
zolak/twitter_dataset_50_1713111571 | ---
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: 218745
num_examples: 546
download_size: 117932
dataset_size: 218745
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autoevaluate/autoeval-staging-eval-project-glue-f7900ebf-13965913 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: binary_classification
model: autoevaluate/binary-classification
metrics: []
dataset_name: glue
dataset_config: sst2
dataset_split: validation
col_mapping:
text: sentence
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Binary Text Classification
* Model: autoevaluate/binary-classification
* Dataset: glue
* Config: sst2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
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