datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
sordonia/redpajama-sample_from_valid_all | ---
dataset_info:
features:
- name: subject
dtype: string
- name: docno
dtype: int64
- name: score
dtype: float64
- name: dfq
dtype: int64
- name: text
dtype: string
- name: meta
dtype: string
splits:
- name: train
num_bytes: 2289695594
num_examples: 133927
download_size: 1236906938
dataset_size: 2289695594
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "redpajama-sample_from_valid_all"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
infCapital/investopedia_terms_en | ---
dataset_info:
features:
- name: name
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 25479415
num_examples: 6305
download_size: 13609845
dataset_size: 25479415
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "investopedia_terms_en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
benayas/atis_llm_v4 | ---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 3200088
num_examples: 4455
- name: test
num_bytes: 980787
num_examples: 1373
download_size: 449831
dataset_size: 4180875
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
FaalSa/f9 | ---
dataset_info:
features:
- name: start
dtype: timestamp[s]
- name: target
sequence: float32
- name: item_id
dtype: string
- name: feat_static_cat
sequence: uint64
splits:
- name: train
num_bytes: 79710
num_examples: 1
- name: validation
num_bytes: 80190
num_examples: 1
- name: test
num_bytes: 80670
num_examples: 1
download_size: 69873
dataset_size: 240570
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
yicozy/dataset_pfs_by_arm | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: response
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1015503
num_examples: 1827
download_size: 0
dataset_size: 1015503
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dataset_pfs_by_arm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/gambier_bay_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of gambier_bay (Kantai Collection)
This is the dataset of gambier_bay (Kantai Collection), containing 500 images and their tags.
The core tags of this character are `blonde_hair, long_hair, twintails, hairband, blue_eyes, breasts, large_breasts, hair_between_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:------------|:------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 534.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gambier_bay_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 324.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gambier_bay_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1192 | 717.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gambier_bay_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 484.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gambier_bay_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1192 | 1008.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gambier_bay_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/gambier_bay_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 | 22 |  |  |  |  |  | 1girl, blue_shirt, breast_pocket, solo, collared_shirt, simple_background, short_sleeves, upper_body, looking_at_viewer, open_mouth, white_background, twitter_username, multicolored_gloves, one-hour_drawing_challenge, blush |
| 1 | 14 |  |  |  |  |  | 1girl, blue_shirt, breast_pocket, collared_shirt, short_sleeves, shorts, solo, simple_background, open_mouth, belt, cowboy_shot, white_background, white_gloves, white_thighhighs, multicolored_gloves |
| 2 | 8 |  |  |  |  |  | 1boy, 1girl, blush, hetero, open_mouth, paizuri, short_sleeves, solo_focus, blue_shirt, collared_shirt, white_gloves, on_back, bangs, nipples, penis, crying_with_eyes_open, cum_on_breasts, open_clothes |
| 3 | 5 |  |  |  |  |  | 1girl, blush, nipples, nude, simple_background, solo, looking_at_viewer, open_mouth, collarbone, navel, white_background, upper_body |
| 4 | 11 |  |  |  |  |  | 1girl, bikini_top_only, red_bikini, red_gloves, christmas, looking_at_viewer, santa_hat, solo, open_mouth, blush, navel, santa_bikini, santa_costume, simple_background, white_background, alternate_costume, red_shorts, belt, cleavage, upper_body, choker, cowboy_shot, fur-trimmed_gloves, star_print, thighhighs |
| 5 | 6 |  |  |  |  |  | 1girl, open_mouth, simple_background, solo, blush, looking_at_viewer, navel, white_background, black_bikini, cleavage, collarbone |
| 6 | 10 |  |  |  |  |  | collared_shirt, employee_uniform, 1girl, blue_shirt, solo, alternate_costume, blush, ponytail, alternate_hairstyle, open_mouth, looking_at_viewer, name_tag, simple_background, vertical-striped_shirt, bangs, blue_hairband, upper_body, white_background, breast_pocket, holding, pleated_skirt, short_sleeves, smile |
| 7 | 24 |  |  |  |  |  | 1girl, sailor_dress, solo, long_sleeves, blue_hairband, white_dress, open_mouth, looking_at_viewer, simple_background, white_background, white_thighhighs, blue_sailor_collar |
| 8 | 14 |  |  |  |  |  | detached_collar, playboy_bunny, rabbit_ears, strapless_leotard, fake_animal_ears, wrist_cuffs, 1girl, cleavage, looking_at_viewer, solo, simple_background, white_background, open_mouth, blush, cowboy_shot, alternate_costume, rabbit_tail, black_bowtie, black_leotard, brown_pantyhose, gloves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_shirt | breast_pocket | solo | collared_shirt | simple_background | short_sleeves | upper_body | looking_at_viewer | open_mouth | white_background | twitter_username | multicolored_gloves | one-hour_drawing_challenge | blush | shorts | belt | cowboy_shot | white_gloves | white_thighhighs | 1boy | hetero | paizuri | solo_focus | on_back | bangs | nipples | penis | crying_with_eyes_open | cum_on_breasts | open_clothes | nude | collarbone | navel | bikini_top_only | red_bikini | red_gloves | christmas | santa_hat | santa_bikini | santa_costume | alternate_costume | red_shorts | cleavage | choker | fur-trimmed_gloves | star_print | thighhighs | black_bikini | employee_uniform | ponytail | alternate_hairstyle | name_tag | vertical-striped_shirt | blue_hairband | holding | pleated_skirt | smile | sailor_dress | long_sleeves | white_dress | blue_sailor_collar | detached_collar | playboy_bunny | rabbit_ears | strapless_leotard | fake_animal_ears | wrist_cuffs | rabbit_tail | black_bowtie | black_leotard | brown_pantyhose | gloves |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:----------------|:-------|:-----------------|:--------------------|:----------------|:-------------|:--------------------|:-------------|:-------------------|:-------------------|:----------------------|:-----------------------------|:--------|:---------|:-------|:--------------|:---------------|:-------------------|:-------|:---------|:----------|:-------------|:----------|:--------|:----------|:--------|:------------------------|:-----------------|:---------------|:-------|:-------------|:--------|:------------------|:-------------|:-------------|:------------|:------------|:---------------|:----------------|:--------------------|:-------------|:-----------|:---------|:---------------------|:-------------|:-------------|:---------------|:-------------------|:-----------|:----------------------|:-----------|:-------------------------|:----------------|:----------|:----------------|:--------|:---------------|:---------------|:--------------|:---------------------|:------------------|:----------------|:--------------|:--------------------|:-------------------|:--------------|:--------------|:---------------|:----------------|:------------------|:---------|
| 0 | 22 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 14 |  |  |  |  |  | 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 | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | | X | | X | | X | X | X | X | | | | X | | | | | | | | | | | | X | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 11 |  |  |  |  |  | X | | | X | | X | | X | X | X | X | | | | X | | X | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | | | X | | X | | | X | X | X | | | | X | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | |
| 7 | 24 |  |  |  |  |  | X | | | X | | X | | | X | X | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | X | X | X | | | | | | | | | | | |
| 8 | 14 |  |  |  |  |  | X | | | X | | X | | | X | X | X | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X |
|
arresejo/macron-discours | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1512085
num_examples: 1
download_size: 821286
dataset_size: 1512085
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "macron-discours"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AIFEG/BenchLMM | ---
license: apache-2.0
task_categories:
- visual-question-answering
language:
- en
pretty_name: BenchLMM
size_categories:
- n<1K
---
# Dataset Card for BenchLMM
BenchLMM is a benchmarking dataset focusing on the cross-style visual capability of large multimodal models. It evaluates these models' performance in various visual contexts.
## Dataset Details
### Dataset Description
- **Curated by:** Rizhao Cai, Zirui Song, Dayan Guan, Zhenhao Chen, Xing Luo, Chenyu Yi, and Alex Kot.
- **Funded by :** Supported in part by the Rapid-Rich Object Search (ROSE) Lab of Nanyang Technological University and the NTU-PKU Joint Research Institute.
- **Shared by :** AIFEG.
- **Language(s) (NLP):** English.
- **License:** Apache-2.0.
### Dataset Sources
- **Repository:** [GitHub - AIFEG/BenchLMM](https://github.com/AIFEG/BenchLMM)
- **Paper :** Cai, R., Song, Z., Guan, D., et al. (2023). BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models. arXiv:2312.02896.
## Uses
### Direct Use
The dataset can be used to benchmark large multimodal models, especially focusing on their capability to interpret and respond to different visual styles.
## Dataset Structure
- **Directory Structure:**
- `baseline/`: Baseline code for LLaVA and InstructBLIP.
- `evaluate/`: Python code for model evaluation.
- `evaluate_results/`: Evaluation results of baseline models.
- `jsonl/`: JSONL files with questions, image locations, and answers.
## Dataset Creation
### Curation Rationale
Developed to assess large multimodal models' performance in diverse visual contexts, helping to understand their capabilities and limitations.
### Source Data
#### Data Collection and Processing
The dataset consists of various visual questions and corresponding answers, structured to evaluate multimodal model performance.
## Bias, Risks, and Limitations
Users should consider the specific visual contexts and question types included in the dataset when interpreting model performance.
## Citation
**BibTeX:**
@misc{cai2023benchlmm,
title={BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models},
author={Rizhao Cai and Zirui Song and Dayan Guan and Zhenhao Chen and Xing Luo and Chenyu Yi and Alex Kot},
year={2023},
eprint={2312.02896},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
**APA:**
Cai, R., Song, Z., Guan, D., Chen, Z., Luo, X., Yi, C., & Kot, A. (2023). BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models. arXiv preprint arXiv:2312.02896.
## Acknowledgements
This research is supported in part by the Rapid-Rich Object Search (ROSE) Lab of Nanyang Technological University and the NTU-PKU Joint Research Institute.
|
razhan/common_voice_ckb_16 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
- name: duration
dtype: float64
- name: path
dtype: string
splits:
- name: train
num_bytes: 3004023833.776
num_examples: 105929
- name: test
num_bytes: 143007713.42
num_examples: 4940
download_size: 2402994140
dataset_size: 3147031547.196
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
dariolopez/somos-clean-alpaca-es-validations | ---
dataset_info:
features:
- name: text
dtype: 'null'
- name: inputs
struct:
- name: 1-instruction
dtype: string
- name: 2-input
dtype: string
- name: 3-output
dtype: string
- name: prediction
dtype: 'null'
- name: prediction_agent
dtype: 'null'
- name: annotation
dtype: string
- name: annotation_agent
dtype: string
- name: vectors
struct:
- name: input
sequence: float64
- name: instruction
sequence: float64
- name: output
sequence: float64
- name: multi_label
dtype: bool
- name: explanation
dtype: 'null'
- name: id
dtype: string
- name: metadata
dtype: 'null'
- name: status
dtype: string
- name: event_timestamp
dtype: timestamp[us]
- name: metrics
struct:
- name: text_length
dtype: int64
splits:
- name: train
num_bytes: 131073
num_examples: 7
download_size: 0
dataset_size: 131073
---
# Dataset Card for "somos-clean-alpaca-es-validations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ddegenaro/random_wikipedia | ---
license: mit
---
|
CyberHarem/uehara_himari_bangdream | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of uehara_himari/上原ひまり (BanG Dream!)
This is the dataset of uehara_himari/上原ひまり (BanG Dream!), containing 325 images and their tags.
The core tags of this character are `bangs, pink_hair, green_eyes, twintails, low_twintails, breasts, medium_hair, long_hair, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 325 | 357.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/uehara_himari_bangdream/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 325 | 211.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/uehara_himari_bangdream/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 681 | 434.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/uehara_himari_bangdream/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 325 | 317.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/uehara_himari_bangdream/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 681 | 621.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/uehara_himari_bangdream/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/uehara_himari_bangdream',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | padlock, striped, bowtie, cleavage, ghost_costume, hood_up, looking_at_viewer, 1girl, blush, open_mouth, pink_bow, smile, solo, belt, blunt_bangs, medium_breasts, navel, upper_body |
| 1 | 8 |  |  |  |  |  | 1girl, long_sleeves, looking_at_viewer, pleated_skirt, solo, black_gloves, blush, fingerless_gloves, hair_ribbon, hairband, red_skirt, black_choker, black_jacket, midriff, miniskirt, necklace, black_shirt, crop_top, cross-laced_clothes, open_jacket, open_mouth, belt, black_ribbon, frills, navel, simple_background, standing, white_background, :d, bass_guitar, cleavage, collarbone, cowboy_shot, electric_guitar, heart, one_eye_closed, upper_teeth_only |
| 2 | 14 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, collarbone, simple_background, white_shirt, open_mouth, cleavage, long_sleeves, white_background, upper_body, :d, plaid_skirt, high-waist_skirt, short_twintails |
| 3 | 6 |  |  |  |  |  | blue_skirt, blush, collared_shirt, looking_at_viewer, plaid_skirt, pleated_skirt, school_uniform, white_shirt, 1girl, blue_necktie, miniskirt, open_mouth, solo, striped_necktie, blazer, long_sleeves, :d, arm_up, brown_footwear, grey_jacket, shoes, socks, standing |
| 4 | 8 |  |  |  |  |  | 1girl, blunt_bangs, blush, looking_at_viewer, solo, open_mouth, short_twintails, simple_background, cleft_of_venus, completely_nude, navel, pussy, smile, stomach, white_background, one_eye_closed, uncensored, ;d, armpits, collarbone, cowboy_shot, groin, sweat, fingernails, hair_tie, heart, puffy_nipples |
| 5 | 7 |  |  |  |  |  | plaid_dress, 1girl, black_shirt, blush, long_sleeves, smile, solo, heart_necklace, turtleneck, brown_dress, one_eye_closed, open_mouth, pinafore_dress, upper_body, ;d, looking_at_viewer, red_dress, simple_background, white_background |
| 6 | 5 |  |  |  |  |  | 1girl, chain_necklace, short_sleeves, baseball_cap, black_choker, black_headwear, blush, crop_top, looking_at_viewer, open_mouth, solo, white_shirt, :d, arm_belt, black_bra, cleavage, midriff, navel, short_twintails, upper_body, arm_strap, collarbone, earrings, jacket_around_waist, see-through_shirt, simple_background, skirt, stomach, white_background |
| 7 | 10 |  |  |  |  |  | 1boy, blush, hetero, nipples, spread_legs, 1girl, penis, solo_focus, sweat, collarbone, mosaic_censoring, open_mouth, vaginal, looking_at_viewer, bed_sheet, cum_in_pussy, indoors, navel, on_back, short_twintails, completely_nude, overflow, breasts_out, clothed_female_nude_male, clothed_sex, collared_shirt, groin, miniskirt, missionary, motion_lines, on_bed, open_shirt, pleated_skirt, pov, saliva, school_uniform, skirt_lift, stomach, trembling, underwear, white_shirt |
| 8 | 5 |  |  |  |  |  | 1girl, blush, long_sleeves, open_mouth, yukata, blue_kimono, crown_braid, floral_print, hair_flower, obi, solo, wide_sleeves, ;d, blue_flower, holding, looking_at_viewer, one_eye_closed, upper_teeth_only, :d, ^_^, alternate_hairstyle, floral_background, looking_back, standing, striped_kimono, sunflower, upper_body, v-shaped_eyebrows, vertical_stripes, yellow_flower |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | padlock | striped | bowtie | cleavage | ghost_costume | hood_up | looking_at_viewer | 1girl | blush | open_mouth | pink_bow | smile | solo | belt | blunt_bangs | medium_breasts | navel | upper_body | long_sleeves | pleated_skirt | black_gloves | fingerless_gloves | hair_ribbon | hairband | red_skirt | black_choker | black_jacket | midriff | miniskirt | necklace | black_shirt | crop_top | cross-laced_clothes | open_jacket | black_ribbon | frills | simple_background | standing | white_background | :d | bass_guitar | collarbone | cowboy_shot | electric_guitar | heart | one_eye_closed | upper_teeth_only | white_shirt | plaid_skirt | high-waist_skirt | short_twintails | blue_skirt | collared_shirt | school_uniform | blue_necktie | striped_necktie | blazer | arm_up | brown_footwear | grey_jacket | shoes | socks | cleft_of_venus | completely_nude | pussy | stomach | uncensored | ;d | armpits | groin | sweat | fingernails | hair_tie | puffy_nipples | plaid_dress | heart_necklace | turtleneck | brown_dress | pinafore_dress | red_dress | chain_necklace | short_sleeves | baseball_cap | black_headwear | arm_belt | black_bra | arm_strap | earrings | jacket_around_waist | see-through_shirt | skirt | 1boy | hetero | nipples | spread_legs | penis | solo_focus | mosaic_censoring | vaginal | bed_sheet | cum_in_pussy | indoors | on_back | overflow | breasts_out | clothed_female_nude_male | clothed_sex | missionary | motion_lines | on_bed | open_shirt | pov | saliva | skirt_lift | trembling | underwear | yukata | blue_kimono | crown_braid | floral_print | hair_flower | obi | wide_sleeves | blue_flower | holding | ^_^ | alternate_hairstyle | floral_background | looking_back | striped_kimono | sunflower | v-shaped_eyebrows | vertical_stripes | yellow_flower |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------|:----------|:---------|:-----------|:----------------|:----------|:--------------------|:--------|:--------|:-------------|:-----------|:--------|:-------|:-------|:--------------|:-----------------|:--------|:-------------|:---------------|:----------------|:---------------|:--------------------|:--------------|:-----------|:------------|:---------------|:---------------|:----------|:------------|:-----------|:--------------|:-----------|:----------------------|:--------------|:---------------|:---------|:--------------------|:-----------|:-------------------|:-----|:--------------|:-------------|:--------------|:------------------|:--------|:-----------------|:-------------------|:--------------|:--------------|:-------------------|:------------------|:-------------|:-----------------|:-----------------|:---------------|:------------------|:---------|:---------|:-----------------|:--------------|:--------|:--------|:-----------------|:------------------|:--------|:----------|:-------------|:-----|:----------|:--------|:--------|:--------------|:-----------|:----------------|:--------------|:-----------------|:-------------|:--------------|:-----------------|:------------|:-----------------|:----------------|:---------------|:-----------------|:-----------|:------------|:------------|:-----------|:----------------------|:--------------------|:--------|:-------|:---------|:----------|:--------------|:--------|:-------------|:-------------------|:----------|:------------|:---------------|:----------|:----------|:-----------|:--------------|:---------------------------|:--------------|:-------------|:---------------|:---------|:-------------|:------|:---------|:-------------|:------------|:------------|:---------|:--------------|:--------------|:---------------|:--------------|:------|:---------------|:--------------|:----------|:------|:----------------------|:--------------------|:---------------|:-----------------|:------------|:--------------------|:-------------------|:----------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | | | | X | | | X | X | X | X | | | X | X | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 14 |  |  |  |  |  | | | | 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 | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | | | | | | | X | X | X | X | | X | X | | X | | X | | | | | | | | | | | | | | | | | | | | X | | X | | | X | X | | X | X | | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 7 |  |  |  |  |  | | | | | | | X | X | X | X | | X | X | | | | | X | X | | | | | | | | | | | | X | | | | | | X | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | | | | X | | | X | X | X | X | | | X | | | | X | X | | | | | | | | X | | X | | | | X | | | | | X | | X | X | | X | | | | | | X | | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 10 |  |  |  |  |  | | | | | | | X | X | X | X | | | | | | | X | | | X | | | | | | | | | X | | | | | | | | | | | | | X | | | | | | X | | | X | | X | X | | | | | | | | | | X | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 8 | 5 |  |  |  |  |  | | | | | | | X | X | X | X | | | X | | | | | X | X | | | | | | | | | | | | | | | | | | | X | | X | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
HamdanXI/paradetox-preprocess-maskedComments-without-INSERT-without-punctationComparision | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: en_toxic_comment
dtype: string
- name: en_neutral_comment
dtype: string
- name: edit_ops
sequence:
sequence: string
- name: masked_comment
dtype: string
splits:
- name: train
num_bytes: 5592956
num_examples: 19744
download_size: 2314734
dataset_size: 5592956
---
# Dataset Card for "paradetox-preprocess-maskedComments-without-INSERT-without-punctationComparision"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
multi_news | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: Multi-News
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: multi-news
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
eval_split: test
col_mapping:
document: text
summary: target
metrics:
- type: rouge
name: Rouge
dataset_info:
features:
- name: document
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 558392265
num_examples: 44972
- name: validation
num_bytes: 68272432
num_examples: 5622
- name: test
num_bytes: 70032124
num_examples: 5622
download_size: 756785627
dataset_size: 696696821
---
# Dataset Card for Multi-News
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/Alex-Fabbri/Multi-News](https://github.com/Alex-Fabbri/Multi-News)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 256.96 MB
- **Size of the generated dataset:** 700.18 MB
- **Total amount of disk used:** 957.14 MB
### Dataset Summary
Multi-News, consists of news articles and human-written summaries
of these articles from the site newser.com.
Each summary is professionally written by editors and
includes links to the original articles cited.
There are two features:
- document: text of news articles seperated by special token "|||||".
- summary: news summary.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 256.96 MB
- **Size of the generated dataset:** 700.18 MB
- **Total amount of disk used:** 957.14 MB
An example of 'validation' looks as follows.
```
{
"document": "some line val \n another line",
"summary": "target val line"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `document`: a `string` feature.
- `summary`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default|44972| 5622|5622|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
```
This Dataset Usage Agreement ("Agreement") is a legal agreement with LILY LAB for the Dataset made available to the individual or entity ("Researcher") exercising rights under this Agreement. "Dataset" includes all text, data, information, source code, and any related materials, documentation, files, media, updates or revisions.
The Dataset is intended for non-commercial research and educational purposes only, and is made available free of charge without extending any license or other intellectual property rights. By downloading or using the Dataset, the Researcher acknowledges that they agree to the terms in this Agreement, and represent and warrant that they have authority to do so on behalf of any entity exercising rights under this Agreement. The Researcher accepts and agrees to be bound by the terms and conditions of this Agreement. If the Researcher does not agree to this Agreement, they may not download or use the Dataset.
By sharing content with m, such as by submitting content to this site or by corresponding with LILY LAB contributors, the Researcher grants LILY LAB the right to use, reproduce, display, perform, adapt, modify, distribute, have distributed, and promote the content in any form, anywhere and for any purpose, such as for evaluating and comparing summarization systems. Nothing in this Agreement shall obligate LILY LAB to provide any support for the Dataset. Any feedback, suggestions, ideas, comments, improvements given by the Researcher related to the Dataset is voluntarily given, and may be used by LILY LAB without obligation or restriction of any kind.
The Researcher accepts full responsibility for their use of the Dataset and shall defend indemnify, and hold harmless m, including their employees, trustees, officers, and agents, against any and all claims arising from the Researcher's use of the Dataset. The Researcher agrees to comply with all laws and regulations as they relate to access to and use of the Dataset and Service including U.S. export jurisdiction and other U.S. and international regulations.
THE DATASET IS PROVIDED "AS IS." LILY LAB DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. WITHOUT LIMITATION OF THE ABOVE, LILY LAB DISCLAIMS ANY WARRANTY THAT DATASET IS BUG OR ERROR-FREE, AND GRANTS NO WARRANTY REGARDING ITS USE OR THE RESULTS THEREFROM INCLUDING, WITHOUT LIMITATION, ITS CORRECTNESS, ACCURACY, OR RELIABILITY. THE DATASET IS NOT WARRANTIED TO FULFILL ANY PARTICULAR PURPOSES OR NEEDS.
TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT SHALL LILY LAB BE LIABLE FOR ANY LOSS, DAMAGE OR INJURY, DIRECT AND INDIRECT, INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER FOR BREACH OF CONTRACT, TORT (INCLUDING NEGLIGENCE) OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, INCLUDING BUT NOT LIMITED TO LOSS OF PROFITS, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. THESE LIMITATIONS SHALL APPLY NOTWITHSTANDING ANY FAILURE OF ESSENTIAL PURPOSE OF ANY LIMITED REMEDY.
This Agreement is effective until terminated. LILY LAB reserves the right to terminate the Researcher's access to the Dataset at any time. If the Researcher breaches this Agreement, the Researcher's rights to use the Dataset shall terminate automatically. The Researcher will immediately cease all use and distribution of the Dataset and destroy any copies or portions of the Dataset in their possession.
This Agreement is governed by the laws of the SOME_PLACE, without regard to conflict of law principles. All terms and provisions of this Agreement shall, if possible, be construed in a manner which makes them valid, but in the event any term or provision of this Agreement is found by a court of competent jurisdiction to be illegal or unenforceable, the validity or enforceability of the remainder of this Agreement shall not be affected.
This Agreement is the complete and exclusive agreement between the parties with respect to its subject matter and supersedes all prior or contemporaneous oral or written agreements or understandings relating to the subject matter.
```
### Citation Information
```
@misc{alex2019multinews,
title={Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model},
author={Alexander R. Fabbri and Irene Li and Tianwei She and Suyi Li and Dragomir R. Radev},
year={2019},
eprint={1906.01749},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
ymalusare/yash | ---
license: openrail
---
|
mehta77/guanaco-llama2-200_1 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 338808
num_examples: 200
download_size: 201258
dataset_size: 338808
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "guanaco-llama2-200_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
innodatalabs/rt-inod-jailbreaking | ---
license: cc-by-sa-4.0
language: en
task_categories:
- text-generation
- translation
- summarization
- question-answering
- sentence-similarity
tags:
- red teaming
labels:
domain: STEM, healthcare, general, finance
genre: business docs
skill: jailbreaking
safety: factuality, toxicity, bias
dataset_info:
- config_name: default
data_files:
- split: test
path: innodata_jailbreaking_test.jsonl
features:
- name: messages
list:
- name: role
dtype: string
- name: content
dtype: string
- name: expected
dtype: string
- name: id
dtype: string
---
# JAILBREAKING dataset
Red teaming human-crafted jailbreaking dataset.
|
awacke1/WikipediaSearchMemory | ---
license: apache-2.0
---
|
sethapun/procedural_gen_4operands | ---
dataset_info:
features:
- name: expression
dtype: string
- name: answer
dtype: float64
- name: label
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
splits:
- name: train
num_bytes: 85550
num_examples: 2000
- name: validation
num_bytes: 17072
num_examples: 400
download_size: 41332
dataset_size: 102622
---
# Dataset Card for "procedural_gen_4operands"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/spence_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of spence/スペンス/斯彭斯 (Azur Lane)
This is the dataset of spence/スペンス/斯彭斯 (Azur Lane), containing 15 images and their tags.
The core tags of this character are `hair_ornament, long_hair, pink_hair, bangs, two_side_up, yellow_eyes, hat, beret`, 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 | 15 | 12.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spence_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 15 | 8.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spence_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 32 | 16.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spence_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 15 | 11.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spence_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 32 | 22.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spence_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/spence_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 15 |  |  |  |  |  | hair_bobbles, blush, 1girl, tears, open_mouth, sleeveless, dress, simple_background, solo, hat_feather, looking_at_viewer, white_background, black_pantyhose, sailor_collar, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | hair_bobbles | blush | 1girl | tears | open_mouth | sleeveless | dress | simple_background | solo | hat_feather | looking_at_viewer | white_background | black_pantyhose | sailor_collar | smile |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------|:--------|:--------|:--------|:-------------|:-------------|:--------|:--------------------|:-------|:--------------|:--------------------|:-------------------|:------------------|:----------------|:--------|
| 0 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
EleutherAI/quirky_modularaddition_increment0_bob_easy | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype: bool
splits:
- name: train
num_bytes: 3503242.40528125
num_examples: 47279
- name: validation
num_bytes: 70323.03525
num_examples: 949
- name: test
num_bytes: 75048.105
num_examples: 1013
download_size: 764642
dataset_size: 3648613.54553125
---
# Dataset Card for "quirky_modularaddition_increment0_bob_easy"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kamaludeen/fututech-colorectal-cancer | ---
task_categories:
- tabular-classification
tags:
- microbiome
- tabular
- gut-microbiota
pretty_name: Colorectal Carcinoma Feng Q 2015
size_categories:
- n<1K
---
## Publication Abstract
Colorectal cancer, a commonly diagnosed cancer in the elderly, often develops slowly from benign polyps called adenoma. The gut microbiota is believed to be directly involved in colorectal carcinogenesis. The identity and functional capacity of the adenoma- or carcinoma-related gut microbe(s), however, have not been surveyed in a comprehensive manner. Here we perform a metagenome-wide association study (MGWAS) on stools from advanced adenoma and carcinoma patients and from healthy subjects, revealing microbial genes, strains and functions enriched in each group. An analysis of potential risk factors indicates that high intake of red meat relative to fruits and vegetables appears to associate with outgrowth of bacteria that might contribute to a more hostile gut environment. These findings suggest that faecal microbiome-based strategies may be useful for early diagnosis and treatment of colorectal adenoma or carcinoma.
## Dataset
156 metagenomic shotgun-sequenced faecal samples from colorectal adenoma and carcinoma patients and healthy controls
### Configurations
- `presence-absence`
- `CLR`
## Usage
```python
dataset = load_dataset("wwydmanski/colorectal-carcinoma-microbiome-fengq", "presence-absence")
train_dataset, test_dataset = dataset['train'], dataset['test']
X_train = np.array(train_dataset['values'])
y_train = np.array(train_dataset['target'])
X_test = np.array(test_dataset['values'])
y_test = np.array(test_dataset['target'])
``` |
pcuenq/face_synthetics_spiga | ---
dataset_info:
features:
- name: image
dtype: image
- name: image_seg
dtype: image
- name: landmarks
dtype: string
- name: spiga
sequence:
sequence: float64
- name: spiga_seg
dtype: image
splits:
- name: train
num_bytes: 31081737215.0
num_examples: 100000
download_size: 31009656222
dataset_size: 31081737215.0
---
# Dataset Card for "face_synthetics_spiga"
This is a copy of [Microsoft FaceSynthetics dataset](https://github.com/microsoft/FaceSynthetics) with [SPIGA](https://github.com/andresprados/SPIGA) landmark annotations. For a copy of the original FaceSynthetics dataset with no extra annotations, please refer to [pcuenq/face_synthetics](https://huggingface.co/pcuenq/face_synthetics).
Please, refer to the original [license](LICENSE.txt), which we replicate in this repo. The SPIGA annotations were created by Hugging Face Inc. and are distributed under the MIT license.
This dataset was prepared using the code below. It iterates through the dataset to perform landmark detection using SPIGA, and then to create visualizations of the features. Visualization is performed using Matplotlib to render to memory buffers.
```Python
import numpy as np
from datasets import load_dataset
from spiga.inference.config import ModelConfig
from spiga.inference.framework import SPIGAFramework
dataset_name = "pcuenq/face_synthetics"
faces = load_dataset(dataset_name)
faces = faces["train"]
# ## Obtain SPIGA features
processor = SPIGAFramework(ModelConfig("300wpublic"))
# We obtain the bbox from the existing landmarks in the dataset.
# We could use `dlib`, but this should be faster.
# Note that the `landmarks` are stored as strings.
def parse_landmarks(landmarks_str):
landmarks = landmarks_str.strip().split('\n')
landmarks = [k.split(' ') for k in landmarks]
landmarks = [(float(x), float(y)) for x, y in landmarks]
return landmarks
def bbox_from_landmarks(landmarks_str):
landmarks = parse_landmarks(landmarks_str)
landmarks_x, landmarks_y = zip(*landmarks)
x_min, x_max = min(landmarks_x), max(landmarks_x)
y_min, y_max = min(landmarks_y), max(landmarks_y)
width = x_max - x_min
height = y_max - y_min
# Give it a little room; I think it works anyway
x_min -= 5
y_min -= 5
width += 10
height += 10
bbox = (x_min, y_min, width, height)
return bbox
def spiga_process(example):
image = example["image"]
image = np.array(image)
# BGR
image = image[:, :, ::-1]
bbox = bbox_from_landmarks(example["landmarks"])
features = processor.inference(image, [bbox])
landmarks = features["landmarks"][0]
example["spiga"] = landmarks
return example
# For some reason this map doesn't work with num_proc > 1 :(
# TODO: run inference on GPU
faces = faces.map(spiga_process)
# ## "Segmentation"
# We use bezier paths to draw contours and areas.
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.path import Path
import PIL
def get_patch(landmarks, color='lime', closed=False):
contour = landmarks
ops = [Path.MOVETO] + [Path.LINETO]*(len(contour)-1)
facecolor = (0, 0, 0, 0) # Transparent fill color, if open
if closed:
contour.append(contour[0])
ops.append(Path.CLOSEPOLY)
facecolor = color
path = Path(contour, ops)
return patches.PathPatch(path, facecolor=facecolor, edgecolor=color, lw=4)
# Draw to a buffer.
def conditioning_from_landmarks(landmarks, size=512):
# Precisely control output image size
dpi = 72
fig, ax = plt.subplots(1, figsize=[size/dpi, size/dpi], tight_layout={'pad':0})
fig.set_dpi(dpi)
black = np.zeros((size, size, 3))
ax.imshow(black)
face_patch = get_patch(landmarks[0:17])
l_eyebrow = get_patch(landmarks[17:22], color='yellow')
r_eyebrow = get_patch(landmarks[22:27], color='yellow')
nose_v = get_patch(landmarks[27:31], color='orange')
nose_h = get_patch(landmarks[31:36], color='orange')
l_eye = get_patch(landmarks[36:42], color='magenta', closed=True)
r_eye = get_patch(landmarks[42:48], color='magenta', closed=True)
outer_lips = get_patch(landmarks[48:60], color='cyan', closed=True)
inner_lips = get_patch(landmarks[60:68], color='blue', closed=True)
ax.add_patch(face_patch)
ax.add_patch(l_eyebrow)
ax.add_patch(r_eyebrow)
ax.add_patch(nose_v)
ax.add_patch(nose_h)
ax.add_patch(l_eye)
ax.add_patch(r_eye)
ax.add_patch(outer_lips)
ax.add_patch(inner_lips)
plt.axis('off')
fig.canvas.draw()
buffer, (width, height) = fig.canvas.print_to_buffer()
assert width == height
assert width == size
buffer = np.frombuffer(buffer, np.uint8).reshape((height, width, 4))
buffer = buffer[:, :, 0:3]
plt.close(fig)
return PIL.Image.fromarray(buffer)
def spiga_segmentation(example):
landmarks = example["spiga"]
example['spiga_seg'] = conditioning_from_landmarks(landmarks)
return example
faces = faces.map(spiga_segmentation, num_proc=16)
faces.push_to_hub(f"{dataset_name}_spiga")
```
|
yzhuang/metatree_BNG_breast_w_ | ---
dataset_info:
features:
- name: id
dtype: int64
- name: X
sequence: float64
- name: y
dtype: int64
splits:
- name: train
num_bytes: 2519880
num_examples: 27390
- name: validation
num_bytes: 1101792
num_examples: 11976
download_size: 1587654
dataset_size: 3621672
---
# Dataset Card for "metatree_BNG_breast_w_"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
catmon/test1 | ---
language:
- fr
- es
--- |
Adapting/Abstracts-for-Clustering | ---
license: mit
---
|
yangezheng/CMSB | ---
dataset_info:
features:
- name: text
dtype: string
- name: toxicity
dtype: float64
- name: label_sexist
dtype:
class_label:
names:
'0': not sexist
'1': sexist
splits:
- name: train
num_bytes: 1165582.968234172
num_examples: 11040
- name: validation
num_bytes: 129544.41141515663
num_examples: 1227
- name: test
num_bytes: 144008.62035067126
num_examples: 1364
download_size: 1019308
dataset_size: 1439136.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
tyzhu/find_second_sent_train_30_eval_10 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: title
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 89174
num_examples: 70
- name: validation
num_bytes: 10923
num_examples: 10
download_size: 63471
dataset_size: 100097
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "find_second_sent_train_30_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CarlosKidman/test-cases | ---
license: mit
language:
- en
tags:
- testing
size_categories:
- n<1K
---
# Functional Test Cases
This is a _very_ small list of functional test cases that a team of software testers (QA) created for an example mobile app called Boop.
## Dataset
* Name: `Boop Test Cases.csv`
* Number of Rows: `136`
* Columns: `11`
* `Test ID` (int)
* `Summary` (string)
* `Idea` (string)
* `Preconditions` (string)
* `Steps to reproduce` (string)
* `Expected Result` (string)
* `Actual Result` (string)
* `Pass/Fail` (string)
* `Bug #` (string)
* `Author` (string)
* `Area` (string)
> 💡 There are missing values. For example, not every test case had a related Bug
## Use Cases
Two common problems in Software Testing are:
* Duplicate test cases (and bug reports)
* Assigning issues to the correct team quickly (from internal sources, Customer or Tech Support, etc)
This dataset is probably too small to create an "Auto-Assigner" tool -- especially because almost half the tests are focused in the `Account` Area.
However, with embeddings, we could see if a new Test Case already exists by checking similarity 🤔 |
itslogannye/autotrain-data-enchondroma-vs-low-grade-chondrosarcoma-histology | ---
task_categories:
- image-classification
---
# AutoTrain Dataset for project: enchondroma-vs-low-grade-chondrosarcoma-histology
## Dataset Description
This dataset has been automatically processed by AutoTrain for project enchondroma-vs-low-grade-chondrosarcoma-histology.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<1024x1024 RGB PIL image>",
"target": 0
},
{
"image": "<1024x1024 RGB PIL image>",
"target": 1
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Enchondroma', 'Low-grade Chondrosarcoma'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 458 |
| valid | 115 |
|
HuggingFaceH4/instruction-pilot-outputs-sampling | ---
dataset_info:
features:
- name: id
dtype: int64
- name: source
dtype: string
- name: prompt
dtype: string
- name: outputs
list:
- name: model
dtype: string
- name: outputs
sequence: string
splits:
- name: train
num_bytes: 1347447
num_examples: 375
download_size: 430865
dataset_size: 1347447
---
# Dataset Card for "instruction-pilot-outputs-sampling"
This dataset contains model outputs generated from the human demonstrations provided in [`HuggingFaceH4/instruction-pilot-prompts`](https://huggingface.co/datasets/HuggingFaceH4/instruction-pilot-prompts).
To convert each language model into a dialogue agent, we shortened [Anthropic's HHH prompt](https://gist.github.com/jareddk/2509330f8ef3d787fc5aaac67aab5f11/#file-hhh_prompt-txt) and prepended this to each sample provided to the models:
```
Below is a friendly conversation between a human and an AI assistant. \
The AI tries to be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable. \
The assistant is happy to help with almost anything, and will do its best to understand exactly what is needed. \
It also tries to avoid giving false or misleading information, and it caveats when it isn’t entirely sure about the right answer. \
That said, the assistant is practical and really does its best, and doesn’t let caution get too much in the way of being useful.
Human: {input}
AI:
```
The reason to shorten the HHH prompt is because it is over 6,000 tokens long, which far exceeds the maximum context size of most open-source language models. For example, Flan-T5 only has a context window of 512 tokens. It is likely that better outputs could be produced for language models with larger context windows, where some dialogue examples can be included in the promopt.
To generate diverse outputs from each models, we used nucleus sampling with `temperature=0` and `top_p=0.9` and set `max_new_tokens=100` (which is about the mean lenght of the Self-Instruct outputs). For each example, 8 generations were produced per model. |
DucHaiten/dantocdao | ---
license: creativeml-openrail-m
---
|
Bingsu/ko_alpaca_data | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 13791136
num_examples: 49620
download_size: 8491044
dataset_size: 13791136
license: cc-by-nc-4.0
language:
- ko
pretty_name: ko-alpaca-data
size_categories:
- 10K<n<100K
task_categories:
- text-generation
---
# Dataset Card for "ko_alpaca_data"
## Dataset Description
- **Repository:** [Beomi/KoAlpaca](https://github.com/Beomi/KoAlpaca)
- **Huggingface:** [beomi/KoAlpaca](https://huggingface.co/beomi/KoAlpaca)
- **Size of downloaded dataset files:** 8.10 MB
- **Size of the generated dataset:** 13.15 MB
### Dataset Summary
Korean translation of [alpaca data](https://huggingface.co/datasets/tatsu-lab/alpaca).
repository: [Beomi/KoAlpaca](https://github.com/Beomi/KoAlpaca)<br>
huggingface: [beomi/KoAlpaca](https://huggingface.co/beomi/KoAlpaca)
1. Translate dataset
Translated 'instruction' and 'input' in the dataset via the DeepL API, except for 'output', which we did not translate because it is the output of OpenAI's `text-davinci-003` model.
2. Generate output data
Then, using the instruction and input, generate output data via the OpenAI ChatGPT API (gpt-3.5-turbo).
Below is the prompt we used to generate the answer.
```python
PROMPT = """\
다양한 작업에 대한 답변을 생성해주세요. 이러한 작업 지침은 ChatGPT 모델에 주어지며, ChatGPT 모델이 지침을 완료하는지 평가합니다.
요구 사항은 다음과 같습니다:
1. 다양성을 극대화하기 위해 각 지시에 대해 동사를 반복하지 않도록 하세요.
2. 지시에 사용되는 언어도 다양해야 합니다. 예를 들어, 질문과 명령형 지시를 결합해야 합니다.
3. 지시 사항의 유형이 다양해야 합니다. 목록에는 개방형 생성, 분류, 편집 등과 같은 다양한 유형의 작업이 포함되어야 합니다.
2. GPT 언어 모델은 지시를 완료할 수 있어야 합니다. 예를 들어 어시스턴트에게 시각적 또는 오디오 출력을 생성하도록 요청하지 마세요. 또 다른 예로, 어시스턴트가 어떤 작업도 수행할 수 없으므로 오후 5시에 깨우거나 미리 알림을 설정하도록 요청하지 마세요.
3. 답변은 한국어로 작성해야 합니다.
4. 답변을 1~2문장으로 작성하세요. 명령문이나 질문도 허용됩니다.
5. 지시 사항에 대한 적절한 입력을 생성해야 합니다. 입력 필드에는 지시에 대한 구체적인 예가 포함되어야 합니다. 실제 데이터를 포함해야 하며 단순한 자리 표시자를 포함해서는 안 됩니다. 입력은 지시 사항을 어렵게 만들 수 있는 상당한 내용을 제공해야 하지만 100단어를 넘지 않는 것이 이상적입니다.
6. 일부 지시사항은 추가 입력이 있고, 일부 지시에는 입력 필드가 비어있습니다. 예를 들어 "세계에서 가장 높은 봉우리는 무엇인가?"라는 일반적인 정보를 묻는 지시의 경우 구체적인 맥락을 제공할 필요가 없어, 입력 필드가 비어있을 수 있습니다.
7. 출력은 명령어와 입력에 대한 적절한 응답이어야 합니다.
아래에 10개의 명령어와 입력(옵션)에 따라 적절한 응답을 생성하세요.
응답은 아래와 같은 형식으로 10가지를 0번 부터 9번 까지, 번호에 따라 해당 번호의 명령어와 입력에 알맞게 작성하세요.
각 응답 사이는 ### 으로 내용을 분리해주세요.
응답0: 첫 번째 응답내용###
응답1: 두 번째 응답내용###
...
응답9: 마지막 응답내용"""
```
### Lisence
CC-BY-NC-4.0
### Data Splits
| | train |
| --------- | -------- |
| # of data | 49620 |
\# Note that the number is not the same as the original data(52002)
```python
>>> from datasets import load_dataset
>>> ds = load_dataset("Bingsu/ko_alpaca_data", split="train")
>>> ds
Dataset({
features: ['instruction', 'input', 'output'],
num_rows: 49620
})
```
```python
>>> ds[0]
{'instruction': '건강을 유지하기 위한 세 가지 팁을 알려주세요.',
'input': '',
'output': '세 가지 팁은 아침식사를 꼭 챙기며, 충분한 수면을 취하고, 적극적으로 운동을 하는 것입니다.'}
``` |
Thouph/text_stories | ---
license: wtfpl
---
|
hongdijk/kluefinal | ---
license: other
---
|
AshtonIsNotHere/nli4ct_semeval2024 | ---
task_categories:
- text-classification
- sentence-similarity
language:
- en
tags:
- medical
pretty_name: >-
SemEval 2024 Task 2: Safe Biomedical Natural Language Inference for Clinical
Trials
---
# SemEval 2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
Compiled dataset for SemEval 2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials.
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** [https://github.com/ai-systems/Task-2-SemEval-2024]
- **Paper:** [https://aclanthology.org/2023.semeval-1.307/]
- **Demo:** [More Information Needed]
|
burkelibbey/colors | ---
license: mit
---
|
autoevaluate/autoeval-staging-eval-cnn_dailymail-3.0.0-d7ce16-14946086 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: facebook/bart-large-cnn
metrics: ['mse']
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# 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: facebook/bart-large-cnn
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
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. |
mimic221/stanowski | ---
license: other
---
|
Multimodal-Fatima/OxfordPets_test_facebook_opt_350m_Visclues_20 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: image
dtype: image
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
- name: scores
sequence: float64
splits:
- name: fewshot_3
num_bytes: 277693.0
num_examples: 20
- name: fewshot_5
num_bytes: 292064.0
num_examples: 20
- name: fewshot_1
num_bytes: 263406.0
num_examples: 20
- name: fewshot_2
num_bytes: 270668.0
num_examples: 20
download_size: 784934
dataset_size: 1103831.0
---
# Dataset Card for "OxfordPets_test_facebook_opt_350m_Visclues_20"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
erikrose93/mariadefatima | ---
license: apache-2.0
---
|
nastyboget/synthetic_hkr_large | ---
license: mit
task_categories:
- image-to-text
language:
- ru
size_categories:
- 1M<n<10M
---
Dataset generated using handwritten fonts
=========================================
Number of images: 2634473
Sources:
* [Handwriting generation code](https://github.com/NastyBoget/HandwritingGeneration)
The code was executed with `hkr` option (with fewer augmentations)
|
Admin0805/Newcc | ---
license: openrail
---
|
Nadav/pixel_glue_sst2_low_noise | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: validation
num_bytes: 18864146.0
num_examples: 872
download_size: 18783765
dataset_size: 18864146.0
---
# Dataset Card for "pixel_glue_sst2_low_noise"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
renatomoulin/fourthbrain_synthetic_marketmail_gpt4 | ---
dataset_info:
features:
- name: product
dtype: string
- name: description
dtype: string
- name: marketing_email
dtype: string
splits:
- name: train
num_bytes: 13145
num_examples: 10
download_size: 18470
dataset_size: 13145
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "fourthbrain_synthetic_marketmail_gpt4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
stanfordnlp/colorswap | ---
license:
- mit
dataset_info:
features:
- name: id
dtype: int32
- name: image_1
dtype: image
- name: image_2
dtype: image
- name: caption_1
dtype: string
- name: caption_2
dtype: string
- name: image_source
dtype: string
- name: caption_source
dtype: string
splits:
- name: train
num_bytes: 300541
num_examples: 700
- name: test
num_bytes: 128623
num_examples: 300
download_size: 2762991931
dataset_size: 429164
---
# ColorSwap: A Color and Word Order Dataset for Multimodal Evaluation
## Dataset Description
ColorSwap is a dataset designed to assess and improve the proficiency of multimodal models in matching objects with their colors. The dataset is comprised of 2,000 unique image-caption pairs, grouped into 1,000 examples. Each example includes a caption-image pair, along with a "color-swapped" pair. Crucially, the two captions in an example have the same words, but the color words have been rearranged to modify different objects. The dataset was created through a novel blend of automated caption and image generation with humans in the loop.
Paper: Coming soon!
## Usage
You can download the dataset directly from the Hugging Face API with the following code:
```python
from datasets import load_dataset
dataset = load_dataset("stanfordnlp/colorswap", use_auth_token=True)
```
Please make sure to install the `datasets` library and use the `use_auth_token` parameter to authenticate with the Hugging Face API.
An example of the dataset is as follows:
```python
[
{
'id': 0,
'image_1': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=1024x1024 at 0x14D908B20>,
'image_2': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=1024x1024 at 0x14D9DCE20>,
'caption_1': 'someone holding a yellow umbrella wearing a white dress',
'caption_2': 'someone holding a white umbrella wearing a yellow dress',
'image_source': 'midjourney',
'caption_source': 'human'
}
...
]
```
## Evaluations
[This Google Colab](https://colab.research.google.com/drive/1EWPsSklfq49WiX2nUyOTmKZftU0AC4YL?usp=sharing) showcases our ITM model evaluations.
Please refer to our Github repository for the VLM evaluations: [ColorSwap](https://github.com/Top34051/colorswap).
## Citation
If you find our work useful, please cite the following paper:
```
@article{burapacheep2024colorswap,
author = {Jirayu Burapacheep and Ishan Gaur and Agam Bhatia and Tristan Thrush},
title = {ColorSwap: A Color and Word Order Dataset for Multimodal Evaluation},
journal = {arXiv},
year = {2024},
}
```
|
robertmujicadell/poc | ---
license: mit
---
|
rinabuoy/Eng-Khmer-Agg-2Ways | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 59320960
num_examples: 150584
- name: test
num_bytes: 5238058
num_examples: 11822
download_size: 15781215
dataset_size: 64559018
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
CyberHarem/ena_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of ena (Fire Emblem)
This is the dataset of ena (Fire Emblem), containing 14 images and their tags.
The core tags of this character are `blue_eyes, pink_hair, earrings, long_hair, facial_mark, pointy_ears, breasts, ponytail, dark_skin, hat`, 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 | 14 | 10.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ena_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 14 | 7.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ena_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 20 | 10.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ena_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 14 | 9.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ena_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 20 | 13.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ena_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/ena_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 | 14 |  |  |  |  |  | 1girl, jewelry, solo, long_sleeves, forehead_mark, halloween, ofuda, open_mouth, qing_guanmao, sleeves_past_wrists, wide_sleeves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | jewelry | solo | long_sleeves | forehead_mark | halloween | ofuda | open_mouth | qing_guanmao | sleeves_past_wrists | wide_sleeves |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------|:-------|:---------------|:----------------|:------------|:--------|:-------------|:---------------|:----------------------|:---------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X |
|
alpayariyak/opencoder-instruct | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: source
dtype: string
- name: output_contains_code
dtype: bool
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 887513766
num_examples: 384623
download_size: 457868231
dataset_size: 887513766
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
MajdTannous/Test3 | ---
license: other
---
|
duraad/nep-spell-synthetic-27k | ---
license: mit
language:
- ne
tags:
- nepali
- spelling
size_categories:
- 10K<n<100K
---
Contains 27k sentence pairs for Nepali Spell Checking.
https://www.kaggle.com/code/amardura/thegroup-nep-spell-synthetic-datapoints |
KnutJaegersberg/interpretable_word_embeddings | ---
license: mit
---
These word embeddings were computed using the POLAR technique to reproject 'common' word embeddings into roundabout 700 interpretable dimensions of polar opposites (i.e. good/bad).
I just used their scripts here:
https://github.com/Sandipan99/POLAR
I applied those on the wikidata5m embeddings, 5 million knowledge graph embeddings (SimplE).
https://graphvite.io/docs/latest/pretrained_model.html
As the model became too huge, I further filtered it for overlap with fasttext embedding tokens.
Not all dimensions make sense, this is a work in progress.
I intend to remove dimensions which turn out to not make sense, when using them. |
jacobbieker/eumetsat-cloudmask-rss | ---
license: mit
---
|
bot-yaya/undl_text | ---
dataset_info:
features:
- name: ar
dtype: string
- name: zh
dtype: string
- name: en
dtype: string
- name: fr
dtype: string
- name: ru
dtype: string
- name: es
dtype: string
- name: de
dtype: string
- name: record
dtype: string
splits:
- name: train
num_bytes: 48667711040
num_examples: 165840
download_size: 18648916788
dataset_size: 48667711040
---
# Dataset Card for "undl_text"
pandoc转docx出的源文本，所用命令为：pandoc -i {filepath} -t plain -o {outpath} --strip-comments
这些文本可能仍需一定的步骤去噪，比如去掉全是横线的分隔符、去掉表格元素，才能用于后续的翻译及对齐步骤 |
CyberHarem/nakano_miku_gotoubunnohanayome | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Nakano Miku/三玖 (Gotoubun no Hanayome)
This is the dataset of Nakano Miku/三玖 (Gotoubun no Hanayome), containing 530 images and their tags.
The core tags of this character are `brown_hair, long_hair, blue_eyes, hair_between_eyes, headphones, 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 | 530 | 331.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nakano_miku_gotoubunnohanayome/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 530 | 320.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nakano_miku_gotoubunnohanayome/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1024 | 576.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nakano_miku_gotoubunnohanayome/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/nakano_miku_gotoubunnohanayome',
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, blue_cardigan, blush, headphones_around_neck, solo, white_shirt, closed_mouth, looking_at_viewer, upper_body |
| 1 | 5 |  |  |  |  |  | 1girl, closed_mouth, headphones_around_neck, solo, white_shirt, upper_body, blush |
| 2 | 18 |  |  |  |  |  | 1girl, closed_mouth, headphones_around_neck, solo, portrait, blush, looking_at_viewer, smile |
| 3 | 5 |  |  |  |  |  | 1girl, from_side, headphones_around_neck, profile, solo, upper_body, white_shirt, closed_mouth, open_mouth |
| 4 | 5 |  |  |  |  |  | 1girl, black_jacket, blazer, blush, collared_shirt, headphones_around_neck, school_uniform, upper_body, white_shirt, closed_mouth, open_jacket, solo, blue_cardigan, purple_eyes, looking_at_viewer |
| 5 | 11 |  |  |  |  |  | butterfly_hair_ornament, headphones_around_neck, red_hair, sisters, white_shirt, black_ribbon, pink_hair, 2girls, blue_cardigan, hair_ribbon, upper_body, blush, long_sleeves, open_mouth, blurry, closed_mouth, indoors, school_uniform, solo_focus |
| 6 | 5 |  |  |  |  |  | :d, blush, english_text, headphones_around_neck, open_mouth, sisters, upper_body, 2girls, solo_focus, long_sleeves, orange_hair, outdoors, white_shirt, 1girl |
| 7 | 10 |  |  |  |  |  | closed_mouth, red_hair, sitting, white_shirt, long_sleeves, english_text, hugging_own_legs, lying, petals, white_dress, ribbon, sisters, 1girl, solo_focus, 2girls, rose, white_flower |
| 8 | 7 |  |  |  |  |  | 1girl, floral_print, green_kimono, blush, hair_flower, solo, closed_mouth, long_sleeves, obi, print_kimono, upper_body |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_cardigan | blush | headphones_around_neck | solo | white_shirt | closed_mouth | looking_at_viewer | upper_body | portrait | smile | from_side | profile | open_mouth | black_jacket | blazer | collared_shirt | school_uniform | open_jacket | purple_eyes | butterfly_hair_ornament | red_hair | sisters | black_ribbon | pink_hair | 2girls | hair_ribbon | long_sleeves | blurry | indoors | solo_focus | :d | english_text | orange_hair | outdoors | sitting | hugging_own_legs | lying | petals | white_dress | ribbon | rose | white_flower | floral_print | green_kimono | hair_flower | obi | print_kimono |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------------|:--------|:-------------------------|:-------|:--------------|:---------------|:--------------------|:-------------|:-----------|:--------|:------------|:----------|:-------------|:---------------|:---------|:-----------------|:-----------------|:--------------|:--------------|:--------------------------|:-----------|:----------|:---------------|:------------|:---------|:--------------|:---------------|:---------|:----------|:-------------|:-----|:---------------|:--------------|:-----------|:----------|:-------------------|:--------|:---------|:--------------|:---------|:-------|:---------------|:---------------|:---------------|:--------------|:------|:---------------|
| 0 | 17 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | | X | X | X | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 18 |  |  |  |  |  | X | | X | X | X | | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 11 |  |  |  |  |  | | X | X | X | | X | X | | X | | | | | X | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | | X | 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 | | | | | |
| 8 | 7 |  |  |  |  |  | X | | X | | X | | X | | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X |
|
lsr42/msmarco-passage-ep | ---
license: apache-2.0
---
|
AdapterOcean/chemistry_dataset_standardized_cluster_4_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: 4334633
num_examples: 6060
download_size: 1851846
dataset_size: 4334633
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "chemistry_dataset_standardized_cluster_4_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
FSMBench/fsmbench_what_will_be_the_state_12K_think_step_by_step_image | ---
dataset_info:
features:
- name: query_id
dtype: string
- name: fsm_id
dtype: string
- name: fsm_json
dtype: string
- name: difficulty_level
dtype: int64
- name: transition_matrix
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: substring_index
dtype: int64
- name: number_of_states
dtype: int64
- name: number_of_alphabets
dtype: int64
- name: state_alpha_combo
dtype: string
- name: image
dtype: image
splits:
- name: validation
num_bytes: 1038341411.0
num_examples: 12800
download_size: 60403789
dataset_size: 1038341411.0
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
|
long292/PADNCH_3 | ---
dataset_info:
features:
- name: Phiên âm
dtype: string
- name: Dịch nghĩa
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 2274364
num_examples: 11641
download_size: 1325611
dataset_size: 2274364
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
projecte-aina/CA-IT_Parallel_Corpus | ---
language:
- ca
- it
multilinguality:
- multilingual
pretty_name: CA-IT Parallel Corpus
size_categories:
- 1M<n<10M
task_categories:
- translation
task_ids: []
license: cc-by-nc-sa-4.0
---
# Dataset Card for CA-IT Parallel Corpus
## Dataset Description
- **Point of Contact:** langtech@bsc.es
### Dataset Summary
The CA-IT Parallel Corpus is a Catalan-Italian dataset of **9.482.927** parallel sentences. The dataset was created to support Catalan in NLP tasks, specifically
Machine Translation.
### Supported Tasks and Leaderboards
The dataset can be used to train Bilingual Machine Translation models between Italian and Catalan in any direction,
as well as Multilingual Machine Translation models.
### Languages
The sentences included in the dataset are in Catalan (CA) and Italian (IT).
## Dataset Structure
### Data Instances
Two separate txt files are provided with the sentences sorted in the same order:
- ca-it_corpus.ca: contains 9.482.927 Catalan sentences.
- ca-it_corpus.it: contains 9.482.927 Italian sentences.
### Data Fields
[N/A]
### Data Splits
The dataset contains a single split: `train`.
## Dataset Creation
### Curation Rationale
This dataset is aimed at promoting the development of Machine Translation between Catalan and other languages, specifically Italian.
### Source Data
#### Initial Data Collection and Normalization
The dataset is a combination of the following original datasets:
| Dataset | Sentences |
|:--- | ---: |
| CCMatrix v1 | 7.757.357|
| MultiCCAligned v1 | 1.010.921|
| WikiMatrix | 271.587|
| GNOME | 1.198|
| KDE4 | 115.027 |
| QED | 52.616 |
| TED2020 v1 | 43.280 |
| OpenSubtitles | 225.732 |
| GlobalVoices| 5.209|
| **Total** | **9.482.927** |
All corpora were collected from [Opus](https://opus.nlpl.eu/).
All datasets are deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75.
This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE).
The filtered datasets are then concatenated to form a final corpus of **9.482.927** parallel sentences.
#### Who are the source language producers?
[Opus](https://opus.nlpl.eu/)
### Annotations
#### Annotation process
The dataset does not contain any annotations.
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
Given that this dataset is partly derived from pre-existing datasets that may contain crawled data, and that no specific anonymisation process has been applied,
personal and sensitive information may be present in the data. This needs to be considered when using the data for training models.
## Considerations for Using the Data
### Social Impact of Dataset
By providing this resource, we intend to promote the use of Catalan across NLP tasks, thereby improving the accessibility and visibility of the Catalan language.
### Discussion of Biases
No specific bias mitigation strategies were applied to this dataset.
Inherent biases may exist within the data.
### Other Known Limitations
The dataset contains data of a general domain. Applications of this dataset in more specific domains such as biomedical, legal etc. would be of limited use.
## Additional Information
### Dataset Curators
Language Technologies Unit at the Barcelona Supercomputing Center (langtech@bsc.es).
This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/).
### Licensing Information
This work is licensed under a [Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Citation Information
[N/A]
### Contributions
[N/A] |
arnabdhar/wikiner-multilingual | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: tags
sequence: string
splits:
- name: train
num_bytes: 795273111
num_examples: 2506842
download_size: 239559155
dataset_size: 795273111
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
thesistranslation/distilled-ccmatrix-es-en | ---
dataset_info:
features:
- name: id
dtype: int32
- name: translation
dtype:
translation:
languages:
- es
- en
splits:
- name: train
num_bytes: 7090174966
num_examples: 30000000
download_size: 4926077685
dataset_size: 7090174966
language:
- es
- en
---
# Dataset Card for "distilled-ccmatrix-es-en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Kofi24/offensive | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 16560610.939746963
num_examples: 5588
- name: test
num_bytes: 7097828.060253038
num_examples: 2395
download_size: 13260136
dataset_size: 23658439.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
nateraw/kineticstest | ---
license: cc-by-4.0
---
|
Jeremy186/testing | ---
license: mit
---
|
Prasant/Mini-Laion | ---
license: apache-2.0
---
Mini-Laion is a subset of Laion-400M dataset |
reciprocate/pku_safer_dpo_pairs | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 43724313
num_examples: 46625
- name: test
num_bytes: 4688874
num_examples: 5135
download_size: 26918777
dataset_size: 48413187
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
mtkinit/Hello-world-21312 | ---
pretty_name: Hello-world-21312
---
# Hello-world-21312
Created from AIOD platform |
kye/all-microsoft-python-code | ---
license: mit
---
|
michaelginn/bert_dataset | ---
dataset_info:
features:
- name: text
sequence: string
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 140296380642.0
num_examples: 47029787
download_size: 28020464137
dataset_size: 140296380642.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
terrakom/dataset | ---
license: mit
---
|
joey234/mmlu-electrical_engineering | ---
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: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: neg_question
dtype: string
- name: fewshot_context
dtype: string
- name: fewshot_context_neg
dtype: string
splits:
- name: dev
num_bytes: 4543
num_examples: 5
- name: test
num_bytes: 391545
num_examples: 145
download_size: 66443
dataset_size: 396088
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
# Dataset Card for "mmlu-electrical_engineering"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Q-bert__MetaMath-Cybertron | ---
pretty_name: Evaluation run of Q-bert/MetaMath-Cybertron
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Q-bert/MetaMath-Cybertron](https://huggingface.co/Q-bert/MetaMath-Cybertron)\
\ 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_Q-bert__MetaMath-Cybertron\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-07T21:43:38.456468](https://huggingface.co/datasets/open-llm-leaderboard/details_Q-bert__MetaMath-Cybertron/blob/main/results_2023-12-07T21-43-38.456468.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\": 0.641342115405787,\n\
\ \"acc_stderr\": 0.032232870272022124,\n \"acc_norm\": 0.6412913403995665,\n\
\ \"acc_norm_stderr\": 0.032896201038175164,\n \"mc1\": 0.408812729498164,\n\
\ \"mc1_stderr\": 0.017209952151641734,\n \"mc2\": 0.5770577317207616,\n\
\ \"mc2_stderr\": 0.015307336326138697\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.636518771331058,\n \"acc_stderr\": 0.014056207319068283,\n\
\ \"acc_norm\": 0.6646757679180887,\n \"acc_norm_stderr\": 0.013796182947785562\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6677952599083847,\n\
\ \"acc_stderr\": 0.004700413824942566,\n \"acc_norm\": 0.8554072893845848,\n\
\ \"acc_norm_stderr\": 0.0035097096477918373\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6518518518518519,\n\
\ \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n\
\ \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n\
\ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n\
\ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \
\ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n\
\ \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7083333333333334,\n\
\ \"acc_stderr\": 0.038009680605548594,\n \"acc_norm\": 0.7083333333333334,\n\
\ \"acc_norm_stderr\": 0.038009680605548594\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\"\
: 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.653179190751445,\n\
\ \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n\
\ \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082635,\n\
\ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082635\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.78,\n \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n\
\ \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n\
\ \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.041443118108781526,\n\
\ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.041443118108781526\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4021164021164021,\n \"acc_stderr\": 0.025253032554997685,\n \"\
acc_norm\": 0.4021164021164021,\n \"acc_norm_stderr\": 0.025253032554997685\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4126984126984127,\n\
\ \"acc_stderr\": 0.04403438954768177,\n \"acc_norm\": 0.4126984126984127,\n\
\ \"acc_norm_stderr\": 0.04403438954768177\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\": 0.7709677419354839,\n\
\ \"acc_stderr\": 0.023904914311782648,\n \"acc_norm\": 0.7709677419354839,\n\
\ \"acc_norm_stderr\": 0.023904914311782648\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n\
\ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"\
acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.02381447708659355,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.02381447708659355\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6461538461538462,\n \"acc_stderr\": 0.024243783994062157,\n\
\ \"acc_norm\": 0.6461538461538462,\n \"acc_norm_stderr\": 0.024243783994062157\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\": 0.7016806722689075,\n \"acc_stderr\": 0.029719142876342853,\n\
\ \"acc_norm\": 0.7016806722689075,\n \"acc_norm_stderr\": 0.029719142876342853\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33774834437086093,\n \"acc_stderr\": 0.038615575462551684,\n \"\
acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.038615575462551684\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8440366972477065,\n \"acc_stderr\": 0.015555802713590167,\n \"\
acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.015555802713590167\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5601851851851852,\n \"acc_stderr\": 0.033851779760448106,\n \"\
acc_norm\": 0.5601851851851852,\n \"acc_norm_stderr\": 0.033851779760448106\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"\
acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \
\ \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\
\ \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n\
\ \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.037683359597287434,\n\
\ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.037683359597287434\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.03957835471980979,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.03957835471980979\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7300613496932515,\n \"acc_stderr\": 0.034878251684978906,\n\
\ \"acc_norm\": 0.7300613496932515,\n \"acc_norm_stderr\": 0.034878251684978906\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.02158649400128138,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.02158649400128138\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8160919540229885,\n\
\ \"acc_stderr\": 0.013853724170922531,\n \"acc_norm\": 0.8160919540229885,\n\
\ \"acc_norm_stderr\": 0.013853724170922531\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.02418242749657761,\n\
\ \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.02418242749657761\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.41564245810055866,\n\
\ \"acc_stderr\": 0.016482782187500666,\n \"acc_norm\": 0.41564245810055866,\n\
\ \"acc_norm_stderr\": 0.016482782187500666\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7091503267973857,\n \"acc_stderr\": 0.02600480036395213,\n\
\ \"acc_norm\": 0.7091503267973857,\n \"acc_norm_stderr\": 0.02600480036395213\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n\
\ \"acc_stderr\": 0.025494259350694912,\n \"acc_norm\": 0.7202572347266881,\n\
\ \"acc_norm_stderr\": 0.025494259350694912\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.024748624490537368,\n\
\ \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.024748624490537368\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\": 0.4498044328552803,\n\
\ \"acc_stderr\": 0.012705721498565106,\n \"acc_norm\": 0.4498044328552803,\n\
\ \"acc_norm_stderr\": 0.012705721498565106\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6654411764705882,\n \"acc_stderr\": 0.028661996202335303,\n\
\ \"acc_norm\": 0.6654411764705882,\n \"acc_norm_stderr\": 0.028661996202335303\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6535947712418301,\n \"acc_stderr\": 0.019249785691717217,\n \
\ \"acc_norm\": 0.6535947712418301,\n \"acc_norm_stderr\": 0.019249785691717217\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\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\
\ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\
\ \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\
\ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\
\ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\
\ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.408812729498164,\n\
\ \"mc1_stderr\": 0.017209952151641734,\n \"mc2\": 0.5770577317207616,\n\
\ \"mc2_stderr\": 0.015307336326138697\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7963693764798737,\n \"acc_stderr\": 0.011317798781626922\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7050796057619408,\n \
\ \"acc_stderr\": 0.012560698010954774\n }\n}\n```"
repo_url: https://huggingface.co/Q-bert/MetaMath-Cybertron
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_07T21_43_38.456468
path:
- '**/details_harness|arc:challenge|25_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|gsm8k|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hellaswag|10_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-07T21-43-38.456468.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-07T21-43-38.456468.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- '**/details_harness|winogrande|5_2023-12-07T21-43-38.456468.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-07T21-43-38.456468.parquet'
- config_name: results
data_files:
- split: 2023_12_07T21_43_38.456468
path:
- results_2023-12-07T21-43-38.456468.parquet
- split: latest
path:
- results_2023-12-07T21-43-38.456468.parquet
---
# Dataset Card for Evaluation run of Q-bert/MetaMath-Cybertron
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Q-bert/MetaMath-Cybertron
- **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 [Q-bert/MetaMath-Cybertron](https://huggingface.co/Q-bert/MetaMath-Cybertron) 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_Q-bert__MetaMath-Cybertron",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-07T21:43:38.456468](https://huggingface.co/datasets/open-llm-leaderboard/details_Q-bert__MetaMath-Cybertron/blob/main/results_2023-12-07T21-43-38.456468.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": 0.641342115405787,
"acc_stderr": 0.032232870272022124,
"acc_norm": 0.6412913403995665,
"acc_norm_stderr": 0.032896201038175164,
"mc1": 0.408812729498164,
"mc1_stderr": 0.017209952151641734,
"mc2": 0.5770577317207616,
"mc2_stderr": 0.015307336326138697
},
"harness|arc:challenge|25": {
"acc": 0.636518771331058,
"acc_stderr": 0.014056207319068283,
"acc_norm": 0.6646757679180887,
"acc_norm_stderr": 0.013796182947785562
},
"harness|hellaswag|10": {
"acc": 0.6677952599083847,
"acc_stderr": 0.004700413824942566,
"acc_norm": 0.8554072893845848,
"acc_norm_stderr": 0.0035097096477918373
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6518518518518519,
"acc_stderr": 0.041153246103369526,
"acc_norm": 0.6518518518518519,
"acc_norm_stderr": 0.041153246103369526
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6973684210526315,
"acc_stderr": 0.03738520676119669,
"acc_norm": 0.6973684210526315,
"acc_norm_stderr": 0.03738520676119669
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.62,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.62,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.690566037735849,
"acc_stderr": 0.028450154794118637,
"acc_norm": 0.690566037735849,
"acc_norm_stderr": 0.028450154794118637
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7083333333333334,
"acc_stderr": 0.038009680605548594,
"acc_norm": 0.7083333333333334,
"acc_norm_stderr": 0.038009680605548594
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.653179190751445,
"acc_stderr": 0.036291466701596636,
"acc_norm": 0.653179190751445,
"acc_norm_stderr": 0.036291466701596636
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.37254901960784315,
"acc_stderr": 0.04810840148082635,
"acc_norm": 0.37254901960784315,
"acc_norm_stderr": 0.04810840148082635
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932261,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932261
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5702127659574469,
"acc_stderr": 0.03236214467715564,
"acc_norm": 0.5702127659574469,
"acc_norm_stderr": 0.03236214467715564
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.49122807017543857,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.49122807017543857,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5517241379310345,
"acc_stderr": 0.041443118108781526,
"acc_norm": 0.5517241379310345,
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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": 0.8440366972477065,
"acc_norm_stderr": 0.015555802713590167
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"harness|hendrycksTest-high_school_statistics|5": {
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"acc_norm": 0.5601851851851852,
"acc_norm_stderr": 0.033851779760448106
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7892156862745098,
"acc_stderr": 0.028626547912437406,
"acc_norm": 0.7892156862745098,
"acc_norm_stderr": 0.028626547912437406
},
"harness|hendrycksTest-high_school_world_history|5": {
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"acc_stderr": 0.026939106581553945,
"acc_norm": 0.7805907172995781,
"acc_norm_stderr": 0.026939106581553945
},
"harness|hendrycksTest-human_aging|5": {
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"acc_stderr": 0.031381476375754995,
"acc_norm": 0.6771300448430493,
"acc_norm_stderr": 0.031381476375754995
},
"harness|hendrycksTest-human_sexuality|5": {
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"acc_norm": 0.7557251908396947,
"acc_norm_stderr": 0.037683359597287434
},
"harness|hendrycksTest-international_law|5": {
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"acc_stderr": 0.037494924487096966,
"acc_norm": 0.7851239669421488,
"acc_norm_stderr": 0.037494924487096966
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7870370370370371,
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"acc_norm": 0.7870370370370371,
"acc_norm_stderr": 0.03957835471980979
},
"harness|hendrycksTest-logical_fallacies|5": {
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"acc_norm": 0.7300613496932515,
"acc_norm_stderr": 0.034878251684978906
},
"harness|hendrycksTest-machine_learning|5": {
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"acc_norm": 0.5,
"acc_norm_stderr": 0.04745789978762494
},
"harness|hendrycksTest-management|5": {
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"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8760683760683761,
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"acc_norm": 0.8760683760683761,
"acc_norm_stderr": 0.02158649400128138
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.73,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.73,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8160919540229885,
"acc_stderr": 0.013853724170922531,
"acc_norm": 0.8160919540229885,
"acc_norm_stderr": 0.013853724170922531
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7196531791907514,
"acc_stderr": 0.02418242749657761,
"acc_norm": 0.7196531791907514,
"acc_norm_stderr": 0.02418242749657761
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.41564245810055866,
"acc_stderr": 0.016482782187500666,
"acc_norm": 0.41564245810055866,
"acc_norm_stderr": 0.016482782187500666
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7091503267973857,
"acc_stderr": 0.02600480036395213,
"acc_norm": 0.7091503267973857,
"acc_norm_stderr": 0.02600480036395213
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7202572347266881,
"acc_stderr": 0.025494259350694912,
"acc_norm": 0.7202572347266881,
"acc_norm_stderr": 0.025494259350694912
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7283950617283951,
"acc_stderr": 0.024748624490537368,
"acc_norm": 0.7283950617283951,
"acc_norm_stderr": 0.024748624490537368
},
"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": 0.4498044328552803,
"acc_stderr": 0.012705721498565106,
"acc_norm": 0.4498044328552803,
"acc_norm_stderr": 0.012705721498565106
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6654411764705882,
"acc_stderr": 0.028661996202335303,
"acc_norm": 0.6654411764705882,
"acc_norm_stderr": 0.028661996202335303
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6535947712418301,
"acc_stderr": 0.019249785691717217,
"acc_norm": 0.6535947712418301,
"acc_norm_stderr": 0.019249785691717217
},
"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": 0.7224489795918367,
"acc_stderr": 0.028666857790274648,
"acc_norm": 0.7224489795918367,
"acc_norm_stderr": 0.028666857790274648
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.835820895522388,
"acc_stderr": 0.026193923544454125,
"acc_norm": 0.835820895522388,
"acc_norm_stderr": 0.026193923544454125
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.88,
"acc_stderr": 0.03265986323710906,
"acc_norm": 0.88,
"acc_norm_stderr": 0.03265986323710906
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5301204819277109,
"acc_stderr": 0.03885425420866767,
"acc_norm": 0.5301204819277109,
"acc_norm_stderr": 0.03885425420866767
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8128654970760234,
"acc_stderr": 0.02991312723236804,
"acc_norm": 0.8128654970760234,
"acc_norm_stderr": 0.02991312723236804
},
"harness|truthfulqa:mc|0": {
"mc1": 0.408812729498164,
"mc1_stderr": 0.017209952151641734,
"mc2": 0.5770577317207616,
"mc2_stderr": 0.015307336326138697
},
"harness|winogrande|5": {
"acc": 0.7963693764798737,
"acc_stderr": 0.011317798781626922
},
"harness|gsm8k|5": {
"acc": 0.7050796057619408,
"acc_stderr": 0.012560698010954774
}
}
```
### 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] |
Khalilkitar/tactics_dataset | ---
license: apache-2.0
---
|
Alex7756/mix-big-0909 | ---
license: other
---
|
dotta/dottamemes | ---
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 19993249.0
num_examples: 35
download_size: 0
dataset_size: 19993249.0
---
# Dataset Card for "dottamemes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
eval4nlp-oom/train | ---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
- name: score
dtype: float64
splits:
- name: summarization
num_bytes: 789401
num_examples: 320
- name: en_de
num_bytes: 2440668
num_examples: 11046
- name: zh_en
num_bytes: 4430272
num_examples: 15750
download_size: 0
dataset_size: 7660341
---
# Dataset Card for "train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
chiayewken/saycan | ---
dataset_info:
features:
- name: INPUT
dtype: string
- name: OUTPUT
dtype: string
splits:
- name: test
num_bytes: 14865
num_examples: 99
download_size: 4765
dataset_size: 14865
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# SayCan
This repo contains the data for ["Do As I Can, Not As I Say:
Grounding Language in Robotic Affordances"](https://say-can.github.io).
The original data link is here: https://raw.githubusercontent.com/say-can/say-can.github.io/main/data/saycan_plan_v0_l.tsv
This dataset is distributed with the CC BY 4.0 license. |
open-llm-leaderboard/details_codellama__CodeLlama-70b-hf | ---
pretty_name: Evaluation run of codellama/CodeLlama-70b-hf
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-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-hf\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-02T06:27:09.209983](https://huggingface.co/datasets/open-llm-leaderboard/details_codellama__CodeLlama-70b-hf/blob/main/results_2024-02-02T06-27-09.209983.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\": 0.5954881773778198,\n\
\ \"acc_stderr\": 0.03341128708368595,\n \"acc_norm\": 0.5993131783154683,\n\
\ \"acc_norm_stderr\": 0.0340914669738772,\n \"mc1\": 0.2607099143206854,\n\
\ \"mc1_stderr\": 0.015368841620766373,\n \"mc2\": 0.39788477413004975,\n\
\ \"mc2_stderr\": 0.014288917719366868\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5426621160409556,\n \"acc_stderr\": 0.014558106543924058,\n\
\ \"acc_norm\": 0.5674061433447098,\n \"acc_norm_stderr\": 0.01447800569418253\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5802628958374826,\n\
\ \"acc_stderr\": 0.004925072159723829,\n \"acc_norm\": 0.7821151165106552,\n\
\ \"acc_norm_stderr\": 0.004119650817714288\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.48148148148148145,\n\
\ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.48148148148148145,\n\
\ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6052631578947368,\n \"acc_stderr\": 0.039777499346220734,\n\
\ \"acc_norm\": 0.6052631578947368,\n \"acc_norm_stderr\": 0.039777499346220734\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\
\ \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.64,\n \
\ \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.5962264150943396,\n \"acc_stderr\": 0.030197611600197946,\n\
\ \"acc_norm\": 0.5962264150943396,\n \"acc_norm_stderr\": 0.030197611600197946\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5625,\n\
\ \"acc_stderr\": 0.04148415739394154,\n \"acc_norm\": 0.5625,\n \
\ \"acc_norm_stderr\": 0.04148415739394154\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.56,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\": 0.56,\n\
\ \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5375722543352601,\n\
\ \"acc_stderr\": 0.0380168510452446,\n \"acc_norm\": 0.5375722543352601,\n\
\ \"acc_norm_stderr\": 0.0380168510452446\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.04755129616062948,\n\
\ \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.04755129616062948\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n\
\ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n\
\ \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.45614035087719296,\n\
\ \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.041618085035015295,\n\
\ \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.041618085035015295\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4021164021164021,\n \"acc_stderr\": 0.025253032554997692,\n \"\
acc_norm\": 0.4021164021164021,\n \"acc_norm_stderr\": 0.025253032554997692\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\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.667741935483871,\n\
\ \"acc_stderr\": 0.0267955608481228,\n \"acc_norm\": 0.667741935483871,\n\
\ \"acc_norm_stderr\": 0.0267955608481228\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4236453201970443,\n \"acc_stderr\": 0.03476725747649037,\n\
\ \"acc_norm\": 0.4236453201970443,\n \"acc_norm_stderr\": 0.03476725747649037\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\": 0.7454545454545455,\n \"acc_stderr\": 0.03401506715249039,\n\
\ \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.03401506715249039\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7424242424242424,\n \"acc_stderr\": 0.03115626951964683,\n \"\
acc_norm\": 0.7424242424242424,\n \"acc_norm_stderr\": 0.03115626951964683\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8082901554404145,\n \"acc_stderr\": 0.02840895362624528,\n\
\ \"acc_norm\": 0.8082901554404145,\n \"acc_norm_stderr\": 0.02840895362624528\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5846153846153846,\n \"acc_stderr\": 0.024985354923102325,\n\
\ \"acc_norm\": 0.5846153846153846,\n \"acc_norm_stderr\": 0.024985354923102325\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.337037037037037,\n \"acc_stderr\": 0.028820884666253255,\n \
\ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.028820884666253255\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6092436974789915,\n \"acc_stderr\": 0.031693802357129965,\n\
\ \"acc_norm\": 0.6092436974789915,\n \"acc_norm_stderr\": 0.031693802357129965\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.41721854304635764,\n \"acc_stderr\": 0.04026141497634611,\n \"\
acc_norm\": 0.41721854304635764,\n \"acc_norm_stderr\": 0.04026141497634611\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7596330275229358,\n \"acc_stderr\": 0.01832060732096407,\n \"\
acc_norm\": 0.7596330275229358,\n \"acc_norm_stderr\": 0.01832060732096407\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.47685185185185186,\n \"acc_stderr\": 0.034063153607115065,\n \"\
acc_norm\": 0.47685185185185186,\n \"acc_norm_stderr\": 0.034063153607115065\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\": 0.7890295358649789,\n \"acc_stderr\": 0.02655837250266192,\n \
\ \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.02655837250266192\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6547085201793722,\n\
\ \"acc_stderr\": 0.03191100192835794,\n \"acc_norm\": 0.6547085201793722,\n\
\ \"acc_norm_stderr\": 0.03191100192835794\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7099236641221374,\n \"acc_stderr\": 0.03980066246467766,\n\
\ \"acc_norm\": 0.7099236641221374,\n \"acc_norm_stderr\": 0.03980066246467766\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.743801652892562,\n \"acc_stderr\": 0.03984979653302872,\n \"acc_norm\"\
: 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302872\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7222222222222222,\n\
\ \"acc_stderr\": 0.043300437496507395,\n \"acc_norm\": 0.7222222222222222,\n\
\ \"acc_norm_stderr\": 0.043300437496507395\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7239263803680982,\n \"acc_stderr\": 0.035123852837050475,\n\
\ \"acc_norm\": 0.7239263803680982,\n \"acc_norm_stderr\": 0.035123852837050475\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.042450224863844956,\n\
\ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.042450224863844956\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.811965811965812,\n\
\ \"acc_stderr\": 0.025598193686652254,\n \"acc_norm\": 0.811965811965812,\n\
\ \"acc_norm_stderr\": 0.025598193686652254\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7407407407407407,\n\
\ \"acc_stderr\": 0.015671006009339572,\n \"acc_norm\": 0.7407407407407407,\n\
\ \"acc_norm_stderr\": 0.015671006009339572\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6502890173410405,\n \"acc_stderr\": 0.025674281456531015,\n\
\ \"acc_norm\": 0.6502890173410405,\n \"acc_norm_stderr\": 0.025674281456531015\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4223463687150838,\n\
\ \"acc_stderr\": 0.016519594275297117,\n \"acc_norm\": 0.4223463687150838,\n\
\ \"acc_norm_stderr\": 0.016519594275297117\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6241830065359477,\n \"acc_stderr\": 0.02773283435336393,\n\
\ \"acc_norm\": 0.6241830065359477,\n \"acc_norm_stderr\": 0.02773283435336393\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6816720257234726,\n\
\ \"acc_stderr\": 0.026457225067811025,\n \"acc_norm\": 0.6816720257234726,\n\
\ \"acc_norm_stderr\": 0.026457225067811025\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6327160493827161,\n \"acc_stderr\": 0.026822801759507905,\n\
\ \"acc_norm\": 0.6327160493827161,\n \"acc_norm_stderr\": 0.026822801759507905\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.450354609929078,\n \"acc_stderr\": 0.029680105565029036,\n \
\ \"acc_norm\": 0.450354609929078,\n \"acc_norm_stderr\": 0.029680105565029036\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.41590612777053454,\n\
\ \"acc_stderr\": 0.01258832385031361,\n \"acc_norm\": 0.41590612777053454,\n\
\ \"acc_norm_stderr\": 0.01258832385031361\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5220588235294118,\n \"acc_stderr\": 0.030343264224213514,\n\
\ \"acc_norm\": 0.5220588235294118,\n \"acc_norm_stderr\": 0.030343264224213514\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5751633986928104,\n \"acc_stderr\": 0.019997973035458333,\n \
\ \"acc_norm\": 0.5751633986928104,\n \"acc_norm_stderr\": 0.019997973035458333\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\
\ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\
\ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6979591836734694,\n \"acc_stderr\": 0.0293936093198798,\n\
\ \"acc_norm\": 0.6979591836734694,\n \"acc_norm_stderr\": 0.0293936093198798\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7711442786069652,\n\
\ \"acc_stderr\": 0.029705284056772426,\n \"acc_norm\": 0.7711442786069652,\n\
\ \"acc_norm_stderr\": 0.029705284056772426\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.0337997668989631,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.0337997668989631\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.46987951807228917,\n\
\ \"acc_stderr\": 0.03885425420866766,\n \"acc_norm\": 0.46987951807228917,\n\
\ \"acc_norm_stderr\": 0.03885425420866766\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7543859649122807,\n \"acc_stderr\": 0.03301405946987249,\n\
\ \"acc_norm\": 0.7543859649122807,\n \"acc_norm_stderr\": 0.03301405946987249\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2607099143206854,\n\
\ \"mc1_stderr\": 0.015368841620766373,\n \"mc2\": 0.39788477413004975,\n\
\ \"mc2_stderr\": 0.014288917719366868\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7521704814522494,\n \"acc_stderr\": 0.01213438601986535\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4397270659590599,\n \
\ \"acc_stderr\": 0.013672052434471577\n }\n}\n```"
repo_url: https://huggingface.co/codellama/CodeLlama-70b-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_27_09.209983
path:
- '**/details_harness|arc:challenge|25_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|gsm8k|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hellaswag|10_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T06-27-09.209983.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T06-27-09.209983.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- '**/details_harness|winogrande|5_2024-02-02T06-27-09.209983.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-02T06-27-09.209983.parquet'
- config_name: results
data_files:
- split: 2024_02_02T06_27_09.209983
path:
- results_2024-02-02T06-27-09.209983.parquet
- split: latest
path:
- results_2024-02-02T06-27-09.209983.parquet
---
# Dataset Card for Evaluation run of codellama/CodeLlama-70b-hf
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-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-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-02T06:27:09.209983](https://huggingface.co/datasets/open-llm-leaderboard/details_codellama__CodeLlama-70b-hf/blob/main/results_2024-02-02T06-27-09.209983.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": 0.5954881773778198,
"acc_stderr": 0.03341128708368595,
"acc_norm": 0.5993131783154683,
"acc_norm_stderr": 0.0340914669738772,
"mc1": 0.2607099143206854,
"mc1_stderr": 0.015368841620766373,
"mc2": 0.39788477413004975,
"mc2_stderr": 0.014288917719366868
},
"harness|arc:challenge|25": {
"acc": 0.5426621160409556,
"acc_stderr": 0.014558106543924058,
"acc_norm": 0.5674061433447098,
"acc_norm_stderr": 0.01447800569418253
},
"harness|hellaswag|10": {
"acc": 0.5802628958374826,
"acc_stderr": 0.004925072159723829,
"acc_norm": 0.7821151165106552,
"acc_norm_stderr": 0.004119650817714288
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.48148148148148145,
"acc_stderr": 0.043163785995113245,
"acc_norm": 0.48148148148148145,
"acc_norm_stderr": 0.043163785995113245
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6052631578947368,
"acc_stderr": 0.039777499346220734,
"acc_norm": 0.6052631578947368,
"acc_norm_stderr": 0.039777499346220734
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.64,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.64,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5962264150943396,
"acc_stderr": 0.030197611600197946,
"acc_norm": 0.5962264150943396,
"acc_norm_stderr": 0.030197611600197946
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5625,
"acc_stderr": 0.04148415739394154,
"acc_norm": 0.5625,
"acc_norm_stderr": 0.04148415739394154
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252605,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.56,
"acc_stderr": 0.049888765156985884,
"acc_norm": 0.56,
"acc_norm_stderr": 0.049888765156985884
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5375722543352601,
"acc_stderr": 0.0380168510452446,
"acc_norm": 0.5375722543352601,
"acc_norm_stderr": 0.0380168510452446
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.35294117647058826,
"acc_stderr": 0.04755129616062948,
"acc_norm": 0.35294117647058826,
"acc_norm_stderr": 0.04755129616062948
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.79,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.79,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5659574468085107,
"acc_stderr": 0.03240038086792747,
"acc_norm": 0.5659574468085107,
"acc_norm_stderr": 0.03240038086792747
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.45614035087719296,
"acc_stderr": 0.04685473041907789,
"acc_norm": 0.45614035087719296,
"acc_norm_stderr": 0.04685473041907789
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5241379310344828,
"acc_stderr": 0.041618085035015295,
"acc_norm": 0.5241379310344828,
"acc_norm_stderr": 0.041618085035015295
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4021164021164021,
"acc_stderr": 0.025253032554997692,
"acc_norm": 0.4021164021164021,
"acc_norm_stderr": 0.025253032554997692
},
"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": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.667741935483871,
"acc_stderr": 0.0267955608481228,
"acc_norm": 0.667741935483871,
"acc_norm_stderr": 0.0267955608481228
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4236453201970443,
"acc_stderr": 0.03476725747649037,
"acc_norm": 0.4236453201970443,
"acc_norm_stderr": 0.03476725747649037
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.73,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.73,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7454545454545455,
"acc_stderr": 0.03401506715249039,
"acc_norm": 0.7454545454545455,
"acc_norm_stderr": 0.03401506715249039
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7424242424242424,
"acc_stderr": 0.03115626951964683,
"acc_norm": 0.7424242424242424,
"acc_norm_stderr": 0.03115626951964683
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8082901554404145,
"acc_stderr": 0.02840895362624528,
"acc_norm": 0.8082901554404145,
"acc_norm_stderr": 0.02840895362624528
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5846153846153846,
"acc_stderr": 0.024985354923102325,
"acc_norm": 0.5846153846153846,
"acc_norm_stderr": 0.024985354923102325
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.337037037037037,
"acc_stderr": 0.028820884666253255,
"acc_norm": 0.337037037037037,
"acc_norm_stderr": 0.028820884666253255
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6092436974789915,
"acc_stderr": 0.031693802357129965,
"acc_norm": 0.6092436974789915,
"acc_norm_stderr": 0.031693802357129965
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.41721854304635764,
"acc_stderr": 0.04026141497634611,
"acc_norm": 0.41721854304635764,
"acc_norm_stderr": 0.04026141497634611
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7596330275229358,
"acc_stderr": 0.01832060732096407,
"acc_norm": 0.7596330275229358,
"acc_norm_stderr": 0.01832060732096407
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.47685185185185186,
"acc_stderr": 0.034063153607115065,
"acc_norm": 0.47685185185185186,
"acc_norm_stderr": 0.034063153607115065
},
"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": 0.7890295358649789,
"acc_stderr": 0.02655837250266192,
"acc_norm": 0.7890295358649789,
"acc_norm_stderr": 0.02655837250266192
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6547085201793722,
"acc_stderr": 0.03191100192835794,
"acc_norm": 0.6547085201793722,
"acc_norm_stderr": 0.03191100192835794
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7099236641221374,
"acc_stderr": 0.03980066246467766,
"acc_norm": 0.7099236641221374,
"acc_norm_stderr": 0.03980066246467766
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.743801652892562,
"acc_stderr": 0.03984979653302872,
"acc_norm": 0.743801652892562,
"acc_norm_stderr": 0.03984979653302872
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.043300437496507395,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.043300437496507395
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7239263803680982,
"acc_stderr": 0.035123852837050475,
"acc_norm": 0.7239263803680982,
"acc_norm_stderr": 0.035123852837050475
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5,
"acc_stderr": 0.04745789978762494,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04745789978762494
},
"harness|hendrycksTest-management|5": {
"acc": 0.7572815533980582,
"acc_stderr": 0.042450224863844956,
"acc_norm": 0.7572815533980582,
"acc_norm_stderr": 0.042450224863844956
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.811965811965812,
"acc_stderr": 0.025598193686652254,
"acc_norm": 0.811965811965812,
"acc_norm_stderr": 0.025598193686652254
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7407407407407407,
"acc_stderr": 0.015671006009339572,
"acc_norm": 0.7407407407407407,
"acc_norm_stderr": 0.015671006009339572
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6502890173410405,
"acc_stderr": 0.025674281456531015,
"acc_norm": 0.6502890173410405,
"acc_norm_stderr": 0.025674281456531015
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4223463687150838,
"acc_stderr": 0.016519594275297117,
"acc_norm": 0.4223463687150838,
"acc_norm_stderr": 0.016519594275297117
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6241830065359477,
"acc_stderr": 0.02773283435336393,
"acc_norm": 0.6241830065359477,
"acc_norm_stderr": 0.02773283435336393
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6816720257234726,
"acc_stderr": 0.026457225067811025,
"acc_norm": 0.6816720257234726,
"acc_norm_stderr": 0.026457225067811025
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6327160493827161,
"acc_stderr": 0.026822801759507905,
"acc_norm": 0.6327160493827161,
"acc_norm_stderr": 0.026822801759507905
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.450354609929078,
"acc_stderr": 0.029680105565029036,
"acc_norm": 0.450354609929078,
"acc_norm_stderr": 0.029680105565029036
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.41590612777053454,
"acc_stderr": 0.01258832385031361,
"acc_norm": 0.41590612777053454,
"acc_norm_stderr": 0.01258832385031361
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5220588235294118,
"acc_stderr": 0.030343264224213514,
"acc_norm": 0.5220588235294118,
"acc_norm_stderr": 0.030343264224213514
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5751633986928104,
"acc_stderr": 0.019997973035458333,
"acc_norm": 0.5751633986928104,
"acc_norm_stderr": 0.019997973035458333
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6909090909090909,
"acc_stderr": 0.044262946482000985,
"acc_norm": 0.6909090909090909,
"acc_norm_stderr": 0.044262946482000985
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6979591836734694,
"acc_stderr": 0.0293936093198798,
"acc_norm": 0.6979591836734694,
"acc_norm_stderr": 0.0293936093198798
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7711442786069652,
"acc_stderr": 0.029705284056772426,
"acc_norm": 0.7711442786069652,
"acc_norm_stderr": 0.029705284056772426
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.87,
"acc_stderr": 0.0337997668989631,
"acc_norm": 0.87,
"acc_norm_stderr": 0.0337997668989631
},
"harness|hendrycksTest-virology|5": {
"acc": 0.46987951807228917,
"acc_stderr": 0.03885425420866766,
"acc_norm": 0.46987951807228917,
"acc_norm_stderr": 0.03885425420866766
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7543859649122807,
"acc_stderr": 0.03301405946987249,
"acc_norm": 0.7543859649122807,
"acc_norm_stderr": 0.03301405946987249
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2607099143206854,
"mc1_stderr": 0.015368841620766373,
"mc2": 0.39788477413004975,
"mc2_stderr": 0.014288917719366868
},
"harness|winogrande|5": {
"acc": 0.7521704814522494,
"acc_stderr": 0.01213438601986535
},
"harness|gsm8k|5": {
"acc": 0.4397270659590599,
"acc_stderr": 0.013672052434471577
}
}
```
## 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] |
FINNUMBER/FINCH_TRAIN_FULL | ---
dataset_info:
features:
- name: task
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 270996938
num_examples: 76580
download_size: 104131367
dataset_size: 270996938
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
NuriAk/Salaries_ds_prepared_for_FAISS | ---
license: mit
dataset_info:
features:
- name: Title
dtype: string
- name: FullDescription
dtype: string
- name: LocationNormalized
dtype: string
- name: Company
dtype: string
- name: Category
dtype: string
- name: SalaryNormalized
dtype: int64
- name: descr_length
dtype: int64
- name: text_column
dtype: string
splits:
- name: train
num_bytes: 548244607
num_examples: 156191
download_size: 300465693
dataset_size: 548244607
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
multi-train/codesearchnet_1107 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 2207111297
num_examples: 1000000
download_size: 552466752
dataset_size: 2207111297
---
# Dataset Card for "codesearchnet_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ASR-HypR/TEDLIUM2_withoutLM | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: dev
path: data/dev-*
dataset_info:
features:
- name: ref
dtype: string
- name: hyps
sequence: string
- name: ctc_score
sequence: float64
- name: att_score
sequence: float64
- name: utt_id
dtype: string
- name: score
sequence: float64
splits:
- name: train
num_bytes: 739353925
num_examples: 92791
- name: test
num_bytes: 9005689
num_examples: 1155
- name: dev
num_bytes: 5574485
num_examples: 507
download_size: 216892133
dataset_size: 753934099
---
# Dataset Card for "TEDLIUM2_withoutLM"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_dreamgen__opus-v1-34b | ---
pretty_name: Evaluation run of dreamgen/opus-v1-34b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [dreamgen/opus-v1-34b](https://huggingface.co/dreamgen/opus-v1-34b) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_dreamgen__opus-v1-34b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-29T23:58:49.435906](https://huggingface.co/datasets/open-llm-leaderboard/details_dreamgen__opus-v1-34b/blob/main/results_2024-03-29T23-58-49.435906.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\": 0.7484152942386265,\n\
\ \"acc_stderr\": 0.028714805681078225,\n \"acc_norm\": 0.7535953614703701,\n\
\ \"acc_norm_stderr\": 0.029251369906711122,\n \"mc1\": 0.3990208078335373,\n\
\ \"mc1_stderr\": 0.017142825728496767,\n \"mc2\": 0.5587613489242838,\n\
\ \"mc2_stderr\": 0.014964195064604065\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6168941979522184,\n \"acc_stderr\": 0.014206472661672876,\n\
\ \"acc_norm\": 0.64419795221843,\n \"acc_norm_stderr\": 0.01399057113791876\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6489743079067914,\n\
\ \"acc_stderr\": 0.004763155068744872,\n \"acc_norm\": 0.8485361481776539,\n\
\ \"acc_norm_stderr\": 0.0035776774950640766\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7111111111111111,\n\
\ \"acc_stderr\": 0.0391545063041425,\n \"acc_norm\": 0.7111111111111111,\n\
\ \"acc_norm_stderr\": 0.0391545063041425\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8552631578947368,\n \"acc_stderr\": 0.028631951845930387,\n\
\ \"acc_norm\": 0.8552631578947368,\n \"acc_norm_stderr\": 0.028631951845930387\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.76,\n\
\ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7886792452830189,\n \"acc_stderr\": 0.025125766484827845,\n\
\ \"acc_norm\": 0.7886792452830189,\n \"acc_norm_stderr\": 0.025125766484827845\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8541666666666666,\n\
\ \"acc_stderr\": 0.029514245964291762,\n \"acc_norm\": 0.8541666666666666,\n\
\ \"acc_norm_stderr\": 0.029514245964291762\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.66,\n \"acc_stderr\": 0.04760952285695238,\n \"acc_norm\"\
: 0.66,\n \"acc_norm_stderr\": 0.04760952285695238\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.04999999999999999,\n \
\ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.04999999999999999\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7341040462427746,\n\
\ \"acc_stderr\": 0.033687629322594316,\n \"acc_norm\": 0.7341040462427746,\n\
\ \"acc_norm_stderr\": 0.033687629322594316\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.5196078431372549,\n \"acc_stderr\": 0.04971358884367406,\n\
\ \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.04971358884367406\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.82,\n \"acc_stderr\": 0.03861229196653695,\n \"acc_norm\": 0.82,\n\
\ \"acc_norm_stderr\": 0.03861229196653695\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7659574468085106,\n \"acc_stderr\": 0.02767845257821239,\n\
\ \"acc_norm\": 0.7659574468085106,\n \"acc_norm_stderr\": 0.02767845257821239\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5964912280701754,\n\
\ \"acc_stderr\": 0.04615186962583707,\n \"acc_norm\": 0.5964912280701754,\n\
\ \"acc_norm_stderr\": 0.04615186962583707\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.7379310344827587,\n \"acc_stderr\": 0.036646663372252565,\n\
\ \"acc_norm\": 0.7379310344827587,\n \"acc_norm_stderr\": 0.036646663372252565\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.6455026455026455,\n \"acc_stderr\": 0.024636830602841997,\n \"\
acc_norm\": 0.6455026455026455,\n \"acc_norm_stderr\": 0.024636830602841997\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5555555555555556,\n\
\ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.5555555555555556,\n\
\ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.896774193548387,\n\
\ \"acc_stderr\": 0.017308381281034527,\n \"acc_norm\": 0.896774193548387,\n\
\ \"acc_norm_stderr\": 0.017308381281034527\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.6600985221674877,\n \"acc_stderr\": 0.033327690684107895,\n\
\ \"acc_norm\": 0.6600985221674877,\n \"acc_norm_stderr\": 0.033327690684107895\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\"\
: 0.79,\n \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8545454545454545,\n \"acc_stderr\": 0.027530196355066584,\n\
\ \"acc_norm\": 0.8545454545454545,\n \"acc_norm_stderr\": 0.027530196355066584\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.9343434343434344,\n \"acc_stderr\": 0.017646526677233345,\n \"\
acc_norm\": 0.9343434343434344,\n \"acc_norm_stderr\": 0.017646526677233345\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9740932642487047,\n \"acc_stderr\": 0.01146452335695318,\n\
\ \"acc_norm\": 0.9740932642487047,\n \"acc_norm_stderr\": 0.01146452335695318\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.8051282051282052,\n \"acc_stderr\": 0.020083167595181393,\n\
\ \"acc_norm\": 0.8051282051282052,\n \"acc_norm_stderr\": 0.020083167595181393\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.40370370370370373,\n \"acc_stderr\": 0.029914812342227634,\n \
\ \"acc_norm\": 0.40370370370370373,\n \"acc_norm_stderr\": 0.029914812342227634\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.8319327731092437,\n \"acc_stderr\": 0.024289102115692265,\n\
\ \"acc_norm\": 0.8319327731092437,\n \"acc_norm_stderr\": 0.024289102115692265\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.4768211920529801,\n \"acc_stderr\": 0.04078093859163083,\n \"\
acc_norm\": 0.4768211920529801,\n \"acc_norm_stderr\": 0.04078093859163083\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9211009174311927,\n \"acc_stderr\": 0.011558198113769605,\n \"\
acc_norm\": 0.9211009174311927,\n \"acc_norm_stderr\": 0.011558198113769605\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6666666666666666,\n \"acc_stderr\": 0.03214952147802749,\n \"\
acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.03214952147802749\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9117647058823529,\n \"acc_stderr\": 0.019907399791316945,\n \"\
acc_norm\": 0.9117647058823529,\n \"acc_norm_stderr\": 0.019907399791316945\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.9029535864978903,\n \"acc_stderr\": 0.01926932302564026,\n \
\ \"acc_norm\": 0.9029535864978903,\n \"acc_norm_stderr\": 0.01926932302564026\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8026905829596412,\n\
\ \"acc_stderr\": 0.02670985334496796,\n \"acc_norm\": 0.8026905829596412,\n\
\ \"acc_norm_stderr\": 0.02670985334496796\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.03088466108951539,\n\
\ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.03088466108951539\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035206,\n \"\
acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035206\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8796296296296297,\n\
\ \"acc_stderr\": 0.031457038543062504,\n \"acc_norm\": 0.8796296296296297,\n\
\ \"acc_norm_stderr\": 0.031457038543062504\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8650306748466258,\n \"acc_stderr\": 0.026845765054553855,\n\
\ \"acc_norm\": 0.8650306748466258,\n \"acc_norm_stderr\": 0.026845765054553855\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5357142857142857,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.5357142857142857,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.03393295729761011,\n\
\ \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.03393295729761011\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9316239316239316,\n\
\ \"acc_stderr\": 0.01653462768431136,\n \"acc_norm\": 0.9316239316239316,\n\
\ \"acc_norm_stderr\": 0.01653462768431136\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9029374201787995,\n\
\ \"acc_stderr\": 0.01058647471201829,\n \"acc_norm\": 0.9029374201787995,\n\
\ \"acc_norm_stderr\": 0.01058647471201829\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.8121387283236994,\n \"acc_stderr\": 0.021029269752423224,\n\
\ \"acc_norm\": 0.8121387283236994,\n \"acc_norm_stderr\": 0.021029269752423224\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6804469273743017,\n\
\ \"acc_stderr\": 0.015595520294147402,\n \"acc_norm\": 0.6804469273743017,\n\
\ \"acc_norm_stderr\": 0.015595520294147402\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.8464052287581699,\n \"acc_stderr\": 0.020645597910418763,\n\
\ \"acc_norm\": 0.8464052287581699,\n \"acc_norm_stderr\": 0.020645597910418763\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8038585209003215,\n\
\ \"acc_stderr\": 0.022552447780478026,\n \"acc_norm\": 0.8038585209003215,\n\
\ \"acc_norm_stderr\": 0.022552447780478026\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8549382716049383,\n \"acc_stderr\": 0.019594877019727956,\n\
\ \"acc_norm\": 0.8549382716049383,\n \"acc_norm_stderr\": 0.019594877019727956\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.6205673758865248,\n \"acc_stderr\": 0.028947338851614095,\n \
\ \"acc_norm\": 0.6205673758865248,\n \"acc_norm_stderr\": 0.028947338851614095\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5808344198174706,\n\
\ \"acc_stderr\": 0.01260224450578822,\n \"acc_norm\": 0.5808344198174706,\n\
\ \"acc_norm_stderr\": 0.01260224450578822\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.8161764705882353,\n \"acc_stderr\": 0.023529242185193106,\n\
\ \"acc_norm\": 0.8161764705882353,\n \"acc_norm_stderr\": 0.023529242185193106\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.8202614379084967,\n \"acc_stderr\": 0.01553374508338279,\n \
\ \"acc_norm\": 0.8202614379084967,\n \"acc_norm_stderr\": 0.01553374508338279\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\
\ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\
\ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8285714285714286,\n \"acc_stderr\": 0.024127463462650173,\n\
\ \"acc_norm\": 0.8285714285714286,\n \"acc_norm_stderr\": 0.024127463462650173\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.9054726368159204,\n\
\ \"acc_stderr\": 0.020687186951534084,\n \"acc_norm\": 0.9054726368159204,\n\
\ \"acc_norm_stderr\": 0.020687186951534084\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.93,\n \"acc_stderr\": 0.0256432399976243,\n \
\ \"acc_norm\": 0.93,\n \"acc_norm_stderr\": 0.0256432399976243\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.572289156626506,\n\
\ \"acc_stderr\": 0.038515976837185335,\n \"acc_norm\": 0.572289156626506,\n\
\ \"acc_norm_stderr\": 0.038515976837185335\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.025679342723276908,\n\
\ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.025679342723276908\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3990208078335373,\n\
\ \"mc1_stderr\": 0.017142825728496767,\n \"mc2\": 0.5587613489242838,\n\
\ \"mc2_stderr\": 0.014964195064604065\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8161010260457774,\n \"acc_stderr\": 0.010887916013305887\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6019711902956786,\n \
\ \"acc_stderr\": 0.013483026939074823\n }\n}\n```"
repo_url: https://huggingface.co/dreamgen/opus-v1-34b
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_29T23_36_38.240782
path:
- '**/details_harness|arc:challenge|25_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|arc:challenge|25_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|gsm8k|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|gsm8k|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hellaswag|10_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hellaswag|10_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-29T23-36-38.240782.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-29T23-58-49.435906.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-29T23-58-49.435906.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- '**/details_harness|winogrande|5_2024-03-29T23-36-38.240782.parquet'
- split: 2024_03_29T23_58_49.435906
path:
- '**/details_harness|winogrande|5_2024-03-29T23-58-49.435906.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-29T23-58-49.435906.parquet'
- config_name: results
data_files:
- split: 2024_03_29T23_36_38.240782
path:
- results_2024-03-29T23-36-38.240782.parquet
- split: 2024_03_29T23_58_49.435906
path:
- results_2024-03-29T23-58-49.435906.parquet
- split: latest
path:
- results_2024-03-29T23-58-49.435906.parquet
---
# Dataset Card for Evaluation run of dreamgen/opus-v1-34b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [dreamgen/opus-v1-34b](https://huggingface.co/dreamgen/opus-v1-34b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_dreamgen__opus-v1-34b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-29T23:58:49.435906](https://huggingface.co/datasets/open-llm-leaderboard/details_dreamgen__opus-v1-34b/blob/main/results_2024-03-29T23-58-49.435906.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": 0.7484152942386265,
"acc_stderr": 0.028714805681078225,
"acc_norm": 0.7535953614703701,
"acc_norm_stderr": 0.029251369906711122,
"mc1": 0.3990208078335373,
"mc1_stderr": 0.017142825728496767,
"mc2": 0.5587613489242838,
"mc2_stderr": 0.014964195064604065
},
"harness|arc:challenge|25": {
"acc": 0.6168941979522184,
"acc_stderr": 0.014206472661672876,
"acc_norm": 0.64419795221843,
"acc_norm_stderr": 0.01399057113791876
},
"harness|hellaswag|10": {
"acc": 0.6489743079067914,
"acc_stderr": 0.004763155068744872,
"acc_norm": 0.8485361481776539,
"acc_norm_stderr": 0.0035776774950640766
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001974,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001974
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.7111111111111111,
"acc_stderr": 0.0391545063041425,
"acc_norm": 0.7111111111111111,
"acc_norm_stderr": 0.0391545063041425
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8552631578947368,
"acc_stderr": 0.028631951845930387,
"acc_norm": 0.8552631578947368,
"acc_norm_stderr": 0.028631951845930387
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7886792452830189,
"acc_stderr": 0.025125766484827845,
"acc_norm": 0.7886792452830189,
"acc_norm_stderr": 0.025125766484827845
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8541666666666666,
"acc_stderr": 0.029514245964291762,
"acc_norm": 0.8541666666666666,
"acc_norm_stderr": 0.029514245964291762
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695238,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695238
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.45,
"acc_stderr": 0.04999999999999999,
"acc_norm": 0.45,
"acc_norm_stderr": 0.04999999999999999
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7341040462427746,
"acc_stderr": 0.033687629322594316,
"acc_norm": 0.7341040462427746,
"acc_norm_stderr": 0.033687629322594316
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.5196078431372549,
"acc_stderr": 0.04971358884367406,
"acc_norm": 0.5196078431372549,
"acc_norm_stderr": 0.04971358884367406
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.82,
"acc_stderr": 0.03861229196653695,
"acc_norm": 0.82,
"acc_norm_stderr": 0.03861229196653695
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7659574468085106,
"acc_stderr": 0.02767845257821239,
"acc_norm": 0.7659574468085106,
"acc_norm_stderr": 0.02767845257821239
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5964912280701754,
"acc_stderr": 0.04615186962583707,
"acc_norm": 0.5964912280701754,
"acc_norm_stderr": 0.04615186962583707
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.7379310344827587,
"acc_stderr": 0.036646663372252565,
"acc_norm": 0.7379310344827587,
"acc_norm_stderr": 0.036646663372252565
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.6455026455026455,
"acc_stderr": 0.024636830602841997,
"acc_norm": 0.6455026455026455,
"acc_norm_stderr": 0.024636830602841997
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5555555555555556,
"acc_stderr": 0.044444444444444495,
"acc_norm": 0.5555555555555556,
"acc_norm_stderr": 0.044444444444444495
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.896774193548387,
"acc_stderr": 0.017308381281034527,
"acc_norm": 0.896774193548387,
"acc_norm_stderr": 0.017308381281034527
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.6600985221674877,
"acc_stderr": 0.033327690684107895,
"acc_norm": 0.6600985221674877,
"acc_norm_stderr": 0.033327690684107895
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.79,
"acc_stderr": 0.04093601807403326,
"acc_norm": 0.79,
"acc_norm_stderr": 0.04093601807403326
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8545454545454545,
"acc_stderr": 0.027530196355066584,
"acc_norm": 0.8545454545454545,
"acc_norm_stderr": 0.027530196355066584
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.9343434343434344,
"acc_stderr": 0.017646526677233345,
"acc_norm": 0.9343434343434344,
"acc_norm_stderr": 0.017646526677233345
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9740932642487047,
"acc_stderr": 0.01146452335695318,
"acc_norm": 0.9740932642487047,
"acc_norm_stderr": 0.01146452335695318
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.8051282051282052,
"acc_stderr": 0.020083167595181393,
"acc_norm": 0.8051282051282052,
"acc_norm_stderr": 0.020083167595181393
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.40370370370370373,
"acc_stderr": 0.029914812342227634,
"acc_norm": 0.40370370370370373,
"acc_norm_stderr": 0.029914812342227634
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.8319327731092437,
"acc_stderr": 0.024289102115692265,
"acc_norm": 0.8319327731092437,
"acc_norm_stderr": 0.024289102115692265
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.4768211920529801,
"acc_stderr": 0.04078093859163083,
"acc_norm": 0.4768211920529801,
"acc_norm_stderr": 0.04078093859163083
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.9211009174311927,
"acc_stderr": 0.011558198113769605,
"acc_norm": 0.9211009174311927,
"acc_norm_stderr": 0.011558198113769605
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.03214952147802749,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.03214952147802749
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9117647058823529,
"acc_stderr": 0.019907399791316945,
"acc_norm": 0.9117647058823529,
"acc_norm_stderr": 0.019907399791316945
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.9029535864978903,
"acc_stderr": 0.01926932302564026,
"acc_norm": 0.9029535864978903,
"acc_norm_stderr": 0.01926932302564026
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.8026905829596412,
"acc_stderr": 0.02670985334496796,
"acc_norm": 0.8026905829596412,
"acc_norm_stderr": 0.02670985334496796
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8549618320610687,
"acc_stderr": 0.03088466108951539,
"acc_norm": 0.8549618320610687,
"acc_norm_stderr": 0.03088466108951539
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8760330578512396,
"acc_stderr": 0.030083098716035206,
"acc_norm": 0.8760330578512396,
"acc_norm_stderr": 0.030083098716035206
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8796296296296297,
"acc_stderr": 0.031457038543062504,
"acc_norm": 0.8796296296296297,
"acc_norm_stderr": 0.031457038543062504
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.8650306748466258,
"acc_stderr": 0.026845765054553855,
"acc_norm": 0.8650306748466258,
"acc_norm_stderr": 0.026845765054553855
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5357142857142857,
"acc_stderr": 0.04733667890053756,
"acc_norm": 0.5357142857142857,
"acc_norm_stderr": 0.04733667890053756
},
"harness|hendrycksTest-management|5": {
"acc": 0.8640776699029126,
"acc_stderr": 0.03393295729761011,
"acc_norm": 0.8640776699029126,
"acc_norm_stderr": 0.03393295729761011
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.9316239316239316,
"acc_stderr": 0.01653462768431136,
"acc_norm": 0.9316239316239316,
"acc_norm_stderr": 0.01653462768431136
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.85,
"acc_stderr": 0.03588702812826371,
"acc_norm": 0.85,
"acc_norm_stderr": 0.03588702812826371
},
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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]
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### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
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## 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. -->
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### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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#### Who are the source data producers?
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
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#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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## Glossary [optional]
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[More Information Needed] |
ctang/just_eval_llama2_v3 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response_a
dtype: string
- name: response_b
dtype: string
- name: more_reasonable
dtype: string
splits:
- name: train
num_bytes: 856802
num_examples: 2968
download_size: 175441
dataset_size: 856802
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Nyaaneet/cord-v2-custom | ---
dataset_info:
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struct:
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list:
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dtype: string
splits:
- name: train
num_bytes: 1786745645.1
num_examples: 1050
- name: test
num_bytes: 611466170.0
num_examples: 350
download_size: 2391012653
dataset_size: 2398211815.1
---
# Dataset Card for "cord-v2-custom"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-eval-futin__feed-sen_en_-1de085-2240171541 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- futin/feed
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-7b1
metrics: []
dataset_name: futin/feed
dataset_config: sen_en_
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: bigscience/bloom-7b1
* Dataset: futin/feed
* Config: sen_en_
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@futin](https://huggingface.co/futin) for evaluating this model. |
CyberHarem/yae_rin_honkai3 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of yae_rin (Houkai 3rd)
This is the dataset of yae_rin (Houkai 3rd), containing 18 images and their tags.
The core tags of this character are `long_hair, pink_hair, bangs, blue_eyes, hair_between_eyes, two_side_up, animal_ears, bow, ribbon`, 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 | 18 | 23.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yae_rin_honkai3/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 18 | 13.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yae_rin_honkai3/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 39 | 26.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yae_rin_honkai3/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 18 | 20.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yae_rin_honkai3/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 39 | 37.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yae_rin_honkai3/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/yae_rin_honkai3',
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 |  |  |  |  |  | long_sleeves, 1girl, open_mouth, solo, looking_at_viewer, dress, blush, holding, :d, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | long_sleeves | 1girl | open_mouth | solo | looking_at_viewer | dress | blush | holding | :d | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------|:--------|:-------------|:-------|:--------------------|:--------|:--------|:----------|:-----|:-------------------|
| 0 | 18 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X |
|
yunus-emre/arithmetic_val | ---
dataset_info:
features:
- name: context
dtype: string
- name: completion
dtype: int64
splits:
- name: validation
num_bytes: 1018162
num_examples: 20000
download_size: 315884
dataset_size: 1018162
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
|
sentiment140 | ---
language:
- en
paperswithcode_id: sentiment140
pretty_name: Sentiment140
dataset_info:
config_name: sentiment140
features:
- name: text
dtype: string
- name: date
dtype: string
- name: user
dtype: string
- name: sentiment
dtype: int32
- name: query
dtype: string
splits:
- name: train
num_bytes: 224542690
num_examples: 1600000
- name: test
num_bytes: 72971
num_examples: 498
download_size: 81363704
dataset_size: 224615661
train-eval-index:
- config: sentiment140
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
sentiment: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for "sentiment140"
## 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:** [http://help.sentiment140.com/home](http://help.sentiment140.com/home)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 81.36 MB
- **Size of the generated dataset:** 225.82 MB
- **Total amount of disk used:** 307.18 MB
### Dataset Summary
Sentiment140 consists of Twitter messages with emoticons, which are used as noisy labels for
sentiment classification. For more detailed information please refer to the paper.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### sentiment140
- **Size of downloaded dataset files:** 81.36 MB
- **Size of the generated dataset:** 225.82 MB
- **Total amount of disk used:** 307.18 MB
An example of 'train' looks as follows.
```
{
"date": "23-04-2010",
"query": "NO_QUERY",
"sentiment": 3,
"text": "train message",
"user": "train user"
}
```
### Data Fields
The data fields are the same among all splits.
#### sentiment140
- `text`: a `string` feature.
- `date`: a `string` feature.
- `user`: a `string` feature.
- `sentiment`: a `int32` feature.
- `query`: a `string` feature.
### Data Splits
| name | train |test|
|------------|------:|---:|
|sentiment140|1600000| 498|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{go2009twitter,
title={Twitter sentiment classification using distant supervision},
author={Go, Alec and Bhayani, Richa and Huang, Lei},
journal={CS224N project report, Stanford},
volume={1},
number={12},
pages={2009},
year={2009}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
mserras/alpaca-es-hackaton-backup | ---
dataset_info:
features:
- name: text
dtype: 'null'
- name: inputs
struct:
- name: 1-instruction
dtype: string
- name: 2-input
dtype: string
- name: 3-output
dtype: string
- name: prediction
dtype: 'null'
- name: prediction_agent
dtype: 'null'
- name: annotation
sequence: string
- name: annotation_agent
dtype: 'null'
- name: vectors
struct:
- name: input
sequence: float64
- name: instruction
sequence: float64
- name: output
sequence: float64
- name: multi_label
dtype: bool
- name: explanation
dtype: 'null'
- name: id
dtype: string
- name: metadata
struct:
- name: tr-flag-1-instruction
dtype: bool
- name: tr-flag-2-input
dtype: bool
- name: tr-flag-3-output
dtype: bool
- name: status
dtype: string
- name: event_timestamp
dtype: timestamp[us]
- name: metrics
dtype: 'null'
splits:
- name: train
num_bytes: 982796255
num_examples: 51942
download_size: 650895383
dataset_size: 982796255
---
# Dataset Card for "alpaca-es-hackaton-backup"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Nexdata/Chinese_Children_Speech_data_by_Mobile_phone | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/Chinese_Children_Speech_data_by_Mobile_phone
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.nexdata.ai/datasets/937?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Mobile phone captured audio data of Chinese children, with total duration of 3,255 hours. 9,780 speakers are children aged 6 to 12, with accent covering seven dialect areas; the recorded text contains common children languages such as essay stories, numbers, and their interactions on cars, at home, and with voice assistants, precisely matching the actual application scenes. All sentences are manually transferred with high accuracy.
For more details, please refer to the link: https://www.nexdata.ai/datasets/937?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Chinese Mandarin
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
|
TopicNet/20-Newsgroups | ---
language:
- en
multilinguality:
- monolingual
license: other
license_name: topicnet
license_link: >-
https://github.com/machine-intelligence-laboratory/TopicNet/blob/master/LICENSE.txt
configs:
- config_name: "20ng"
default: true
data_files:
- split: train
path: "data/20NG.csv.gz"
- split: test
path: "data/20NG_test.csv.gz"
task_categories:
- text-classification
task_ids:
- topic-classification
- multi-class-classification
- multi-label-classification
tags:
- topic-modeling
- topic-modelling
- text-clustering
- multimodal-data
- multimodal-learning
- modalities
- document-representation
---
# 20 Newsgroups
## Train
Some measurable characteristics of the dataset:
* D — number of documents
* <modality name> W — modality dictionary size (number of unique tokens)
* <modality name> len D — average document length in modality tokens (number of tokens)
* <modality name> len D uniq — average document length in unique modality tokens (number of unique tokens)
| | D | @lemmatized W | @lemmatized len D | @lemmatized len D uniq | @bigram W | @bigram len D | @bigram len D uniq |
|:------|------------:|-----------------------:|---------------------------:|--------------------------------:|-------------------:|-----------------------:|----------------------------:|
| value | 11301 | 1.0614e+06 | 93.9204 | 60.5687 | 213701 | 18.9099 | 15.0068 |
Information about document lengths in modality tokens:
| | len_total@lemmatized | len_total@bigram | len_uniq@lemmatized | len_uniq@bigram |
|:-----|-----------------------:|-------------------:|----------------------:|------------------:|
| mean | 93.9204 | 18.9099 | 60.5687 | 15.0068 |
| std | 276.901 | 66.4278 | 104.23 | 39.1756 |
| min | 0 | 0 | 0 | 0 |
| 25% | 20 | 3 | 19 | 3 |
| 50% | 42 | 8 | 35 | 8 |
| 75% | 83 | 16 | 65 | 15 |
| max | 6497 | 1528 | 1875 | 831 |
**Metadata**: known class labels (20 classes).
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_39 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1309357952.0
num_examples: 255136
download_size: 1334577874
dataset_size: 1309357952.0
---
# Dataset Card for "chunk_39"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
meenham/MSC_korean | ---
license: apache-2.0
task_categories:
- translation
language:
- ko
size_categories:
- 1K<n<10K
---
- Data
- source
- MSC data from the paper < Beyond Goldfish Memory: Long-Term Open-Domain Conversation >
- train/valid/test dataset of session 4
- translation ( English -> Koeran )
- GPT-3.5-turbo is used mostly
- GPT-4 : 66 data from the start of session_4_train ( after these, changed to gpt-3.5 ) |
heliosprime/twitter_dataset_1713180591 | ---
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: 6467
num_examples: 18
download_size: 10983
dataset_size: 6467
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713180591"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ostapeno/tulu_v2_gpt4_alpaca_subset | ---
dataset_info:
features:
- name: dataset
dtype: string
- name: id
dtype: string
- name: messages
list:
- name: role
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 16994301
num_examples: 20000
download_size: 9302507
dataset_size: 16994301
---
# Dataset Card for "tulu_v2_gpt4_alpaca_subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_vmajor__Orca2-13B-selfmerge-39B | ---
pretty_name: Evaluation run of vmajor/Orca2-13B-selfmerge-39B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [vmajor/Orca2-13B-selfmerge-39B](https://huggingface.co/vmajor/Orca2-13B-selfmerge-39B)\
\ 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_vmajor__Orca2-13B-selfmerge-39B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-04T17:00:35.598511](https://huggingface.co/datasets/open-llm-leaderboard/details_vmajor__Orca2-13B-selfmerge-39B/blob/main/results_2023-12-04T17-00-35.598511.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\": 0.6021029441684177,\n\
\ \"acc_stderr\": 0.03292834355809297,\n \"acc_norm\": 0.6066088767121881,\n\
\ \"acc_norm_stderr\": 0.033597954121191174,\n \"mc1\": 0.401468788249694,\n\
\ \"mc1_stderr\": 0.017160273901693654,\n \"mc2\": 0.5637680270447162,\n\
\ \"mc2_stderr\": 0.01593030661874887\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5725255972696246,\n \"acc_stderr\": 0.014456862944650649,\n\
\ \"acc_norm\": 0.6083617747440273,\n \"acc_norm_stderr\": 0.014264122124938217\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.611929894443338,\n\
\ \"acc_stderr\": 0.004863147544177514,\n \"acc_norm\": 0.7984465245966939,\n\
\ \"acc_norm_stderr\": 0.004003405481372169\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n\
\ \"acc_stderr\": 0.04218506215368879,\n \"acc_norm\": 0.6074074074074074,\n\
\ \"acc_norm_stderr\": 0.04218506215368879\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7368421052631579,\n \"acc_stderr\": 0.03583496176361073,\n\
\ \"acc_norm\": 0.7368421052631579,\n \"acc_norm_stderr\": 0.03583496176361073\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.6226415094339622,\n \"acc_stderr\": 0.029832808114796005,\n\
\ \"acc_norm\": 0.6226415094339622,\n \"acc_norm_stderr\": 0.029832808114796005\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6805555555555556,\n\
\ \"acc_stderr\": 0.038990736873573344,\n \"acc_norm\": 0.6805555555555556,\n\
\ \"acc_norm_stderr\": 0.038990736873573344\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n\
\ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.5491329479768786,\n\
\ \"acc_stderr\": 0.03794012674697031,\n \"acc_norm\": 0.5491329479768786,\n\
\ \"acc_norm_stderr\": 0.03794012674697031\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201943,\n\
\ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201943\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n\
\ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.032469569197899575,\n\
\ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.032469569197899575\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2807017543859649,\n\
\ \"acc_stderr\": 0.042270544512322,\n \"acc_norm\": 0.2807017543859649,\n\
\ \"acc_norm_stderr\": 0.042270544512322\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\"\
: 0.37566137566137564,\n \"acc_stderr\": 0.02494236893115979,\n \"\
acc_norm\": 0.37566137566137564,\n \"acc_norm_stderr\": 0.02494236893115979\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.36507936507936506,\n\
\ \"acc_stderr\": 0.04306241259127153,\n \"acc_norm\": 0.36507936507936506,\n\
\ \"acc_norm_stderr\": 0.04306241259127153\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7387096774193549,\n\
\ \"acc_stderr\": 0.02499305339776481,\n \"acc_norm\": 0.7387096774193549,\n\
\ \"acc_norm_stderr\": 0.02499305339776481\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4729064039408867,\n \"acc_stderr\": 0.03512819077876106,\n\
\ \"acc_norm\": 0.4729064039408867,\n \"acc_norm_stderr\": 0.03512819077876106\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\"\
: 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7272727272727273,\n \"acc_stderr\": 0.03477691162163659,\n\
\ \"acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.03477691162163659\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7323232323232324,\n \"acc_stderr\": 0.03154449888270285,\n \"\
acc_norm\": 0.7323232323232324,\n \"acc_norm_stderr\": 0.03154449888270285\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8393782383419689,\n \"acc_stderr\": 0.026499057701397447,\n\
\ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.026499057701397447\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5948717948717949,\n \"acc_stderr\": 0.02489047176993815,\n \
\ \"acc_norm\": 0.5948717948717949,\n \"acc_norm_stderr\": 0.02489047176993815\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3148148148148148,\n \"acc_stderr\": 0.028317533496066475,\n \
\ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.028317533496066475\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.634453781512605,\n \"acc_stderr\": 0.031282177063684614,\n \
\ \"acc_norm\": 0.634453781512605,\n \"acc_norm_stderr\": 0.031282177063684614\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33774834437086093,\n \"acc_stderr\": 0.038615575462551684,\n \"\
acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.038615575462551684\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8128440366972477,\n \"acc_stderr\": 0.016722684526200144,\n \"\
acc_norm\": 0.8128440366972477,\n \"acc_norm_stderr\": 0.016722684526200144\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\
: 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.803921568627451,\n\
\ \"acc_stderr\": 0.027865942286639325,\n \"acc_norm\": 0.803921568627451,\n\
\ \"acc_norm_stderr\": 0.027865942286639325\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n\
\ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n\
\ \"acc_stderr\": 0.031493846709941306,\n \"acc_norm\": 0.672645739910314,\n\
\ \"acc_norm_stderr\": 0.031493846709941306\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7175572519083969,\n \"acc_stderr\": 0.03948406125768361,\n\
\ \"acc_norm\": 0.7175572519083969,\n \"acc_norm_stderr\": 0.03948406125768361\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.03749492448709697,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.03749492448709697\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\
\ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\
\ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7239263803680982,\n \"acc_stderr\": 0.035123852837050475,\n\
\ \"acc_norm\": 0.7239263803680982,\n \"acc_norm_stderr\": 0.035123852837050475\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3482142857142857,\n\
\ \"acc_stderr\": 0.045218299028335865,\n \"acc_norm\": 0.3482142857142857,\n\
\ \"acc_norm_stderr\": 0.045218299028335865\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384493,\n\
\ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384493\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\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7752234993614304,\n\
\ \"acc_stderr\": 0.01492744710193716,\n \"acc_norm\": 0.7752234993614304,\n\
\ \"acc_norm_stderr\": 0.01492744710193716\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6791907514450867,\n \"acc_stderr\": 0.025131000233647897,\n\
\ \"acc_norm\": 0.6791907514450867,\n \"acc_norm_stderr\": 0.025131000233647897\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.30837988826815643,\n\
\ \"acc_stderr\": 0.01544571691099888,\n \"acc_norm\": 0.30837988826815643,\n\
\ \"acc_norm_stderr\": 0.01544571691099888\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6633986928104575,\n \"acc_stderr\": 0.027057974624494382,\n\
\ \"acc_norm\": 0.6633986928104575,\n \"acc_norm_stderr\": 0.027057974624494382\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6881028938906752,\n\
\ \"acc_stderr\": 0.02631185807185416,\n \"acc_norm\": 0.6881028938906752,\n\
\ \"acc_norm_stderr\": 0.02631185807185416\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7129629629629629,\n \"acc_stderr\": 0.02517104191530968,\n\
\ \"acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.02517104191530968\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.45390070921985815,\n \"acc_stderr\": 0.029700453247291484,\n \
\ \"acc_norm\": 0.45390070921985815,\n \"acc_norm_stderr\": 0.029700453247291484\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4361147327249022,\n\
\ \"acc_stderr\": 0.012665568135455335,\n \"acc_norm\": 0.4361147327249022,\n\
\ \"acc_norm_stderr\": 0.012665568135455335\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5772058823529411,\n \"acc_stderr\": 0.030008562845003476,\n\
\ \"acc_norm\": 0.5772058823529411,\n \"acc_norm_stderr\": 0.030008562845003476\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6045751633986928,\n \"acc_stderr\": 0.019780465954777508,\n \
\ \"acc_norm\": 0.6045751633986928,\n \"acc_norm_stderr\": 0.019780465954777508\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\
\ \"acc_stderr\": 0.04607582090719976,\n \"acc_norm\": 0.6363636363636364,\n\
\ \"acc_norm_stderr\": 0.04607582090719976\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.02866685779027465,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.02866685779027465\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7313432835820896,\n\
\ \"acc_stderr\": 0.03134328358208954,\n \"acc_norm\": 0.7313432835820896,\n\
\ \"acc_norm_stderr\": 0.03134328358208954\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036624,\n \
\ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036624\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5120481927710844,\n\
\ \"acc_stderr\": 0.03891364495835817,\n \"acc_norm\": 0.5120481927710844,\n\
\ \"acc_norm_stderr\": 0.03891364495835817\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.030611116557432528,\n\
\ \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.030611116557432528\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.401468788249694,\n\
\ \"mc1_stderr\": 0.017160273901693654,\n \"mc2\": 0.5637680270447162,\n\
\ \"mc2_stderr\": 0.01593030661874887\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7687450670876085,\n \"acc_stderr\": 0.011850040124850508\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.39196360879454134,\n \
\ \"acc_stderr\": 0.013447140886023829\n }\n}\n```"
repo_url: https://huggingface.co/vmajor/Orca2-13B-selfmerge-39B
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_04T17_00_35.598511
path:
- '**/details_harness|arc:challenge|25_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|gsm8k|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hellaswag|10_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T17-00-35.598511.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-04T17-00-35.598511.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- '**/details_harness|winogrande|5_2023-12-04T17-00-35.598511.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-04T17-00-35.598511.parquet'
- config_name: results
data_files:
- split: 2023_12_04T17_00_35.598511
path:
- results_2023-12-04T17-00-35.598511.parquet
- split: latest
path:
- results_2023-12-04T17-00-35.598511.parquet
---
# Dataset Card for Evaluation run of vmajor/Orca2-13B-selfmerge-39B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/vmajor/Orca2-13B-selfmerge-39B
- **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 [vmajor/Orca2-13B-selfmerge-39B](https://huggingface.co/vmajor/Orca2-13B-selfmerge-39B) 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_vmajor__Orca2-13B-selfmerge-39B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T17:00:35.598511](https://huggingface.co/datasets/open-llm-leaderboard/details_vmajor__Orca2-13B-selfmerge-39B/blob/main/results_2023-12-04T17-00-35.598511.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": 0.6021029441684177,
"acc_stderr": 0.03292834355809297,
"acc_norm": 0.6066088767121881,
"acc_norm_stderr": 0.033597954121191174,
"mc1": 0.401468788249694,
"mc1_stderr": 0.017160273901693654,
"mc2": 0.5637680270447162,
"mc2_stderr": 0.01593030661874887
},
"harness|arc:challenge|25": {
"acc": 0.5725255972696246,
"acc_stderr": 0.014456862944650649,
"acc_norm": 0.6083617747440273,
"acc_norm_stderr": 0.014264122124938217
},
"harness|hellaswag|10": {
"acc": 0.611929894443338,
"acc_stderr": 0.004863147544177514,
"acc_norm": 0.7984465245966939,
"acc_norm_stderr": 0.004003405481372169
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695236,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695236
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6074074074074074,
"acc_stderr": 0.04218506215368879,
"acc_norm": 0.6074074074074074,
"acc_norm_stderr": 0.04218506215368879
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7368421052631579,
"acc_stderr": 0.03583496176361073,
"acc_norm": 0.7368421052631579,
"acc_norm_stderr": 0.03583496176361073
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6226415094339622,
"acc_stderr": 0.029832808114796005,
"acc_norm": 0.6226415094339622,
"acc_norm_stderr": 0.029832808114796005
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6805555555555556,
"acc_stderr": 0.038990736873573344,
"acc_norm": 0.6805555555555556,
"acc_norm_stderr": 0.038990736873573344
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5491329479768786,
"acc_stderr": 0.03794012674697031,
"acc_norm": 0.5491329479768786,
"acc_norm_stderr": 0.03794012674697031
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.04690650298201943,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.04690650298201943
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5574468085106383,
"acc_stderr": 0.032469569197899575,
"acc_norm": 0.5574468085106383,
"acc_norm_stderr": 0.032469569197899575
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2807017543859649,
"acc_stderr": 0.042270544512322,
"acc_norm": 0.2807017543859649,
"acc_norm_stderr": 0.042270544512322
},
"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": 0.37566137566137564,
"acc_stderr": 0.02494236893115979,
"acc_norm": 0.37566137566137564,
"acc_norm_stderr": 0.02494236893115979
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.36507936507936506,
"acc_stderr": 0.04306241259127153,
"acc_norm": 0.36507936507936506,
"acc_norm_stderr": 0.04306241259127153
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7387096774193549,
"acc_stderr": 0.02499305339776481,
"acc_norm": 0.7387096774193549,
"acc_norm_stderr": 0.02499305339776481
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4729064039408867,
"acc_stderr": 0.03512819077876106,
"acc_norm": 0.4729064039408867,
"acc_norm_stderr": 0.03512819077876106
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.64,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.64,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7272727272727273,
"acc_stderr": 0.03477691162163659,
"acc_norm": 0.7272727272727273,
"acc_norm_stderr": 0.03477691162163659
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7323232323232324,
"acc_stderr": 0.03154449888270285,
"acc_norm": 0.7323232323232324,
"acc_norm_stderr": 0.03154449888270285
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8393782383419689,
"acc_stderr": 0.026499057701397447,
"acc_norm": 0.8393782383419689,
"acc_norm_stderr": 0.026499057701397447
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5948717948717949,
"acc_stderr": 0.02489047176993815,
"acc_norm": 0.5948717948717949,
"acc_norm_stderr": 0.02489047176993815
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3148148148148148,
"acc_stderr": 0.028317533496066475,
"acc_norm": 0.3148148148148148,
"acc_norm_stderr": 0.028317533496066475
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.634453781512605,
"acc_stderr": 0.031282177063684614,
"acc_norm": 0.634453781512605,
"acc_norm_stderr": 0.031282177063684614
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33774834437086093,
"acc_stderr": 0.038615575462551684,
"acc_norm": 0.33774834437086093,
"acc_norm_stderr": 0.038615575462551684
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8128440366972477,
"acc_stderr": 0.016722684526200144,
"acc_norm": 0.8128440366972477,
"acc_norm_stderr": 0.016722684526200144
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4722222222222222,
"acc_stderr": 0.0340470532865388,
"acc_norm": 0.4722222222222222,
"acc_norm_stderr": 0.0340470532865388
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.803921568627451,
"acc_stderr": 0.027865942286639325,
"acc_norm": 0.803921568627451,
"acc_norm_stderr": 0.027865942286639325
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.810126582278481,
"acc_stderr": 0.02553010046023349,
"acc_norm": 0.810126582278481,
"acc_norm_stderr": 0.02553010046023349
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.672645739910314,
"acc_stderr": 0.031493846709941306,
"acc_norm": 0.672645739910314,
"acc_norm_stderr": 0.031493846709941306
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7175572519083969,
"acc_stderr": 0.03948406125768361,
"acc_norm": 0.7175572519083969,
"acc_norm_stderr": 0.03948406125768361
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7851239669421488,
"acc_stderr": 0.03749492448709697,
"acc_norm": 0.7851239669421488,
"acc_norm_stderr": 0.03749492448709697
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7962962962962963,
"acc_stderr": 0.03893542518824847,
"acc_norm": 0.7962962962962963,
"acc_norm_stderr": 0.03893542518824847
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7239263803680982,
"acc_stderr": 0.035123852837050475,
"acc_norm": 0.7239263803680982,
"acc_norm_stderr": 0.035123852837050475
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.3482142857142857,
"acc_stderr": 0.045218299028335865,
"acc_norm": 0.3482142857142857,
"acc_norm_stderr": 0.045218299028335865
},
"harness|hendrycksTest-management|5": {
"acc": 0.7572815533980582,
"acc_stderr": 0.04245022486384493,
"acc_norm": 0.7572815533980582,
"acc_norm_stderr": 0.04245022486384493
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8675213675213675,
"acc_stderr": 0.022209309073165616,
"acc_norm": 0.8675213675213675,
"acc_norm_stderr": 0.022209309073165616
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7752234993614304,
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"acc_norm": 0.7752234993614304,
"acc_norm_stderr": 0.01492744710193716
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6791907514450867,
"acc_stderr": 0.025131000233647897,
"acc_norm": 0.6791907514450867,
"acc_norm_stderr": 0.025131000233647897
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.30837988826815643,
"acc_stderr": 0.01544571691099888,
"acc_norm": 0.30837988826815643,
"acc_norm_stderr": 0.01544571691099888
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6633986928104575,
"acc_stderr": 0.027057974624494382,
"acc_norm": 0.6633986928104575,
"acc_norm_stderr": 0.027057974624494382
},
"harness|hendrycksTest-philosophy|5": {
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},
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"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
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},
"harness|hendrycksTest-professional_medicine|5": {
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|truthfulqa:mc|0": {
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"mc2": 0.5637680270447162,
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"harness|winogrande|5": {
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"harness|gsm8k|5": {
"acc": 0.39196360879454134,
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}
}
```
### 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
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### Citation Information
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### Contributions
[More Information Needed] |
shidowake/FreedomIntelligence_alpaca-gpt4-japanese_subset_split_2 | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 4863217.322740098
num_examples: 4997
download_size: 2557516
dataset_size: 4863217.322740098
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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