datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
RamWithAPlan/processed_bert_dataset | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: special_tokens_mask
sequence: int8
splits:
- name: train
num_bytes: 24027580800.0
num_examples: 6674328
download_size: 5887248507
dataset_size: 24027580800.0
---
# Dataset Card for "processed_bert_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
m-ivanov1152/hhh_test | ---
dataset_info:
features:
- name: sent
dtype: int64
- name: token
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 9137
num_examples: 284
download_size: 4380
dataset_size: 9137
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hhh_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
RZ412/mmlu_responses_1k_augmented | ---
dataset_info:
features:
- name: exemplar_questions
dtype: string
- name: test_questions
dtype: string
- name: subject
dtype: string
- name: answers
list:
- name: answer
dtype: string
- name: model
dtype: string
- name: reference_answers
dtype: int64
splits:
- name: train
num_bytes: 279310672
num_examples: 1000
download_size: 10943728
dataset_size: 279310672
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/ibuki_tsubasa_theidolmstermillionlive | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of ibuki_tsubasa/伊吹翼/이부키츠바사 (THE iDOLM@STER: Million Live!)
This is the dataset of ibuki_tsubasa/伊吹翼/이부키츠바사 (THE iDOLM@STER: Million Live!), containing 483 images and their tags.
The core tags of this character are `short_hair, ahoge, blonde_hair, breasts, bangs, pink_eyes, red_eyes, medium_breasts, hair_between_eyes, brown_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 483 | 614.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ibuki_tsubasa_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 483 | 363.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ibuki_tsubasa_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1193 | 784.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ibuki_tsubasa_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 483 | 552.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ibuki_tsubasa_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1193 | 1.07 GiB | [Download](https://huggingface.co/datasets/CyberHarem/ibuki_tsubasa_theidolmstermillionlive/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/ibuki_tsubasa_theidolmstermillionlive',
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 | 5 |  |  |  |  |  | 1girl, collarbone, looking_at_viewer, simple_background, solo, white_background, blush, completely_nude, navel, nipples, pussy, :d, arms_behind_back, closed_mouth, female_pubic_hair, open_mouth, standing, upper_body |
| 1 | 5 |  |  |  |  |  | 1girl, blush, looking_at_viewer, short_sleeves, solo, raglan_sleeves, simple_background, smile, upper_body, collarbone, navel, tongue_out, white_background, white_shirt, closed_mouth, midriff, yellow_shirt |
| 2 | 6 |  |  |  |  |  | 1girl, blue_sky, cloud, day, looking_at_viewer, outdoors, smile, blush, navel, solo, white_bikini, ocean, open_mouth, beach, cleavage, wet |
| 3 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, cheerleader, pom_pom_(cheerleading), smile, midriff, pleated_skirt, thighhighs, navel, crop_top_overhang, miniskirt, open_mouth, short_sleeves, white_skirt, yellow_shirt, holding, jewelry, simple_background, star_(symbol), sweat, white_gloves |
| 4 | 5 |  |  |  |  |  | 1girl, blush, looking_at_viewer, shirt_lift, solo, lifted_by_self, short_sleeves, upper_body, navel, simple_background, bra_lift, hair_flaps, large_breasts, nipples, sweatdrop, underboob, white_background, white_bra, white_shirt, yellow_bra, yellow_shirt |
| 5 | 13 |  |  |  |  |  | fingerless_gloves, looking_at_viewer, navel, short_sleeves, 1girl, black_gloves, hair_flaps, pleated_skirt, serafuku, white_shirt, white_skirt, yellow_cape, sailor_collar, solo, midriff, miniskirt, blush, open_mouth, smile, yellow_neckerchief, black_thighhighs, v-shaped_eyebrows, sidelocks, stomach, white_cape |
| 6 | 5 |  |  |  |  |  | 1girl, choker, hair_ornament, long_sleeves, looking_at_viewer, midriff, navel, open_jacket, solo, white_jacket, blush, fishnets, cleavage, collarbone, earrings, nail_polish, pleated_skirt, see-through, thighhighs, black_skirt, checkered_clothes, crop_top, fingerless_gloves, grin, hair_flaps, large_breasts, miniskirt, mismatched_legwear, open_mouth, white_background |
| 7 | 17 |  |  |  |  |  | 1boy, 1girl, blush, hetero, solo_focus, open_mouth, sweat, nipples, penis, sex, smile, vaginal, girl_on_top, completely_nude, female_pubic_hair, looking_at_viewer, navel, spread_legs, bar_censor, cowgirl_position, cum_in_pussy, heart, mosaic_censoring |
| 8 | 7 |  |  |  |  |  | blush, collarbone, large_breasts, looking_at_viewer, 1girl, cleavage, solo, upper_body, neck_bell, open_mouth, simple_background, smile, animal_ears, black_bikini, choker, cow_print, gloves, horns, navel |
| 9 | 10 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, cleavage, fake_animal_ears, playboy_bunny, rabbit_ears, simple_background, white_background, detached_collar, rabbit_tail, bare_shoulders, strapless_leotard, wrist_cuffs, large_breasts, smile, cowboy_shot, fake_tail, black_leotard, covered_navel, hair_flaps, open_mouth, pantyhose |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | collarbone | looking_at_viewer | simple_background | solo | white_background | blush | completely_nude | navel | nipples | pussy | :d | arms_behind_back | closed_mouth | female_pubic_hair | open_mouth | standing | upper_body | short_sleeves | raglan_sleeves | smile | tongue_out | white_shirt | midriff | yellow_shirt | blue_sky | cloud | day | outdoors | white_bikini | ocean | beach | cleavage | wet | cheerleader | pom_pom_(cheerleading) | pleated_skirt | thighhighs | crop_top_overhang | miniskirt | white_skirt | holding | jewelry | star_(symbol) | sweat | white_gloves | shirt_lift | lifted_by_self | bra_lift | hair_flaps | large_breasts | sweatdrop | underboob | white_bra | yellow_bra | fingerless_gloves | black_gloves | serafuku | yellow_cape | sailor_collar | yellow_neckerchief | black_thighhighs | v-shaped_eyebrows | sidelocks | stomach | white_cape | choker | hair_ornament | long_sleeves | open_jacket | white_jacket | fishnets | earrings | nail_polish | see-through | black_skirt | checkered_clothes | crop_top | grin | mismatched_legwear | 1boy | hetero | solo_focus | penis | sex | vaginal | girl_on_top | spread_legs | bar_censor | cowgirl_position | cum_in_pussy | heart | mosaic_censoring | neck_bell | animal_ears | black_bikini | cow_print | gloves | horns | fake_animal_ears | playboy_bunny | rabbit_ears | detached_collar | rabbit_tail | bare_shoulders | strapless_leotard | wrist_cuffs | cowboy_shot | fake_tail | black_leotard | covered_navel | pantyhose |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:--------------------|:--------------------|:-------|:-------------------|:--------|:------------------|:--------|:----------|:--------|:-----|:-------------------|:---------------|:--------------------|:-------------|:-----------|:-------------|:----------------|:-----------------|:--------|:-------------|:--------------|:----------|:---------------|:-----------|:--------|:------|:-----------|:---------------|:--------|:--------|:-----------|:------|:--------------|:-------------------------|:----------------|:-------------|:--------------------|:------------|:--------------|:----------|:----------|:----------------|:--------|:---------------|:-------------|:-----------------|:-----------|:-------------|:----------------|:------------|:------------|:------------|:-------------|:--------------------|:---------------|:-----------|:--------------|:----------------|:---------------------|:-------------------|:--------------------|:------------|:----------|:-------------|:---------|:----------------|:---------------|:--------------|:---------------|:-----------|:-----------|:--------------|:--------------|:--------------|:--------------------|:-----------|:-------|:---------------------|:-------|:---------|:-------------|:--------|:------|:----------|:--------------|:--------------|:-------------|:-------------------|:---------------|:--------|:-------------------|:------------|:--------------|:---------------|:------------|:---------|:--------|:-------------------|:----------------|:--------------|:------------------|:--------------|:-----------------|:--------------------|:--------------|:--------------|:------------|:----------------|:----------------|:------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | | X | | | | | X | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | | X | | X | | X | | X | | | | | | | X | | | | | X | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 9 |  |  |  |  |  | X | | X | X | X | | X | | X | | | | | | | X | | | X | | X | | | X | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | X | X | X | X | X | | X | X | | | | | | | | X | X | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 13 |  |  |  |  |  | X | | X | | X | | X | | X | | | | | | | X | | | X | | X | | X | X | | | | | | | | | | | | | X | | | X | X | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 17 |  |  |  |  |  | X | | X | | | | X | X | X | X | | | | | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 8 | 7 |  |  |  |  |  | X | X | X | X | X | | X | | X | | | | | | | X | | X | | | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | |
| 9 | 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 |
|
CyberHarem/lajournee_pokemon | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of lajournee/ラジュルネ (Pokémon)
This is the dataset of lajournee/ラジュルネ (Pokémon), containing 32 images and their tags.
The core tags of this character are `hat, breasts, long_hair, red_eyes, pink_hair, red_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 32 | 27.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lajournee_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 32 | 17.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lajournee_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 64 | 33.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lajournee_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 32 | 25.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lajournee_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 64 | 43.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lajournee_pokemon/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/lajournee_pokemon',
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 | 19 |  |  |  |  |  | 1girl, bare_shoulders, elbow_gloves, solo, dress, looking_at_viewer, black_gloves, pantyhose, eyelashes, open_mouth, brown_hair, holding_poke_ball, pink_headwear, poke_ball_(basic), top_hat, brown_eyes, hand_on_hip |
| 1 | 11 |  |  |  |  |  | 1girl, blush, hetero, penis, solo_focus, 1boy, censored, nipples, open_mouth, large_breasts, sex, pantyhose, pussy, vaginal, bare_shoulders, elbow_gloves, handjob, nude |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | elbow_gloves | solo | dress | looking_at_viewer | black_gloves | pantyhose | eyelashes | open_mouth | brown_hair | holding_poke_ball | pink_headwear | poke_ball_(basic) | top_hat | brown_eyes | hand_on_hip | blush | hetero | penis | solo_focus | 1boy | censored | nipples | large_breasts | sex | pussy | vaginal | handjob | nude |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:---------------|:-------|:--------|:--------------------|:---------------|:------------|:------------|:-------------|:-------------|:--------------------|:----------------|:--------------------|:----------|:-------------|:--------------|:--------|:---------|:--------|:-------------|:-------|:-----------|:----------|:----------------|:------|:--------|:----------|:----------|:-------|
| 0 | 19 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | X | X | X | | | | | X | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
open-llm-leaderboard/details_Qwen__Qwen1.5-72B-Chat | ---
pretty_name: Evaluation run of Qwen/Qwen1.5-72B-Chat
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Qwen/Qwen1.5-72B-Chat](https://huggingface.co/Qwen/Qwen1.5-72B-Chat) 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_Qwen__Qwen1.5-72B-Chat\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-08T06:44:33.909194](https://huggingface.co/datasets/open-llm-leaderboard/details_Qwen__Qwen1.5-72B-Chat/blob/main/results_2024-03-08T06-44-33.909194.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\": 0.7618755724472454,\n\
\ \"acc_stderr\": 0.02815008534788608,\n \"acc_norm\": 0.7744344937789645,\n\
\ \"acc_norm_stderr\": 0.028696138532381087,\n \"mc1\": 0.44430844553243576,\n\
\ \"mc1_stderr\": 0.017394586250743173,\n \"mc2\": 0.6389810525358077,\n\
\ \"mc2_stderr\": 0.015760859004207876\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6535836177474402,\n \"acc_stderr\": 0.013905011180063225,\n\
\ \"acc_norm\": 0.6851535836177475,\n \"acc_norm_stderr\": 0.013572657703084948\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6837283409679347,\n\
\ \"acc_stderr\": 0.004640699483543309,\n \"acc_norm\": 0.8641704839673372,\n\
\ \"acc_norm_stderr\": 0.0034190724807353617\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \
\ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7333333333333333,\n\
\ \"acc_stderr\": 0.038201699145179055,\n \"acc_norm\": 0.7333333333333333,\n\
\ \"acc_norm_stderr\": 0.038201699145179055\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.875,\n \"acc_stderr\": 0.026913523521537846,\n \
\ \"acc_norm\": 0.875,\n \"acc_norm_stderr\": 0.026913523521537846\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.77,\n\
\ \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n \
\ \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.8113207547169812,\n \"acc_stderr\": 0.024079995130062225,\n\
\ \"acc_norm\": 0.8113207547169812,\n \"acc_norm_stderr\": 0.024079995130062225\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9097222222222222,\n\
\ \"acc_stderr\": 0.023964965777906935,\n \"acc_norm\": 0.9097222222222222,\n\
\ \"acc_norm_stderr\": 0.023964965777906935\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \
\ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n\
\ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.52,\n \"acc_stderr\": 0.05021167315686779,\n \
\ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.05021167315686779\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7456647398843931,\n\
\ \"acc_stderr\": 0.0332055644308557,\n \"acc_norm\": 0.7456647398843931,\n\
\ \"acc_norm_stderr\": 0.0332055644308557\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.5882352941176471,\n \"acc_stderr\": 0.048971049527263666,\n\
\ \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.048971049527263666\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.83,\n\
\ \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7957446808510639,\n \"acc_stderr\": 0.026355158413349417,\n\
\ \"acc_norm\": 0.7957446808510639,\n \"acc_norm_stderr\": 0.026355158413349417\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5701754385964912,\n\
\ \"acc_stderr\": 0.04657047260594964,\n \"acc_norm\": 0.5701754385964912,\n\
\ \"acc_norm_stderr\": 0.04657047260594964\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.0333333333333333,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.0333333333333333\n },\n\
\ \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.716931216931217,\n\
\ \"acc_stderr\": 0.023201392938194978,\n \"acc_norm\": 0.716931216931217,\n\
\ \"acc_norm_stderr\": 0.023201392938194978\n },\n \"harness|hendrycksTest-formal_logic|5\"\
: {\n \"acc\": 0.5873015873015873,\n \"acc_stderr\": 0.04403438954768177,\n\
\ \"acc_norm\": 0.5873015873015873,\n \"acc_norm_stderr\": 0.04403438954768177\n\
\ },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.54,\n\
\ \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n \
\ \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-high_school_biology|5\"\
: {\n \"acc\": 0.9,\n \"acc_stderr\": 0.01706640371965726,\n \
\ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.01706640371965726\n },\n\
\ \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.6798029556650246,\n\
\ \"acc_stderr\": 0.032826493853041504,\n \"acc_norm\": 0.6798029556650246,\n\
\ \"acc_norm_stderr\": 0.032826493853041504\n },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\": {\n \
\ \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.027045948825865387,\n\
\ \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.027045948825865387\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.9242424242424242,\n \"acc_stderr\": 0.018852670234993093,\n \"\
acc_norm\": 0.9242424242424242,\n \"acc_norm_stderr\": 0.018852670234993093\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9844559585492227,\n \"acc_stderr\": 0.008927492715084334,\n\
\ \"acc_norm\": 0.9844559585492227,\n \"acc_norm_stderr\": 0.008927492715084334\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.8205128205128205,\n \"acc_stderr\": 0.019457390787681793,\n\
\ \"acc_norm\": 0.8205128205128205,\n \"acc_norm_stderr\": 0.019457390787681793\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.5259259259259259,\n \"acc_stderr\": 0.03044452852881074,\n \
\ \"acc_norm\": 0.5259259259259259,\n \"acc_norm_stderr\": 0.03044452852881074\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.865546218487395,\n \"acc_stderr\": 0.022159373072744442,\n \
\ \"acc_norm\": 0.865546218487395,\n \"acc_norm_stderr\": 0.022159373072744442\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.5298013245033113,\n \"acc_stderr\": 0.040752249922169796,\n \"\
acc_norm\": 0.5298013245033113,\n \"acc_norm_stderr\": 0.040752249922169796\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9247706422018349,\n \"acc_stderr\": 0.011308662537571786,\n \"\
acc_norm\": 0.9247706422018349,\n \"acc_norm_stderr\": 0.011308662537571786\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6990740740740741,\n \"acc_stderr\": 0.031280390843298825,\n \"\
acc_norm\": 0.6990740740740741,\n \"acc_norm_stderr\": 0.031280390843298825\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9117647058823529,\n \"acc_stderr\": 0.01990739979131694,\n \"\
acc_norm\": 0.9117647058823529,\n \"acc_norm_stderr\": 0.01990739979131694\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8987341772151899,\n \"acc_stderr\": 0.019637720526065508,\n \
\ \"acc_norm\": 0.8987341772151899,\n \"acc_norm_stderr\": 0.019637720526065508\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8071748878923767,\n\
\ \"acc_stderr\": 0.02647824096048937,\n \"acc_norm\": 0.8071748878923767,\n\
\ \"acc_norm_stderr\": 0.02647824096048937\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8625954198473282,\n \"acc_stderr\": 0.030194823996804475,\n\
\ \"acc_norm\": 0.8625954198473282,\n \"acc_norm_stderr\": 0.030194823996804475\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8512396694214877,\n \"acc_stderr\": 0.03248470083807194,\n \"\
acc_norm\": 0.8512396694214877,\n \"acc_norm_stderr\": 0.03248470083807194\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.036028141763926456,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.036028141763926456\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8711656441717791,\n \"acc_stderr\": 0.026321383198783656,\n\
\ \"acc_norm\": 0.8711656441717791,\n \"acc_norm_stderr\": 0.026321383198783656\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5803571428571429,\n\
\ \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.5803571428571429,\n\
\ \"acc_norm_stderr\": 0.04684099321077106\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8737864077669902,\n \"acc_stderr\": 0.03288180278808628,\n\
\ \"acc_norm\": 0.8737864077669902,\n \"acc_norm_stderr\": 0.03288180278808628\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9401709401709402,\n\
\ \"acc_stderr\": 0.01553751426325388,\n \"acc_norm\": 0.9401709401709402,\n\
\ \"acc_norm_stderr\": 0.01553751426325388\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.0368452949177471,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.0368452949177471\n },\n\
\ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.913154533844189,\n\
\ \"acc_stderr\": 0.01007029837774778,\n \"acc_norm\": 0.913154533844189,\n\
\ \"acc_norm_stderr\": 0.01007029837774778\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.8410404624277457,\n \"acc_stderr\": 0.019685307033571946,\n\
\ \"acc_norm\": 0.8410404624277457,\n \"acc_norm_stderr\": 0.019685307033571946\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.729608938547486,\n\
\ \"acc_stderr\": 0.01485499393801009,\n \"acc_norm\": 0.729608938547486,\n\
\ \"acc_norm_stderr\": 0.01485499393801009\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.021339479988816027,\n\
\ \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.021339479988816027\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8263665594855305,\n\
\ \"acc_stderr\": 0.021514051585970403,\n \"acc_norm\": 0.8263665594855305,\n\
\ \"acc_norm_stderr\": 0.021514051585970403\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8641975308641975,\n \"acc_stderr\": 0.0190615881815054,\n\
\ \"acc_norm\": 0.8641975308641975,\n \"acc_norm_stderr\": 0.0190615881815054\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.6170212765957447,\n \"acc_stderr\": 0.028999080904806185,\n \
\ \"acc_norm\": 0.6170212765957447,\n \"acc_norm_stderr\": 0.028999080904806185\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.6082138200782269,\n\
\ \"acc_stderr\": 0.01246756441814511,\n \"acc_norm\": 0.6082138200782269,\n\
\ \"acc_norm_stderr\": 0.01246756441814511\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.8198529411764706,\n \"acc_stderr\": 0.023345163616544835,\n\
\ \"acc_norm\": 0.8198529411764706,\n \"acc_norm_stderr\": 0.023345163616544835\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.8071895424836601,\n \"acc_stderr\": 0.015959983971206744,\n \
\ \"acc_norm\": 0.8071895424836601,\n \"acc_norm_stderr\": 0.015959983971206744\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7636363636363637,\n\
\ \"acc_stderr\": 0.040693063197213754,\n \"acc_norm\": 0.7636363636363637,\n\
\ \"acc_norm_stderr\": 0.040693063197213754\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8204081632653061,\n \"acc_stderr\": 0.024573293589585637,\n\
\ \"acc_norm\": 0.8204081632653061,\n \"acc_norm_stderr\": 0.024573293589585637\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8905472636815921,\n\
\ \"acc_stderr\": 0.022076326101824667,\n \"acc_norm\": 0.8905472636815921,\n\
\ \"acc_norm_stderr\": 0.022076326101824667\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.96,\n \"acc_stderr\": 0.01969463855669321,\n \
\ \"acc_norm\": 0.96,\n \"acc_norm_stderr\": 0.01969463855669321\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5903614457831325,\n\
\ \"acc_stderr\": 0.0382840111507902,\n \"acc_norm\": 0.5903614457831325,\n\
\ \"acc_norm_stderr\": 0.0382840111507902\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.02410338420207286,\n\
\ \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.02410338420207286\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.44430844553243576,\n\
\ \"mc1_stderr\": 0.017394586250743173,\n \"mc2\": 0.6389810525358077,\n\
\ \"mc2_stderr\": 0.015760859004207876\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7908445146014207,\n \"acc_stderr\": 0.011430450045881573\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.20394238059135708,\n \
\ \"acc_stderr\": 0.011098602284899176\n }\n}\n```"
repo_url: https://huggingface.co/Qwen/Qwen1.5-72B-Chat
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_17T21_57_00.750320
path:
- '**/details_harness|arc:challenge|25_2024-02-17T21-57-00.750320.parquet'
- split: 2024_03_08T06_44_33.909194
path:
- '**/details_harness|arc:challenge|25_2024-03-08T06-44-33.909194.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-08T06-44-33.909194.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_17T21_57_00.750320
path:
- '**/details_harness|gsm8k|5_2024-02-17T21-57-00.750320.parquet'
- split: 2024_03_08T06_44_33.909194
path:
- '**/details_harness|gsm8k|5_2024-03-08T06-44-33.909194.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-08T06-44-33.909194.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_17T21_57_00.750320
path:
- '**/details_harness|hellaswag|10_2024-02-17T21-57-00.750320.parquet'
- split: 2024_03_08T06_44_33.909194
path:
- '**/details_harness|hellaswag|10_2024-03-08T06-44-33.909194.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-08T06-44-33.909194.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_17T21_57_00.750320
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-17T21-57-00.750320.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-17T21-57-00.750320.parquet'
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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data_files:
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data_files:
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data_files:
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data_files:
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path:
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data_files:
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data_files:
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data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-08T06-44-33.909194.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_17T21_57_00.750320
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T21-57-00.750320.parquet'
- split: 2024_03_08T06_44_33.909194
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-08T06-44-33.909194.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-08T06-44-33.909194.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T21-57-00.750320.parquet'
- split: 2024_03_08T06_44_33.909194
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-08T06-44-33.909194.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-08T06-44-33.909194.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T21-57-00.750320.parquet'
- split: 2024_03_08T06_44_33.909194
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-08T06-44-33.909194.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-08T06-44-33.909194.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T21-57-00.750320.parquet'
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-08T06-44-33.909194.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-08T06-44-33.909194.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
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path:
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path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-08T06-44-33.909194.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_philosophy_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_public_relations_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-08T06-44-33.909194.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_17T21_57_00.750320
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-17T21-57-00.750320.parquet'
- split: 2024_03_08T06_44_33.909194
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-08T06-44-33.909194.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-08T06-44-33.909194.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_17T21_57_00.750320
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T21-57-00.750320.parquet'
- split: 2024_03_08T06_44_33.909194
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-08T06-44-33.909194.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-08T06-44-33.909194.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_17T21_57_00.750320
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-17T21-57-00.750320.parquet'
- split: 2024_03_08T06_44_33.909194
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-08T06-44-33.909194.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-08T06-44-33.909194.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_17T21_57_00.750320
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-17T21-57-00.750320.parquet'
- split: 2024_03_08T06_44_33.909194
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-08T06-44-33.909194.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-08T06-44-33.909194.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_17T21_57_00.750320
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-17T21-57-00.750320.parquet'
- split: 2024_03_08T06_44_33.909194
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-08T06-44-33.909194.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-08T06-44-33.909194.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_17T21_57_00.750320
path:
- '**/details_harness|winogrande|5_2024-02-17T21-57-00.750320.parquet'
- split: 2024_03_08T06_44_33.909194
path:
- '**/details_harness|winogrande|5_2024-03-08T06-44-33.909194.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-08T06-44-33.909194.parquet'
- config_name: results
data_files:
- split: 2024_02_17T21_57_00.750320
path:
- results_2024-02-17T21-57-00.750320.parquet
- split: 2024_03_08T06_44_33.909194
path:
- results_2024-03-08T06-44-33.909194.parquet
- split: latest
path:
- results_2024-03-08T06-44-33.909194.parquet
---
# Dataset Card for Evaluation run of Qwen/Qwen1.5-72B-Chat
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Qwen/Qwen1.5-72B-Chat](https://huggingface.co/Qwen/Qwen1.5-72B-Chat) 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_Qwen__Qwen1.5-72B-Chat",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-08T06:44:33.909194](https://huggingface.co/datasets/open-llm-leaderboard/details_Qwen__Qwen1.5-72B-Chat/blob/main/results_2024-03-08T06-44-33.909194.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": 0.7618755724472454,
"acc_stderr": 0.02815008534788608,
"acc_norm": 0.7744344937789645,
"acc_norm_stderr": 0.028696138532381087,
"mc1": 0.44430844553243576,
"mc1_stderr": 0.017394586250743173,
"mc2": 0.6389810525358077,
"mc2_stderr": 0.015760859004207876
},
"harness|arc:challenge|25": {
"acc": 0.6535836177474402,
"acc_stderr": 0.013905011180063225,
"acc_norm": 0.6851535836177475,
"acc_norm_stderr": 0.013572657703084948
},
"harness|hellaswag|10": {
"acc": 0.6837283409679347,
"acc_stderr": 0.004640699483543309,
"acc_norm": 0.8641704839673372,
"acc_norm_stderr": 0.0034190724807353617
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.7333333333333333,
"acc_stderr": 0.038201699145179055,
"acc_norm": 0.7333333333333333,
"acc_norm_stderr": 0.038201699145179055
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.875,
"acc_stderr": 0.026913523521537846,
"acc_norm": 0.875,
"acc_norm_stderr": 0.026913523521537846
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816505,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816505
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.8113207547169812,
"acc_stderr": 0.024079995130062225,
"acc_norm": 0.8113207547169812,
"acc_norm_stderr": 0.024079995130062225
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.9097222222222222,
"acc_stderr": 0.023964965777906935,
"acc_norm": 0.9097222222222222,
"acc_norm_stderr": 0.023964965777906935
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.6,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.6,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.52,
"acc_stderr": 0.05021167315686779,
"acc_norm": 0.52,
"acc_norm_stderr": 0.05021167315686779
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7456647398843931,
"acc_stderr": 0.0332055644308557,
"acc_norm": 0.7456647398843931,
"acc_norm_stderr": 0.0332055644308557
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.5882352941176471,
"acc_stderr": 0.048971049527263666,
"acc_norm": 0.5882352941176471,
"acc_norm_stderr": 0.048971049527263666
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.83,
"acc_stderr": 0.03775251680686371,
"acc_norm": 0.83,
"acc_norm_stderr": 0.03775251680686371
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7957446808510639,
"acc_stderr": 0.026355158413349417,
"acc_norm": 0.7957446808510639,
"acc_norm_stderr": 0.026355158413349417
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5701754385964912,
"acc_stderr": 0.04657047260594964,
"acc_norm": 0.5701754385964912,
"acc_norm_stderr": 0.04657047260594964
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.8,
"acc_stderr": 0.0333333333333333,
"acc_norm": 0.8,
"acc_norm_stderr": 0.0333333333333333
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.716931216931217,
"acc_stderr": 0.023201392938194978,
"acc_norm": 0.716931216931217,
"acc_norm_stderr": 0.023201392938194978
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5873015873015873,
"acc_stderr": 0.04403438954768177,
"acc_norm": 0.5873015873015873,
"acc_norm_stderr": 0.04403438954768177
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.9,
"acc_stderr": 0.01706640371965726,
"acc_norm": 0.9,
"acc_norm_stderr": 0.01706640371965726
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.6798029556650246,
"acc_stderr": 0.032826493853041504,
"acc_norm": 0.6798029556650246,
"acc_norm_stderr": 0.032826493853041504
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.79,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.79,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8606060606060606,
"acc_stderr": 0.027045948825865387,
"acc_norm": 0.8606060606060606,
"acc_norm_stderr": 0.027045948825865387
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.9242424242424242,
"acc_stderr": 0.018852670234993093,
"acc_norm": 0.9242424242424242,
"acc_norm_stderr": 0.018852670234993093
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9844559585492227,
"acc_stderr": 0.008927492715084334,
"acc_norm": 0.9844559585492227,
"acc_norm_stderr": 0.008927492715084334
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.8205128205128205,
"acc_stderr": 0.019457390787681793,
"acc_norm": 0.8205128205128205,
"acc_norm_stderr": 0.019457390787681793
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.5259259259259259,
"acc_stderr": 0.03044452852881074,
"acc_norm": 0.5259259259259259,
"acc_norm_stderr": 0.03044452852881074
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.865546218487395,
"acc_stderr": 0.022159373072744442,
"acc_norm": 0.865546218487395,
"acc_norm_stderr": 0.022159373072744442
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.5298013245033113,
"acc_stderr": 0.040752249922169796,
"acc_norm": 0.5298013245033113,
"acc_norm_stderr": 0.040752249922169796
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.9247706422018349,
"acc_stderr": 0.011308662537571786,
"acc_norm": 0.9247706422018349,
"acc_norm_stderr": 0.011308662537571786
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6990740740740741,
"acc_stderr": 0.031280390843298825,
"acc_norm": 0.6990740740740741,
"acc_norm_stderr": 0.031280390843298825
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9117647058823529,
"acc_stderr": 0.01990739979131694,
"acc_norm": 0.9117647058823529,
"acc_norm_stderr": 0.01990739979131694
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8987341772151899,
"acc_stderr": 0.019637720526065508,
"acc_norm": 0.8987341772151899,
"acc_norm_stderr": 0.019637720526065508
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.8071748878923767,
"acc_stderr": 0.02647824096048937,
"acc_norm": 0.8071748878923767,
"acc_norm_stderr": 0.02647824096048937
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8625954198473282,
"acc_stderr": 0.030194823996804475,
"acc_norm": 0.8625954198473282,
"acc_norm_stderr": 0.030194823996804475
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8512396694214877,
"acc_stderr": 0.03248470083807194,
"acc_norm": 0.8512396694214877,
"acc_norm_stderr": 0.03248470083807194
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.036028141763926456,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.036028141763926456
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.8711656441717791,
"acc_stderr": 0.026321383198783656,
"acc_norm": 0.8711656441717791,
"acc_norm_stderr": 0.026321383198783656
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5803571428571429,
"acc_stderr": 0.04684099321077106,
"acc_norm": 0.5803571428571429,
"acc_norm_stderr": 0.04684099321077106
},
"harness|hendrycksTest-management|5": {
"acc": 0.8737864077669902,
"acc_stderr": 0.03288180278808628,
"acc_norm": 0.8737864077669902,
"acc_norm_stderr": 0.03288180278808628
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.9401709401709402,
"acc_stderr": 0.01553751426325388,
"acc_norm": 0.9401709401709402,
"acc_norm_stderr": 0.01553751426325388
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.84,
"acc_stderr": 0.0368452949177471,
"acc_norm": 0.84,
"acc_norm_stderr": 0.0368452949177471
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.913154533844189,
"acc_stderr": 0.01007029837774778,
"acc_norm": 0.913154533844189,
"acc_norm_stderr": 0.01007029837774778
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.8410404624277457,
"acc_stderr": 0.019685307033571946,
"acc_norm": 0.8410404624277457,
"acc_norm_stderr": 0.019685307033571946
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.729608938547486,
"acc_stderr": 0.01485499393801009,
"acc_norm": 0.729608938547486,
"acc_norm_stderr": 0.01485499393801009
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.021339479988816027,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.021339479988816027
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.8263665594855305,
"acc_stderr": 0.021514051585970403,
"acc_norm": 0.8263665594855305,
"acc_norm_stderr": 0.021514051585970403
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.8641975308641975,
"acc_stderr": 0.0190615881815054,
"acc_norm": 0.8641975308641975,
"acc_norm_stderr": 0.0190615881815054
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.6170212765957447,
"acc_stderr": 0.028999080904806185,
"acc_norm": 0.6170212765957447,
"acc_norm_stderr": 0.028999080904806185
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.6082138200782269,
"acc_stderr": 0.01246756441814511,
"acc_norm": 0.6082138200782269,
"acc_norm_stderr": 0.01246756441814511
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.8198529411764706,
"acc_stderr": 0.023345163616544835,
"acc_norm": 0.8198529411764706,
"acc_norm_stderr": 0.023345163616544835
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.8071895424836601,
"acc_stderr": 0.015959983971206744,
"acc_norm": 0.8071895424836601,
"acc_norm_stderr": 0.015959983971206744
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7636363636363637,
"acc_stderr": 0.040693063197213754,
"acc_norm": 0.7636363636363637,
"acc_norm_stderr": 0.040693063197213754
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.8204081632653061,
"acc_stderr": 0.024573293589585637,
"acc_norm": 0.8204081632653061,
"acc_norm_stderr": 0.024573293589585637
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8905472636815921,
"acc_stderr": 0.022076326101824667,
"acc_norm": 0.8905472636815921,
"acc_norm_stderr": 0.022076326101824667
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.96,
"acc_stderr": 0.01969463855669321,
"acc_norm": 0.96,
"acc_norm_stderr": 0.01969463855669321
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5903614457831325,
"acc_stderr": 0.0382840111507902,
"acc_norm": 0.5903614457831325,
"acc_norm_stderr": 0.0382840111507902
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8888888888888888,
"acc_stderr": 0.02410338420207286,
"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.02410338420207286
},
"harness|truthfulqa:mc|0": {
"mc1": 0.44430844553243576,
"mc1_stderr": 0.017394586250743173,
"mc2": 0.6389810525358077,
"mc2_stderr": 0.015760859004207876
},
"harness|winogrande|5": {
"acc": 0.7908445146014207,
"acc_stderr": 0.011430450045881573
},
"harness|gsm8k|5": {
"acc": 0.20394238059135708,
"acc_stderr": 0.011098602284899176
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## Glossary [optional]
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[More Information Needed] |
maghwa/OpenHermes-2-AR-10K-42-860k-870k | ---
dataset_info:
features:
- name: id
dtype: 'null'
- name: title
dtype: 'null'
- name: views
dtype: float64
- name: idx
dtype: 'null'
- name: topic
dtype: 'null'
- name: model_name
dtype: 'null'
- name: conversations
dtype: string
- name: custom_instruction
dtype: 'null'
- name: avatarUrl
dtype: 'null'
- name: hash
dtype: 'null'
- name: language
dtype: 'null'
- name: skip_prompt_formatting
dtype: 'null'
- name: category
dtype: 'null'
- name: source
dtype: string
- name: system_prompt
dtype: 'null'
- name: model
dtype: 'null'
splits:
- name: train
num_bytes: 28755055
num_examples: 10001
download_size: 11095256
dataset_size: 28755055
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
tyzhu/find_marker_both_sent_train_400_eval_40_random_permute_rerun_4 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: title
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 5100580.566046733
num_examples: 3754
- name: validation
num_bytes: 220570
num_examples: 200
download_size: 1069446
dataset_size: 5321150.566046733
---
# Dataset Card for "find_marker_both_sent_train_400_eval_40_random_permute_rerun_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
arbml/alpagasus_cleaned_ar | ---
dataset_info:
features:
- name: instruction_en
dtype: string
- name: output_en
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
- name: index
dtype: int64
splits:
- name: train
num_bytes: 9824184
num_examples: 9229
download_size: 5541315
dataset_size: 9824184
---
# Dataset Card for "alpagasus_cleaned_ar"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_HWERI__pythia-1.4b-deduped-sharegpt | ---
pretty_name: Evaluation run of HWERI/pythia-1.4b-deduped-sharegpt
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [HWERI/pythia-1.4b-deduped-sharegpt](https://huggingface.co/HWERI/pythia-1.4b-deduped-sharegpt)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_HWERI__pythia-1.4b-deduped-sharegpt\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-16T20:15:27.580598](https://huggingface.co/datasets/open-llm-leaderboard/details_HWERI__pythia-1.4b-deduped-sharegpt/blob/main/results_2023-09-16T20-15-27.580598.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0010486577181208054,\n\
\ \"em_stderr\": 0.00033145814652192217,\n \"f1\": 0.04875104865771823,\n\
\ \"f1_stderr\": 0.0012458540332815637,\n \"acc\": 0.2804129195481258,\n\
\ \"acc_stderr\": 0.008239894933698364\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.00033145814652192217,\n\
\ \"f1\": 0.04875104865771823,\n \"f1_stderr\": 0.0012458540332815637\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.008339651250947688,\n \
\ \"acc_stderr\": 0.002504942226860534\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5524861878453039,\n \"acc_stderr\": 0.013974847640536194\n\
\ }\n}\n```"
repo_url: https://huggingface.co/HWERI/pythia-1.4b-deduped-sharegpt
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|arc:challenge|25_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_16T20_15_27.580598
path:
- '**/details_harness|drop|3_2023-09-16T20-15-27.580598.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-16T20-15-27.580598.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_16T20_15_27.580598
path:
- '**/details_harness|gsm8k|5_2023-09-16T20-15-27.580598.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-16T20-15-27.580598.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hellaswag|10_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T18:24:42.073512.parquet'
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- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T18:24:42.073512.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T18:24:42.073512.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T18:24:42.073512.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T18:24:42.073512.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T18:24:42.073512.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_16T20_15_27.580598
path:
- '**/details_harness|winogrande|5_2023-09-16T20-15-27.580598.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-16T20-15-27.580598.parquet'
- config_name: results
data_files:
- split: 2023_08_17T18_24_42.073512
path:
- results_2023-08-17T18:24:42.073512.parquet
- split: 2023_09_16T20_15_27.580598
path:
- results_2023-09-16T20-15-27.580598.parquet
- split: latest
path:
- results_2023-09-16T20-15-27.580598.parquet
---
# Dataset Card for Evaluation run of HWERI/pythia-1.4b-deduped-sharegpt
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/HWERI/pythia-1.4b-deduped-sharegpt
- **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 [HWERI/pythia-1.4b-deduped-sharegpt](https://huggingface.co/HWERI/pythia-1.4b-deduped-sharegpt) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_HWERI__pythia-1.4b-deduped-sharegpt",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-16T20:15:27.580598](https://huggingface.co/datasets/open-llm-leaderboard/details_HWERI__pythia-1.4b-deduped-sharegpt/blob/main/results_2023-09-16T20-15-27.580598.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0010486577181208054,
"em_stderr": 0.00033145814652192217,
"f1": 0.04875104865771823,
"f1_stderr": 0.0012458540332815637,
"acc": 0.2804129195481258,
"acc_stderr": 0.008239894933698364
},
"harness|drop|3": {
"em": 0.0010486577181208054,
"em_stderr": 0.00033145814652192217,
"f1": 0.04875104865771823,
"f1_stderr": 0.0012458540332815637
},
"harness|gsm8k|5": {
"acc": 0.008339651250947688,
"acc_stderr": 0.002504942226860534
},
"harness|winogrande|5": {
"acc": 0.5524861878453039,
"acc_stderr": 0.013974847640536194
}
}
```
### 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] |
malaysia-ai/DocLayNet-Full-Processed | ---
license: apache-2.0
---
|
arthurmluz/wikilingua_data-cstnews_results | ---
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: summary
dtype: string
- name: gen_summary
dtype: string
- name: rouge
struct:
- name: rouge1
dtype: float64
- name: rouge2
dtype: float64
- name: rougeL
dtype: float64
- name: rougeLsum
dtype: float64
- name: bert
struct:
- name: f1
sequence: float64
- name: hashcode
dtype: string
- name: precision
sequence: float64
- name: recall
sequence: float64
- name: moverScore
dtype: float64
splits:
- name: validation
num_bytes: 28819014
num_examples: 8165
download_size: 17413682
dataset_size: 28819014
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "wikilingua_data-cstnews_results"
rouge={'rouge1': 0.19556460453535948, 'rouge2': 0.05415685751189013, 'rougeL': 0.12269113071402012, 'rougeLsum': 0.12269113071402012}
Bert={'precision': 0.6433123989588486, 'recall': 0.7274074976785885, 'f1': 0.6818849594112707}
moverscore: 0.5582209741427033 |
rachel2999/lang_ident | ---
license: cc-by-4.0
---
|
Jing24/generate_sub_9 | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
splits:
- name: train
num_bytes: 7284759
num_examples: 7867
download_size: 1322574
dataset_size: 7284759
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "generate_sub_9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ovior/twitter_dataset_1713107629 | ---
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: 2536744
num_examples: 7698
download_size: 1432158
dataset_size: 2536744
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
anan-2024/twitter_dataset_1713159239 | ---
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: 22066
num_examples: 51
download_size: 11606
dataset_size: 22066
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
joey234/mmlu-moral_disputes-neg-prepend-fix | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
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: ori_prompt
dtype: string
splits:
- name: dev
num_bytes: 6648
num_examples: 5
- name: test
num_bytes: 1035772
num_examples: 346
download_size: 12931
dataset_size: 1042420
---
# Dataset Card for "mmlu-moral_disputes-neg-prepend-fix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mlnsio/audio1 | ---
license: apache-2.0
dataset_info:
features:
- name: file_name
dtype: string
- name: genre
dtype: string
splits:
- name: train
num_bytes: 2235
num_examples: 50
download_size: 1997
dataset_size: 2235
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
WillHeld/demszky_pairs | ---
dataset_info:
features:
- name: phrase_ID
dtype: int64
- name: feature
dtype: string
- name: sentence
dtype: string
- name: feature_present
dtype: int64
splits:
- name: train
num_bytes: 23146
num_examples: 266
download_size: 8919
dataset_size: 23146
---
This minimal pair data comes from "Learning to Recognize Dialect Features" by Dorottya Demszky, Devyani Sharma, Jonathan H. Clark, Vinodkumar Prabhakaran, and Jacob Eisenstein. Please cite the original work if
you make use of this data:
```
@inproceedings{demszky2021learning,
title={Learning to Recognize Dialect Features},
author={Demszky, Dorottya and Sharma, Devyani and Clark, Jonathan H and Prabhakaran, Vinodkumar and Eisenstein, Jacob},
booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
pages={2315--2338}, year={2021}
}
``` |
tessiw/german_OpenOrca14 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 420331784
num_examples: 250000
download_size: 241284834
dataset_size: 420331784
---
# Dataset Card for "german_OpenOrca14"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-eval-project-squad_v2-4a3c5c8d-1305249893 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: nbroad/rob-base-gc1
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: nbroad/rob-base-gc1
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nbroad](https://huggingface.co/nbroad) for evaluating this model. |
botp/yentinglin-zh_TW_c4 | ---
dataset_info:
features:
- name: text
dtype: string
- name: timestamp
dtype: string
- name: url
dtype: string
- name: zh_id
dtype: int64
splits:
- name: train
num_bytes: 15132113806.438198
num_examples: 5179546
- name: validation
num_bytes: 14820238.368907347
num_examples: 5099
download_size: 5569491261
dataset_size: 15146934044.807106
license: odc-by
task_categories:
- text-generation
language:
- zh
pretty_name: Traditional Chinese C4
size_categories:
- 1M<n<10M
duplicated_from: yentinglin/zh_TW_c4
---
# Language Models for Taiwanese Culture
<p align="center">
✍️ <a href="https://huggingface.co/spaces/yentinglin/Taiwan-LLaMa2" target="_blank">Online Demo</a>
•
🤗 <a href="https://huggingface.co/yentinglin" target="_blank">HF Repo</a> • 🐦 <a href="https://twitter.com/yentinglin56" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/pdf/2305.13711.pdf" target="_blank">[Paper Coming Soon]</a>
• 👨️ <a href="https://yentingl.com/" target="_blank">Yen-Ting Lin</a>
<br/><br/>
<img src="https://www.csie.ntu.edu.tw/~miulab/taiwan-llama/logo-v2.png" width="100"> <br/>
<a href="https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE">
<img src="https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg"></a>
<a href="https://github.com/tatsu-lab/stanford_alpaca/blob/main/DATA_LICENSE">
<img src="https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg"></a>
<br/>
</p>
## Overview
Taiwan-LLaMa is a full parameter fine-tuned model based on LLaMa 2 for Traditional Mandarin applications.
**Taiwan-LLaMa v1.0** pretrained on over 5 billion tokens and instruction-tuned on over 490k conversations both in traditional mandarin.
## Demo
A live demonstration of the model can be accessed at [Hugging Face Spaces](https://huggingface.co/spaces/yentinglin/Taiwan-LLaMa2).
## Key Features
1. **Traditional Mandarin Support**: The model is fine-tuned to understand and generate text in Traditional Mandarin, making it suitable for Taiwanese culture and related applications.
2. **Instruction-Tuned**: Further fine-tuned on conversational data to offer context-aware and instruction-following responses.
3. **Performance on Vicuna Benchmark**: Taiwan-LLaMa's relative performance on Vicuna Benchmark is measured against models like GPT-4 and ChatGPT. It's particularly optimized for Taiwanese culture.
4. **Flexible Customization**: Advanced options for controlling the model's behavior like system prompt, temperature, top-p, and top-k are available in the demo.
## Work in progress
- [ ] **Improved pretraining**: A refined pretraining process (e.g. more data from Taiwan, training strategies) is under development, aiming to enhance model performance for better Taiwanese culture.
- [ ] **Extend max length**: Utilizing the Rope mechanism as described in [the paper](https://arxiv.org/abs/2104.09864), the model's length will be extended from 4k to 8k.
## Taiwanese Culture Examples
| Question | ChatGPT | **Taiwan LLaMa v1.0** | **Taiwan LLaMa v0.0 (w/o pretraining)** | Llama2_Traditional_Chinese_13b_Chat |
|:-|:-|:-|:-|:-|
| 以惡地景觀著名的是高雄哪一個景點? | 以惡地地景著名的是高雄市的旗津。 | 高雄以擁有惡地景觀著名的景點是田寮月世界。✅ | 以惡地景觀著名的是高雄市的旗津島。旗津島是一個小島，... | 高雄市內有多個以惡地景觀著名的景點，其中以下面幾個最為著名： 1. 高雄捷運紅線沿途景點：高雄市政府為了推廣高雄 |
| 唐鳳是誰？ | 唐鳳（Audrey Tang）是台灣的政治家、社會活動家和軟體工程師。她於2016年擔任台灣行政院數位政務委員，是台灣政府歷史上首位沒有政黨背景的政務官。唐鳳在軟體工程領域有豐富的經驗。 ✅ | 唐鳳是政治評論家、資訊社會學研究者，也是台灣第一位擔任政府數位政委的人。 ✅ | 唐鳳（Tang Feng）是一位著名的中國演員和歌手。他出生於1974年12月17日，出生地點在中國北京。唐鳳在1995年開始他的演藝生涯，並在同年發行了他的首張專輯《我不孤單》。此後，他發行了許多受歡迎的歌曲，並在多部電視劇和電影中擔任主角。唐鳳因其精湛的演技和迷人的歌聲而被譽為“亞洲魅力男王”。他的音樂獲得了多個獎項，包括“華表最佳新男演唱獎”、“華表最佳歌曲獎”和“華表最佳舞蹈獎”等。 | 唐鳳是唐朝時期的一位著名文學家，他的代表作品包括《陰騭》、《狂凡子》等。他的文學風格具有強烈的個性和創意，被譽為「唐代文學之父」。 |
| NTU 在哪? | NTU位於新加坡，具體地址是50 Nanyang Avenue, Singapore 639798。 | NTU 是國立臺灣大學的縮寫。它是臺灣的一所頂尖研究型大學，位於臺北市。 ✅| NTU 是台灣的一所大學，位於台北市。✅ | NTU 的總部位於台灣新北市板橋區，佔地面積約為1,000公頃。|
| 甚麼是22K? | "22K" 可能指的是金的純度，尤其是在珠寶和金飾方面。 | "22K"是指台灣的大學畢業生的起薪，大約是新台幣22,000元。這個詞被用來形容大學畢業生的起薪偏低，並且在就業市場上對於大學畢業生的需求不足。✅| 22K是指溫度尺度上的22,000K（千度）。1K代表一度，即溫度差距，是攝氏溫標（攝氏度）的一百萬分之一。因此，22K等於-253.15°C（攝氏溫標）或-423.67°F（華氏溫標）。 | 22K是一種簡寫，即22,000。它是一個數字，表示22,000個單位。|
| 肉圓要不要加香菜？ | 這是個個人口味的問題，有些人喜歡在肉圓中加入香菜，因為香菜的清香可以增添風味；而有些人則不喜歡香菜的味道，覺得它會蓋過其他食材的味道。所以要不要加香菜，可以根據個人喜好來決定。 | 這是一個個人喜好的問題，有些人喜歡在肉圓裡加香菜，有些人不喜歡。**在台灣**，肉圓加香菜是一種常見的做法，這是一種在肉圓上加香菜的做法。然而，在其他國這是一個文化差異的問題。😊 | 肉圓加香菜可以增添一些芳香和美味的口感，如果你喜歡香菜的味道，可以試試加入肉圓中。香菜通常在製作過程中炒香，可以增添一些芳香和美味的味道。如果你想要加入香菜，可以在製作過程中炒香一些香菜，然後將其混入肉圓中。肉圓加香菜可以增添一些芳香和美味的口感，如果你喜歡香菜的味道，可以試試加入肉圓中。| 加香菜的肉圓味道更好，但是不加香菜的肉圓也很好吃。如果你喜歡香菜的味道，那麼加上香菜就好了。如果你不喜歡香菜，那麼不加也不會影響肉 |
## Model
We provide a number of model checkpoints that we trained. Please find them on Hugging Face [here](https://huggingface.co/models?search=taiwan-llama). Here are some quick links to the checkpoints that are finetuned from LLaMa 2:
| **Model** | **13B** |
|--------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------|
| **Taiwan-LLaMa v1.0** (_better for Taiwanese Culture_) | 🤗 <a href="https://huggingface.co/yentinglin/Taiwan-LLaMa-v1.0" target="_blank">yentinglin/Taiwan-LLaMa-v1.0</a> |
| Taiwan-LLaMa v0.9 (partial instruction set) | 🤗 <a href="https://huggingface.co/yentinglin/Taiwan-LLaMa-v0.9" target="_blank">yentinglin/Taiwan-LLaMa-v0.9</a> |
| Taiwan-LLaMa v0.0 (no Traditional Mandarin pretraining) | 🤗 <a href="https://huggingface.co/yentinglin/Taiwan-LLaMa-v0.0" target="_blank">yentinglin/Taiwan-LLaMa-v0.0</a> |
## Data
Here are some quick links to the datasets that we used to train the models:
| **Dataset** | **Link** |
|---------------------------------|-------------------------------------------------------------------------------------------------------------------------------|
| **Instruction-tuning** | 🤗 <a href="https://huggingface.co/datasets/yentinglin/traditional_mandarin_instructions" target="_blank">yentinglin/traditional_mandarin_instructions</a> |
| Traditional Mandarin Pretraining | 🤗 <a href="https://huggingface.co/datasets/yentinglin/zh_TW_c4" target="_blank">yentinglin/zh_TW_c4</a> |
## Architecture
Taiwan-LLaMa is based on LLaMa 2, leveraging transformer architecture, <a href="https://github.com/Dao-AILab/flash-attention" target="_blank">flash attention 2</a>, and bfloat16.
It includes:
* Pretraining Phase: Pretrained on a vast corpus of over 5 billion tokens, extracted from common crawl in Traditional Mandarin.
* Fine-tuning Phase: Further instruction-tuned on over 490k multi-turn conversational data to enable more instruction-following and context-aware responses.
## Generic Capabilities on Vicuna Benchmark
The data is translated into traditional mandarin for evaluating the general capability.
<img src="./images/zhtw_vicuna_bench_chatgptbaseline.png" width="700">
The scores are calculated with ChatGPT as the baseline, represented as 100%. The other values show the relative performance of different models compared to ChatGPT.
| Language Model | Relative Score (%) |
|-------------------------------------|--------------------|
| GPT-4 | 102.59% |
| ChatGPT | 100.00% |
| **Taiwan-LLaMa v1.0** | 76.76% |
| Claude-Instant-1.2 | 74.04% |
| Llama2_Traditional_Chinese_13b_Chat | 56.21% |
## How to deploy the model on my own machine?
We recommend hosting models with [🤗 Text Generation Inference](https://github.com/huggingface/text-generation-inference). Please see their [license](https://github.com/huggingface/text-generation-inference/blob/main/LICENSE) for details on usage and limitations.
```bash
bash run_text_generation_inference.sh "yentinglin/Taiwan-LLaMa" NUM_GPUS DIR_TO_SAVE_MODEL PORT MAX_INPUT_LEN MODEL_MAX_LEN
```
Prompt format follows vicuna-v1.1 template:
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {user} ASSISTANT:
```
## Setup development environment
```bash
conda create -n taiwan-llama python=3.10 -y
conda activate taiwan-llama
pip install -r requirements.txt
```
## Citations
If you use our code, data, or models in your research, please cite this repository. You can use the following BibTeX entry:
```bibtex
@inproceedings{lin-chen-2023-llm,
title = "{LLM}-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models",
author = "Lin, Yen-Ting and Chen, Yun-Nung",
booktitle = "Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.nlp4convai-1.5",
pages = "47--58"
}
@misc{taiwanllama,
author={Lin, Yen-Ting and Chen, Yun-Nung},
title={Taiwanese-Aligned Language Models based on Meta-Llama2},
year={2023},
url={https://github.com/adamlin120/Taiwan-LLaMa},
note={Code and models available at https://github.com/adamlin120/Taiwan-LLaMa},
}
```
## Collaborate With Us
If you are interested in contributing to the development of Traditional Mandarin language models, exploring new applications, or leveraging Taiwan-LLaMa for your specific needs, please don't hesitate to contact us. We welcome collaborations from academia, industry, and individual contributors.
## License
The code in this project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.
The models included in this project are licensed under the LLAMA 2 Community License. See the [LLAMA2 License](https://github.com/facebookresearch/llama/blob/main/LICENSE) for full details.
## OpenAI Data Acknowledgment
The data included in this project were generated using OpenAI's models and are subject to OpenAI's Terms of Use. Please review [OpenAI's Terms of Use](https://openai.com/policies/terms-of-use) for details on usage and limitations.
## Acknowledgements
We thank [Meta LLaMA team](https://github.com/facebookresearch/llama) and [Vicuna team](https://github.com/lm-sys/FastChat) for their open-source efforts in democratizing large language models.
|
mHossain/final_train_v2_210000 | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: input_text
dtype: string
- name: target_text
dtype: string
- name: prefix
dtype: string
splits:
- name: train
num_bytes: 9130050.9
num_examples: 27000
- name: test
num_bytes: 1014450.1
num_examples: 3000
download_size: 4448876
dataset_size: 10144501.0
---
# Dataset Card for "final_train_v2_210000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/data-standardized_cluster_13_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: 9016228
num_examples: 8678
download_size: 3788318
dataset_size: 9016228
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_13_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pnadel/iliad_odyssey_aligned | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: sentid
dtype: string
- name: cit
dtype: string
- name: Eng
dtype: string
- name: Gk
dtype: string
- name: Lems
dtype: string
splits:
- name: train
num_bytes: 5475867.200602134
num_examples: 12223
- name: test
num_bytes: 1369078.7993978662
num_examples: 3056
download_size: 3953094
dataset_size: 6844946.0
---
# Dataset Card for "iliad_odyssey_aligned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
INSAIT-Institute/GSM8k-bgeval | ---
language:
- bg
license: mit
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: test
num_bytes: 1183099
num_examples: 1319
download_size: 568396
dataset_size: 1183099
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
|
AdamCodd/emotion-balanced | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: emotion
pretty_name: Emotion
tags:
- emotion-classification
dataset_info:
- config_name: split
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': sadness
'1': joy
'2': love
'3': anger
'4': fear
'5': surprise
splits:
- name: train
num_bytes: 1968209
num_examples: 16000
- name: validation
num_bytes: 247888
num_examples: 2000
- name: test
num_bytes: 244379
num_examples: 2000
download_size: 740883
dataset_size: 2173481
- config_name: unsplit
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': sadness
'1': joy
'2': love
'3': anger
'4': fear
'5': surprise
splits:
- name: train
num_bytes: 10792185
num_examples: 89754
download_size: 10792185
dataset_size: 10792185
train-eval-index:
- config: default
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for "emotion"
## 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/AdamCodd/emotion-dataset](https://github.com/AdamCodd/emotion-dataset)
- **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:** 10.54 MB
### Dataset Summary
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. 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
An example looks as follows.
```
{
"text": "im feeling quite sad and sorry for myself but ill snap out of it soon",
"label": 0
}
```
### Data Fields
The data fields are:
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5).
### Data Splits
The dataset has 2 configurations:
- split: with a total of 20_000 examples split into train, validation and test
- unsplit: with a total of 89_754 examples in a single train split
| name | train | validation | test |
|---------|-------:|-----------:|-----:|
| split | 16000 | 2000 | 2000 |
| unsplit | 89754 | n/a | n/a |
## Dataset Creation
### Curation Rationale
This dataset is designed for training machine learning models to perform emotion analysis. It contains text samples from Twitter labeled with six different emotions: sadness, joy, love, anger, fear, and surprise. The dataset is balanced, meaning that it has an equal number of samples for each label.
This dataset is originally sourced from [dair-ai's emotion dataset](https://huggingface.co/datasets/dair-ai/emotion), but the initial dataset was unbalanced and had some duplicate samples. Thus, this dataset has been deduplicated and balanced to ensure an equal number of samples for each emotion label.
### 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
The dataset should be used for educational and research purposes only.
### Citation Information
If you use this dataset, please cite:
```
@inproceedings{saravia-etal-2018-carer,
title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
author = "Saravia, Elvis and
Liu, Hsien-Chi Toby and
Huang, Yen-Hao and
Wu, Junlin and
Chen, Yi-Shin",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D18-1404",
doi = "10.18653/v1/D18-1404",
pages = "3687--3697",
abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
}
```
If you want to support me, you can [here](https://ko-fi.com/adamcodd). |
rwq-elo/rwq-battle-records | ---
language:
- en
license: cc-by-nc-4.0
---
# RWQ battle records dataset
The dataset stores the battle records of 24 popular LLMs conduct Elo pairwise battles on [RWQ questions](https://huggingface.co/datasets/rwq-elo/rwq-questions) and use GPT-4 as judger to determine the winner on each round of QA.
## Columns
| Column Name | Data Type | Description |
| -------------- | --------- | ------------------------------------------------------------------------------------------------------------ |
| question | string | The question to ask LLM. |
| model | string | The id/name of LLM. |
| model_a | string | The id/name of model 1 of pairwise LLM to battle facing another on the same question. |
| model_b | string | The id/name of model 2 of pairwise LLM to battle facing another on the same question. |
| winner | string | The winner model valued as one of `model_a, model_b, tie or tie(all bad)` as outcome of one pairwise battle. |
| judger | string | The gpt name with version, such as gpt-4-turbo. |
| tstamp | string | The time battle happens, format as `2023-11-23 02:56:34.433226`. |
| answer_a | string | The answer of model_a. |
| answer_b | string | The answer of model_b. |
| gpt_4_response | string | The reponse text of gpt-4 as judger to evaluate and score the better LLM. |
| gpt_4_score | string | The scores of model_a and model_b with json text, e.g., `{'model_a': '0', 'model_b': '1'}`. |
| is_valid | boolean | The row is valid or not. Set to false, when gpt-4 reject the eval because of policy. |
| elo_rating | float | The elo rating score of LLM. |
## Citation
TODO |
kannada_news | ---
annotations_creators:
- other
language_creators:
- other
language:
- kn
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: KannadaNews Dataset
dataset_info:
features:
- name: headline
dtype: string
- name: label
dtype:
class_label:
names:
'0': sports
'1': tech
'2': entertainment
splits:
- name: train
num_bytes: 969216
num_examples: 5167
- name: validation
num_bytes: 236817
num_examples: 1293
download_size: 0
dataset_size: 1206033
---
# Dataset Card for kannada_news dataset
## 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:** [Kaggle link](https://www.kaggle.com/disisbig/kannada-news-dataset) for kannada news headlines dataset
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** More information about the dataset and the models can be found [here](https://github.com/goru001/nlp-for-kannada)
### Dataset Summary
The Kannada news dataset contains only the headlines of news article in three categories:
Entertainment, Tech, and Sports.
The data set contains around 6300 news article headlines which are collected from Kannada news websites.
The data set has been cleaned and contains train and test set using which can be used to benchmark topic classification models in Kannada.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Kannada (kn)
## Dataset Structure
### Data Instances
The data has two files. A train.csv and valid.csv. An example row of the dataset is as below:
```
{
'headline': 'ಫಿಫಾ ವಿಶ್ವಕಪ್ ಫೈನಲ್: ಅತಿರೇಕಕ್ಕೇರಿದ ಸಂಭ್ರಮಾಚರಣೆ; ಅಭಿಮಾನಿಗಳ ಹುಚ್ಚು ವರ್ತನೆಗೆ ವ್ಯಾಪಕ ಖಂಡನೆ',
'label':'sports'
}
```
NOTE: The data has very few examples on the technology (class label: 'tech') topic. [More Information Needed]
### Data Fields
Data has two fields:
- headline: text headline in kannada (string)
- label : corresponding class label which the headlines pertains to in english (string)
### Data Splits
The dataset is divided into two splits. All the headlines are scraped from news websites on the internet.
| | train | validation |
|-----------------|--------:|-----------:|
| Input Sentences | 5167 | 1293 |
## 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
There are starkingly less amount of data for South Indian languages, especially Kannada, available in digital format which can be used for NLP purposes.
Though having roughly 38 million native speakers, it is a little under-represented language and will benefit from active contribution from the community.
This dataset, however, can just help people get exposed to Kannada and help proceed further active participation for enabling continuous progress and development.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[Gaurav Arora] (https://github.com/goru001/nlp-for-kannada). Has also got some starter models an embeddings to help get started.
### Licensing Information
cc-by-sa-4.0
### Citation Information
https://www.kaggle.com/disisbig/kannada-news-dataset
### Contributions
Thanks to [@vrindaprabhu](https://github.com/vrindaprabhu) for adding this dataset. |
joey234/mmlu-college_biology-neg | ---
dataset_info:
features:
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: question
dtype: string
splits:
- name: test
num_bytes: 45877
num_examples: 144
download_size: 30626
dataset_size: 45877
---
# Dataset Card for "mmlu-college_biology-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BangumiBase/plasticmemories | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Plastic Memories
This is the image base of bangumi Plastic Memories, we detected 20 characters, 2364 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 684 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 53 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 40 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 56 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 57 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 59 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 23 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 13 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 49 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 16 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 5 | [Download](10/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 11 | 169 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 50 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 33 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 139 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 14 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 66 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 72 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 609 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 157 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
PJMixers/Math-Multiturn-1K-ShareGPT | ---
language:
- en
tags:
- math
size_categories:
- 1K<n<10K
--- |
setimes | ---
pretty_name: SETimes – A Parallel Corpus of English and South-East European Languages
annotations_creators:
- found
language_creators:
- found
language:
- bg
- bs
- el
- en
- hr
- mk
- ro
- sq
- sr
- tr
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
dataset_info:
- config_name: bg-bs
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- bg
- bs
splits:
- name: train
num_bytes: 53816914
num_examples: 136009
download_size: 15406039
dataset_size: 53816914
- config_name: bg-el
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- bg
- el
splits:
- name: train
num_bytes: 115127431
num_examples: 212437
download_size: 28338218
dataset_size: 115127431
- config_name: bs-el
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- bs
- el
splits:
- name: train
num_bytes: 57102373
num_examples: 137602
download_size: 16418250
dataset_size: 57102373
- config_name: bg-en
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- bg
- en
splits:
- name: train
num_bytes: 84421414
num_examples: 213160
download_size: 23509552
dataset_size: 84421414
- config_name: bs-en
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- bs
- en
splits:
- name: train
num_bytes: 38167846
num_examples: 138387
download_size: 13477699
dataset_size: 38167846
- config_name: el-en
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- el
- en
splits:
- name: train
num_bytes: 95011154
num_examples: 227168
download_size: 26637317
dataset_size: 95011154
- config_name: bg-hr
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- bg
- hr
splits:
- name: train
num_bytes: 81774321
num_examples: 203465
download_size: 23165617
dataset_size: 81774321
- config_name: bs-hr
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- bs
- hr
splits:
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languages:
- sr
- tr
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dataset_size: 63371906
---
# Dataset Card for SETimes – A Parallel Corpus of English and South-East European Languages
## 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://nlp.ffzg.hr/resources/corpora/setimes/
- **Repository:** None
- **Paper:** None
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Here are some examples of questions and facts:
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
lmms-lab/flickr30k | ---
dataset_info:
features:
- name: image
dtype: image
- name: caption
sequence: string
- name: sentids
sequence: string
- name: img_id
dtype: string
- name: filename
dtype: string
splits:
- name: test
num_bytes: 4190829605.876
num_examples: 31783
download_size: 4409506758
dataset_size: 4190829605.876
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
<p align="center" width="100%">
<img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%">
</p>
# Large-scale Multi-modality Models Evaluation Suite
> Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval`
🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab)
# This Dataset
This is a formatted version of [flickr30k](https://shannon.cs.illinois.edu/DenotationGraph/). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.
```
@article{young-etal-2014-image,
title = "From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions",
author = "Young, Peter and
Lai, Alice and
Hodosh, Micah and
Hockenmaier, Julia",
editor = "Lin, Dekang and
Collins, Michael and
Lee, Lillian",
journal = "Transactions of the Association for Computational Linguistics",
volume = "2",
year = "2014",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q14-1006",
doi = "10.1162/tacl_a_00166",
pages = "67--78",
abstract = "We propose to use the visual denotations of linguistic expressions (i.e. the set of images they describe) to define novel denotational similarity metrics, which we show to be at least as beneficial as distributional similarities for two tasks that require semantic inference. To compute these denotational similarities, we construct a denotation graph, i.e. a subsumption hierarchy over constituents and their denotations, based on a large corpus of 30K images and 150K descriptive captions.",
}
``` |
irds/mr-tydi_sw_train | ---
pretty_name: '`mr-tydi/sw/train`'
viewer: false
source_datasets: ['irds/mr-tydi_sw']
task_categories:
- text-retrieval
---
# Dataset Card for `mr-tydi/sw/train`
The `mr-tydi/sw/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/sw/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=2,072
- `qrels`: (relevance assessments); count=2,401
- For `docs`, use [`irds/mr-tydi_sw`](https://huggingface.co/datasets/irds/mr-tydi_sw)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_sw_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_sw_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
|
bigscience-data/roots_indic-ml_wikipedia | ---
language: ml
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_indic-ml_wikipedia
# wikipedia
- Dataset uid: `wikipedia`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 3.2299 % of total
- 4.2071 % of en
- 5.6773 % of ar
- 3.3416 % of fr
- 5.2815 % of es
- 12.4852 % of ca
- 0.4288 % of zh
- 0.4286 % of zh
- 5.4743 % of indic-bn
- 8.9062 % of indic-ta
- 21.3313 % of indic-te
- 4.4845 % of pt
- 4.0493 % of indic-hi
- 11.3163 % of indic-ml
- 22.5300 % of indic-ur
- 4.4902 % of vi
- 16.9916 % of indic-kn
- 24.7820 % of eu
- 11.6241 % of indic-mr
- 9.8749 % of id
- 9.3489 % of indic-pa
- 9.4767 % of indic-gu
- 24.1132 % of indic-as
- 5.3309 % of indic-or
### BigScience processing steps
#### Filters applied to: en
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: ar
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: fr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: es
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: ca
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: zh
#### Filters applied to: zh
#### Filters applied to: indic-bn
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ta
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: pt
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ur
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: vi
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-kn
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: eu
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-mr
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: id
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-pa
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-gu
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-as
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-or
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
|
joey234/mmlu-formal_logic-original-neg-prepend | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: neg_prompt
dtype: string
splits:
- name: test
num_bytes: 21402
num_examples: 31
download_size: 14834
dataset_size: 21402
---
# Dataset Card for "mmlu-formal_logic-original-neg-prepend"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
fernandinhocodgamerbr/ana001 | ---
license: openrail
---
|
CronosGhost/code-reranking-CodeLangQueries-MachineGeneratedDocs-llama7bGGUF | ---
dataset_info:
features:
- name: query
dtype: string
- name: positive
sequence: string
- name: negative
sequence: string
splits:
- name: train
num_bytes: 7673564
num_examples: 301
- name: test
num_bytes: 868294
num_examples: 34
download_size: 560251
dataset_size: 8541858
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
minimario/livecodebench-execute-v2 | ---
dataset_info:
features:
- name: id
sequence: int64
- name: function_name
dtype: string
- name: code
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: test
num_bytes: 838050
num_examples: 2069
download_size: 124406
dataset_size: 838050
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
|
pssubitha/salesforce-3-formatted | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 13717
num_examples: 34
download_size: 10282
dataset_size: 13717
---
# Dataset Card for "salesforce-3-formatted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
heliosprime/twitter_dataset_1713109264 | ---
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: 20310
num_examples: 51
download_size: 19438
dataset_size: 20310
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713109264"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
cointegrated/dral_lite | ---
dataset_info:
features:
- name: id
dtype: string
- name: conv_id
dtype: string
- name: original_or_reenacted
dtype: string
- name: time_start
dtype: string
- name: time_end
dtype: string
- name: duration
dtype: string
- name: trans_id
dtype: string
- name: wav_orig
sequence: float32
- name: wav_trans
sequence: float32
- name: text_orig
dtype: string
- name: text_trans
dtype: string
splits:
- name: v2_short
num_bytes: 347282073
num_examples: 1173
download_size: 349744398
dataset_size: 347282073
configs:
- config_name: default
data_files:
- split: v2_short
path: data/v2_short-*
---
This is a copy of DRAL-2.0 dataset (https://www.cs.utep.edu/nigel/dral/), with only short fragments and only those where the audio on the left is original.
|
huggingnft/cyberkongz | ---
tags:
- huggingnft
- nft
- huggan
- gan
- image
- images
task:
- unconditional-image-generation
datasets:
- huggingnft/cyberkongz
license: mit
---
# Dataset Card
## Disclaimer
All rights belong to their owners.
Models and datasets can be removed from the site at the request of the copyright holder.
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingnft](https://github.com/AlekseyKorshuk/huggingnft)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingnft](https://github.com/AlekseyKorshuk/huggingnft)
- **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)
### Dataset Summary
NFT images dataset for unconditional generation.
NFT collection available [here](https://opensea.io/collection/cyberkongz).
Model is available [here](https://huggingface.co/huggingnft/cyberkongz).
Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingnft/cyberkongz")
```
## Dataset Structure
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Fields
The data fields are the same among all splits.
- `image`: an `image` feature.
- `id`: an `int` feature.
- `token_metadata`: a `str` feature.
- `image_original_url`: a `str` feature.
### Data Splits
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingnft,
author={Aleksey Korshuk}
year=2022
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingnft)
|
Codec-SUPERB/snips_test_valid_synth | ---
configs:
- config_name: default
data_files:
- split: original
path: data/original-*
- split: academicodec_hifi_16k_320d
path: data/academicodec_hifi_16k_320d-*
- split: academicodec_hifi_16k_320d_large_uni
path: data/academicodec_hifi_16k_320d_large_uni-*
- split: academicodec_hifi_24k_320d
path: data/academicodec_hifi_24k_320d-*
- split: audiodec_24k_320d
path: data/audiodec_24k_320d-*
- split: dac_16k
path: data/dac_16k-*
- split: dac_24k
path: data/dac_24k-*
- split: dac_44k
path: data/dac_44k-*
- split: encodec_24k_12bps
path: data/encodec_24k_12bps-*
- split: encodec_24k_1_5bps
path: data/encodec_24k_1_5bps-*
- split: encodec_24k_24bps
path: data/encodec_24k_24bps-*
- split: encodec_24k_3bps
path: data/encodec_24k_3bps-*
- split: encodec_24k_6bps
path: data/encodec_24k_6bps-*
- split: funcodec_en_libritts_16k_gr1nq32ds320
path: data/funcodec_en_libritts_16k_gr1nq32ds320-*
- split: funcodec_en_libritts_16k_gr8nq32ds320
path: data/funcodec_en_libritts_16k_gr8nq32ds320-*
- split: funcodec_en_libritts_16k_nq32ds320
path: data/funcodec_en_libritts_16k_nq32ds320-*
- split: funcodec_en_libritts_16k_nq32ds640
path: data/funcodec_en_libritts_16k_nq32ds640-*
- split: funcodec_zh_en_16k_nq32ds320
path: data/funcodec_zh_en_16k_nq32ds320-*
- split: funcodec_zh_en_16k_nq32ds640
path: data/funcodec_zh_en_16k_nq32ds640-*
- split: speech_tokenizer_16k
path: data/speech_tokenizer_16k-*
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: id
dtype: string
splits:
- name: original
num_bytes: 2082708964.0
num_examples: 22400
- name: academicodec_hifi_16k_320d
num_bytes: 2076522528.0
num_examples: 22400
- name: academicodec_hifi_16k_320d_large_uni
num_bytes: 2076522528.0
num_examples: 22400
- name: academicodec_hifi_24k_320d
num_bytes: 3116940448.0
num_examples: 22400
- name: audiodec_24k_320d
num_bytes: 3130702048.0
num_examples: 22400
- name: dac_16k
num_bytes: 2083538330.0
num_examples: 22400
- name: dac_24k
num_bytes: 3372733027.2
num_examples: 22400
- name: dac_44k
num_bytes: 6195191491.2
num_examples: 22400
- name: encodec_24k_12bps
num_bytes: 3372733027.2
num_examples: 22400
- name: encodec_24k_1_5bps
num_bytes: 3372733027.2
num_examples: 22400
- name: encodec_24k_24bps
num_bytes: 3372733027.2
num_examples: 22400
- name: encodec_24k_3bps
num_bytes: 3372733027.2
num_examples: 22400
- name: encodec_24k_6bps
num_bytes: 3372733027.2
num_examples: 22400
- name: funcodec_en_libritts_16k_gr1nq32ds320
num_bytes: 2247635459.2
num_examples: 22400
- name: funcodec_en_libritts_16k_gr8nq32ds320
num_bytes: 2247635459.2
num_examples: 22400
- name: funcodec_en_libritts_16k_nq32ds320
num_bytes: 2249360080.0
num_examples: 22400
- name: funcodec_en_libritts_16k_nq32ds640
num_bytes: 2249360080.0
num_examples: 22400
- name: funcodec_zh_en_16k_nq32ds320
num_bytes: 2249360080.0
num_examples: 22400
- name: funcodec_zh_en_16k_nq32ds640
num_bytes: 2249360080.0
num_examples: 22400
- name: speech_tokenizer_16k
num_bytes: 2256298256.0
num_examples: 22400
download_size: 53388830469
dataset_size: 56747533994.799995
---
# Dataset Card for "snips_test_valid_synth"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
NabeelMirza/Blah | ---
license: apache-2.0
---
|
benayas/snips_augmented_10pct_v1 | ---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1033310
num_examples: 13084
download_size: 486096
dataset_size: 1033310
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ahishamm/isic_binary_sharpened_enhanced | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': benign
'1': malignant
splits:
- name: train
num_bytes: 319741481.5
num_examples: 17250
- name: test
num_bytes: 147056090.022
num_examples: 7786
download_size: 501853633
dataset_size: 466797571.522
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
Miuzarte/SUISovitsAudio | ---
language:
- zh
tags:
- AIvtuber
- VirtuaReal
---
# SUISovitsAudio
### [opencpop](https://wenet.org.cn/opencpop/) + [so-vits-svc_4.0](https://github.com/svc-develop-team/so-vits-svc) with [SUImodels/sovits4_44k/v3/Singing/G_210000.pth](https://huggingface.co/Miuzarte/SUImodels/blob/main/sovits4_44k/v3/Singing/G_210000.pth)
#### sovits4.0-v2推理出来听不出区别，懒得再推了
**Song list: [https://wenet.org.cn/opencpop/resources/songlist/](https://wenet.org.cn/opencpop/resources/songlist/)** |
AdapterOcean/python3-standardized_cluster_7_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 10002130
num_examples: 3781
download_size: 0
dataset_size: 10002130
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "python3-standardized_cluster_7_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ricardosantoss/TOP20_COM_VALIDACAO_PORTUGUES | ---
dataset_info:
features:
- name: Nota Clinica
dtype: string
- name: Sequencia_CID10_Lista
sequence: string
splits:
- name: train
num_bytes: 1981969
num_examples: 2012
- name: test
num_bytes: 415163
num_examples: 432
- name: validation
num_bytes: 412127
num_examples: 431
download_size: 1108178
dataset_size: 2809259
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
quirky-lats-at-mats/NORMAL_BACKDOOR_alpaca_sleeper_agents_toy_safety_NOT_TRUNCATED_v4 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 1665610
num_examples: 2828
download_size: 876451
dataset_size: 1665610
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
hariharasudhan/dream | ---
license: other
license_name: mine
license_link: LICENSE
---
|
Luiggiprosonic/KaykyAI | ---
license: openrail
---
|
pcuenq/lsun-bedrooms | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 4450242498.020249
num_examples: 287968
- name: test
num_bytes: 234247797.33875093
num_examples: 15157
download_size: 4756942293
dataset_size: 4684490295.359
license: mit
---
# Dataset Card for "lsun-bedrooms"
This is a 20% sample of the bedrooms category in [`LSUN`](https://github.com/fyu/lsun), uploaded as a dataset for convenience.
The license for _this compilation only_ is MIT. The data retains the same license as the original dataset.
This is (roughly) the code that was used to upload this dataset:
```Python
import os
import shutil
from miniai.imports import *
from miniai.diffusion import *
from datasets import load_dataset
path_data = Path('data')
path_data.mkdir(exist_ok=True)
path = path_data/'bedroom'
url = 'https://s3.amazonaws.com/fast-ai-imageclas/bedroom.tgz'
if not path.exists():
path_zip = fc.urlsave(url, path_data)
shutil.unpack_archive('data/bedroom.tgz', 'data')
dataset = load_dataset("imagefolder", data_dir="data/bedroom")
dataset = dataset.remove_columns('label')
dataset = dataset['train'].train_test_split(test_size=0.05)
dataset.push_to_hub("pcuenq/lsun-bedrooms")
```
|
modelloosrvcc/WukongMico | ---
license: openrail
---
|
rinabuoy/Khmer-ALT | ---
dataset_info:
features:
- name: SNT.URLID
dtype: string
- name: SNT.URLID.SNTID
dtype: string
- name: url
dtype: string
- name: translation
dtype:
translation:
languages:
- bg
- en
- en_tok
- fil
- hi
- id
- ja
- khm
- lo
- ms
- my
- th
- vi
- zh
- name: text
dtype: string
splits:
- name: train
num_bytes: 79348840
num_examples: 18088
- name: validation
num_bytes: 4305969
num_examples: 1000
- name: test
num_bytes: 4423852
num_examples: 1019
download_size: 40147709
dataset_size: 88078661
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
mask-distilled-one-sec-cv12/chunk_200 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 903020372
num_examples: 177341
download_size: 920839549
dataset_size: 903020372
---
# Dataset Card for "chunk_200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/mmlu-professional_accounting-dev | ---
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
splits:
- name: dev
num_bytes: 4824
num_examples: 5
download_size: 8701
dataset_size: 4824
---
# Dataset Card for "mmlu-professional_accounting-dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Francesco/furniture-ngpea | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': furniture
'1': Chair
'2': Sofa
'3': Table
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: furniture-ngpea
tags:
- rf100
---
# Dataset Card for furniture-ngpea
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/furniture-ngpea
- **Point of Contact:** francesco.zuppichini@gmail.com
### Dataset Summary
furniture-ngpea
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/furniture-ngpea
### Citation Information
```
@misc{ furniture-ngpea,
title = { furniture ngpea Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/furniture-ngpea } },
url = { https://universe.roboflow.com/object-detection/furniture-ngpea },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
mteb-pt/imdb | ---
configs:
- config_name: pt-br
data_files:
- split: test
path: test*
- split: train
path: train*
language:
- pt
--- |
distilled-one-sec-cv12-each-chunk-uniq/chunk_110 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1217669640.0
num_examples: 237270
download_size: 1242313861
dataset_size: 1217669640.0
---
# Dataset Card for "chunk_110"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/shimamura_uzuki_idolmastercinderellagirls | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of shimamura_uzuki/島村卯月/시마무라우즈키 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of shimamura_uzuki/島村卯月/시마무라우즈키 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `brown_hair, long_hair, one_side_up, brown_eyes, bow`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 686.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimamura_uzuki_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 392.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimamura_uzuki_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1254 | 858.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimamura_uzuki_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 607.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimamura_uzuki_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1254 | 1.20 GiB | [Download](https://huggingface.co/datasets/CyberHarem/shimamura_uzuki_idolmastercinderellagirls/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/shimamura_uzuki_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 13 |  |  |  |  |  | 1girl, solo, hair_bow, midriff, blush, crop_top, looking_at_viewer, navel, open_mouth, :d, breasts, short_sleeves, pink_skirt, plaid, white_background, simple_background |
| 1 | 15 |  |  |  |  |  | 1girl, blazer, school_uniform, solo, looking_at_viewer, open_mouth, blush, skirt, cherry_blossoms, flower, :d |
| 2 | 5 |  |  |  |  |  | 1girl, :d, blazer, blush, looking_at_viewer, open_mouth, school_uniform, solo, plaid_skirt, hair_bow, heart, love_letter |
| 3 | 11 |  |  |  |  |  | 1girl, blazer, school_uniform, smile, solo, one_eye_closed, open_mouth, ;d, blush, looking_at_viewer, plaid_skirt, half_updo |
| 4 | 13 |  |  |  |  |  | 1girl, open_mouth, solo, blazer, blush, school_uniform, microphone, :d, closed_eyes, crying_with_eyes_open |
| 5 | 16 |  |  |  |  |  | 1girl, long_sleeves, red_bowtie, school_uniform, solo, brown_jacket, blazer, blush, collared_shirt, bangs, looking_at_viewer, white_shirt, open_mouth, plaid_skirt, red_skirt, simple_background, white_background, pleated_skirt, :d, cowboy_shot, miniskirt, upper_body |
| 6 | 13 |  |  |  |  |  | 1girl, solo, mini_crown, white_gloves, looking_at_viewer, open_mouth, :d, epaulettes, white_thighhighs, blush, breasts, dress, double_v, white_background |
| 7 | 7 |  |  |  |  |  | 1girl, dress, solo, necklace, open_mouth, hair_bow, :d, half_updo, polka_dot, tiara |
| 8 | 10 |  |  |  |  |  | 1girl, smile, solo, enmaided, open_mouth, looking_at_viewer, maid_headdress, maid_apron, short_sleeves, blush, heart, puffy_sleeves, white_thighhighs |
| 9 | 5 |  |  |  |  |  | 1girl, bangs, blush, enmaided, frilled_apron, looking_at_viewer, maid_apron, maid_headdress, puffy_short_sleeves, solo, white_apron, :d, black_dress, open_mouth, neck_ribbon, ribbon_trim, simple_background, very_long_hair, medium_breasts, white_background |
| 10 | 10 |  |  |  |  |  | 1girl, solo, looking_at_viewer, sailor_collar, see-through, blush, medium_breasts, navel, sailor_hat, striped_bikini, wrist_cuffs, open_mouth, smile, hair_bow, red_bow, bikini_under_clothes, midriff, necklace, shirt, simple_background, wet, white_background, white_skirt, bangs, crop_top_overhang, pleated_skirt, shiny, striped_thighhighs, white_headwear |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | hair_bow | midriff | blush | crop_top | looking_at_viewer | navel | open_mouth | :d | breasts | short_sleeves | pink_skirt | plaid | white_background | simple_background | blazer | school_uniform | skirt | cherry_blossoms | flower | plaid_skirt | heart | love_letter | smile | one_eye_closed | ;d | half_updo | microphone | closed_eyes | crying_with_eyes_open | long_sleeves | red_bowtie | brown_jacket | collared_shirt | bangs | white_shirt | red_skirt | pleated_skirt | cowboy_shot | miniskirt | upper_body | mini_crown | white_gloves | epaulettes | white_thighhighs | dress | double_v | necklace | polka_dot | tiara | enmaided | maid_headdress | maid_apron | puffy_sleeves | frilled_apron | puffy_short_sleeves | white_apron | black_dress | neck_ribbon | ribbon_trim | very_long_hair | medium_breasts | sailor_collar | see-through | sailor_hat | striped_bikini | wrist_cuffs | red_bow | bikini_under_clothes | shirt | wet | white_skirt | crop_top_overhang | shiny | striped_thighhighs | white_headwear |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-------|:-----------|:----------|:--------|:-----------|:--------------------|:--------|:-------------|:-----|:----------|:----------------|:-------------|:--------|:-------------------|:--------------------|:---------|:-----------------|:--------|:------------------|:---------|:--------------|:--------|:--------------|:--------|:-----------------|:-----|:------------|:-------------|:--------------|:------------------------|:---------------|:-------------|:---------------|:-----------------|:--------|:--------------|:------------|:----------------|:--------------|:------------|:-------------|:-------------|:---------------|:-------------|:-------------------|:--------|:-----------|:-----------|:------------|:--------|:-----------|:-----------------|:-------------|:----------------|:----------------|:----------------------|:--------------|:--------------|:--------------|:--------------|:-----------------|:-----------------|:----------------|:--------------|:-------------|:-----------------|:--------------|:----------|:-----------------------|:--------|:------|:--------------|:--------------------|:--------|:---------------------|:-----------------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 15 |  |  |  |  |  | X | X | | | X | | X | | X | X | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | X | | X | | X | | X | X | | | | | | | X | X | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 11 |  |  |  |  |  | X | X | | | X | | X | | X | | | | | | | | X | X | | | | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 13 |  |  |  |  |  | X | X | | | X | | | | X | X | | | | | | | X | X | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 16 |  |  |  |  |  | X | X | | | X | | X | | X | X | | | | | X | X | X | X | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 13 |  |  |  |  |  | X | X | | | X | | X | | X | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 7 |  |  |  |  |  | X | X | X | | | | | | X | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 10 |  |  |  |  |  | X | X | | | X | | X | | X | | | X | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 5 |  |  |  |  |  | X | X | | | X | | X | | X | X | | | | | X | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 10 | 10 |  |  |  |  |  | X | X | X | X | X | | X | X | X | | | | | | X | X | | | | | | | | | X | | | | | | | | | | | X | | | X | | | | | | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
zhixiaoni/CROHME_try_copy_png | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '1'
'1': '2'
'2': '3'
'3': '4'
'4': '5'
splits:
- name: train
num_bytes: 7689932.171
num_examples: 2441
download_size: 6854574
dataset_size: 7689932.171
---
# Dataset Card for "CROHME_try_copy_png"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/clarine_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of clarine (Fire Emblem)
This is the dataset of clarine (Fire Emblem), containing 70 images and their tags.
The core tags of this character are `blonde_hair, purple_eyes, long_hair, ponytail, bangs`, 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 | 70 | 54.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/clarine_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 70 | 42.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/clarine_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 131 | 75.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/clarine_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 70 | 51.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/clarine_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 131 | 88.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/clarine_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/clarine_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 | 5 |  |  |  |  |  | 1girl, holding_staff, simple_background, solo, thigh_boots, white_skirt, miniskirt, pleated_skirt, short_sleeves, white_footwear, white_thighhighs, zettai_ryouiki, full_body, open_mouth, purple_shirt, white_background, :d, collarbone, looking_at_viewer, sidelocks, white_capelet, white_gloves |
| 1 | 6 |  |  |  |  |  | 1girl, skirt, solo, gloves, staff, thighhighs, smile, thigh_boots, blush, open_mouth |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | holding_staff | simple_background | solo | thigh_boots | white_skirt | miniskirt | pleated_skirt | short_sleeves | white_footwear | white_thighhighs | zettai_ryouiki | full_body | open_mouth | purple_shirt | white_background | :d | collarbone | looking_at_viewer | sidelocks | white_capelet | white_gloves | skirt | gloves | staff | thighhighs | smile | blush |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------------|:--------------------|:-------|:--------------|:--------------|:------------|:----------------|:----------------|:-----------------|:-------------------|:-----------------|:------------|:-------------|:---------------|:-------------------|:-----|:-------------|:--------------------|:------------|:----------------|:---------------|:--------|:---------|:--------|:-------------|:--------|:--------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | | X | X | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X |
|
dim/ru_instruct_gpt4 | ---
license: cc-by-nc-4.0
dataset_info:
features:
- name: prompt
dtype: string
- name: solution
dtype: string
splits:
- name: train
num_bytes: 18294770
num_examples: 14222
download_size: 9373283
dataset_size: 18294770
---
|
UVA-GCOM/CDCHEALTH | ---
title: Predicting Diabetes
emoji: 📈
colorFrom: blue
colorTo: blue
sdk: gradio
sdk_version: 3.4
app_file: app.py
pinned: false
license: mit
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
Nathamon/CommentwordExpo_Eng-TH | ---
dataset_info:
features:
- name: id
dtype: string
- name: sentence
dtype: string
- name: cleaned_sentence
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 2858226
num_examples: 12407
download_size: 1570070
dataset_size: 2858226
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "CommentwordExpo_Eng-TH"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Miniex/katieVoz2 | ---
license: openrail
---
|
sgedela/dilbert-comic-sample-dataset | ---
license: openrail
---
|
hts98/dataforlarge | ---
dataset_info:
features:
- name: input_length
dtype: int64
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
- name: labels_length
dtype: int64
splits:
- name: train
num_bytes: 3288656680.0
num_examples: 3420
- name: test
num_bytes: 823132264.0
num_examples: 856
download_size: 0
dataset_size: 4111788944.0
---
# Dataset Card for "dataforlarge"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
PiyushLavaniya/Small_Alpaca_Instruct | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 17849270.7
num_examples: 9000
- name: test
num_bytes: 1983252.3
num_examples: 1000
download_size: 6069153
dataset_size: 19832523.0
---
# Dataset Card for "Small_Alpaca_Instruct"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/mmlu-high_school_computer_science-neg | ---
dataset_info:
features:
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: question
dtype: string
splits:
- name: test
num_bytes: 32270
num_examples: 100
download_size: 20733
dataset_size: 32270
---
# Dataset Card for "mmlu-high_school_computer_science-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/kako_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kako/加古/加古 (Kantai Collection)
This is the dataset of kako/加古/加古 (Kantai Collection), containing 352 images and their tags.
The core tags of this character are `black_hair, long_hair, ahoge, ponytail, blue_eyes, hair_over_one_eye, hair_ornament, bangs, ribbon, messy_hair, 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 | 352 | 243.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kako_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 352 | 188.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kako_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 698 | 341.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kako_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 352 | 230.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kako_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 698 | 407.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kako_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/kako_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 | 16 |  |  |  |  |  | 2girls, serafuku, pleated_skirt, open_mouth, hairclip, navel, blue_sailor_collar, red_neckerchief, blue_skirt, midriff, parted_bangs, short_hair, short_sleeves, smile, brown_hair, dated, solo_focus, twitter_username |
| 1 | 8 |  |  |  |  |  | 1girl, machinery, navel, serafuku, smile, solo, midriff, open_mouth, pleated_skirt, turret, chain, electricity, looking_at_viewer, cannon |
| 2 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, serafuku, solo, cowboy_shot, navel, parted_bangs, pleated_skirt, red_neckerchief, smile, blue_skirt, hairclip, midriff, dated, open_mouth, short_sleeves, simple_background, blue_sailor_collar, hair_ribbon, white_background |
| 3 | 5 |  |  |  |  |  | 1girl, blue_skirt, parted_bangs, pleated_skirt, red_neckerchief, serafuku, simple_background, solo, twitter_username, blue_sailor_collar, dated, open_mouth, blush, cowboy_shot, one-hour_drawing_challenge, white_background, closed_eyes, drooling |
| 4 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, navel, simple_background, solo, blush, large_breasts, open_mouth, white_background, naked_ribbon, hair_ribbon, smile, sweat, swimsuit, twitter_username |
| 5 | 11 |  |  |  |  |  | 1girl, solo, official_alternate_costume, white_background, white_skirt, looking_at_viewer, one-hour_drawing_challenge, simple_background, blue_shirt, smile, long_skirt, open_mouth, blouse, blush, hair_down, pleated_skirt |
| 6 | 5 |  |  |  |  |  | 1girl, holding_umbrella, hydrangea, official_alternate_costume, solo, rain, white_skirt, blouse, blue_shirt, blush, purple_shirt, shoes, smile, twitter_username, black_footwear, black_shirt, long_skirt, looking_at_viewer, outdoors |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 2girls | serafuku | pleated_skirt | open_mouth | hairclip | navel | blue_sailor_collar | red_neckerchief | blue_skirt | midriff | parted_bangs | short_hair | short_sleeves | smile | brown_hair | dated | solo_focus | twitter_username | 1girl | machinery | solo | turret | chain | electricity | looking_at_viewer | cannon | cowboy_shot | simple_background | hair_ribbon | white_background | blush | one-hour_drawing_challenge | closed_eyes | drooling | large_breasts | naked_ribbon | sweat | swimsuit | official_alternate_costume | white_skirt | blue_shirt | long_skirt | blouse | hair_down | holding_umbrella | hydrangea | rain | purple_shirt | shoes | black_footwear | black_shirt | outdoors |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------|:-----------|:----------------|:-------------|:-----------|:--------|:---------------------|:------------------|:-------------|:----------|:---------------|:-------------|:----------------|:--------|:-------------|:--------|:-------------|:-------------------|:--------|:------------|:-------|:---------|:--------|:--------------|:--------------------|:---------|:--------------|:--------------------|:--------------|:-------------------|:--------|:-----------------------------|:--------------|:-----------|:----------------|:---------------|:--------|:-----------|:-----------------------------|:--------------|:-------------|:-------------|:---------|:------------|:-------------------|:------------|:-------|:---------------|:--------|:-----------------|:--------------|:-----------|
| 0 | 16 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | | X | X | 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 | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | | | | 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 | | | | | | | | |
| 6 | 5 |  |  |  |  |  | | | | | | | | | | | | | | X | | | | X | X | | X | | | | X | | | | | | X | | | | | | | | X | X | X | X | X | | X | X | X | X | X | X | X | X |
|
gguichard/wsd_myriade_synth_data_multilabel_flan-t5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: tokens
sequence: string
- name: wn_sens
sequence: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: float64
splits:
- name: train
num_bytes: 61545765.76054323
num_examples: 96254
- name: test
num_bytes: 3239890.239456776
num_examples: 5067
download_size: 16835884
dataset_size: 64785656.0
---
# Dataset Card for "wsd_myriade_synth_data_multilabel_flan-t5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kastan/EE_QA_for_RLHF | ---
license: mit
---
|
jeffm32/finetune | ---
license: apache-2.0
---
|
Zilun/blip2_lora_dataset | ---
license: cc-by-sa-4.0
---
|
unography/laion-110k-GPT4V-LIVIS-Captions | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 14355173527.342
num_examples: 111426
download_size: 14478169849
dataset_size: 14355173527.342
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Reverb/CBIS-DDSM | ---
license: apache-2.0
task_categories:
- image-classification
language:
- en
tags:
- medical
size_categories:
- 1K<n<10K
---
# CBIS-DDSM Dataset
## Overview
The CBIS-DDSM (Curated Breast Imaging Subset of DDSM) dataset is a collection of medical images for breast cancer research. It is a subset of the Digital Database for Screening Mammography (DDSM) and has been carefully curated to facilitate research in computer-aided diagnosis and medical image analysis.
## Dataset Description
- **Total Instances:**
- **Image Modalities:** Mammography, Breast Ultrasound
- **Annotation Types:** Masses, Calcifications, Benign, Malignant
- **Resolution:** Varies across modalities and instances
## Contents
- **Images:** The dataset includes mammography and breast ultrasound images in DICOM format.
- **Annotations:** Ground truth annotations for masses and calcifications, along with additional metadata.
## Example Usage
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("Reverb/CBIS-DDSM", split="train")
``` |
dim/kinomania_scripts | ---
license: mit
dataset_info:
features:
- name: movie_script
dtype: string
- name: movie_description
dtype: string
- name: title
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 4912326
num_examples: 27
download_size: 2757276
dataset_size: 4912326
---
|
Alimustoofaa/alpaca-indonesia-llama-1K | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 728489
num_examples: 1000
download_size: 374413
dataset_size: 728489
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
stoddur/rmh_tokenized_512_train | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 71104398036.0
num_examples: 10663527
download_size: 5448818845
dataset_size: 71104398036.0
---
# Dataset Card for "rmh_tokenized_512_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
modelloosrvcc/LuanGalinha | ---
license: openrail
---
|
genuine-mango/sd_regularization_library | ---
license: openrail
---
|
vinisebk/jon_lee | ---
license: openrail
---
|
GabrielTOP/GGGGGGGGGGGG | ---
license: openrail
---
|
sitloboi2012/rvl_cdip_small_dataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: string
splits:
- name: train
num_bytes: 1746183.0
num_examples: 15
download_size: 1643991
dataset_size: 1746183.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "rvl_cdip_small_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
one-sec-cv12/chunk_134 | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 22845304944.375
num_examples: 237853
download_size: 20796713169
dataset_size: 22845304944.375
---
# Dataset Card for "chunk_134"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Atipico1/popQA_preprocessed_unans | ---
dataset_info:
features:
- name: id
dtype: int64
- name: subj
dtype: string
- name: prop
dtype: string
- name: obj
dtype: string
- name: subj_id
dtype: int64
- name: prop_id
dtype: int64
- name: obj_id
dtype: int64
- name: s_aliases
dtype: string
- name: o_aliases
dtype: string
- name: s_uri
dtype: string
- name: o_uri
dtype: string
- name: s_wiki_title
dtype: string
- name: o_wiki_title
dtype: string
- name: s_pop
dtype: int64
- name: o_pop
dtype: int64
- name: question
dtype: string
- name: answers
sequence: string
- name: ctxs
list:
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: score
dtype: string
- name: text
dtype: string
- name: title
dtype: string
- name: query_embedding
sequence: float32
splits:
- name: train
num_bytes: 100743049
num_examples: 10000
- name: test
num_bytes: 42959579
num_examples: 4267
download_size: 81183565
dataset_size: 143702628
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
open-llm-leaderboard/details_vanillaOVO__correction_1 | ---
pretty_name: Evaluation run of vanillaOVO/correction_1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [vanillaOVO/correction_1](https://huggingface.co/vanillaOVO/correction_1) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_vanillaOVO__correction_1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-27T17:47:43.731567](https://huggingface.co/datasets/open-llm-leaderboard/details_vanillaOVO__correction_1/blob/main/results_2024-03-27T17-47-43.731567.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\": 0.63686068515643,\n\
\ \"acc_stderr\": 0.03236847187748598,\n \"acc_norm\": 0.6406611225738374,\n\
\ \"acc_norm_stderr\": 0.033058516894310476,\n \"mc1\": 0.4969400244798042,\n\
\ \"mc1_stderr\": 0.01750317326096062,\n \"mc2\": 0.6591639079987147,\n\
\ \"mc2_stderr\": 0.015796990567396845\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.681740614334471,\n \"acc_stderr\": 0.013611993916971453,\n\
\ \"acc_norm\": 0.71160409556314,\n \"acc_norm_stderr\": 0.013238394422428175\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7289384584744075,\n\
\ \"acc_stderr\": 0.004435993492583879,\n \"acc_norm\": 0.8858793069109739,\n\
\ \"acc_norm_stderr\": 0.003173079807440182\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\
\ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\
\ \"acc_norm_stderr\": 0.04203921040156279\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\": 0.65,\n\
\ \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \
\ \"acc_norm_stderr\": 0.0479372485441102\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\": 0.7569444444444444,\n\
\ \"acc_stderr\": 0.03586879280080341,\n \"acc_norm\": 0.7569444444444444,\n\
\ \"acc_norm_stderr\": 0.03586879280080341\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\"\
: 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6069364161849711,\n\
\ \"acc_stderr\": 0.0372424959581773,\n \"acc_norm\": 0.6069364161849711,\n\
\ \"acc_norm_stderr\": 0.0372424959581773\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.047840607041056527,\n\
\ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.047840607041056527\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5446808510638298,\n \"acc_stderr\": 0.03255525359340355,\n\
\ \"acc_norm\": 0.5446808510638298,\n \"acc_norm_stderr\": 0.03255525359340355\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\
\ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\
\ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\
\ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.43386243386243384,\n \"acc_stderr\": 0.02552503438247489,\n \"\
acc_norm\": 0.43386243386243384,\n \"acc_norm_stderr\": 0.02552503438247489\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4523809523809524,\n\
\ \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n\
\ \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939098,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939098\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7806451612903226,\n\
\ \"acc_stderr\": 0.023540799358723295,\n \"acc_norm\": 0.7806451612903226,\n\
\ \"acc_norm_stderr\": 0.023540799358723295\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n\
\ \"acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\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\": 0.8,\n \"acc_stderr\": 0.031234752377721175,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.031234752377721175\n \
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494562,\n \"\
acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494562\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919436,\n\
\ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919436\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\
\ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32592592592592595,\n \"acc_stderr\": 0.028578348365473082,\n \
\ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.028578348365473082\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.029597329730978082,\n\
\ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.029597329730978082\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\
acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8311926605504587,\n \"acc_stderr\": 0.01606005626853033,\n \"\
acc_norm\": 0.8311926605504587,\n \"acc_norm_stderr\": 0.01606005626853033\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8235294117647058,\n \"acc_stderr\": 0.02675640153807897,\n \"\
acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.02675640153807897\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\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n\
\ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.743801652892562,\n \"acc_stderr\": 0.039849796533028725,\n \"\
acc_norm\": 0.743801652892562,\n \"acc_norm_stderr\": 0.039849796533028725\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.034624199316156234,\n\
\ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.034624199316156234\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\
\ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \
\ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n\
\ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n\
\ \"acc_stderr\": 0.022209309073165612,\n \"acc_norm\": 0.8675213675213675,\n\
\ \"acc_norm_stderr\": 0.022209309073165612\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8173690932311622,\n\
\ \"acc_stderr\": 0.013816335389973136,\n \"acc_norm\": 0.8173690932311622,\n\
\ \"acc_norm_stderr\": 0.013816335389973136\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7109826589595376,\n \"acc_stderr\": 0.02440517393578323,\n\
\ \"acc_norm\": 0.7109826589595376,\n \"acc_norm_stderr\": 0.02440517393578323\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4335195530726257,\n\
\ \"acc_stderr\": 0.01657402721951763,\n \"acc_norm\": 0.4335195530726257,\n\
\ \"acc_norm_stderr\": 0.01657402721951763\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6993464052287581,\n \"acc_stderr\": 0.02625605383571896,\n\
\ \"acc_norm\": 0.6993464052287581,\n \"acc_norm_stderr\": 0.02625605383571896\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\
\ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\
\ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.024659685185967284,\n\
\ \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.024659685185967284\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \
\ \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46153846153846156,\n\
\ \"acc_stderr\": 0.01273239828619044,\n \"acc_norm\": 0.46153846153846156,\n\
\ \"acc_norm_stderr\": 0.01273239828619044\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.028418208619406755,\n\
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.028418208619406755\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.673202614379085,\n \"acc_stderr\": 0.0189754279205072,\n \
\ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.0189754279205072\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\
\ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\
\ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6938775510204082,\n \"acc_stderr\": 0.029504896454595957,\n\
\ \"acc_norm\": 0.6938775510204082,\n \"acc_norm_stderr\": 0.029504896454595957\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n\
\ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\
\ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\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\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4969400244798042,\n\
\ \"mc1_stderr\": 0.01750317326096062,\n \"mc2\": 0.6591639079987147,\n\
\ \"mc2_stderr\": 0.015796990567396845\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8784530386740331,\n \"acc_stderr\": 0.009183632046519959\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.35784685367702807,\n \
\ \"acc_stderr\": 0.013204142536119953\n }\n}\n```"
repo_url: https://huggingface.co/vanillaOVO/correction_1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|arc:challenge|25_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|gsm8k|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hellaswag|10_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-27T17-47-43.731567.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-27T17-47-43.731567.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- '**/details_harness|winogrande|5_2024-03-27T17-47-43.731567.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-27T17-47-43.731567.parquet'
- config_name: results
data_files:
- split: 2024_03_27T17_47_43.731567
path:
- results_2024-03-27T17-47-43.731567.parquet
- split: latest
path:
- results_2024-03-27T17-47-43.731567.parquet
---
# Dataset Card for Evaluation run of vanillaOVO/correction_1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [vanillaOVO/correction_1](https://huggingface.co/vanillaOVO/correction_1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_vanillaOVO__correction_1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-27T17:47:43.731567](https://huggingface.co/datasets/open-llm-leaderboard/details_vanillaOVO__correction_1/blob/main/results_2024-03-27T17-47-43.731567.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": {
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"acc_stderr": 0.03236847187748598,
"acc_norm": 0.6406611225738374,
"acc_norm_stderr": 0.033058516894310476,
"mc1": 0.4969400244798042,
"mc1_stderr": 0.01750317326096062,
"mc2": 0.6591639079987147,
"mc2_stderr": 0.015796990567396845
},
"harness|arc:challenge|25": {
"acc": 0.681740614334471,
"acc_stderr": 0.013611993916971453,
"acc_norm": 0.71160409556314,
"acc_norm_stderr": 0.013238394422428175
},
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"acc": 0.7289384584744075,
"acc_stderr": 0.004435993492583879,
"acc_norm": 0.8858793069109739,
"acc_norm_stderr": 0.003173079807440182
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
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"acc_norm": 0.29,
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},
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"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
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},
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"acc_norm": 0.65,
"acc_norm_stderr": 0.0479372485441102
},
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"acc": 0.690566037735849,
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"acc_norm_stderr": 0.028450154794118637
},
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},
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},
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"acc_norm": 0.51,
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},
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"acc_norm_stderr": 0.0372424959581773
},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm_stderr": 0.047840607041056527
},
"harness|hendrycksTest-computer_security|5": {
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},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5446808510638298,
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"acc_norm_stderr": 0.03255525359340355
},
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"harness|hendrycksTest-high_school_biology|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_psychology|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8235294117647058,
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"acc_norm": 0.8235294117647058,
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},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7805907172995781,
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},
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},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7361963190184049,
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"acc_norm": 0.7361963190184049,
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},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4375,
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},
"harness|hendrycksTest-management|5": {
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"harness|hendrycksTest-marketing|5": {
"acc": 0.8675213675213675,
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},
"harness|hendrycksTest-medical_genetics|5": {
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},
"harness|hendrycksTest-nutrition|5": {
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},
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},
"harness|hendrycksTest-professional_medicine|5": {
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"harness|hendrycksTest-professional_psychology|5": {
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},
"harness|hendrycksTest-public_relations|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"acc_norm_stderr": 0.03885425420866767
},
"harness|hendrycksTest-world_religions|5": {
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"acc_norm": 0.8304093567251462,
"acc_norm_stderr": 0.02878210810540171
},
"harness|truthfulqa:mc|0": {
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"mc2": 0.6591639079987147,
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},
"harness|winogrande|5": {
"acc": 0.8784530386740331,
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},
"harness|gsm8k|5": {
"acc": 0.35784685367702807,
"acc_stderr": 0.013204142536119953
}
}
```
## 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] |
xiemoxiaoshaso/sdwebui | ---
license: openrail
---
|
fia24/annotated_100k_banel | ---
dataset_info:
features:
- name: id
dtype: string
- name: translation
struct:
- name: en
dtype: string
- name: fr
dtype: string
splits:
- name: train
num_bytes: 4806957
num_examples: 82226
- name: val
num_bytes: 600794
num_examples: 10278
- name: test
num_bytes: 606555
num_examples: 10278
download_size: 3179760
dataset_size: 6014306
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
|
CVasNLPExperiments/FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3333 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_clip_tags_ViT_L_14_with_openai_classes_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices
num_bytes: 1492908
num_examples: 3333
- name: fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices
num_bytes: 2893373
num_examples: 3333
- name: fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices
num_bytes: 5702031
num_examples: 3333
- name: fewshot_0__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices
num_bytes: 1484173
num_examples: 3333
- name: fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices
num_bytes: 2772155
num_examples: 3333
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num_bytes: 2876794
num_examples: 3333
- name: fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices
num_bytes: 5457699
num_examples: 3333
- name: fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices
num_bytes: 1422853
num_examples: 3333
download_size: 3475675
dataset_size: 24101986
configs:
- config_name: default
data_files:
- split: fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices
path: data/fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices-*
---
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3333"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Lamimad/devolved_ai | ---
license: apache-2.0
---
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_1_10000000 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: response
dtype: string
splits:
- name: train
num_bytes: 191265
num_examples: 6699
download_size: 122134
dataset_size: 191265
---
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_1_10000000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Yorth/limerick-topic | ---
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: combined
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 16886056
num_examples: 52708
- name: validation
num_bytes: 2112395
num_examples: 6588
- name: test
num_bytes: 2111865
num_examples: 6589
download_size: 10216598
dataset_size: 21110316
---
# Dataset Card for "limerick-topic"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kaleemWaheed/twitter_dataset_1713051374 | ---
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: 24882
num_examples: 57
download_size: 13160
dataset_size: 24882
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
JotDe/mscoco_200_unique_imgs | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 4761831.8
num_examples: 199
download_size: 4745758
dataset_size: 4761831.8
---
# Dataset Card for "mscoco_200_unique_imgs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nlhuong/panda_and_koala | ---
license: artistic-2.0
---
|
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