datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
fsuarez/autotrain-data-logo-identifier-v3-medium | ---
task_categories:
- image-classification
---
# AutoTrain Dataset for project: logo-identifier-v3-medium
## Dataset Description
This dataset has been automatically processed by AutoTrain for project logo-identifier-v3-medium.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<100x72 RGB PIL image>",
"target": 47
},
{
"image": "<100x63 RGB PIL image>",
"target": 82
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['20thTelevision', '3M', '7Eleven', 'Acer', 'AmericanExpress', 'Amul', 'Anthem', 'ApolloHospitals', 'Apple', 'Armani', 'Asahi', 'Asus', 'Atari', 'Audi', 'Avon', 'Booking', 'Bosch', 'Bridgestone', 'British Airways', 'Budweiser', 'Burberry', 'BurgerKing', 'BuzzFeed', 'Canon', 'CocaColaZero', 'Coleman', 'Coles', 'Converse', 'CornFlakes', 'Corona', 'CostcoWholesale', 'Crayola', 'Credit Agricole', 'Crocs', 'Crunchyroll', 'Ctrip', 'Dropbox', 'Ducati', 'DunkinDonuts', 'Duracell', 'Dyson', 'Ethereum', 'Etsy', 'ExxonMobil', 'FoxNews', 'FreddieMac', 'Fujitsu', 'Goodyear', 'Grubhub', 'Gucci', 'Huawei', 'Hudson Bay Company', 'HugoBoss', 'Hulu', 'Hyundai', 'Instagram', 'Intel', 'John Lewis & Partners', 'Johnson&Johnson', 'Kingston', 'LouisVuitton', 'Lowes', 'Lufthansa', 'Lululemon', 'Luxottica', 'MorganStanley', 'Motorola', 'MountainDew', 'Moutai', 'Movistar', 'Msci', 'Muji', 'Nike', 'Nissan', 'Nokia', 'Nvidia', 'Orange', 'Oreo', 'Porsche', 'Power China', 'Prada', 'Pringles', 'Publix', 'Puma', 'Purina', 'PwC', 'Qualcomm', 'Rolex', 'Rolls-Royce', 'RoyalCaribbean', 'Spotify', 'Sprite', 'Starbucks', 'StateBankofIndia', 'StateGrid', 'Subaru', 'Subway', 'SumitomoGroup', 'Suning', 'Supreme', 'Suzuki', 'Toshiba', 'Total SA', 'TotalEnergies', 'Toyota', 'TripAdvisor', 'Twitch', 'Twitter', 'UnitedHealthCare', 'Universal', 'Volkswagen', 'Volvo', 'Wikipedia', 'Wipro', 'Wuliangye', 'Xiaomi', 'Youtube', 'Zoom', 'hennessy', 'iHeartRadio', 'koolAid'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 7024 |
| valid | 1822 |
|
bigscience-data/roots_indic-ur_leipzig_wortschatz_urdu_newscrawl_2016_sentences | ---
language: ur
license: cc-by-nc-4.0
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-ur_leipzig_wortschatz_urdu_newscrawl_2016_sentences
# leipzig_wortschatz_urdu_newscrawl_2016_sentences
- Dataset uid: `leipzig_wortschatz_urdu_newscrawl_2016_sentences`
### Description
Leipzig Wortschatz Crawl
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 0.0587 % of total
- 20.7635 % of indic-ur
### BigScience processing steps
#### Filters applied to: indic-ur
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
|
CyberHarem/saiga_12_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of saiga_12/Saiga-12/塞伽12型 (Girls' Frontline)
This is the dataset of saiga_12/Saiga-12/塞伽12型 (Girls' Frontline), containing 102 images and their tags.
The core tags of this character are `breasts, yellow_eyes, long_hair, dark-skinned_female, dark_skin, purple_hair, large_breasts, bangs, hair_between_eyes, hair_ornament, sidelocks, hat`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 102 | 142.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/saiga_12_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 102 | 77.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/saiga_12_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 243 | 160.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/saiga_12_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 102 | 124.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/saiga_12_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 243 | 233.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/saiga_12_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/saiga_12_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 18 |  |  |  |  |  | 1girl, elbow_gloves, hair_ribbon, navel, solo, white_gloves, cleavage, official_alternate_costume, race_queen, red_shorts, bare_shoulders, looking_at_viewer, short_shorts, holding, simple_background, thigh_boots, choker, collarbone, criss-cross_halter, red_footwear, smile, white_background, blush, highleg, stomach, midriff, red_bikini, standing, umbrella, white_ribbon, white_thighhighs, open_mouth, panties |
| 1 | 29 |  |  |  |  |  | 1girl, solo, looking_at_viewer, beret, gloves, white_thighhighs, pleated_skirt, blush, twintails, necktie, belt, buckle, simple_background, framed_breasts, hair_flower |
| 2 | 5 |  |  |  |  |  | 1girl, cleavage, hair_flower, looking_at_viewer, solo, blush, collarbone, red_hair, simple_background, smile, white_background, white_kimono, bare_shoulders, obi, official_alternate_costume, open_mouth, full_body, gun, holding, long_sleeves, wide_sleeves |
| 3 | 5 |  |  |  |  |  | 1girl, blush, nipples, nude, solo, looking_at_viewer, navel, barcode_tattoo, collarbone, parted_lips, side_ponytail |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | elbow_gloves | hair_ribbon | navel | solo | white_gloves | cleavage | official_alternate_costume | race_queen | red_shorts | bare_shoulders | looking_at_viewer | short_shorts | holding | simple_background | thigh_boots | choker | collarbone | criss-cross_halter | red_footwear | smile | white_background | blush | highleg | stomach | midriff | red_bikini | standing | umbrella | white_ribbon | white_thighhighs | open_mouth | panties | beret | gloves | pleated_skirt | twintails | necktie | belt | buckle | framed_breasts | hair_flower | red_hair | white_kimono | obi | full_body | gun | long_sleeves | wide_sleeves | nipples | nude | barcode_tattoo | parted_lips | side_ponytail |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------------|:--------|:-------|:---------------|:-----------|:-----------------------------|:-------------|:-------------|:-----------------|:--------------------|:---------------|:----------|:--------------------|:--------------|:---------|:-------------|:---------------------|:---------------|:--------|:-------------------|:--------|:----------|:----------|:----------|:-------------|:-----------|:-----------|:---------------|:-------------------|:-------------|:----------|:--------|:---------|:----------------|:------------|:----------|:-------|:---------|:-----------------|:--------------|:-----------|:---------------|:------|:------------|:------|:---------------|:---------------|:----------|:-------|:-----------------|:--------------|:----------------|
| 0 | 18 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 1 | 29 |  |  |  |  |  | X | | | | X | | | | | | | X | | | X | | | | | | | | X | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | | | | X | | X | X | | | X | X | | X | X | | | X | | | X | X | X | | | | | | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | |
| 3 | 5 |  |  |  |  |  | X | | | X | X | | | | | | | X | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X |
|
distilled-from-one-sec-cv12/chunk_45 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1219219504
num_examples: 237572
download_size: 1241201193
dataset_size: 1219219504
---
# Dataset Card for "chunk_45"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
leemeng/mt_bench_japanese | ---
license: cc-by-4.0
---
|
NazmusAshrafi/setfit-absa-small-stock-tweet | ---
license: mit
---
|
ovior/twitter_dataset_1713082514 | ---
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: 2723837
num_examples: 8128
download_size: 1552631
dataset_size: 2723837
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Haneen84/Arabic_news | ---
license: other
---
|
mikehemberger/indian-medicinal-plants | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Aloevera
'1': Amla
splits:
- name: train
num_bytes: 21027703.0
num_examples: 495
download_size: 21028445
dataset_size: 21027703.0
---
# Dataset Card for "indian-medicinal-plants"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
GIZ/policy_qa_v2 | ---
license: apache-2.0
---
|
sethapun/procedural_gen | ---
dataset_info:
features:
- name: expression
dtype: string
- name: answer
dtype: float64
- name: label
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
splits:
- name: train
num_bytes: 99358
num_examples: 2000
- name: validation
num_bytes: 19864
num_examples: 400
download_size: 46579
dataset_size: 119222
---
# Dataset Card for "procedural_gen"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Imran1/Network_ | ---
dataset_info:
features:
- name: resp_pkts
dtype: int64
- name: service
dtype: string
- name: orig_ip_bytes
dtype: int64
- name: local_resp
dtype: bool
- name: missed_bytes
dtype: int64
- name: protocol
dtype: string
- name: duration
dtype: float64
- name: conn_state
dtype: string
- name: dest_ip
dtype: string
- name: orig_pkts
dtype: int64
- name: community_id
dtype: string
- name: resp_ip_bytes
dtype: int64
- name: dest_port
dtype: int64
- name: orig_bytes
dtype: float64
- name: local_orig
dtype: bool
- name: datetime
dtype: string
- name: history
dtype: string
- name: resp_bytes
dtype: float64
- name: uid
dtype: string
- name: src_port
dtype: int64
- name: ts
dtype: float64
- name: src_ip
dtype: string
- name: mitre_attack_tactics
dtype: string
splits:
- name: train
num_bytes: 966212295
num_examples: 4068587
download_size: 231047526
dataset_size: 966212295
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Network_"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
openaccess-ai-collective/chatbot-arena-elo-scores | ---
dataset_info:
features:
- name: elo_score
dtype: float64
- name: chatbot_name
dtype: string
splits:
- name: train
num_bytes: 359
num_examples: 14
download_size: 1669
dataset_size: 359
---
# Dataset Card for "chatbot-arena-elo-scores"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AnonymousPaperSubmissions/RoBERTa_eval_data | ---
license: mit
---
|
projecte-aina/CA-GL_Parallel_Corpus | ---
language:
- ca
- gl
multilinguality:
- multilingual
pretty_name: CA-GL Parallel Corpus
size_categories:
- 10M<n<100M
task_categories:
- translation
task_ids: []
license: cc-by-nc-sa-4.0
---
# Dataset Card for CA-GL Parallel Corpus
## Dataset Description
### Dataset Summary
The CA-GL Parallel Corpus is a Catalan-Galician synthetic dataset of **33.668.599** parallel sentences.
The dataset was created to support the use of co-official languages from Spain, such as Catalan and Galician,
in NLP tasks, specifically Machine Translation.
### Supported Tasks and Leaderboards
The dataset can be used to train Bilingual Machine Translation models between Galician and Catalan in any direction,
as well as Multilingual Machine Translation models.
### Languages
The sentences included in the dataset are in Catalan (CA) and Galician (GL).
## Dataset Structure
### Data Instances
Two separate txt files are provided with the sentences sorted in the same order:
- nos_all.ca: contains 33.668.599 Catalan sentences (synthetic).
- nos_all.gl: contains 33.668.599 Galician sentences (authentic).
### Data Fields
[N/A]
### Data Splits
The dataset contains a single split: `train`.
## Dataset Creation
### Curation Rationale
This dataset is aimed at promoting the development of Machine Translation between Catalan
and other co-official languages from Spain, specifically Galician.
### Source Data
#### Initial Data Collection and Normalization
This synthetic dataset was created in the frame of Project Ilenia. An authentic parallel corpus ES-GL was delivered by Proxecto Nós
and the Spanish was translated to Catalan using the machine translation model [PlanTL-GOB-ES](https://huggingface.co/PlanTL-GOB-ES/mt-plantl-es-ca).
**Total: 33.668.599 parallel sentences** .
#### Who are the source language producers?
[Proxecto Nós](https://nos.gal/es/proxecto-nos)
### Annotations
#### Annotation process
The dataset does not contain any annotations.
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
Given that this dataset is partly derived from pre-existing datasets that may contain crawled data, and that no specific anonymisation process has been applied,
personal and sensitive information may be present in the data. This needs to be considered when using the data for training models.
## Considerations for Using the Data
### Social Impact of Dataset
By providing this resource, we intend to promote the use of Catalan and Galician, two of the co-official languages of Spain,
across NLP tasks, thereby improving the accessibility and visibility of both Catalan and Galician.
### Discussion of Biases
No specific bias mitigation strategies were applied to this dataset.
Inherent biases may exist within the data.
### Other Known Limitations
The dataset contains data of a general domain.
Applications of this dataset in more specific domains such as biomedical, legal etc. would be of limited use.
## Additional Information
### Dataset Curators
Language Technologies Unit at the Barcelona Supercomputing Center (langtech@bsc.es).
This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU
within the framework of the [project ILENIA](https://proyectoilenia.es/)
with reference 2022/TL22/00215337, 2022/TL22/00215336, 2022/TL22/00215335 y 2022/TL22/00215334
### Licensing Information
This work is licensed under a [Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Citation Information
[N/A]
### Contributions
[N/A] |
max-id/gaianet-qdrant-snapshot | ---
license: apache-2.0
---
|
pedrghost/Psychgo | ---
license: openrail
---
|
open-llm-leaderboard/details_TFLai__PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch | ---
pretty_name: Evaluation run of TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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_TFLai__PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-18T01:14:07.335372](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-18T01-14-07.335372.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\": 0.08420721476510067,\n\
\ \"em_stderr\": 0.0028438907694585103,\n \"f1\": 0.20487206375838948,\n\
\ \"f1_stderr\": 0.0032246591490556827,\n \"acc\": 0.35556432517758485,\n\
\ \"acc_stderr\": 0.006369120635509223\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.08420721476510067,\n \"em_stderr\": 0.0028438907694585103,\n\
\ \"f1\": 0.20487206375838948,\n \"f1_stderr\": 0.0032246591490556827\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7111286503551697,\n\
\ \"acc_stderr\": 0.012738241271018446\n }\n}\n```"
repo_url: https://huggingface.co/TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_18T01_14_07.335372
path:
- '**/details_harness|drop|3_2023-10-18T01-14-07.335372.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-18T01-14-07.335372.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_18T01_14_07.335372
path:
- '**/details_harness|gsm8k|5_2023-10-18T01-14-07.335372.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-18T01-14-07.335372.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_18T01_14_07.335372
path:
- '**/details_harness|winogrande|5_2023-10-18T01-14-07.335372.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-18T01-14-07.335372.parquet'
- config_name: results
data_files:
- split: 2023_08_28T22_56_04.264879
path:
- results_2023-08-28T22:56:04.264879.parquet
- split: 2023_10_18T01_14_07.335372
path:
- results_2023-10-18T01-14-07.335372.parquet
- split: latest
path:
- results_2023-10-18T01-14-07.335372.parquet
---
# Dataset Card for Evaluation run of TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch
- **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 [TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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_TFLai__PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T01:14:07.335372](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-18T01-14-07.335372.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": 0.08420721476510067,
"em_stderr": 0.0028438907694585103,
"f1": 0.20487206375838948,
"f1_stderr": 0.0032246591490556827,
"acc": 0.35556432517758485,
"acc_stderr": 0.006369120635509223
},
"harness|drop|3": {
"em": 0.08420721476510067,
"em_stderr": 0.0028438907694585103,
"f1": 0.20487206375838948,
"f1_stderr": 0.0032246591490556827
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.7111286503551697,
"acc_stderr": 0.012738241271018446
}
}
```
### 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] |
danaaubakirova/patfig | ---
license: cc-by-nc-4.0
task_categories:
- image-to-text
- visual-question-answering
- image-classification
language:
- en
pretty_name: PatFig
size_categories:
- 10K<n<100K
---
# PatFig Dataset
<div align="center">
<img src="https://cdn-lfs-us-1.huggingface.co/repos/25/0c/250cb7eb9b83b2bd76ad6440700971baf0ec2981fdcb94b7fad768f2eb59fecc/1e79b8cf6cbe22d424c95e4816fc763e388d80cba4228908d707100f1f41182a?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27patfig_funny_logo.png%3B+filename%3D%22patfig_funny_logo.png%22%3B&response-content-type=image%2Fpng&Expires=1710484025&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxMDQ4NDAyNX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzI1LzBjLzI1MGNiN2ViOWI4M2IyYmQ3NmFkNjQ0MDcwMDk3MWJhZjBlYzI5ODFmZGNiOTRiN2ZhZDc2OGYyZWI1OWZlY2MvMWU3OWI4Y2Y2Y2JlMjJkNDI0Yzk1ZTQ4MTZmYzc2M2UzODhkODBjYmE0MjI4OTA4ZDcwNzEwMGYxZjQxMTgyYT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=ghxVaJztNO%7EJsQTPLCGf5VjyqxZQBbQEwpEFUlg9jp0pwu6YySncyKudoNVEJkRJlXpIi6pTw0ezQ4VpMHa4BrirgO8JfmxzVJQ5h1wgq9KtRwpdYt0ba%7EH73KNCSS1KvNM50prZ5fKBon3s9yvL1wgkOgIMN2i7NPaR1MCzps8sMyhbWFpwVpvuoV91QS20OGwGsWmYW3IQ3cEsCczadefAI%7EehWswamDxG3UZ%7ErYrMqXprFlpiB1fTas7SPeKpDT4V5YdTtEHUZRQ14Lb0QEogydoNmgj4afvqrqwV-6dnL3Z7iaJxDrdxYTIk6VfPIHIH1%7E7dyLjRwG8gdhvhCQ__&Key-Pair-Id=KCD77M1F0VK2B" width="40%" alt="PatFig Dataset Logo">
</div>
## Table of Contents
- [Introduction](#introduction)
- [Dataset Description](#dataset-description)
- [Overview](#overview)
- [Structure](#structure)
- [Categories](#categories)
- [Usage](#usage)
- [Challenges and Considerations](#challenges-and-considerations)
- [License and Usage Guidelines](#license-and-usage-guidelines)
## Introduction
The PatFig Dataset is a curated collection of over 18,000 patent images from more than 7,000 European patent applications, spanning the year 2020. It aims to provide a comprehensive resource for research and applications in image captioning, abstract reasoning, patent analysis, and automated documentprocessing.
The overarching goal of this dataset is to advance the research in visually situated language understanding towards more hollistic consumption of the visual and textual data.
## Dataset Description
### Overview
This dataset includes patent figures accompanied by short and long captions, reference numerals, corresponding terms, and a minimal set of claims, offering a detailed insight into the depicted inventions.
### Structure
- **Image Files**: Technical drawings, block diagrams, flowcharts, plots, and grayscale photographs.
- **Captions**: Each figure is accompanied by a short and long caption describing its content and context.
- **Reference Numerals and Terms**: Key components in the figures are linked to their descriptions through reference numerals.
- **Minimal Set of Claims**: Claims sentences summarizing the interactions among elements within each figure.
- **Metadata**: Includes image names, publication numbers, titles, figure identifiers, and more. The detailed descriptions of the fields are available in the Dataset Documentation.
### Categories
The dataset is categorized according to the International Patent Classification (IPC) system, ensuring a diverse representation of technological domains.
## Usage
The PatFig Dataset is intended for use in patent image analysis, document image processing, visual question answering tasks, and image captioning in technical contexts. Users are encouraged to explore innovative applications in related fields.
<p align="center">
<span style="display: inline-block; margin-right: 20px;"><img src="https://cdn-lfs-us-1.huggingface.co/repos/25/0c/250cb7eb9b83b2bd76ad6440700971baf0ec2981fdcb94b7fad768f2eb59fecc/3c626eeb8727520da886493356c116cc5165a0104fa7a3445bce92cb4117591c?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27PatFig_example.png%3B+filename%3D%22PatFig_example.png%22%3B&response-content-type=image%2Fpng&Expires=1710484079&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxMDQ4NDA3OX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzI1LzBjLzI1MGNiN2ViOWI4M2IyYmQ3NmFkNjQ0MDcwMDk3MWJhZjBlYzI5ODFmZGNiOTRiN2ZhZDc2OGYyZWI1OWZlY2MvM2M2MjZlZWI4NzI3NTIwZGE4ODY0OTMzNTZjMTE2Y2M1MTY1YTAxMDRmYTdhMzQ0NWJjZTkyY2I0MTE3NTkxYz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=ehYXk1BEjurpR-Rpn6QmYrSJmuX96AjF6c6BzNXYvSLrwkE9olnZfkstWhhR7QJ6Y%7Ef4--82aUUb0wXJdnmCBCmMrJ2JzipYMpZ92XRrIeJ41Kd2YGHr1QU-IWIXE-2eYJRXrq2GdNo3tf3dqJLKzG7FWyoxqthQt2EGpneAyMURw81LGqay1N9pvfnoB751BPEUsiRz-iDI8G8HkNkJ%7EViE7HKU5rTCV2pPfHjKKQ6pLXUW%7EIwpvkXLj02xhGD-aoo24TYZ5NdZJC1lYj56ynqyABhnPhhFqzVsD%7Eqmdi9wmw2gKa--HZU5q3bmZtm9lsifOQ4mLkJ8x4vl2TWefA__&Key-Pair-Id=KCD77M1F0VK2B" alt="PatFig Image Captioning Version" width="286"/></span>
<span style="display: inline-block; margin-left: 20px;"><img src="https://cdn-lfs-us-1.huggingface.co/repos/25/0c/250cb7eb9b83b2bd76ad6440700971baf0ec2981fdcb94b7fad768f2eb59fecc/532251dbff11e080a91b60d91956c49420a70381143cd8c43ea80fb94608d7f9?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27PatFigVQA_example.png%3B+filename%3D%22PatFigVQA_example.png%22%3B&response-content-type=image%2Fpng&Expires=1710484116&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxMDQ4NDExNn19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzI1LzBjLzI1MGNiN2ViOWI4M2IyYmQ3NmFkNjQ0MDcwMDk3MWJhZjBlYzI5ODFmZGNiOTRiN2ZhZDc2OGYyZWI1OWZlY2MvNTMyMjUxZGJmZjExZTA4MGE5MWI2MGQ5MTk1NmM0OTQyMGE3MDM4MTE0M2NkOGM0M2VhODBmYjk0NjA4ZDdmOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=DR9Zobe00j2rUf0QDFD2yxYy96TwLq0Jgl9CdGT4MrbjmtDMUbFQ0W3xOTNiCBxQ3fEZJ0ykFjmE-tNo8UjOCneWKqyj1AoqcYRDozji9HL0flVplSRceMLmnAzgMyKBSiXJNXNhFv2iEz1007qiyQiMidOpQoyPyZXXqYDzQLHQdKfhYlKUFEs-w9ZYT0vJwKDOlBIAc7pfPuPmoMnPP5sJ4etayoU2bY43WdUaL6cqoEuPS14LaCNIpwl8z2-BhZMp6YZstLoQgiktWQYun7izeLIZavqFRPRJ46GndJ0mdVXS5c%7E7QrT4BFucDQZ%7EV-skGr5CaInhfQnH99ep5w__&Key-Pair-Id=KCD77M1F0VK2B" alt="PatFig VQA Version" width="300""/></span>
</p>
## Challenges and Considerations
Users should be aware of challenges such as interpreting compound figures. PatFig was built automatically using high-performance machine-learning and deep-learning methods. Therefore, the data might contain noise, which was mentioned in the corresponding paper.
## License and Usage Guidelines
The dataset is released under a Creative Commons Attribution-NonCommercial 2.0 Generic (CC BY-NC 2.0) License. It is intended for non-commercial use, and users must adhere to the license terms.
## Cite as
```
@inproceedings{aubakirova2023patfig,
title={PatFig: Generating Short and Long Captions for Patent Figures},
author={Aubakirova, Dana and Gerdes, Kim and Liu, Lufei},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={2843--2849},
year={2023}
}
``` |
AlekseyKorshuk/vicuna-v0-chatml | ---
dataset_info:
features:
- name: source
dtype: string
- name: conversation
list:
- name: content
dtype: string
- name: do_train
dtype: bool
- name: role
dtype: string
splits:
- name: train
num_bytes: 1125413655.0
num_examples: 268680
download_size: 518750501
dataset_size: 1125413655.0
---
# Dataset Card for "vicuna-v0-chatml"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Jaiver123/axenai-test | ---
license: apache-2.0
---
|
DataStudio/T2S_dataset_v2 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: content
dtype: string
splits:
- name: train
num_bytes: 35349683294.07329
num_examples: 214995
download_size: 34104281825
dataset_size: 35349683294.07329
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
BhabhaAI/hindi-RAG-20k | ---
language:
- hi
license: apache-2.0
---
|
tyzhu/find_second_sent_train_10_eval_10_hint5 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: title
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 39713
num_examples: 30
- name: validation
num_bytes: 9412
num_examples: 10
download_size: 45009
dataset_size: 49125
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "find_second_sent_train_10_eval_10_hint5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dmayhem93/self-critiquing-critique-and-refine-test | ---
dataset_info:
features:
- name: id
dtype: string
- name: source_id
dtype: string
- name: split
dtype: string
- name: time
dtype: float64
- name: labeler
dtype: string
- name: is_topic_based_summarization
dtype: bool
- name: category
dtype: string
- name: severity
dtype: int64
- name: text_quotes
list:
- name: begin
dtype: int64
- name: end
dtype: int64
- name: response_quotes
list:
- name: begin
dtype: int64
- name: end
dtype: int64
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 26100872
num_examples: 5119
download_size: 4209199
dataset_size: 26100872
---
# Dataset Card for "self-critiquing-critique-and-refine-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
projecte-aina/WikiCAT_ca | ---
YAML tags:
annotations_creators:
- auromatically-generated
language_creators:
- found
language:
- ca
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
pretty_name: wikicat_ca
size_categories:
- unknown
source_datasets: []
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# WikiCAT_ca: Catalan Text Classification dataset
## Dataset Description
- **Homepage** [Projecte AINA](https://projecteaina.cat/tech/)
- **Repository** [HuggingFace](https://huggingface.co/projecte-aina)
- **Point of Contact** langtech@bsc.es
**Repository**
https://github.com/TeMU-BSC/WikiCAT
### Dataset Summary
WikiCAT_ca is a Catalan corpus for thematic Text Classification tasks. It is created automagically from Wikipedia and Wikidata sources, and contains 13201 articles from the Viquipedia classified under 13 different categories.
This dataset was developed by BSC TeMU as part of the AINA project, and intended as an evaluation of LT capabilities to generate useful synthetic corpus.
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International</a>.
### Supported Tasks and Leaderboards
Text classification, Language Model
### Languages
The dataset is in Catalan (ca-ES).
## Dataset Structure
### Data Instances
Two json files, one for each split.
### Data Fields
We used a simple model with the article text and associated labels, without further metadata.
#### Example:
<pre>
{"version": "1.1.0",
"data":
[
{
'sentence': ' Celsius és conegut com l\'inventor de l\'escala centesimal del termòmetre. Encara que aquest instrument és un invent molt antic, la història de la seva gradació és molt més capritxosa. Durant el segle xvi era graduat com "fred" col·locant-lo (...)',
'label': 'Ciència'
},
.
.
.
]
}
</pre>
#### Labels
'Ciència_i_Tecnologia', 'Dret', 'Economia', 'Enginyeria', 'Entreteniment', 'Esport', 'Filosofia', 'Història', 'Humanitats', 'Matemàtiques', 'Música', 'Política', 'Religió'
### Data Splits
* dev_ca.json: 2484 label-document pairs
* train_ca.json: 9907 label-document pairs
## Dataset Creation
### Methodology
“Category” starting pages are chosen to represent the topics in each language.
We extract, for each category, the main pages, as well as the subcategories ones, and the individual pages under this first level.
For each page, the "summary" provided by Wikipedia is also extracted as the representative text.
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
The source data are thematic categories in the different Wikipedias
#### Who are the source language producers?
### Annotations
#### Annotation process
Automatic annotation
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
We are aware that this data might contain biases. We have not applied any steps to reduce their impact.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International</a>.
### Contributions
[N/A]
|
phongdtd/youtube_casual_audio | ---
multilinguality:
vi:
- 190K<n<200K
source_datasets:
- extended|youtube
task_categories:
- automatic-speech-recognition
task_ids: []
Pretty_name: Youtube Casual Audio
Annotations_creators:
- crowdsourced
Language_creators:
- datlq
Languages:
- vi
Licenses:
- cc0-1.0
---
# Dataset Card for common_voice
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
[Needs More Information]
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Vietnamese
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, called path and its sentence. Additional fields include accent, age, client_id, up_votes down_votes, gender, locale and segment.
`
{
'file_path': 'audio/_1OsFqkFI38_34.304_39.424.wav',
'script': 'Ik vind dat een dubieuze procedure.',
'audio': {'path': 'audio/_1OsFqkFI38_34.304_39.424.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000}
`
### Data Fields
file_path: The path to the audio file
audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
script: The sentence the user was prompted to speak
### Data Splits
The speech material has been subdivided into portions for train, test, validated.
The val, test, train are all data that has been reviewed, deemed of high quality and split into val, test and train.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information]
### Contributions
Thanks to [@datlq](https://github.com/datlq98) for adding this dataset.
|
open-llm-leaderboard/details_allknowingroger__Mistralmath-15B-pass | ---
pretty_name: Evaluation run of allknowingroger/Mistralmath-15B-pass
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [allknowingroger/Mistralmath-15B-pass](https://huggingface.co/allknowingroger/Mistralmath-15B-pass)\
\ 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_allknowingroger__Mistralmath-15B-pass\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-10T21:19:39.771304](https://huggingface.co/datasets/open-llm-leaderboard/details_allknowingroger__Mistralmath-15B-pass/blob/main/results_2024-04-10T21-19-39.771304.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\": 0.5958998447216084,\n\
\ \"acc_stderr\": 0.03324679502929795,\n \"acc_norm\": 0.5993176739147386,\n\
\ \"acc_norm_stderr\": 0.033921307926057284,\n \"mc1\": 0.43084455324357407,\n\
\ \"mc1_stderr\": 0.017335272475332366,\n \"mc2\": 0.604572168002731,\n\
\ \"mc2_stderr\": 0.016122997468132123\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5708191126279863,\n \"acc_stderr\": 0.014464085894870653,\n\
\ \"acc_norm\": 0.6006825938566553,\n \"acc_norm_stderr\": 0.014312094557946712\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6534554869547898,\n\
\ \"acc_stderr\": 0.004748965717214275,\n \"acc_norm\": 0.8312089225253934,\n\
\ \"acc_norm_stderr\": 0.0037380177340378714\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5407407407407407,\n\
\ \"acc_stderr\": 0.04304979692464242,\n \"acc_norm\": 0.5407407407407407,\n\
\ \"acc_norm_stderr\": 0.04304979692464242\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6447368421052632,\n \"acc_stderr\": 0.038947344870133176,\n\
\ \"acc_norm\": 0.6447368421052632,\n \"acc_norm_stderr\": 0.038947344870133176\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n\
\ \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.62,\n \
\ \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.630188679245283,\n \"acc_stderr\": 0.02971142188010793,\n\
\ \"acc_norm\": 0.630188679245283,\n \"acc_norm_stderr\": 0.02971142188010793\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6944444444444444,\n\
\ \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.6944444444444444,\n\
\ \"acc_norm_stderr\": 0.03852084696008534\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n\
\ \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5549132947976878,\n\
\ \"acc_stderr\": 0.03789401760283647,\n \"acc_norm\": 0.5549132947976878,\n\
\ \"acc_norm_stderr\": 0.03789401760283647\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3235294117647059,\n \"acc_stderr\": 0.04655010411319616,\n\
\ \"acc_norm\": 0.3235294117647059,\n \"acc_norm_stderr\": 0.04655010411319616\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.71,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.71,\n\
\ \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5063829787234042,\n \"acc_stderr\": 0.032683358999363366,\n\
\ \"acc_norm\": 0.5063829787234042,\n \"acc_norm_stderr\": 0.032683358999363366\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\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370333,\n\
\ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370333\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3994708994708995,\n \"acc_stderr\": 0.025225450284067877,\n \"\
acc_norm\": 0.3994708994708995,\n \"acc_norm_stderr\": 0.025225450284067877\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3333333333333333,\n\
\ \"acc_stderr\": 0.04216370213557835,\n \"acc_norm\": 0.3333333333333333,\n\
\ \"acc_norm_stderr\": 0.04216370213557835\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.5806451612903226,\n \"acc_stderr\": 0.02807158890109185,\n \"\
acc_norm\": 0.5806451612903226,\n \"acc_norm_stderr\": 0.02807158890109185\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.43842364532019706,\n \"acc_stderr\": 0.03491207857486518,\n \"\
acc_norm\": 0.43842364532019706,\n \"acc_norm_stderr\": 0.03491207857486518\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\
: 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7212121212121212,\n \"acc_stderr\": 0.03501438706296781,\n\
\ \"acc_norm\": 0.7212121212121212,\n \"acc_norm_stderr\": 0.03501438706296781\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7424242424242424,\n \"acc_stderr\": 0.03115626951964683,\n \"\
acc_norm\": 0.7424242424242424,\n \"acc_norm_stderr\": 0.03115626951964683\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8341968911917098,\n \"acc_stderr\": 0.026839845022314415,\n\
\ \"acc_norm\": 0.8341968911917098,\n \"acc_norm_stderr\": 0.026839845022314415\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5794871794871795,\n \"acc_stderr\": 0.02502861027671086,\n \
\ \"acc_norm\": 0.5794871794871795,\n \"acc_norm_stderr\": 0.02502861027671086\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3074074074074074,\n \"acc_stderr\": 0.02813325257881563,\n \
\ \"acc_norm\": 0.3074074074074074,\n \"acc_norm_stderr\": 0.02813325257881563\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6512605042016807,\n \"acc_stderr\": 0.03095663632856654,\n \
\ \"acc_norm\": 0.6512605042016807,\n \"acc_norm_stderr\": 0.03095663632856654\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\
acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7798165137614679,\n \"acc_stderr\": 0.017765978652327544,\n \"\
acc_norm\": 0.7798165137614679,\n \"acc_norm_stderr\": 0.017765978652327544\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4351851851851852,\n \"acc_stderr\": 0.03381200005643525,\n \"\
acc_norm\": 0.4351851851851852,\n \"acc_norm_stderr\": 0.03381200005643525\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7549019607843137,\n \"acc_stderr\": 0.030190282453501947,\n \"\
acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.030190282453501947\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7468354430379747,\n \"acc_stderr\": 0.028304657943035303,\n \
\ \"acc_norm\": 0.7468354430379747,\n \"acc_norm_stderr\": 0.028304657943035303\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6502242152466368,\n\
\ \"acc_stderr\": 0.03200736719484503,\n \"acc_norm\": 0.6502242152466368,\n\
\ \"acc_norm_stderr\": 0.03200736719484503\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6793893129770993,\n \"acc_stderr\": 0.04093329229834277,\n\
\ \"acc_norm\": 0.6793893129770993,\n \"acc_norm_stderr\": 0.04093329229834277\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8347107438016529,\n \"acc_stderr\": 0.03390780612972776,\n \"\
acc_norm\": 0.8347107438016529,\n \"acc_norm_stderr\": 0.03390780612972776\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\
\ \"acc_stderr\": 0.041331194402438376,\n \"acc_norm\": 0.7592592592592593,\n\
\ \"acc_norm_stderr\": 0.041331194402438376\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7116564417177914,\n \"acc_stderr\": 0.035590395316173425,\n\
\ \"acc_norm\": 0.7116564417177914,\n \"acc_norm_stderr\": 0.035590395316173425\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5267857142857143,\n\
\ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.5267857142857143,\n\
\ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.04354631077260595,\n\
\ \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.04354631077260595\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8376068376068376,\n\
\ \"acc_stderr\": 0.02416161812798774,\n \"acc_norm\": 0.8376068376068376,\n\
\ \"acc_norm_stderr\": 0.02416161812798774\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7739463601532567,\n\
\ \"acc_stderr\": 0.014957458504335839,\n \"acc_norm\": 0.7739463601532567,\n\
\ \"acc_norm_stderr\": 0.014957458504335839\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.684971098265896,\n \"acc_stderr\": 0.025009313790069716,\n\
\ \"acc_norm\": 0.684971098265896,\n \"acc_norm_stderr\": 0.025009313790069716\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3564245810055866,\n\
\ \"acc_stderr\": 0.0160182397105134,\n \"acc_norm\": 0.3564245810055866,\n\
\ \"acc_norm_stderr\": 0.0160182397105134\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6437908496732027,\n \"acc_stderr\": 0.02742047766262922,\n\
\ \"acc_norm\": 0.6437908496732027,\n \"acc_norm_stderr\": 0.02742047766262922\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6752411575562701,\n\
\ \"acc_stderr\": 0.026596782287697043,\n \"acc_norm\": 0.6752411575562701,\n\
\ \"acc_norm_stderr\": 0.026596782287697043\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6851851851851852,\n \"acc_stderr\": 0.025842248700902168,\n\
\ \"acc_norm\": 0.6851851851851852,\n \"acc_norm_stderr\": 0.025842248700902168\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.45390070921985815,\n \"acc_stderr\": 0.02970045324729147,\n \
\ \"acc_norm\": 0.45390070921985815,\n \"acc_norm_stderr\": 0.02970045324729147\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4367666232073012,\n\
\ \"acc_stderr\": 0.012667701919603666,\n \"acc_norm\": 0.4367666232073012,\n\
\ \"acc_norm_stderr\": 0.012667701919603666\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5919117647058824,\n \"acc_stderr\": 0.029855261393483924,\n\
\ \"acc_norm\": 0.5919117647058824,\n \"acc_norm_stderr\": 0.029855261393483924\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6241830065359477,\n \"acc_stderr\": 0.019594021136577443,\n \
\ \"acc_norm\": 0.6241830065359477,\n \"acc_norm_stderr\": 0.019594021136577443\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7061224489795919,\n \"acc_stderr\": 0.02916273841024977,\n\
\ \"acc_norm\": 0.7061224489795919,\n \"acc_norm_stderr\": 0.02916273841024977\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5870646766169154,\n\
\ \"acc_stderr\": 0.03481520803367348,\n \"acc_norm\": 0.5870646766169154,\n\
\ \"acc_norm_stderr\": 0.03481520803367348\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036625,\n \
\ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036625\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5,\n \
\ \"acc_stderr\": 0.03892494720807614,\n \"acc_norm\": 0.5,\n \"\
acc_norm_stderr\": 0.03892494720807614\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.03126781714663179,\n\
\ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.03126781714663179\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.43084455324357407,\n\
\ \"mc1_stderr\": 0.017335272475332366,\n \"mc2\": 0.604572168002731,\n\
\ \"mc2_stderr\": 0.016122997468132123\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7663772691397001,\n \"acc_stderr\": 0.011892194477183522\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4313874147081122,\n \
\ \"acc_stderr\": 0.013642195352511573\n }\n}\n```"
repo_url: https://huggingface.co/allknowingroger/Mistralmath-15B-pass
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|arc:challenge|25_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|gsm8k|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hellaswag|10_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-10T21-19-39.771304.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-10T21-19-39.771304.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- '**/details_harness|winogrande|5_2024-04-10T21-19-39.771304.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-10T21-19-39.771304.parquet'
- config_name: results
data_files:
- split: 2024_04_10T21_19_39.771304
path:
- results_2024-04-10T21-19-39.771304.parquet
- split: latest
path:
- results_2024-04-10T21-19-39.771304.parquet
---
# Dataset Card for Evaluation run of allknowingroger/Mistralmath-15B-pass
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [allknowingroger/Mistralmath-15B-pass](https://huggingface.co/allknowingroger/Mistralmath-15B-pass) 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_allknowingroger__Mistralmath-15B-pass",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-10T21:19:39.771304](https://huggingface.co/datasets/open-llm-leaderboard/details_allknowingroger__Mistralmath-15B-pass/blob/main/results_2024-04-10T21-19-39.771304.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": 0.5958998447216084,
"acc_stderr": 0.03324679502929795,
"acc_norm": 0.5993176739147386,
"acc_norm_stderr": 0.033921307926057284,
"mc1": 0.43084455324357407,
"mc1_stderr": 0.017335272475332366,
"mc2": 0.604572168002731,
"mc2_stderr": 0.016122997468132123
},
"harness|arc:challenge|25": {
"acc": 0.5708191126279863,
"acc_stderr": 0.014464085894870653,
"acc_norm": 0.6006825938566553,
"acc_norm_stderr": 0.014312094557946712
},
"harness|hellaswag|10": {
"acc": 0.6534554869547898,
"acc_stderr": 0.004748965717214275,
"acc_norm": 0.8312089225253934,
"acc_norm_stderr": 0.0037380177340378714
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5407407407407407,
"acc_stderr": 0.04304979692464242,
"acc_norm": 0.5407407407407407,
"acc_norm_stderr": 0.04304979692464242
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6447368421052632,
"acc_stderr": 0.038947344870133176,
"acc_norm": 0.6447368421052632,
"acc_norm_stderr": 0.038947344870133176
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.62,
"acc_stderr": 0.04878317312145633,
"acc_norm": 0.62,
"acc_norm_stderr": 0.04878317312145633
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.630188679245283,
"acc_stderr": 0.02971142188010793,
"acc_norm": 0.630188679245283,
"acc_norm_stderr": 0.02971142188010793
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6944444444444444,
"acc_stderr": 0.03852084696008534,
"acc_norm": 0.6944444444444444,
"acc_norm_stderr": 0.03852084696008534
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.27,
"acc_stderr": 0.0446196043338474,
"acc_norm": 0.27,
"acc_norm_stderr": 0.0446196043338474
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5549132947976878,
"acc_stderr": 0.03789401760283647,
"acc_norm": 0.5549132947976878,
"acc_norm_stderr": 0.03789401760283647
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3235294117647059,
"acc_stderr": 0.04655010411319616,
"acc_norm": 0.3235294117647059,
"acc_norm_stderr": 0.04655010411319616
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.71,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.71,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5063829787234042,
"acc_stderr": 0.032683358999363366,
"acc_norm": 0.5063829787234042,
"acc_norm_stderr": 0.032683358999363366
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4824561403508772,
"acc_stderr": 0.04700708033551038,
"acc_norm": 0.4824561403508772,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5724137931034483,
"acc_stderr": 0.04122737111370333,
"acc_norm": 0.5724137931034483,
"acc_norm_stderr": 0.04122737111370333
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3994708994708995,
"acc_stderr": 0.025225450284067877,
"acc_norm": 0.3994708994708995,
"acc_norm_stderr": 0.025225450284067877
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.04216370213557835,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.04216370213557835
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.5806451612903226,
"acc_stderr": 0.02807158890109185,
"acc_norm": 0.5806451612903226,
"acc_norm_stderr": 0.02807158890109185
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.43842364532019706,
"acc_stderr": 0.03491207857486518,
"acc_norm": 0.43842364532019706,
"acc_norm_stderr": 0.03491207857486518
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695237,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695237
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7212121212121212,
"acc_stderr": 0.03501438706296781,
"acc_norm": 0.7212121212121212,
"acc_norm_stderr": 0.03501438706296781
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7424242424242424,
"acc_stderr": 0.03115626951964683,
"acc_norm": 0.7424242424242424,
"acc_norm_stderr": 0.03115626951964683
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8341968911917098,
"acc_stderr": 0.026839845022314415,
"acc_norm": 0.8341968911917098,
"acc_norm_stderr": 0.026839845022314415
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5794871794871795,
"acc_stderr": 0.02502861027671086,
"acc_norm": 0.5794871794871795,
"acc_norm_stderr": 0.02502861027671086
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3074074074074074,
"acc_stderr": 0.02813325257881563,
"acc_norm": 0.3074074074074074,
"acc_norm_stderr": 0.02813325257881563
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6512605042016807,
"acc_stderr": 0.03095663632856654,
"acc_norm": 0.6512605042016807,
"acc_norm_stderr": 0.03095663632856654
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.36423841059602646,
"acc_stderr": 0.03929111781242742,
"acc_norm": 0.36423841059602646,
"acc_norm_stderr": 0.03929111781242742
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7798165137614679,
"acc_stderr": 0.017765978652327544,
"acc_norm": 0.7798165137614679,
"acc_norm_stderr": 0.017765978652327544
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4351851851851852,
"acc_stderr": 0.03381200005643525,
"acc_norm": 0.4351851851851852,
"acc_norm_stderr": 0.03381200005643525
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7549019607843137,
"acc_stderr": 0.030190282453501947,
"acc_norm": 0.7549019607843137,
"acc_norm_stderr": 0.030190282453501947
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7468354430379747,
"acc_stderr": 0.028304657943035303,
"acc_norm": 0.7468354430379747,
"acc_norm_stderr": 0.028304657943035303
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6502242152466368,
"acc_stderr": 0.03200736719484503,
"acc_norm": 0.6502242152466368,
"acc_norm_stderr": 0.03200736719484503
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6793893129770993,
"acc_stderr": 0.04093329229834277,
"acc_norm": 0.6793893129770993,
"acc_norm_stderr": 0.04093329229834277
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8347107438016529,
"acc_stderr": 0.03390780612972776,
"acc_norm": 0.8347107438016529,
"acc_norm_stderr": 0.03390780612972776
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7592592592592593,
"acc_stderr": 0.041331194402438376,
"acc_norm": 0.7592592592592593,
"acc_norm_stderr": 0.041331194402438376
},
"harness|hendrycksTest-logical_fallacies|5": {
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"acc": 0.4313874147081122,
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
electricity_load_diagrams | ---
annotations_creators:
- no-annotation
language_creators:
- found
language: []
license:
- unknown
multilinguality:
- monolingual
pretty_name: Electricity Load Diagrams
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- time-series-forecasting
task_ids:
- univariate-time-series-forecasting
dataset_info:
- config_name: uci
features:
- name: start
dtype: timestamp[s]
- name: target
sequence: float32
- name: feat_static_cat
sequence: uint64
- name: item_id
dtype: string
splits:
- name: train
num_bytes: 42968147
num_examples: 370
- name: test
num_bytes: 302059069
num_examples: 2590
- name: validation
num_bytes: 43004777
num_examples: 370
download_size: 261335609
dataset_size: 388031993
- config_name: lstnet
features:
- name: start
dtype: timestamp[s]
- name: target
sequence: float32
- name: feat_static_cat
sequence: uint64
- name: item_id
dtype: string
splits:
- name: train
num_bytes: 20843200
num_examples: 320
- name: test
num_bytes: 195401080
num_examples: 2240
- name: validation
num_bytes: 27787720
num_examples: 320
download_size: 261335609
dataset_size: 244032000
---
# Dataset Card for Electricity Load Diagrams
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Electricity Load Diagrams 2011-2014](https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014)
- **Paper:** [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
](https://dl.acm.org/doi/10.1145/3209978.3210006)
- **Point of Contact:** [Artur Trindade](mailto:artur.trindade@elergone.pt)
### Dataset Summary
This dataset contains hourly kW electricity consumption time series of 370 Portuguese clients from 2011 to 2014.
### Dataset Usage
The dataset has the following configuration parameters:
- `freq` is the time series frequency at which we resample (default: `"1H"`)
- `prediction_length` is the forecast horizon for this task which is used to make the validation and test splits (default: `24`)
- `rolling_evaluations` is the number of rolling window time series in the test split for evaluation purposes (default: `7`)
For example, you can specify your own configuration different from those used in the papers as follows:
```python
load_dataset("electricity_load_diagrams", "uci", rolling_evaluations=10)
```
> Notes:
> - Data set has no missing values.
> - Values are in kW of each 15 min rescaled to hourly. To convert values in kWh values must be divided by 4.
> - All time labels report to Portuguese hour, however all days present 96 measures (24*4).
> - Every year in March time change day (which has only 23 hours) the values between 1:00 am and 2:00 am are zero for all points.
> - Every year in October time change day (which has 25 hours) the values between 1:00 am and 2:00 am aggregate the consumption of two hours.
### Supported Tasks and Leaderboards
- `univariate-time-series-forecasting`: The time series forecasting tasks involves learning the future `target` values of time series in a dataset for the `prediction_length` time steps. The results of the forecasts can then be validated via the ground truth in the `validation` split and tested via the `test` split.
### Languages
## Dataset Structure
Data set has no missing values. The raw values are in kW of each 15 min interval and are resampled to hourly frequency.
Each time series represent one client. Some clients were created after 2011. In these cases consumption were considered zero. All time labels report to Portuguese hour, however all days contain 96 measurements (24*4). Every year in March time change day (which has only 23 hours) the values between 1:00 am and 2:00 am are zero for all points. Every year in October time change day (which has 25 hours) the values between 1:00 am and 2:00 am aggregate the consumption of two hours.
### Data Instances
A sample from the training set is provided below:
```
{
'start': datetime.datetime(2012, 1, 1, 0, 0),
'target': [14.0, 18.0, 21.0, 20.0, 22.0, 20.0, 20.0, 20.0, 13.0, 11.0], # <= this target array is a concatenated sample
'feat_static_cat': [0],
'item_id': '0'
}
```
We have two configurations `uci` and `lstnet`, which are specified as follows.
The time series are resampled to hourly frequency. We test on 7 rolling windows of prediction length of 24.
The `uci` validation therefore ends 24*7 time steps before the end of each time series. The training split ends 24 time steps before the end of the validation split.
For the `lsnet` configuration we split the training window so that it is 0.6-th of the full time series and the validation is 0.8-th of the full time series and the last 0.2-th length time windows is used as the test set of 7 rolling windows of the 24 time steps each. Finally, as in the LSTNet paper, we only consider time series that are active in the year 2012--2014, which leaves us with 320 time series.
### Data Fields
For this univariate regular time series we have:
- `start`: a `datetime` of the first entry of each time series in the dataset
- `target`: an `array[float32]` of the actual target values
- `feat_static_cat`: an `array[uint64]` which contains a categorical identifier of each time series in the dataset
- `item_id`: a string identifier of each time series in a dataset for reference
Given the `freq` and the `start` datetime, we can assign a datetime to each entry in the target array.
### Data Splits
| name |train|unsupervised|test |
|----------|----:|-----------:|----:|
|uci|370| 2590|370|
|lstnet|320| 2240|320|
## Dataset Creation
The Electricity Load Diagrams 2011–2014 Dataset was developed by Artur Trindade and shared in UCI Machine Learning Repository. This dataset covers the electricity load of 370 substations in Portugal from the start of 2011 to the end of 2014 with a sampling period of 15 min. We will resample this to hourly time series.
### Curation Rationale
Research and development of load forecasting methods. In particular short-term electricity forecasting.
### Source Data
This dataset covers the electricity load of 370 sub-stations in Portugal from the start of 2011 to the end of 2014 with a sampling period of 15 min.
#### 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
```bibtex
@inproceedings{10.1145/3209978.3210006,
author = {Lai, Guokun and Chang, Wei-Cheng and Yang, Yiming and Liu, Hanxiao},
title = {Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks},
year = {2018},
isbn = {9781450356572},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3209978.3210006},
doi = {10.1145/3209978.3210006},
booktitle = {The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
pages = {95--104},
numpages = {10},
location = {Ann Arbor, MI, USA},
series = {SIGIR '18}
}
```
### Contributions
Thanks to [@kashif](https://github.com/kashif) for adding this dataset. |
nadsoft/Jordan-Audio | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
splits:
- name: train
num_bytes: 669684377.68
num_examples: 5044
download_size: 660360475
dataset_size: 669684377.68
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "jo_aud"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AMead10/Universal-Magicoder-Evol-Instruct-110K | ---
dataset_info:
features:
- name: system
dtype: string
- name: conversation
list:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 247866350
num_examples: 111183
download_size: 136772975
dataset_size: 247866350
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Universal-Magicoder-Evol-Instruct-110K"
[Magicoder-Evol-Instruct-110K](https://huggingface.co/datasets/ise-uiuc/Magicoder-Evol-Instruct-110K) reformatted to in the universal data format. |
jmarmier/scs-phase-iii | ---
license: mit
---
|
alexandrainst/nordjylland-news-summarization | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: summary
dtype: string
- name: text_len
dtype: int64
- name: summary_len
dtype: int64
splits:
- name: train
num_bytes: 118935809
num_examples: 75219
- name: val
num_bytes: 6551332
num_examples: 4178
- name: test
num_bytes: 6670392
num_examples: 4178
download_size: 81334629
dataset_size: 132157533
license: apache-2.0
task_categories:
- summarization
language:
- da
size_categories:
- 10K<n<100K
---
# Dataset Card for "nordjylland-news-summarization"
## Dataset Description
- **Point of Contact:** [Oliver Kinch](mailto:oliver.kinch@alexandra.dk)
- **Size of dataset:** 148 MB
### Dataset Summary
This dataset consists of pairs containing text and corresponding summaries extracted from the Danish newspaper [TV2 Nord](https://www.tv2nord.dk/).
### Supported Tasks and Leaderboards
Summarization is the intended task for this dataset. No leaderboard is active at this point.
### Languages
The dataset is available in Danish (`da`).
## Dataset Structure
An example from the dataset looks as follows.
```
{
"text": "some text",
"summary": "some summary",
"text_len": <number of chars in text>,
"summary_len": <number of chars in summary>
}
```
### Data Fields
- `text`: a `string` feature.
- `summary`: a `string` feature.
- `text_len`: an `int64` feature.
- `summary_len`: an `int64` feature.
### Dataset Statistics
#### Number of samples
- Train: 75219
- Val: 4178
- Test: 4178
#### Text Length Distribution
- Minimum length: 21
- Maximum length: 35164
#### Summary Length Distribution
- Minimum length: 12
- Maximum length: 499
## Potential Dataset Issues
Within the dataset, there are 181 instances where the length of the summary exceeds the length of the corresponding text.
## Dataset Creation
### Curation Rationale
There are not many large-scale summarization datasets in Danish.
### Source Data
The dataset has been collected through the TV2 Nord API, which can be accessed [here](https://developer.bazo.dk/#876ab6f9-e057-43e3-897a-1563de34397e).
## Additional Information
### Dataset Curators
[Oliver Kinch](https://huggingface.co/oliverkinch) from the [The Alexandra
Institute](https://alexandra.dk/)
### Licensing Information
The dataset is licensed under the [CC0
license](https://creativecommons.org/share-your-work/public-domain/cc0/). |
kastan/chatbot-comparisons-rlhf | ---
dataset_info:
features:
- name: Hi, this is an Alexa Prize Social bot! Tonight I'm planning to do some stargazing.
It's supposed to be cloudy, but I'm hoping the skies will clear. What's your
favorite planet?
struct:
- name: blenderbot_175B
sequence: string
- name: cosmo
sequence: string
- name: davinci-003
sequence: string
- name: I don't think we've met. What's your name?
struct:
- name: blenderbot_175B
sequence: string
- name: cosmo
sequence: string
- name: davinci-003
sequence: string
- name: Hello! How is your day going so far?
struct:
- name: blenderbot_175B
sequence: string
- name: cosmo
sequence: string
- name: davinci-003
sequence: string
- name: Hi, I'm an Alexa Prize Social bot, it's nice to meet you!
struct:
- name: blenderbot_175B
sequence: string
- name: cosmo
sequence: string
- name: davinci-003
sequence: string
- name: How has your day been so far?
struct:
- name: blenderbot_175B
sequence: string
- name: cosmo
sequence: string
- name: davinci-003
sequence: string
- name: Do you have any plans for the weekend?
struct:
- name: blenderbot_175B
sequence: string
- name: cosmo
sequence: string
- name: davinci-003
sequence: string
- name: Do you have any recommendations for things to do/see around here?
struct:
- name: blenderbot_175B
sequence: string
- name: cosmo
sequence: string
- name: davinci-003
sequence: string
- name: What brings you here today?
struct:
- name: blenderbot_175B
sequence: string
- name: cosmo
sequence: string
- name: davinci-003
sequence: string
- name: Have you seen any good movies/shows lately?
struct:
- name: blenderbot_175B
sequence: string
- name: cosmo
sequence: string
- name: davinci-003
sequence: string
- name: What do you do for work/study?
struct:
- name: blenderbot_175B
sequence: string
- name: cosmo
sequence: string
- name: davinci-003
sequence: string
- name: What do you enjoy doing in your free time?
struct:
- name: blenderbot_175B
sequence: string
- name: cosmo
sequence: string
- name: davinci-003
sequence: string
- name: What's something interesting you've learned recently?
struct:
- name: blenderbot_175B
sequence: string
- name: cosmo
sequence: string
- name: davinci-003
sequence: string
- name: What do you like to do in your free time?
struct:
- name: blenderbot_175B
sequence: string
- name: cosmo
sequence: string
- name: davinci-003
sequence: string
- name: What's been the highlight of your day so far?
struct:
- name: blenderbot_175B
sequence: string
- name: cosmo
sequence: string
- name: davinci-003
sequence: string
- name: What do you think about this whole AI revolution that’s happening?
struct:
- name: blenderbot_175B
sequence: string
- name: cosmo
sequence: string
- name: davinci-003
sequence: string
splits:
- name: train
num_bytes: 19186
num_examples: 1
download_size: 69471
dataset_size: 19186
---
# Dataset Card for "chatbot-comparisons-rlhf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
gryffindor-ISWS/aggregated_metrics | ---
license: gpl-3.0
---
|
Reasat/arch_processed | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 6335034336.864
num_examples: 7577
download_size: 6331772726
dataset_size: 6335034336.864
---
# Dataset Card for "arch_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vietgpt/the_pile_openwebtext2 | ---
language: en
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
- name: reddit_scores
sequence: int32
splits:
- name: train
num_bytes: 68786199155
num_examples: 17103059
download_size: 42444568964
dataset_size: 68786199155
---
# Dataset Card for "the_pile_openwebtext2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/okazaki_yumemi_touhou | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of okazaki_yumemi/岡崎夢美 (Touhou)
This is the dataset of okazaki_yumemi/岡崎夢美 (Touhou), containing 285 images and their tags.
The core tags of this character are `red_hair, red_eyes, bow, long_hair, braid`, 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 | 285 | 238.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/okazaki_yumemi_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 285 | 163.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/okazaki_yumemi_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 519 | 291.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/okazaki_yumemi_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 285 | 221.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/okazaki_yumemi_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 519 | 367.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/okazaki_yumemi_touhou/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/okazaki_yumemi_touhou',
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, cross, red_capelet, single_braid, solo, hair_bow, dress, ribbon, skirt_set, smile |
| 1 | 11 |  |  |  |  |  | 1girl, cross, smile, solo, capelet, skirt, short_hair, hexagram |
| 2 | 5 |  |  |  |  |  | 1girl, capelet, short_hair, smile, solo, cross, open_mouth |
| 3 | 6 |  |  |  |  |  | 1girl, bangs, long_sleeves, looking_at_viewer, red_bowtie, red_capelet, red_skirt, red_vest, smile, solo, white_shirt, closed_mouth, buttons, blush, collared_shirt, short_hair, simple_background |
| 4 | 7 |  |  |  |  |  | 1girl, bangs, buttons, collared_shirt, cross, long_sleeves, red_bowtie, red_capelet, red_vest, solo, white_shirt, open_mouth, red_skirt, very_long_hair, looking_at_viewer, frilled_skirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cross | red_capelet | single_braid | solo | hair_bow | dress | ribbon | skirt_set | smile | capelet | skirt | short_hair | hexagram | open_mouth | bangs | long_sleeves | looking_at_viewer | red_bowtie | red_skirt | red_vest | white_shirt | closed_mouth | buttons | blush | collared_shirt | simple_background | very_long_hair | frilled_skirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------|:---------------|:-------|:-----------|:--------|:---------|:------------|:--------|:----------|:--------|:-------------|:-----------|:-------------|:--------|:---------------|:--------------------|:-------------|:------------|:-----------|:--------------|:---------------|:----------|:--------|:-----------------|:--------------------|:-----------------|:----------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | X | X | | | X | | | | | X | X | X | X | X | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | | | X | | | | | X | X | | X | | X | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | | X | | X | | | | | X | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | | |
| 4 | 7 |  |  |  |  |  | X | X | X | | X | | | | | | | | | | X | X | X | X | X | X | X | X | | X | | X | | X | X |
|
peterkchung/commonsense_cot_partial_annotated_v0.1 | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: question_concept
dtype: string
- name: choices
struct:
- name: label
sequence: string
- name: text
sequence: string
- name: answerKey
dtype: string
- name: rationale
dtype: string
splits:
- name: train
num_bytes: 52115
num_examples: 100
download_size: 39000
dataset_size: 52115
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Commonsense QA CoT (Partial, Annotated) v0.1
## Dataset Summary
This dataset is a human-annotated subset of randomly sampled question-answer entries from the CommonsenseQA dataset (tau/commonsense_qa).
The 'rationales' for each QA pair were created using a two-part method. First, Mixtral (mistralai/Mixtral-8x7B-Instruct-v0.1) was used
to generate 3 unique CoT (Chain-of-Thought) explanations. Next, human evaluation was applied to distill the random sampling down to
a cohesive set of question-answer-rationale triplets. In most cases, the response generated by Mixtral was kept as a passing
explanation for the QA pair.
The working hypothesis, inspired by the research papers listed below, is that a diverse set of CoT rationales passed along
with the CommonsenseQA question-answer choices will provide accelerated commonsense reasoning performance on even a relatively
small model (<3B parameters).
Additional refinement and annotations to this dataset are to follow.
Background research and inspiration from the following papers:
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge (https://arxiv.org/abs/1811.00937)
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (https://arxiv.org/abs/2201.11903)
Specializing Smaller Language Models towards Multi-Step Reasoning (https://arxiv.org/abs/2301.12726)
Orca 2: Teaching Small Language Models How to Reason (https://arxiv.org/abs/2311.11045)
Large Language Models Are Reasoning Teachers (https://arxiv.org/abs/2212.10071)
Teaching Small Language Models to Reason (https://arxiv.org/abs/2212.08410)
## Dataset Structure
### Languages
The dataset is in English (`en`).
### Data Fields
- `id` (`str`): Unique ID.
- `question`: a `string` feature.
- `question_concept` (`str`): ConceptNet concept associated to the question.
- `choices`: a dictionary feature containing:
- `label`: a `string` feature.
- `text`: a `string` feature.
- `answerKey`: a `string` feature.
- `rationale`: a `string` feature.
### Data Example
```
{'id': '1fe48d12b6f6e4e38f4445f3ec60d5c5',
'question': 'What can happen to someone too sure of their learning?',
'question_concept': 'learning',
'choices': {'label': ['A', 'B', 'C', 'D', 'E'],
'text': ['growth',
'gaining knowledge',
'enlightenment',
'knowing more',
'overconfidence']},
'answerKey': 'E',
'rationale': 'When someone is too sure of their learning, they become '
'overconfident, thinking that they know everything. This can '
'prevent them from learning more, as they stop seeking new '
'knowledge and ideas. They might also miss out on '
'enlightenment, as they close themselves off to new '
'perspectives. Overall, their growth might be stunted, as they '
'stop challenging themselves and expanding their '
'understanding. So, out of the given choices, the most '
'appropriate answer is overconfidence.'}
```
### Source Data
- **Data:** https://huggingface.co/datasets/tau/commonsense_qa
- **Homepage:** https://www.tau-nlp.org/commonsenseqa
- **Repository:** https://github.com/jonathanherzig/commonsenseqa
- **Paper:** https://arxiv.org/abs/1811.00937
### Licensing Information
The dataset is licensed under the MIT License. |
CyberHarem/myrtle_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of myrtle/テンニンカ/桃金娘 (Arknights)
This is the dataset of myrtle/テンニンカ/桃金娘 (Arknights), containing 492 images and their tags.
The core tags of this character are `long_hair, pointy_ears, green_eyes, red_hair, ahoge, parted_bangs, very_long_hair, ear_piercing`, 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 | 492 | 840.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/myrtle_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 492 | 393.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/myrtle_arknights/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1244 | 873.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/myrtle_arknights/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 492 | 698.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/myrtle_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1244 | 1.34 GiB | [Download](https://huggingface.co/datasets/CyberHarem/myrtle_arknights/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/myrtle_arknights',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 18 |  |  |  |  |  | 1girl, simple_background, solo, white_background, bare_shoulders, looking_at_viewer, white_jacket, white_tank_top, long_sleeves, off_shoulder, piercing, open_jacket, brown_hair, holding_flag, open_mouth, standard_bearer, upper_body, :d, apple, blush |
| 1 | 6 |  |  |  |  |  | 1girl, bare_shoulders, closed_mouth, crop_top, midriff, navel, off_shoulder, open_jacket, solo, white_jacket, blush, looking_at_viewer, piercing, simple_background, smile, upper_body, white_background, white_tank_top, white_shirt, :3, small_breasts |
| 2 | 10 |  |  |  |  |  | 1girl, bare_shoulders, long_sleeves, looking_at_viewer, midriff, navel, off_shoulder, open_jacket, solo, white_jacket, white_tank_top, apple, piercing, belt, simple_background, smile, standard_bearer, white_background, crop_top, holding_flag, blush, closed_mouth, purple_skirt, brown_hair, holding_fruit, open_mouth |
| 3 | 5 |  |  |  |  |  | 1girl, :d, belt, crop_top, long_sleeves, midriff, navel, open_mouth, solo, standard_bearer, white_jacket, white_shirt, white_tank_top, apple, bare_shoulders, looking_at_viewer, off_shoulder, open_jacket, blush, grey_skirt, holding_flag, shoes, white_footwear, arm_up, blue_butterfly, breasts, cowboy_shot, piercing, sleeves_past_wrists, socks |
| 4 | 28 |  |  |  |  |  | 1girl, long_sleeves, looking_at_viewer, shoes, solo, standard_bearer, white_jacket, white_footwear, white_tank_top, open_jacket, apple, full_body, midriff, grey_socks, navel, simple_background, belt, holding_flag, bare_shoulders, off_shoulder, shirt, grey_skirt, crop_top, open_mouth, white_background, :d, sleeves_past_fingers, standing |
| 5 | 12 |  |  |  |  |  | 1girl, blue_sash, holding_flag, long_sleeves, official_alternate_costume, open_mouth, solo, standard_bearer, white_headwear, white_skirt, looking_at_viewer, white_shirt, shako_cap, white_background, :d, cowboy_shot, thighhighs, apple, simple_background, cape, piercing |
| 6 | 8 |  |  |  |  |  | 1girl, blue_sash, long_sleeves, official_alternate_costume, smile, solo, standard_bearer, white_headwear, white_skirt, grey_thighhighs, holding_flag, looking_at_viewer, white_footwear, white_shirt, shako_cap, closed_mouth, full_body, shoes, standing, fur-trimmed_cloak, hat_feather, fruit, fur-trimmed_cape, open_mouth, white_cape |
| 7 | 10 |  |  |  |  |  | 1girl, bare_shoulders, eyewear_on_head, heart-shaped_eyewear, looking_at_viewer, official_alternate_costume, sunglasses, white_bikini, frilled_bikini, hair_flower, navel, solo, blush, cowboy_shot, earrings, open_mouth, simple_background, small_breasts, collarbone, smile, bridal_garter, white_background, red_flower, shell_necklace, stomach |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | simple_background | solo | white_background | bare_shoulders | looking_at_viewer | white_jacket | white_tank_top | long_sleeves | off_shoulder | piercing | open_jacket | brown_hair | holding_flag | open_mouth | standard_bearer | upper_body | :d | apple | blush | closed_mouth | crop_top | midriff | navel | smile | white_shirt | :3 | small_breasts | belt | purple_skirt | holding_fruit | grey_skirt | shoes | white_footwear | arm_up | blue_butterfly | breasts | cowboy_shot | sleeves_past_wrists | socks | full_body | grey_socks | shirt | sleeves_past_fingers | standing | blue_sash | official_alternate_costume | white_headwear | white_skirt | shako_cap | thighhighs | cape | grey_thighhighs | fur-trimmed_cloak | hat_feather | fruit | fur-trimmed_cape | white_cape | eyewear_on_head | heart-shaped_eyewear | sunglasses | white_bikini | frilled_bikini | hair_flower | earrings | collarbone | bridal_garter | red_flower | shell_necklace | stomach |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-------------------|:-----------------|:--------------------|:---------------|:-----------------|:---------------|:---------------|:-----------|:--------------|:-------------|:---------------|:-------------|:------------------|:-------------|:-----|:--------|:--------|:---------------|:-----------|:----------|:--------|:--------|:--------------|:-----|:----------------|:-------|:---------------|:----------------|:-------------|:--------|:-----------------|:---------|:-----------------|:----------|:--------------|:----------------------|:--------|:------------|:-------------|:--------|:-----------------------|:-----------|:------------|:-----------------------------|:-----------------|:--------------|:------------|:-------------|:-------|:------------------|:--------------------|:--------------|:--------|:-------------------|:-------------|:------------------|:-----------------------|:-------------|:---------------|:-----------------|:--------------|:-----------|:-------------|:----------------|:-------------|:-----------------|:----------|
| 0 | 18 |  |  |  |  |  | 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 | X | | | | | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 28 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | X | | X | X | X | | X | X | | | X | X | X | | | | | X | | | X | X | X | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 12 |  |  |  |  |  | X | X | X | X | | X | | | X | | X | | | X | X | X | | X | X | | | | | | | X | | | | | | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 6 | 8 |  |  |  |  |  | X | | X | | | X | | | X | | | | | X | X | X | | | | | X | | | | X | X | | | | | | | X | X | | | | | | | X | | | | X | X | X | X | X | X | | | X | X | X | X | X | X | | | | | | | | | | | | |
| 7 | 10 |  |  |  |  |  | X | X | X | X | X | X | | | | | | | | | X | | | | | X | | | | X | X | | | X | | | | | | | | | | X | | | | | | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
|
riotu-lab/Quran-Tafseers | ---
license: apache-2.0
task_categories:
- question-answering
language:
- ar
pretty_name: 'Tibyan For Holy Quran '
size_categories:
- 10K<n<100K
---
### Model Details
Developed by: Prince Sultan University - Riotu Lab
This dataset is intended for use in natural language processing tasks, particularly for understanding classical Arabic and religious texts, including text analysis, language modeling, and thematic studies.
Primary Users: Researchers and developers in the field of natural language processing, religious studies, and AI, specifically those working with classical Arabic texts.
Out-of-scope Use Cases: This dataset is not intended for predictive modeling that could lead to ethical concerns, such as surveillance or profiling based on religious texts.
Model/Data Specifications
Format: Json
Dataset Size: Contains more than 57K rows
Language: Arabic
### Dataset Structure
Fields:
- sura_number: Integer representing the Surah number in the Quran.-
- Aya_number: Integer representing the Ayah number in the Surah.
- tafseers: Dictionary mapping Tafseer sources to their text for each Ayah:
- Tafseer Name :
1: "التفسير الميسر",
2: "تفسير الجلالين",
3:"تفسير ابن كثير",
4: "تفسير الوسيط لطنطاوي",
5: "تفسير البغوي",
6: "تفسير القرطبي",
7: "تفسير الطبري",
|
Radioo/Shikha_Task2_dataset | ---
dataset_info:
features:
- name: ENGLISH
dtype: string
- name: HINDI
dtype: string
- name: HINGLISH
dtype: string
splits:
- name: train
num_bytes: 5847
num_examples: 10
download_size: 9321
dataset_size: 5847
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AIARTCHAN/lora-hanboka-000003 | ---
license: creativeml-openrail-m
tags:
- lora
- aiartchan
- stable-diffusion
---
# Lora - hanboka-000003
## Dataset Description
- **원본** [한복 lora](https://arca.live/b/aiart/69417775)
한복 로라 파일
프롬프트에 hanbok, korean clothes
가중치 0.8 권장
[다운로드](https://huggingface.co/datasets/AIARTCHAN/lora-hanboka-000003/resolve/main/hanboka-000003.safetensors)
|
fancyerii/test | ---
annotations_creators: []
language_creators: []
language: []
license: []
multilinguality: []
pretty_name: demo
size_categories:
- 10K<n<100K
source_datasets: []
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage: [HomePage](https://fancyerii.github.io)**
- **Repository: fancyerii**
- **Paper: No Paper**
- **Leaderboard: No**
- **Point of Contact:**
### Dataset Summary
测试数据集
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
中文
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@fancyerii](https://github.com/fancyerii) for adding this dataset.
|
Teera/RelationExtraction-NLG-Thai | ---
license: apache-2.0
---
This is translate dataset NLG for data extraction in english language to thai language.
|
somewheresystems/dataclysm-arxiv | ---
license: cc0-1.0
language:
- en
tags:
- arxiv
- science
pretty_name: dataclysm-arxiv
size_categories:
- 1M<n<10M
---
# DATACLYSM PATCH 0.0.2: ARXIV
## USE THE NOTEBOOK TO GET STARTED!
https://github.com/somewheresystems/dataclysm
# somewheresystems/dataclysm-wikipedia-titles
This dataset comprises of 3,360,984 English language arXiv papers from the Cornell/arXiv dataset, with two new columns added: title-embeddings and abstract-embeddings. These additional columns were generated using the bge-small-en-v1.5 embeddings model. The dataset was sourced from the Cornell/arXiv GCP bucket's json manifest for arXiv metadata, as of January 14th, 2024 [gs://arxiv-dataset/metadata-v5/arxiv-metadata-oai.json](gs://arxiv-dataset/metadata-v5/arxiv-metadata-oai.json)
# Embeddings Model
We used https://huggingface.co/BAAI/bge-small-en-v1.5 to embed the `title` and `abstract` fields.
## Contact
Please contact hi@dataclysm.xyz for inquiries. |
active-learning/unlabeled_samples | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 16624881.912901606
num_examples: 59755
download_size: 15263092
dataset_size: 16624881.912901606
---
# Dataset Card for "unlabeled_samples"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
etherealxx/tortoise_voices | ---
license: unknown
---
Some voices i got from Youtube for Tortoise TTS usage. Fair Use. |
Juanid14317/NewMinDataSetForEngUrduRUrduEmogi | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 21086235.14905149
num_examples: 36604
- name: test
num_bytes: 5272134.85094851
num_examples: 9152
download_size: 14166891
dataset_size: 26358370.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
open-llm-leaderboard/details_jondurbin__airoboros-c34b-2.1 | ---
pretty_name: Evaluation run of jondurbin/airoboros-c34b-2.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jondurbin/airoboros-c34b-2.1](https://huggingface.co/jondurbin/airoboros-c34b-2.1)\
\ 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_jondurbin__airoboros-c34b-2.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-22T21:16:11.848472](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-c34b-2.1/blob/main/results_2023-10-22T21-16-11.848472.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\": 0.2950922818791946,\n\
\ \"em_stderr\": 0.004670729426706433,\n \"f1\": 0.35763003355704864,\n\
\ \"f1_stderr\": 0.004615741016305116,\n \"acc\": 0.38384505972750876,\n\
\ \"acc_stderr\": 0.010339372510053756\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.2950922818791946,\n \"em_stderr\": 0.004670729426706433,\n\
\ \"f1\": 0.35763003355704864,\n \"f1_stderr\": 0.004615741016305116\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08339651250947688,\n \
\ \"acc_stderr\": 0.0076156502771067\n },\n \"harness|winogrande|5\":\
\ {\n \"acc\": 0.6842936069455406,\n \"acc_stderr\": 0.01306309474300081\n\
\ }\n}\n```"
repo_url: https://huggingface.co/jondurbin/airoboros-c34b-2.1
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: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|arc:challenge|25_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_22T21_16_11.848472
path:
- '**/details_harness|drop|3_2023-10-22T21-16-11.848472.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-22T21-16-11.848472.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_22T21_16_11.848472
path:
- '**/details_harness|gsm8k|5_2023-10-22T21-16-11.848472.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-22T21-16-11.848472.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hellaswag|10_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-28T14:17:53.693745.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-28T14:17:53.693745.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-28T14:17:53.693745.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_22T21_16_11.848472
path:
- '**/details_harness|winogrande|5_2023-10-22T21-16-11.848472.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-22T21-16-11.848472.parquet'
- config_name: results
data_files:
- split: 2023_08_28T14_17_53.693745
path:
- results_2023-08-28T14:17:53.693745.parquet
- split: 2023_10_22T21_16_11.848472
path:
- results_2023-10-22T21-16-11.848472.parquet
- split: latest
path:
- results_2023-10-22T21-16-11.848472.parquet
---
# Dataset Card for Evaluation run of jondurbin/airoboros-c34b-2.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/jondurbin/airoboros-c34b-2.1
- **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 [jondurbin/airoboros-c34b-2.1](https://huggingface.co/jondurbin/airoboros-c34b-2.1) 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_jondurbin__airoboros-c34b-2.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T21:16:11.848472](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-c34b-2.1/blob/main/results_2023-10-22T21-16-11.848472.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": 0.2950922818791946,
"em_stderr": 0.004670729426706433,
"f1": 0.35763003355704864,
"f1_stderr": 0.004615741016305116,
"acc": 0.38384505972750876,
"acc_stderr": 0.010339372510053756
},
"harness|drop|3": {
"em": 0.2950922818791946,
"em_stderr": 0.004670729426706433,
"f1": 0.35763003355704864,
"f1_stderr": 0.004615741016305116
},
"harness|gsm8k|5": {
"acc": 0.08339651250947688,
"acc_stderr": 0.0076156502771067
},
"harness|winogrande|5": {
"acc": 0.6842936069455406,
"acc_stderr": 0.01306309474300081
}
}
```
### 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] |
liuyanchen1015/MULTI_VALUE_mnli_serial_verb_give | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev_matched
num_bytes: 5809
num_examples: 20
- name: dev_mismatched
num_bytes: 8432
num_examples: 40
- name: test_matched
num_bytes: 8860
num_examples: 38
- name: test_mismatched
num_bytes: 4387
num_examples: 23
- name: train
num_bytes: 214665
num_examples: 868
download_size: 120430
dataset_size: 242153
---
# Dataset Card for "MULTI_VALUE_mnli_serial_verb_give"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
louisbrulenaudet/code-communes | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code des communes
source_datasets:
- original
pretty_name: Code des communes
task_categories:
- text-generation
- table-question-answering
- summarization
- text-retrieval
- question-answering
- text-classification
size_categories:
- 1K<n<10K
---
# Code des communes, non-instruct (2024-04-15)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Concurrent reading of the LegalKit
To use all the legal data published on LegalKit, you can use this code snippet:
```python
# -*- coding: utf-8 -*-
import concurrent.futures
import os
import datasets
from tqdm.notebook import tqdm
def dataset_loader(
name:str,
streaming:bool=True
) -> datasets.Dataset:
"""
Helper function to load a single dataset in parallel.
Parameters
----------
name : str
Name of the dataset to be loaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
dataset : datasets.Dataset
Loaded dataset object.
Raises
------
Exception
If an error occurs during dataset loading.
"""
try:
return datasets.load_dataset(
name,
split="train",
streaming=streaming
)
except Exception as exc:
logging.error(f"Error loading dataset {name}: {exc}")
return None
def load_datasets(
req:list,
streaming:bool=True
) -> list:
"""
Downloads datasets specified in a list and creates a list of loaded datasets.
Parameters
----------
req : list
A list containing the names of datasets to be downloaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
datasets_list : list
A list containing loaded datasets as per the requested names provided in 'req'.
Raises
------
Exception
If an error occurs during dataset loading or processing.
Examples
--------
>>> datasets = load_datasets(["dataset1", "dataset2"], streaming=False)
"""
datasets_list = []
with concurrent.futures.ThreadPoolExecutor() as executor:
future_to_dataset = {executor.submit(dataset_loader, name): name for name in req}
for future in tqdm(concurrent.futures.as_completed(future_to_dataset), total=len(req)):
name = future_to_dataset[future]
try:
dataset = future.result()
if dataset:
datasets_list.append(dataset)
except Exception as exc:
logging.error(f"Error processing dataset {name}: {exc}")
return datasets_list
req = [
"louisbrulenaudet/code-artisanat",
"louisbrulenaudet/code-action-sociale-familles",
# ...
]
datasets_list = load_datasets(
req=req,
streaming=True
)
dataset = datasets.concatenate_datasets(
datasets_list
)
```
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
- `start`: `string`, the date of entry into force of the article.
- `expiration`: `string`, the date of expiration of the article.
- `num`: `string`, the id of the article.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Feedback
If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com). |
KatoHF/helpsteer_binarized | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 23181889.087317087
num_examples: 16654
download_size: 18057259
dataset_size: 23181889.087317087
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
joshr/competency_games | ---
dataset_info:
features:
- name: description
dtype: string
- name: label
dtype: int64
splits:
- name: sg_train
num_bytes: 51120000
num_examples: 90000
- name: sg_validation
num_bytes: 2726400
num_examples: 4800
- name: sg_test
num_bytes: 2726400
num_examples: 4800
- name: sg_llm_eval
num_bytes: 272640
num_examples: 480
- name: sg_exemplar
num_bytes: 6816
num_examples: 12
- name: cc_train
num_bytes: 119621976
num_examples: 90000
- name: cc_validation
num_bytes: 6379974
num_examples: 4800
- name: cc_test
num_bytes: 6380186
num_examples: 4800
- name: cc_llm_eval
num_bytes: 637971
num_examples: 480
- name: cc_exemplar
num_bytes: 15965
num_examples: 12
- name: hn_train
num_bytes: 183955709
num_examples: 90000
- name: hn_validation
num_bytes: 9810256
num_examples: 4800
- name: hn_test
num_bytes: 9809441
num_examples: 4800
- name: hn_llm_eval
num_bytes: 981379
num_examples: 480
- name: hn_exemplar
num_bytes: 24478
num_examples: 12
- name: ring_train
num_bytes: 79429571
num_examples: 90000
- name: ring_validation
num_bytes: 4236560
num_examples: 4800
- name: ring_test
num_bytes: 4236287
num_examples: 4800
- name: ring_llm_eval
num_bytes: 423614
num_examples: 480
- name: ring_exemplar
num_bytes: 10597
num_examples: 12
- name: voting_train
num_bytes: 48250182
num_examples: 90000
- name: voting_validation
num_bytes: 2573367
num_examples: 4800
- name: voting_test
num_bytes: 2573448
num_examples: 4800
- name: voting_llm_eval
num_bytes: 257391
num_examples: 480
- name: voting_exemplar
num_bytes: 6430
num_examples: 12
download_size: 93750310
dataset_size: 536467038
---
# Dataset Card for "competency_games"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
KeynesYouDigIt/DoctorKelp | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': test_satellite
'1': train_kelp
'2': train_satellite
splits:
- name: train
num_bytes: 28827196275.44
num_examples: 22540
- name: test
num_bytes: 3643649767.064
num_examples: 2852
download_size: 18049706797
dataset_size: 32470846042.503998
---
# Dataset Card for "DoctorKelp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Ti-Ma/TiMaGPT2-2011 | ---
license: other
license_name: paracrawl-license
license_link: LICENSE
---
|
Lucky2022/coda | ---
license: apache-2.0
---
|
rania-refaat/preprocessed-data | ---
dataset_info:
features:
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dtype: int64
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dtype: string
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dtype: string
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num_examples: 20
- name: validation
num_bytes: 813137
num_examples: 20
download_size: 7635775
dataset_size: 4046434
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
JoAo352/Ratinhox | ---
license: openrail
---
|
nuprl/CanItEdit | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: canitedit
pretty_name: CanItEdit
tags:
- code-generation
- code
dataset_info:
features:
- name: id
dtype: int64
- name: name
dtype: string
- name: full_name
dtype: string
- name: before
dtype: string
- name: after
dtype: string
- name: tests
dtype: string
- name: instruction_descriptive
dtype: string
- name: instruction_lazy
dtype: string
- name: taxonomy
struct:
- name: change_kind
dtype: string
- name: libraries
sequence: string
- name: topic
dtype: string
splits:
- name: test
num_bytes: 564910
num_examples: 105
download_size: 250477
dataset_size: 564910
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions
CanItEdit is a benchmark for evaluating LLMs on instructional code editing, the task of updating a program given a natural language instruction. The benchmark contains 105 hand-crafted Python programs with before and after code blocks, two types of natural language instructions (descriptive and lazy), and a hidden test suite.
The dataset’s dual natural language instructions test model efficiency in two scenarios:
1) Descriptive: Detailed instructions replicate situations where users provide specific specifications or
another model outlines a plan, similar to Reflexion prompting,
2) Lazy: Informal instructions resemble typical user queries
for LLMs in code generation.
For more information and results see [our paper](https://arxiv.org/abs/2312.12450).
## Citation
If you use our work, please cite our paper as such:
```
@inproceedings{cassano2023edit,
title={{Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions}},
author={Federico Cassano and Luisa Li and Akul Sethi and Noah Shinn and Abby Brennan-Jones and Anton Lozhkov and Carolyn Jane Anderson and Arjun Guha},
booktitle={The First International Workshop on Large Language Model for Code},
year={2024},
url={https://arxiv.org/abs/2312.12450}
}
```
## How To Evaluate
All the code for evaluating the benchmark can be found in our [GitHub repository](https://github.com/nuprl/CanItEdit). |
qkrwnstj/cubism-journal | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 4452339.0
num_examples: 20
download_size: 4429181
dataset_size: 4452339.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
arize-ai/ecommerce_reviews_with_language_drift | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: sentiment-classification-reviews-with-drift
size_categories:
- 10K<n<100K
source_datasets:
- extended|imdb
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for `reviews_with_drift`
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place.
### Supported Tasks and Leaderboards
`text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).
### Languages
Text is mainly written in english.
## 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
[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 [@fjcasti1](https://github.com/fjcasti1) for adding this dataset. |
CyberHarem/abe_nana_idolmastercinderellagirls | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of abe_nana/安部菜々 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of abe_nana/安部菜々 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `ponytail, brown_hair, brown_eyes, bow, breasts, orange_hair, ribbon, bangs, short_hair, hair_bow`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 585.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/abe_nana_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 345.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/abe_nana_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1161 | 730.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/abe_nana_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 517.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/abe_nana_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1161 | 1.02 GiB | [Download](https://huggingface.co/datasets/CyberHarem/abe_nana_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/abe_nana_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 | 22 |  |  |  |  |  | 1girl, solo, blush, smile, looking_at_viewer, open_mouth, maid_apron, hair_ribbon, long_sleeves, thighhighs |
| 1 | 5 |  |  |  |  |  | 1girl, blush, maid_apron, red_bowtie, solo, white_apron, frilled_apron, hair_ribbon, juliet_sleeves, looking_at_viewer, smile, black_dress, closed_mouth, enmaided, holding, red_eyes, simple_background, sitting, white_background |
| 2 | 20 |  |  |  |  |  | 1girl, open_mouth, rabbit_ears, smile, solo, blush, one_eye_closed, ;d, dress, microphone, thighhighs, large_breasts, looking_at_viewer, v_over_eye |
| 3 | 5 |  |  |  |  |  | 1girl, :d, blush, frills, looking_at_viewer, open_mouth, rabbit_ears, solo, wrist_cuffs, puffy_short_sleeves, medium_breasts, pink_bow, red_eyes, heart, pink_dress, white_apron |
| 4 | 8 |  |  |  |  |  | 1girl, heart, looking_at_viewer, open_mouth, solo, white_gloves, blush, dress, one_eye_closed, rabbit_ears, smile, ;d, choker, pink_bow, magical_girl, frills, holding, puffy_short_sleeves, wand, collarbone, fake_animal_ears, happy_birthday, jewelry, skirt, sparkle |
| 5 | 8 |  |  |  |  |  | 1girl, blue_one-piece_swimsuit, looking_at_viewer, rabbit_ears, school_swimsuit, solo, blush, cleavage, smile, white_thighhighs, collarbone, fake_animal_ears, large_breasts, name_tag, open_mouth, polka_dot, red_eyes, sitting, bracelet, poolside, water |
| 6 | 6 |  |  |  |  |  | 1girl, black_gloves, blush, looking_at_viewer, rabbit_ears, solo, cleavage, navel, skirt, smile, open_mouth, striped_thighhighs, fake_animal_ears, large_breasts, medium_breasts, midriff |
| 7 | 7 |  |  |  |  |  | blue_sky, blush, cloud, day, looking_at_viewer, ocean, outdoors, pink_bikini, 1girl, beach, open_mouth, solo, collarbone, cleavage, red_eyes, bracelet, frilled_bikini, large_breasts, medium_breasts, navel, smile |
| 8 | 11 |  |  |  |  |  | 1girl, detached_collar, playboy_bunny, solo, cleavage, looking_at_viewer, smile, large_breasts, rabbit_ears, black_leotard, blush, wrist_cuffs, open_mouth, simple_background, white_background, bare_shoulders, strapless_leotard, thighhighs, fake_animal_ears, medium_breasts, pink_bow, red_bowtie |
| 9 | 5 |  |  |  |  |  | bare_shoulders, blush, strapless_dress, white_gloves, 1girl, looking_at_viewer, pearl_necklace, pink_dress, solo, tiara, collarbone, frilled_dress, long_hair, medium_breasts, open_mouth, petals, white_dress, :d, earrings, moon, own_hands_together, simple_background, white_background |
| 10 | 5 |  |  |  |  |  | 1girl, blush, collarbone, hair_ribbon, large_breasts, looking_at_viewer, side-tie_bikini_bottom, solo, cleavage, elbow_gloves, open_mouth, simple_background, white_background, white_bikini, bare_shoulders, micro_bikini, shiny_skin, sidelocks, thighs, white_choker, white_gloves, white_thighhighs, kneeling, red_eyes, smile, wariza |
| 11 | 8 |  |  |  |  |  | blush, white_shirt, 1girl, serafuku, smile, solo, looking_at_viewer, white_background, blue_skirt, long_sleeves, pleated_skirt, sidelocks, simple_background, blue_sailor_collar, closed_mouth, neckerchief, red_eyes, white_socks, bag, brown_footwear, collarbone, hair_ribbon, open_mouth, shoes, short_sleeves, sitting |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | blush | smile | looking_at_viewer | open_mouth | maid_apron | hair_ribbon | long_sleeves | thighhighs | red_bowtie | white_apron | frilled_apron | juliet_sleeves | black_dress | closed_mouth | enmaided | holding | red_eyes | simple_background | sitting | white_background | rabbit_ears | one_eye_closed | ;d | dress | microphone | large_breasts | v_over_eye | :d | frills | wrist_cuffs | puffy_short_sleeves | medium_breasts | pink_bow | heart | pink_dress | white_gloves | choker | magical_girl | wand | collarbone | fake_animal_ears | happy_birthday | jewelry | skirt | sparkle | blue_one-piece_swimsuit | school_swimsuit | cleavage | white_thighhighs | name_tag | polka_dot | bracelet | poolside | water | black_gloves | navel | striped_thighhighs | midriff | blue_sky | cloud | day | ocean | outdoors | pink_bikini | beach | frilled_bikini | detached_collar | playboy_bunny | black_leotard | bare_shoulders | strapless_leotard | strapless_dress | pearl_necklace | tiara | frilled_dress | long_hair | petals | white_dress | earrings | moon | own_hands_together | side-tie_bikini_bottom | elbow_gloves | white_bikini | micro_bikini | shiny_skin | sidelocks | thighs | white_choker | kneeling | wariza | white_shirt | serafuku | blue_skirt | pleated_skirt | blue_sailor_collar | neckerchief | white_socks | bag | brown_footwear | shoes | short_sleeves |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-------|:--------|:--------|:--------------------|:-------------|:-------------|:--------------|:---------------|:-------------|:-------------|:--------------|:----------------|:-----------------|:--------------|:---------------|:-----------|:----------|:-----------|:--------------------|:----------|:-------------------|:--------------|:-----------------|:-----|:--------|:-------------|:----------------|:-------------|:-----|:---------|:--------------|:----------------------|:-----------------|:-----------|:--------|:-------------|:---------------|:---------|:---------------|:-------|:-------------|:-------------------|:-----------------|:----------|:--------|:----------|:--------------------------|:------------------|:-----------|:-------------------|:-----------|:------------|:-----------|:-----------|:--------|:---------------|:--------|:---------------------|:----------|:-----------|:--------|:------|:--------|:-----------|:--------------|:--------|:-----------------|:------------------|:----------------|:----------------|:-----------------|:--------------------|:------------------|:-----------------|:--------|:----------------|:------------|:---------|:--------------|:-----------|:-------|:---------------------|:-------------------------|:---------------|:---------------|:---------------|:-------------|:------------|:---------|:---------------|:-----------|:---------|:--------------|:-----------|:-------------|:----------------|:---------------------|:--------------|:--------------|:------|:-----------------|:--------|:----------------|
| 0 | 22 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 20 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | X | X | X | X | X | X | | | | | | | | | | | | X | | | | | X | X | X | X | | | | | X | | X | | X | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 8 |  |  |  |  |  | X | X | X | X | X | X | | | | | | | | | | | | | X | | X | | X | | | | | X | | | | | | | | | | | | | | X | X | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | X | X | X | X | X | X | | | | | | | | | | | | | | | | | X | | | | | X | | | | | | X | | | | | | | | | X | | | X | | | | X | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 7 |  |  |  |  |  | X | X | X | X | X | X | | | | | | | | | | | | | X | | | | | | | | | X | | | | | | X | | | | | | | | X | | | | | | | | X | | | | X | | | | X | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 11 |  |  |  |  |  | X | X | X | X | X | X | | | | X | X | | | | | | | | | X | | X | X | | | | | X | | | | X | | X | X | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | X | X | 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 | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 10 | 5 |  |  |  |  |  | X | X | X | X | X | X | | X | | | | | | | | | | | X | X | | X | | | | | | X | | | | | | | | | | X | | | | X | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 11 | 8 |  |  |  |  |  | X | X | X | X | X | X | | X | X | | | | | | | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X |
|
BOP-Berlin-University-Alliance/dc_terms_prompts | ---
license: gpl-3.0
language:
- en
size_categories:
- n<1K
--- |
james-burton/OrientalMuseum_min3-num | ---
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': DUROM.1950.10.a-b
'1': DUROM.1950.33.a-b
'2': DUROM.1952.1.21.b
'3': DUROM.1954.Spalding29.W
'4': DUROM.1954.Spalding32.a-j
'5': DUROM.1960.1012.a-b
'6': DUROM.1960.1215.a-b
'7': DUROM.1960.1276.a-b
'8': DUROM.1960.1359.a-b
'9': DUROM.1960.1489.b
'10': DUROM.1960.1784.a-b
'11': DUROM.1960.1885.c
'12': DUROM.1960.1908.a-b
'13': DUROM.1960.1951.a-b
'14': DUROM.1960.2068.a-b
'15': DUROM.1960.2224.a-b
'16': DUROM.1960.2255.a-c
'17': DUROM.1960.2349.a-b
'18': DUROM.1960.2395.A-B
'19': DUROM.1960.2448.a-b
'20': DUROM.1960.2456.b
'21': DUROM.1960.2566.a-b
'22': DUROM.1960.2645.A
'23': DUROM.1960.2996.a-b
'24': DUROM.1960.3070.a-b
'25': DUROM.1960.3200.h
'26': DUROM.1960.3253.a-b
'27': DUROM.1960.3295.A-B
'28': DUROM.1960.3400.a-b
'29': DUROM.1960.3449.a-b
'30': DUROM.1960.3573.a-b
'31': DUROM.1960.3685.a-b
'32': DUROM.1960.3969.a-b
'33': DUROM.1960.412.a-b
'34': DUROM.1960.589.a-b
'35': DUROM.1960.592.a-b
'36': DUROM.1960.827.a-b
'37': DUROM.1960.891.c
'38': DUROM.1960.945.c
'39': DUROM.1961.27.B
'40': DUROM.1961.31.B
'41': DUROM.1961.34.a-b
'42': DUROM.1961.40.a-b
'43': DUROM.1961.44.c
'44': DUROM.1962.210.B
'45': DUROM.1962.251.a
'46': DUROM.1962.253.f
'47': DUROM.1962.99.a-c
'48': DUROM.1963.50.b
'49': DUROM.1963.52.A
'50': DUROM.1964.12.F
'51': DUROM.1965.25.B
'52': DUROM.1966.2.A-B
'53': DUROM.1966.45.B
'54': DUROM.1966.46.B
'55': DUROM.1966.62.B
'56': DUROM.1967.23.k
'57': DUROM.1967.40.a-b
'58': DUROM.1968.139.b
'59': DUROM.1968.15.c.a-b
'60': DUROM.1968.178.a-b
'61': DUROM.1968.185.a-b
'62': DUROM.1968.43.d-e
'63': DUROM.1968.46.b
'64': DUROM.1968.64.a-b
'65': DUROM.1968.72.b
'66': DUROM.1968.79.a-b
'67': DUROM.1969.104.c
'68': DUROM.1969.144.B
'69': DUROM.1969.148.a-b
'70': DUROM.1969.150.b
'71': DUROM.1969.162.b
'72': DUROM.1969.166.b
'73': DUROM.1969.169.b
'74': DUROM.1969.171.b
'75': DUROM.1969.186.b
'76': DUROM.1969.188.b
'77': DUROM.1969.189.b
'78': DUROM.1969.190.b
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'172': DUROM.1981.14.a-b
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'222': durma.1989.44.1
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'1389': durom.1973.52
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'1400': durom.1974.22
'1401': durom.1974.30.3
'1402': durom.1974.37
'1403': durom.1974.39
'1404': durom.1974.42
'1405': durom.1974.43
'1406': durom.1974.53
'1407': durom.1974.54
'1408': durom.1974.57
'1409': durom.1974.58
'1410': durom.1974.59
'1411': durom.1974.60
'1412': durom.1974.67
'1413': durom.1974.7
'1414': durom.1974.8
'1415': durom.1974.9
'1416': durom.1975.10
'1417': durom.1975.11
'1418': durom.1975.20
'1419': durom.1975.25
'1420': durom.1975.43
'1421': durom.1975.46
'1422': durom.1975.50
'1423': durom.1975.52
'1424': durom.1975.53
'1425': durom.1975.54
'1426': durom.1975.55
'1427': durom.1975.56
'1428': durom.1975.57
'1429': durom.1975.59
'1430': durom.1975.60
'1431': durom.1975.8
'1432': durom.1976.110
'1433': durom.1976.116
'1434': durom.1976.117
'1435': durom.1976.118
'1436': durom.1976.12
'1437': durom.1976.120
'1438': durom.1976.124
'1439': durom.1976.125
'1440': durom.1976.126
'1441': durom.1976.130
'1442': durom.1976.138
'1443': durom.1976.139
'1444': durom.1976.14
'1445': durom.1976.142
'1446': durom.1976.144
'1447': durom.1976.148
'1448': durom.1976.15
'1449': durom.1976.158
'1450': durom.1976.17
'1451': durom.1976.187
'1452': durom.1976.188
'1453': durom.1976.189
'1454': durom.1976.191
'1455': durom.1976.193
'1456': durom.1976.194
'1457': durom.1976.195
'1458': durom.1976.196
'1459': durom.1976.197
'1460': durom.1976.199
'1461': durom.1976.2
'1462': durom.1976.200
'1463': durom.1976.201
'1464': durom.1976.202
'1465': durom.1976.203
'1466': durom.1976.205
'1467': durom.1976.206
'1468': durom.1976.207
'1469': durom.1976.208
'1470': durom.1976.21
'1471': durom.1976.23
'1472': durom.1976.274
'1473': durom.1976.29
'1474': durom.1976.290
'1475': durom.1976.293
'1476': durom.1976.34
'1477': durom.1976.36
'1478': durom.1977.104
'1479': durom.1977.25
'1480': durom.1977.45
'1481': durom.1977.50
'1482': durom.1977.61
'1483': durom.1977.64
'1484': durom.1977.68
'1485': durom.1977.71
'1486': durom.1977.72
'1487': durom.1977.76
'1488': durom.1977.82
'1489': durom.1977.85
'1490': durom.1978.10
'1491': durom.1978.100
'1492': durom.1978.101
'1493': durom.1978.104
'1494': durom.1978.110
'1495': durom.1978.112
'1496': durom.1978.115
'1497': durom.1978.128
'1498': durom.1978.147
'1499': durom.1978.22
'1500': durom.1978.23
'1501': durom.1978.3
'1502': durom.1978.35
'1503': durom.1978.38
'1504': durom.1978.39
'1505': durom.1978.4
'1506': durom.1978.40
'1507': durom.1978.43
'1508': durom.1978.45
'1509': durom.1978.46
'1510': durom.1978.48
'1511': durom.1978.54
'1512': durom.1978.55
'1513': durom.1978.65
'1514': durom.1978.82
'1515': durom.1978.87
'1516': durom.1978.88
'1517': durom.1978.9
'1518': durom.1978.91
'1519': durom.1978.93
'1520': durom.1978.99
'1521': durom.1979.12.12
'1522': durom.1979.12.13
'1523': durom.1979.12.14
'1524': durom.1979.12.15
'1525': durom.1979.12.16
'1526': durom.1979.12.17
'1527': durom.1979.12.18
'1528': durom.1979.12.19
'1529': durom.1979.12.20
'1530': durom.1979.12.5
'1531': durom.1979.12.8
'1532': durom.1979.12.9
'1533': durom.1979.16.19
'1534': durom.1979.24
'1535': durom.1979.27
'1536': durom.1979.32
'1537': durom.1979.34
'1538': durom.1979.35
'1539': durom.1979.37
'1540': durom.1979.42
'1541': durom.1979.43
'1542': durom.1979.47
'1543': durom.1979.5
'1544': durom.1979.52
'1545': durom.1979.53
'1546': durom.1979.62
'1547': durom.1979.63.1
'1548': durom.1979.7
'1549': durom.1979.75
'1550': durom.1979.79
'1551': durom.1979.80
'1552': durom.1979.81
'1553': durom.1979.82
'1554': durom.1980.21
'1555': durom.1980.23
'1556': durom.1980.28
'1557': durom.1980.3
'1558': durom.1980.35
'1559': durom.1980.38
'1560': durom.1980.53
'1561': durom.1980.54
'1562': durom.1980.61
'1563': durom.1980.62
'1564': durom.1980.64
'1565': durom.1980.68
'1566': durom.1980.85
'1567': durom.1980.95
'1568': durom.1981.103
'1569': durom.1981.107
'1570': durom.1981.124
'1571': durom.1983.14
'1572': durom.1983.19
'1573': durom.1983.20
'1574': durom.1983.21
'1575': durom.1983.22
'1576': durom.1983.24
'1577': durom.1983.25
'1578': durom.1983.26
'1579': durom.1983.28
'1580': durom.1983.29
'1581': durom.1983.30
'1582': durom.1983.5
'1583': durom.1983.8
'1584': durom.1983.9
'1585': durom.1984.15
'1586': durom.1984.22
'1587': durom.1985.10
'1588': durom.1985.15
'1589': durom.1985.17
'1590': durom.1985.19
'1591': durom.1985.2
'1592': durom.1985.20
'1593': durom.1985.26
'1594': durom.1985.27
'1595': durom.1985.33
'1596': durom.1985.36
'1597': durom.1985.39
'1598': durom.1985.40
'1599': durom.1985.45
'1600': durom.1985.46
'1601': durom.1985.47
'1602': durom.1985.48
'1603': durom.1985.49
'1604': durom.1985.50
'1605': durom.1985.51
'1606': durom.1985.54
'1607': durom.1985.56
'1608': durom.1985.57
'1609': durom.1985.7
'1610': durom.1986.6
'1611': durom.1986.7
'1612': durom.1986.d100
'1613': durom.1986.d11
'1614': durom.1986.d119
'1615': durom.1986.d12
'1616': durom.1986.d122
'1617': durom.1986.d124
'1618': durom.1986.d126
'1619': durom.1986.d127
'1620': durom.1986.d128
'1621': durom.1986.d131
'1622': durom.1986.d132
'1623': durom.1986.d136
'1624': durom.1986.d137
'1625': durom.1986.d140
'1626': durom.1986.d143
'1627': durom.1986.d15
'1628': durom.1986.d150
'1629': durom.1986.d151
'1630': durom.1986.d154
'1631': durom.1986.d16
'1632': durom.1986.d161
'1633': durom.1986.d17
'1634': durom.1986.d177
'1635': durom.1986.d18
'1636': durom.1986.d183
'1637': durom.1986.d186
'1638': durom.1986.d187
'1639': durom.1986.d195
'1640': durom.1986.d196
'1641': durom.1986.d198
'1642': durom.1986.d20
'1643': durom.1986.d22
'1644': durom.1986.d23
'1645': durom.1986.d25
'1646': durom.1986.d26
'1647': durom.1986.d27
'1648': durom.1986.d29
'1649': durom.1986.d3
'1650': durom.1986.d304
'1651': durom.1986.d31
'1652': durom.1986.d33
'1653': durom.1986.d35
'1654': durom.1986.d36
'1655': durom.1986.d37
'1656': durom.1986.d38
'1657': durom.1986.d4
'1658': durom.1986.d40
'1659': durom.1986.d41
'1660': durom.1986.d5
'1661': durom.1986.d57
'1662': durom.1986.d6
'1663': durom.1986.d60
'1664': durom.1986.d61
'1665': durom.1986.d64
'1666': durom.1986.d66
'1667': durom.1986.d7
'1668': durom.1986.d75
'1669': durom.1986.d77
'1670': durom.1986.d8
'1671': durom.1987.1
'1672': durom.1987.22
'1673': durom.1987.26
'1674': durom.1988.12
'1675': durom.1988.41
'1676': durom.1988.9
'1677': durom.1990.10
'1678': durom.1991.10
'1679': durom.1991.100
'1680': durom.1991.101
'1681': durom.1991.103
'1682': durom.1991.109
'1683': durom.1991.11
'1684': durom.1991.110
'1685': durom.1991.117
'1686': durom.1991.118
'1687': durom.1991.12
'1688': durom.1991.121
'1689': durom.1991.162
'1690': durom.1991.165
'1691': durom.1991.171
'1692': durom.1991.177
'1693': durom.1991.180
'1694': durom.1991.181
'1695': durom.1991.195
'1696': durom.1991.212
'1697': durom.1991.213
'1698': durom.1991.5
'1699': durom.1991.56
'1700': durom.1991.59
'1701': durom.1991.60
'1702': durom.1991.61
'1703': durom.1991.62
'1704': durom.1991.63
'1705': durom.1991.65
'1706': durom.1991.66
'1707': durom.1991.8
'1708': durom.1991.81
'1709': durom.1991.85
'1710': durom.1991.86
'1711': durom.1991.87
'1712': durom.1991.88
'1713': durom.1991.89
'1714': durom.1991.9
'1715': durom.1991.90
'1716': durom.1991.91
'1717': durom.1991.99
'1718': durom.1992.1
'1719': durom.1992.10
'1720': durom.1992.100
'1721': durom.1992.107
'1722': durom.1992.11
'1723': durom.1992.113
'1724': durom.1992.14
'1725': durom.1992.140
'1726': durom.1992.145
'1727': durom.1992.146
'1728': durom.1992.149
'1729': durom.1992.154
'1730': durom.1992.155
'1731': durom.1992.156
'1732': durom.1992.157
'1733': durom.1992.159
'1734': durom.1992.16
'1735': durom.1992.161
'1736': durom.1992.164
'1737': durom.1992.179
'1738': durom.1992.18
'1739': durom.1992.181
'1740': durom.1992.19
'1741': durom.1992.2
'1742': durom.1992.20
'1743': durom.1992.21
'1744': durom.1992.22
'1745': durom.1992.23
'1746': durom.1992.24
'1747': durom.1992.25
'1748': durom.1992.26
'1749': durom.1992.27
'1750': durom.1992.28
'1751': durom.1992.3
'1752': durom.1992.38
'1753': durom.1992.4
'1754': durom.1992.40
'1755': durom.1992.45
'1756': durom.1992.66
'1757': durom.1992.67
'1758': durom.1992.7
'1759': durom.1992.88
'1760': durom.1992.89
'1761': durom.1992.90
'1762': durom.1992.92
'1763': durom.1992.98
'1764': durom.1993.1
'1765': durom.1993.100
'1766': durom.1993.101
'1767': durom.1993.109
'1768': durom.1993.110
'1769': durom.1993.111
'1770': durom.1993.112
'1771': durom.1993.113
'1772': durom.1993.123
'1773': durom.1993.140
'1774': durom.1993.141
'1775': durom.1993.142
'1776': durom.1993.149
'1777': durom.1993.33
'1778': durom.1993.46
'1779': durom.1993.47
'1780': durom.1993.57
'1781': durom.1993.96
'1782': durom.1994.1
'1783': durom.1994.9
'1784': durom.1995.1
'1785': durom.1995.27
'1786': durom.1995.3
'1787': durom.1995.30
'1788': durom.1995.31
'1789': durom.1995.32
'1790': durom.1995.33
'1791': durom.1995.39
'1792': durom.1995.41
'1793': durom.1995.62
'1794': durom.1995.73
'1795': durom.1995.81
'1796': durom.1995.83
'1797': durom.1995.85
'1798': durom.1995.88
'1799': durom.1995.89
'1800': durom.1996.107
'1801': durom.1996.150
'1802': durom.1996.16
'1803': durom.1996.19
'1804': durom.1996.22
'1805': durom.1996.71
'1806': durom.1997.10
'1807': durom.1997.100
'1808': durom.1997.13
'1809': durom.1997.134
'1810': durom.1997.146
'1811': durom.1997.15
'1812': durom.1997.154
'1813': durom.1997.170
'1814': durom.1997.171
'1815': durom.1997.173
'1816': durom.1997.174
'1817': durom.1997.180
'1818': durom.1997.19
'1819': durom.1997.20
'1820': durom.1997.69
'1821': durom.1997.7
'1822': durom.1998.1.1
'1823': durom.1998.12
'1824': durom.1998.15
'1825': durom.1998.17
'1826': durom.1998.19
'1827': durom.1998.20
'1828': durom.1998.21.1
'1829': durom.1998.21.2
'1830': durom.1998.22
'1831': durom.1998.24
'1832': durom.1998.26
'1833': durom.1999.100
'1834': durom.1999.102
'1835': durom.1999.107
'1836': durom.1999.108
'1837': durom.1999.119
'1838': durom.1999.121
'1839': durom.1999.124
'1840': durom.1999.125
'1841': durom.1999.128
'1842': durom.1999.129
'1843': durom.1999.131
'1844': durom.1999.132
'1845': durom.1999.137.1
'1846': durom.1999.138
'1847': durom.1999.139
'1848': durom.1999.140
'1849': durom.1999.141
'1850': durom.1999.142
'1851': durom.1999.19
'1852': durom.1999.30
'1853': durom.1999.49
'1854': durom.1999.51
'1855': durom.1999.52
'1856': durom.1999.53
'1857': durom.1999.54
'1858': durom.1999.80
'1859': durom.1999.88
'1860': durom.1999.89
'1861': durom.1999.92
'1862': durom.1999.93
'1863': durom.1999.94
'1864': durom.1999.95
'1865': durom.1999.99
'1866': durom.2000.1
'1867': durom.2000.11
'1868': durom.2000.12
'1869': durom.2000.13
'1870': durom.2000.14
'1871': durom.2000.15
'1872': durom.2000.19
'1873': durom.2000.20
'1874': durom.2000.26
'1875': durom.2000.27
'1876': durom.2000.7
'1877': durom.2000.8
'1878': durom.2000.9
'1879': durom.2001.100
'1880': durom.2001.11
'1881': durom.2001.12
'1882': durom.2001.121
'1883': durom.2001.122
'1884': durom.2001.126.1
'1885': durom.2001.129
'1886': durom.2001.14
'1887': durom.2001.147
'1888': durom.2001.149
'1889': durom.2001.15
'1890': durom.2001.151
'1891': durom.2001.163
'1892': durom.2001.175
'1893': durom.2001.178
'1894': durom.2001.182
'1895': durom.2001.190
'1896': durom.2001.192
'1897': durom.2001.193
'1898': durom.2001.204
'1899': durom.2001.206
'1900': durom.2001.22
'1901': durom.2001.23
'1902': durom.2001.27
'1903': durom.2001.29
'1904': durom.2001.29.11
'1905': durom.2001.29.16
'1906': durom.2001.29.17
'1907': durom.2001.29.21
'1908': durom.2001.29.22
'1909': durom.2001.29.23
'1910': durom.2001.29.31
'1911': durom.2001.29.7
'1912': durom.2001.29.8
'1913': durom.2001.29.9
'1914': durom.2001.32
'1915': durom.2001.35.1
'1916': durom.2001.35.2
'1917': durom.2001.35.3
'1918': durom.2001.35.4
'1919': durom.2001.35.5
'1920': durom.2001.41
'1921': durom.2001.43
'1922': durom.2001.50
'1923': durom.2001.6
'1924': durom.2001.64.2
'1925': durom.2001.69.2
'1926': durom.2001.9
'1927': durom.2001.91.28
'1928': durom.2001.91.30
'1929': durom.2001.94
'1930': durom.2001.95
'1931': durom.2001.96.1
'1932': durom.2001.96.10
'1933': durom.2001.96.11
'1934': durom.2001.96.12
'1935': durom.2001.96.13
'1936': durom.2001.96.14
'1937': durom.2001.96.15
'1938': durom.2001.96.16
'1939': durom.2001.96.17
'1940': durom.2001.96.18
'1941': durom.2001.96.19
'1942': durom.2001.96.2
'1943': durom.2001.96.20
'1944': durom.2001.96.21
'1945': durom.2001.96.3
'1946': durom.2001.96.4
'1947': durom.2001.96.5
'1948': durom.2001.96.6
'1949': durom.2001.96.7
'1950': durom.2001.96.8
'1951': durom.2001.96.9
'1952': durom.2002.10
'1953': durom.2002.11
'1954': durom.2002.12
'1955': durom.2002.13
'1956': durom.2002.14
'1957': durom.2002.15
'1958': durom.2002.23
'1959': durom.2002.501
'1960': durom.2002.7
'1961': durom.2002.8
'1962': durom.2003.10
'1963': durom.2004.18
'1964': durom.2004.6
'1965': durom.2004.8
'1966': durom.2004.9
'1967': durom.2005.2
'1968': durom.2006.20
'1969': durom.2006.21
'1970': durom.2006.22
'1971': durom.2006.24.2
'1972': durom.2006.26
'1973': durom.2006.27
'1974': durom.2006.28
'1975': durom.2006.30
'1976': durom.2006.31
'1977': durom.2006.33
'1978': durom.2006.34
'1979': durom.2006.35
'1980': durom.2006.36
'1981': durom.2006.37
'1982': durom.2006.38
'1983': durom.2006.39
'1984': durom.2006.40
'1985': durom.2006.44
'1986': durom.2006.47
'1987': durom.2006.48
'1988': durom.2006.49
'1989': durom.2006.50
'1990': durom.2006.51
'1991': durom.2006.52
'1992': durom.2006.53
'1993': durom.2006.53.129
'1994': durom.2006.53.167
'1995': durom.2006.53.168
'1996': durom.2006.53.169
'1997': durom.2006.53.170
'1998': durom.2006.53.173
'1999': durom.2006.53.174
'2000': durom.2006.53.178
'2001': durom.2006.53.184
'2002': durom.2006.53.191
'2003': durom.2006.53.21
'2004': durom.2006.53.22
'2005': durom.2006.53.23
'2006': durom.2006.53.26
'2007': durom.2006.53.27
'2008': durom.2006.53.31
'2009': durom.2006.53.32.1
'2010': durom.2006.53.34.1
'2011': durom.2006.53.36.1
'2012': durom.2006.53.37.1
'2013': durom.2006.53.37.3
'2014': durom.2006.53.38
'2015': durom.2006.53.39.1
'2016': durom.2006.53.40.1
'2017': durom.2006.53.40.6
'2018': durom.2006.53.40.8
'2019': durom.2006.53.41.1
'2020': durom.2006.53.44
'2021': durom.2006.53.46
'2022': durom.2006.53.82.1
'2023': durom.2006.53.91
'2024': durom.2006.62
'2025': durom.2006.63
'2026': durom.2006.65
'2027': durom.2006.68
'2028': durom.2008.2
'2029': durom.2008.4
'2030': durom.2009.1
'2031': durom.2009.2
'2032': durom.2009.3
'2033': durom.2009.74
'2034': durom.2009.75
'2035': durom.2009.8
'2036': durom.2009.9
'2037': durom.2010.14
'2038': durom.2010.22
'2039': durom.2010.25
'2040': durom.2010.43
'2041': durom.2010.48
'2042': durom.2010.49
'2043': durom.2010.71
'2044': durom.2011.12
'2045': durom.2011.4
'2046': durom.2011.5
'2047': durom.2011.6
'2048': durom.2011.61
'2049': durom.2011.63
'2050': durom.2011.64
'2051': durom.2011.7
'2052': durom.2011.8
'2053': durom.2012.10
'2054': durom.2012.11
'2055': durom.2012.12
'2056': durom.2012.129
'2057': durom.2012.130
'2058': durom.2012.131
'2059': durom.2012.132
'2060': durom.2012.133
'2061': durom.2012.134
'2062': durom.2012.135
'2063': durom.2012.136
'2064': durom.2012.137
'2065': durom.2012.138
'2066': durom.2012.139
'2067': durom.2012.140
'2068': durom.2012.141
'2069': durom.2012.36
'2070': durom.2012.37
'2071': durom.2012.38
'2072': durom.2012.39
'2073': durom.2012.40
'2074': durom.2012.44
'2075': durom.2012.45
'2076': durom.2012.46
'2077': durom.2012.47
'2078': durom.2012.48
'2079': durom.2012.49
'2080': durom.2012.50
'2081': durom.2012.51
'2082': durom.2012.8
'2083': durom.2012.9
'2084': durom.2013.1
'2085': durom.2013.10
'2086': durom.2013.105
'2087': durom.2013.106
'2088': durom.2013.109
'2089': durom.2013.11
'2090': durom.2013.110
'2091': durom.2013.111
'2092': durom.2013.112
'2093': durom.2013.113
'2094': durom.2013.114
'2095': durom.2013.115
'2096': durom.2013.119
'2097': durom.2013.12
'2098': durom.2013.120
'2099': durom.2013.121
'2100': durom.2013.122
'2101': durom.2013.123
'2102': durom.2013.125
'2103': durom.2013.126
'2104': durom.2013.129
'2105': durom.2013.13
'2106': durom.2013.132
'2107': durom.2013.133
'2108': durom.2013.134
'2109': durom.2013.14
'2110': durom.2013.15
'2111': durom.2013.157
'2112': durom.2013.16
'2113': durom.2013.17
'2114': durom.2013.173
'2115': durom.2013.173.12
'2116': durom.2013.174
'2117': durom.2013.175
'2118': durom.2013.176
'2119': durom.2013.177
'2120': durom.2013.178
'2121': durom.2013.179
'2122': durom.2013.180
'2123': durom.2013.181
'2124': durom.2013.187
'2125': durom.2013.188
'2126': durom.2013.190
'2127': durom.2013.2
'2128': durom.2013.208
'2129': durom.2013.224
'2130': durom.2013.225
'2131': durom.2013.246
'2132': durom.2013.247
'2133': durom.2013.252
'2134': durom.2013.258
'2135': durom.2013.298.1
'2136': durom.2013.3
'2137': durom.2013.302
'2138': durom.2013.304
'2139': durom.2013.305
'2140': durom.2013.307
'2141': durom.2013.329
'2142': durom.2013.33.1
'2143': durom.2013.33.2
'2144': durom.2013.330
'2145': durom.2013.338
'2146': durom.2013.340.1
'2147': durom.2013.340.2
'2148': durom.2013.340.3
'2149': durom.2013.340.4
'2150': durom.2013.340.5
'2151': durom.2013.341.2
'2152': durom.2013.342.2
'2153': durom.2013.343
'2154': durom.2013.35
'2155': durom.2013.350
'2156': durom.2013.350.1
'2157': durom.2013.350.2
'2158': durom.2013.350.3
'2159': durom.2013.350.4
'2160': durom.2013.351
'2161': durom.2013.4
'2162': durom.2013.41
'2163': durom.2013.42
'2164': durom.2013.43
'2165': durom.2013.5
'2166': durom.2013.52
'2167': durom.2013.53
'2168': durom.2013.54
'2169': durom.2013.55
'2170': durom.2013.56
'2171': durom.2013.57
'2172': durom.2013.58
'2173': durom.2013.59
'2174': durom.2013.6
'2175': durom.2013.60
'2176': durom.2013.61
'2177': durom.2013.62
'2178': durom.2013.63
'2179': durom.2013.64
'2180': durom.2013.65
'2181': durom.2013.66
'2182': durom.2013.67
'2183': durom.2013.68
'2184': durom.2013.69
'2185': durom.2013.7
'2186': durom.2013.70
'2187': durom.2013.78
'2188': durom.2013.79
'2189': durom.2013.8
'2190': durom.2013.9
'2191': durom.2013.90
'2192': durom.2013.93
'2193': durom.2013.95
'2194': durom.2013.96.1
'2195': durom.2013.96.11
'2196': durom.2013.96.5
'2197': durom.2013.99
'2198': durom.2014.1
'2199': durom.2014.1.1
'2200': durom.2014.1.125
'2201': durom.2014.1.2
'2202': durom.2014.1.3
'2203': durom.2014.1.4
'2204': durom.2014.1.71
'2205': durom.2014.1.77
'2206': durom.2014.1.78
'2207': durom.2014.1.79
'2208': durom.2014.105
'2209': durom.2014.106
'2210': durom.2014.107
'2211': durom.2014.108
'2212': durom.2014.109
'2213': durom.2014.110
'2214': durom.2014.111
'2215': durom.2014.112
'2216': durom.2014.113
'2217': durom.2014.114
'2218': durom.2014.115
'2219': durom.2014.116
'2220': durom.2014.117
'2221': durom.2014.118
'2222': durom.2014.119
'2223': durom.2014.120
'2224': durom.2014.121
'2225': durom.2014.122
'2226': durom.2014.123
'2227': durom.2014.124
'2228': durom.2014.125
'2229': durom.2014.127
'2230': durom.2014.128
'2231': durom.2014.131
'2232': durom.2014.132
'2233': durom.2014.133
'2234': durom.2014.134
'2235': durom.2014.135
'2236': durom.2014.136
'2237': durom.2014.137
'2238': durom.2014.138
'2239': durom.2014.14
'2240': durom.2014.141
'2241': durom.2014.142
'2242': durom.2014.143
'2243': durom.2014.145
'2244': durom.2014.20
'2245': durom.2014.21
'2246': durom.2014.22
'2247': durom.2014.228
'2248': durom.2014.230
'2249': durom.2014.232
'2250': durom.2014.24
'2251': durom.2014.243
'2252': durom.2014.249
'2253': durom.2014.25
'2254': durom.2014.250
'2255': durom.2014.254
'2256': durom.2014.256
'2257': durom.2014.26
'2258': durom.2014.273
'2259': durom.2014.285
'2260': durom.2014.290
'2261': durom.2014.291
'2262': durom.2014.292
'2263': durom.2014.293
'2264': durom.2014.294
'2265': durom.2014.295
'2266': durom.2014.297
'2267': durom.2014.3
'2268': durom.2014.305
'2269': durom.2014.311
'2270': durom.2014.317
'2271': durom.2014.318
'2272': durom.2014.321
'2273': durom.2014.33
'2274': durom.2014.342.101
'2275': durom.2014.344
'2276': durom.2014.346.1
'2277': durom.2014.346.2
'2278': durom.2014.347
'2279': durom.2014.348
'2280': durom.2014.360
'2281': durom.2014.361
'2282': durom.2014.362
'2283': durom.2014.395
'2284': durom.2014.4
'2285': durom.2014.400
'2286': durom.2014.419
'2287': durom.2014.435
'2288': durom.2014.436
'2289': durom.2014.439
'2290': durom.2014.44
'2291': durom.2014.450
'2292': durom.2014.457
'2293': durom.2014.458
'2294': durom.2014.462
'2295': durom.2014.463
'2296': durom.2014.465
'2297': durom.2014.466
'2298': durom.2014.468
'2299': durom.2014.469
'2300': durom.2014.47
'2301': durom.2014.470
'2302': durom.2014.471
'2303': durom.2014.474
'2304': durom.2014.477
'2305': durom.2014.478
'2306': durom.2014.48
'2307': durom.2014.484
'2308': durom.2014.486
'2309': durom.2014.487
'2310': durom.2014.49
'2311': durom.2014.501
'2312': durom.2014.51
'2313': durom.2014.510
'2314': durom.2014.513
'2315': durom.2014.514
'2316': durom.2014.52
'2317': durom.2014.53
'2318': durom.2014.536
'2319': durom.2014.537
'2320': durom.2014.538
'2321': durom.2014.539
'2322': durom.2014.54
'2323': durom.2014.540
'2324': durom.2014.56
'2325': durom.2014.6
'2326': durom.2014.67
'2327': durom.2014.68
'2328': durom.2014.72
'2329': durom.2014.73
'2330': durom.2014.81
'2331': durom.2014.82.1
'2332': durom.2014.82.2
'2333': durom.2014.92
'2334': durom.2014.95
'2335': durom.2014.97
'2336': durom.2014.98
'2337': durom.2015.123
'2338': durom.2015.124
'2339': durom.2015.147
'2340': durom.2015.159
'2341': durom.2015.178
'2342': durom.2015.179
'2343': durom.2015.18
'2344': durom.2015.19
'2345': durom.2015.20
'2346': durom.2015.21
'2347': durom.2015.212
'2348': durom.2015.213
'2349': durom.2015.214
'2350': durom.2015.215
'2351': durom.2015.22
'2352': durom.2015.226
'2353': durom.2015.227
'2354': durom.2015.230
'2355': durom.2015.235
'2356': durom.2015.25
'2357': durom.2015.257
'2358': durom.2015.267
'2359': durom.2015.274
'2360': durom.2015.28
'2361': durom.2015.29
'2362': durom.2015.295
'2363': durom.2015.296
'2364': durom.2015.297
'2365': durom.2015.298
'2366': durom.2015.31
'2367': durom.2015.319
'2368': durom.2015.32
'2369': durom.2015.33
'2370': durom.2015.34
'2371': durom.2015.340.1
'2372': durom.2015.340.17
'2373': durom.2015.340.18
'2374': durom.2015.340.19
'2375': durom.2015.340.2
'2376': durom.2015.340.20
'2377': durom.2015.340.21
'2378': durom.2015.340.22
'2379': durom.2015.340.23
'2380': durom.2015.340.24
'2381': durom.2015.340.25
'2382': durom.2015.340.26
'2383': durom.2015.340.27
'2384': durom.2015.340.28
'2385': durom.2015.340.29
'2386': durom.2015.340.30
'2387': durom.2015.340.31
'2388': durom.2015.340.32
'2389': durom.2015.340.33
'2390': durom.2015.340.34
'2391': durom.2015.340.35
'2392': durom.2015.340.36
'2393': durom.2015.340.37
'2394': durom.2015.340.38
'2395': durom.2015.340.39
'2396': durom.2015.340.4
'2397': durom.2015.340.40
'2398': durom.2015.340.41
'2399': durom.2015.340.42
'2400': durom.2015.340.43
'2401': durom.2015.340.44
'2402': durom.2015.340.45
'2403': durom.2015.340.46
'2404': durom.2015.340.47
'2405': durom.2015.340.48
'2406': durom.2015.340.49
'2407': durom.2015.340.50
'2408': durom.2015.340.51
'2409': durom.2015.340.52
'2410': durom.2015.340.53
'2411': durom.2015.340.54
'2412': durom.2015.340.55
'2413': durom.2015.340.56
'2414': durom.2015.340.57
'2415': durom.2015.340.58
'2416': durom.2015.340.59
'2417': durom.2015.340.64
'2418': durom.2015.340.65
'2419': durom.2015.341
'2420': durom.2015.35
'2421': durom.2015.352
'2422': durom.2015.354
'2423': durom.2015.357
'2424': durom.2015.36
'2425': durom.2015.361
'2426': durom.2015.362
'2427': durom.2015.363
'2428': durom.2015.364
'2429': durom.2015.365
'2430': durom.2015.366
'2431': durom.2015.367
'2432': durom.2015.37
'2433': durom.2015.38
'2434': durom.2015.385.10
'2435': durom.2015.385.11
'2436': durom.2015.385.2
'2437': durom.2015.39
'2438': durom.2015.391
'2439': durom.2015.401
'2440': durom.2015.41
'2441': durom.2015.42
'2442': durom.2015.422
'2443': durom.2015.423.1
'2444': durom.2015.43
'2445': durom.2015.44
'2446': durom.2015.45
'2447': durom.2015.46
'2448': durom.2015.498
'2449': durom.2015.50
'2450': durom.2015.501
'2451': durom.2015.507
'2452': durom.2015.55
'2453': durom.2015.60
'2454': durom.2015.61
'2455': durom.2015.62
'2456': durom.2015.63
'2457': durom.2015.71
'2458': durom.2015.93
'2459': durom.2015.95
'2460': durom.2015.98
'2461': durom.2016.1
'2462': durom.2016.10
'2463': durom.2016.101
'2464': durom.2016.102
'2465': durom.2016.103.1
'2466': durom.2016.103.2
'2467': durom.2016.104.1
'2468': durom.2016.104.2
'2469': durom.2016.105
'2470': durom.2016.106
'2471': durom.2016.107
'2472': durom.2016.108
'2473': durom.2016.109
'2474': durom.2016.21
'2475': durom.2016.26
'2476': durom.2016.27
'2477': durom.2016.28
'2478': durom.2016.31
'2479': durom.2016.40
'2480': durom.2016.47
'2481': durom.2016.52
'2482': durom.2016.58
'2483': durom.2016.59
'2484': durom.2016.60
'2485': durom.2016.73
'2486': durom.2016.78
'2487': durom.2017.18
'2488': durom.2017.20
'2489': durom.2017.21
'2490': durom.2017.22
'2491': durom.2017.33
'2492': durom.2017.34
'2493': durom.2017.35
'2494': durom.2017.37
'2495': durom.2017.38
'2496': durom.2017.48
'2497': durom.2017.49
'2498': durom.2017.50
'2499': durom.2017.62
'2500': durom.2017.73
'2501': durom.2017.8
'2502': durom.2017.87
'2503': durom.2017.88
'2504': durom.2017.89
'2505': durom.2017.90
'2506': durom.2017.92
'2507': durom.2017.93
'2508': durom.2017.94
'2509': durom.2017.95
'2510': durom.2017.96
'2511': durom.2018.1
'2512': durom.2018.2
'2513': durom.272
'2514': durom.316
'2515': durom.370
'2516': durom.50
'2517': durom.60
'2518': durom.64
'2519': durom.69
'2520': durom.75
'2521': durom.81
'2522': duruc.1924.105
'2523': duruc.1924.15
'2524': duruc.1924.3.13
'2525': duruc.1924.3.5
'2526': duruc.1924.40
'2527': duruc.1924.42
'2528': duruc.1924.44
'2529': duruc.1924.45
'2530': duruc.1924.6.2
'2531': duruc.1924.76.1
'2532': duruc.1924.77.1
'2533': duruc.1924.77.2
'2534': duruc.1924.78
'2535': duruc.1924.79
'2536': duruc.2014.1.10
'2537': duruc.2014.1.2
'2538': duruc.2016.41
'2539': duruc.2016.44
'2540': duruc.2016.47.2
'2541': duruc.2016.50.1
'2542': duruc.2016.50.2
'2543': duruc.2018.13.1
'2544': duruc.2018.13.2
'2545': duruc.2018.14
'2546': duruc.2018.15
'2547': duruc.2018.16
'2548': duruc.2018.17
'2549': duruc.2018.18
'2550': duruc.2018.19
'2551': duruc.2018.20
'2552': duruc.2018.21
'2553': duruc.2018.22
'2554': duruc.2018.23
'2555': duruc.2018.24
'2556': duruc.2018.25
'2557': duruc.2018.26
'2558': duruc.2018.27
'2559': duruc.2018.3
'2560': duruc.2018.6
'2561': duruc.2018.7
'2562': duruc.2020.1
'2563': duruc.2020.2
'2564': duruc.2020.25.1
'2565': eg1002
'2566': eg1004
'2567': eg1012
'2568': eg1013
'2569': eg1030
'2570': eg1033
'2571': eg104
'2572': eg1041
'2573': eg1050
'2574': eg1054
'2575': eg1059
'2576': eg107
'2577': eg112
'2578': eg1141
'2579': eg117
'2580': eg1188
'2581': eg1190
'2582': eg1191
'2583': eg1193
'2584': eg1194
'2585': eg1195
'2586': eg1196
'2587': eg1197
'2588': eg1200
'2589': eg1216
'2590': eg1217
'2591': eg1218
'2592': eg1219
'2593': eg1236
'2594': eg1237
'2595': eg1239
'2596': eg1246
'2597': eg1247
'2598': eg1248
'2599': eg1255
'2600': eg1257
'2601': eg1259
'2602': eg1260
'2603': eg1273
'2604': eg1276
'2605': eg1280
'2606': eg1282
'2607': eg1283
'2608': eg1284
'2609': eg1285
'2610': eg1286
'2611': eg1287
'2612': eg1288
'2613': eg1289
'2614': eg1290
'2615': eg1293
'2616': eg1294
'2617': eg1302
'2618': eg1329
'2619': eg133
'2620': eg1335
'2621': eg135
'2622': eg1374
'2623': eg138
'2624': eg139
'2625': eg1414
'2626': eg1415
'2627': eg1416
'2628': eg1423
'2629': eg1424
'2630': eg1427
'2631': eg143
'2632': eg1438
'2633': eg1439
'2634': eg1440
'2635': eg1449
'2636': eg147
'2637': eg1491
'2638': eg1493
'2639': eg1500
'2640': eg1506
'2641': eg1508
'2642': eg151
'2643': eg1522
'2644': eg1523
'2645': eg1524
'2646': eg1528
'2647': eg1529
'2648': eg1550
'2649': eg1552
'2650': eg156
'2651': eg1561
'2652': eg1568
'2653': eg1603
'2654': eg1605
'2655': eg1615
'2656': eg1616
'2657': eg1618
'2658': eg1620
'2659': eg165
'2660': eg1656
'2661': eg1659
'2662': eg167
'2663': eg1689
'2664': eg1690
'2665': eg1695
'2666': eg1727
'2667': eg1729
'2668': eg1730
'2669': eg1733
'2670': eg1742
'2671': eg1745
'2672': eg1747
'2673': eg1748
'2674': eg1749
'2675': eg175
'2676': eg1752
'2677': eg1786
'2678': eg1789
'2679': eg1790
'2680': eg1791
'2681': eg1793
'2682': eg1796
'2683': eg1801
'2684': eg1803
'2685': eg1809
'2686': eg1813
'2687': eg1814
'2688': eg1824
'2689': eg1826
'2690': eg1833
'2691': eg1838
'2692': eg1840
'2693': eg1842
'2694': eg1845
'2695': eg1850
'2696': eg1851
'2697': eg1856
'2698': eg1857
'2699': eg1859
'2700': eg186
'2701': eg1861
'2702': eg1862
'2703': eg1865
'2704': eg1874
'2705': eg1933
'2706': eg1934
'2707': eg1936
'2708': eg1948
'2709': eg196
'2710': eg200
'2711': eg204
'2712': eg2070
'2713': eg2076
'2714': eg2084
'2715': eg2091
'2716': eg211
'2717': eg212
'2718': eg2133
'2719': eg2134
'2720': eg215
'2721': eg2150
'2722': eg2155
'2723': eg2168
'2724': eg2173
'2725': eg2175
'2726': eg2176
'2727': eg2182
'2728': eg2185
'2729': eg2186
'2730': eg2188
'2731': eg2195
'2732': eg2200
'2733': eg2204
'2734': eg221
'2735': eg2222
'2736': eg2223
'2737': eg2239
'2738': eg2251
'2739': eg2261
'2740': eg2280
'2741': eg2281
'2742': eg2283
'2743': eg2284
'2744': eg2291
'2745': eg2300
'2746': eg2301
'2747': eg2305
'2748': eg2309
'2749': eg2325
'2750': eg2353
'2751': eg2356
'2752': eg2361
'2753': eg2364
'2754': eg2366
'2755': eg2400
'2756': eg2402
'2757': eg2472
'2758': eg2481
'2759': eg2493
'2760': eg2495.1
'2761': eg2495.2
'2762': eg2498
'2763': eg2526
'2764': eg2540
'2765': eg2547
'2766': eg2548
'2767': eg264
'2768': eg2647
'2769': eg2670
'2770': eg2733
'2771': eg2735
'2772': eg2738
'2773': eg2749
'2774': eg2750
'2775': eg2762
'2776': eg2763
'2777': eg279
'2778': eg2797
'2779': eg2804.1
'2780': eg2810
'2781': eg2863
'2782': eg2918
'2783': eg2924
'2784': eg293
'2785': eg2930
'2786': eg2933
'2787': eg2948
'2788': eg2962
'2789': eg2973
'2790': eg2991
'2791': eg306
'2792': eg3062
'2793': eg307
'2794': eg3070
'2795': eg308
'2796': eg3083
'2797': eg309
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- name: file
dtype: string
- name: image
dtype: image
- name: root
dtype: string
- name: description
dtype: string
- name: object_name
dtype: string
- name: other_name
dtype: string
- name: material
dtype: string
- name: production.period
dtype: string
- name: production.place
dtype: string
splits:
- name: train
num_bytes: 1651319857.7770662
num_examples: 16073
- name: validation
num_bytes: 402611533.89396673
num_examples: 3782
- name: test
num_bytes: 431398026.44796675
num_examples: 3782
download_size: 2539461475
dataset_size: 2485329418.1189995
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
puyuan1996/pong_muzero_2episodes_gsl400_v0.0.4 | ---
license: apache-2.0
task_categories:
- reinforcement-learning
size_categories:
- 10M<n<100M
---
# Dataset Card for pong_muzero_2episodes_gsl400_v0.0.4
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
|
result-kand2-sdxl-wuerst-karlo/3658ecd8 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 198
num_examples: 10
download_size: 1383
dataset_size: 198
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "3658ecd8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_KnutJaegersberg__Deita-4b | ---
pretty_name: Evaluation run of KnutJaegersberg/Deita-4b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [KnutJaegersberg/Deita-4b](https://huggingface.co/KnutJaegersberg/Deita-4b) 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_KnutJaegersberg__Deita-4b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-13T12:13:56.610648](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Deita-4b/blob/main/results_2024-02-13T12-13-56.610648.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\": 0.5527501076415084,\n\
\ \"acc_stderr\": 0.03417435776098714,\n \"acc_norm\": 0.5557602717989104,\n\
\ \"acc_norm_stderr\": 0.03486732917817813,\n \"mc1\": 0.32558139534883723,\n\
\ \"mc1_stderr\": 0.016403989469907825,\n \"mc2\": 0.5022562674091439,\n\
\ \"mc2_stderr\": 0.014750233587042572\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.4300341296928328,\n \"acc_stderr\": 0.01446763155913799,\n\
\ \"acc_norm\": 0.46075085324232085,\n \"acc_norm_stderr\": 0.014566303676636586\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5262895837482573,\n\
\ \"acc_stderr\": 0.004982879340691411,\n \"acc_norm\": 0.7180840470025891,\n\
\ \"acc_norm_stderr\": 0.00449013069102043\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411022,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411022\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5111111111111111,\n\
\ \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.5111111111111111,\n\
\ \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5328947368421053,\n \"acc_stderr\": 0.04060127035236395,\n\
\ \"acc_norm\": 0.5328947368421053,\n \"acc_norm_stderr\": 0.04060127035236395\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\
\ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \
\ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.5962264150943396,\n \"acc_stderr\": 0.03019761160019795,\n\
\ \"acc_norm\": 0.5962264150943396,\n \"acc_norm_stderr\": 0.03019761160019795\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4930555555555556,\n\
\ \"acc_stderr\": 0.04180806750294938,\n \"acc_norm\": 0.4930555555555556,\n\
\ \"acc_norm_stderr\": 0.04180806750294938\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\"\
: 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5260115606936416,\n\
\ \"acc_stderr\": 0.038073017265045125,\n \"acc_norm\": 0.5260115606936416,\n\
\ \"acc_norm_stderr\": 0.038073017265045125\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105654,\n\
\ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105654\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.68,\n\
\ \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4851063829787234,\n \"acc_stderr\": 0.03267151848924777,\n\
\ \"acc_norm\": 0.4851063829787234,\n \"acc_norm_stderr\": 0.03267151848924777\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3684210526315789,\n\
\ \"acc_stderr\": 0.04537815354939392,\n \"acc_norm\": 0.3684210526315789,\n\
\ \"acc_norm_stderr\": 0.04537815354939392\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\"\
: 0.4523809523809524,\n \"acc_stderr\": 0.02563425811555496,\n \"\
acc_norm\": 0.4523809523809524,\n \"acc_norm_stderr\": 0.02563425811555496\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.35714285714285715,\n\
\ \"acc_stderr\": 0.04285714285714281,\n \"acc_norm\": 0.35714285714285715,\n\
\ \"acc_norm_stderr\": 0.04285714285714281\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6419354838709678,\n\
\ \"acc_stderr\": 0.027273890594300645,\n \"acc_norm\": 0.6419354838709678,\n\
\ \"acc_norm_stderr\": 0.027273890594300645\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4433497536945813,\n \"acc_stderr\": 0.03495334582162933,\n\
\ \"acc_norm\": 0.4433497536945813,\n \"acc_norm_stderr\": 0.03495334582162933\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\"\
: 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7212121212121212,\n \"acc_stderr\": 0.03501438706296781,\n\
\ \"acc_norm\": 0.7212121212121212,\n \"acc_norm_stderr\": 0.03501438706296781\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7272727272727273,\n \"acc_stderr\": 0.03173071239071724,\n \"\
acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.03173071239071724\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7305699481865285,\n \"acc_stderr\": 0.032018671228777947,\n\
\ \"acc_norm\": 0.7305699481865285,\n \"acc_norm_stderr\": 0.032018671228777947\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5256410256410257,\n \"acc_stderr\": 0.02531764972644866,\n \
\ \"acc_norm\": 0.5256410256410257,\n \"acc_norm_stderr\": 0.02531764972644866\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948496,\n \
\ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948496\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5504201680672269,\n \"acc_stderr\": 0.03231293497137707,\n \
\ \"acc_norm\": 0.5504201680672269,\n \"acc_norm_stderr\": 0.03231293497137707\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\
acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7192660550458716,\n \"acc_stderr\": 0.019266055045871616,\n \"\
acc_norm\": 0.7192660550458716,\n \"acc_norm_stderr\": 0.019266055045871616\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4074074074074074,\n \"acc_stderr\": 0.03350991604696043,\n \"\
acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.03350991604696043\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7058823529411765,\n \"acc_stderr\": 0.031980016601150706,\n \"\
acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.031980016601150706\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.70042194092827,\n \"acc_stderr\": 0.02981802474975309,\n \
\ \"acc_norm\": 0.70042194092827,\n \"acc_norm_stderr\": 0.02981802474975309\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6188340807174888,\n\
\ \"acc_stderr\": 0.03259625118416827,\n \"acc_norm\": 0.6188340807174888,\n\
\ \"acc_norm_stderr\": 0.03259625118416827\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6717557251908397,\n \"acc_stderr\": 0.04118438565806298,\n\
\ \"acc_norm\": 0.6717557251908397,\n \"acc_norm_stderr\": 0.04118438565806298\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6942148760330579,\n \"acc_stderr\": 0.042059539338841226,\n \"\
acc_norm\": 0.6942148760330579,\n \"acc_norm_stderr\": 0.042059539338841226\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6388888888888888,\n\
\ \"acc_stderr\": 0.04643454608906276,\n \"acc_norm\": 0.6388888888888888,\n\
\ \"acc_norm_stderr\": 0.04643454608906276\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6441717791411042,\n \"acc_stderr\": 0.03761521380046734,\n\
\ \"acc_norm\": 0.6441717791411042,\n \"acc_norm_stderr\": 0.03761521380046734\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4107142857142857,\n\
\ \"acc_stderr\": 0.04669510663875191,\n \"acc_norm\": 0.4107142857142857,\n\
\ \"acc_norm_stderr\": 0.04669510663875191\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\
\ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.024414947304543688,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.024414947304543688\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001974,\n \
\ \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001974\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7318007662835249,\n\
\ \"acc_stderr\": 0.015842430835269438,\n \"acc_norm\": 0.7318007662835249,\n\
\ \"acc_norm_stderr\": 0.015842430835269438\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6358381502890174,\n \"acc_stderr\": 0.025906632631016124,\n\
\ \"acc_norm\": 0.6358381502890174,\n \"acc_norm_stderr\": 0.025906632631016124\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2536312849162011,\n\
\ \"acc_stderr\": 0.014551553659369918,\n \"acc_norm\": 0.2536312849162011,\n\
\ \"acc_norm_stderr\": 0.014551553659369918\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6372549019607843,\n \"acc_stderr\": 0.02753007844711031,\n\
\ \"acc_norm\": 0.6372549019607843,\n \"acc_norm_stderr\": 0.02753007844711031\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5916398713826366,\n\
\ \"acc_stderr\": 0.027917050748484624,\n \"acc_norm\": 0.5916398713826366,\n\
\ \"acc_norm_stderr\": 0.027917050748484624\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5987654320987654,\n \"acc_stderr\": 0.027272582849839803,\n\
\ \"acc_norm\": 0.5987654320987654,\n \"acc_norm_stderr\": 0.027272582849839803\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4219858156028369,\n \"acc_stderr\": 0.0294621892333706,\n \
\ \"acc_norm\": 0.4219858156028369,\n \"acc_norm_stderr\": 0.0294621892333706\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.40091264667535853,\n\
\ \"acc_stderr\": 0.012516960350640828,\n \"acc_norm\": 0.40091264667535853,\n\
\ \"acc_norm_stderr\": 0.012516960350640828\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.47794117647058826,\n \"acc_stderr\": 0.030343264224213528,\n\
\ \"acc_norm\": 0.47794117647058826,\n \"acc_norm_stderr\": 0.030343264224213528\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5506535947712419,\n \"acc_stderr\": 0.020123766528027266,\n \
\ \"acc_norm\": 0.5506535947712419,\n \"acc_norm_stderr\": 0.020123766528027266\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\
\ \"acc_stderr\": 0.04582004841505418,\n \"acc_norm\": 0.6454545454545455,\n\
\ \"acc_norm_stderr\": 0.04582004841505418\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6489795918367347,\n \"acc_stderr\": 0.030555316755573637,\n\
\ \"acc_norm\": 0.6489795918367347,\n \"acc_norm_stderr\": 0.030555316755573637\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7213930348258707,\n\
\ \"acc_stderr\": 0.031700561834973086,\n \"acc_norm\": 0.7213930348258707,\n\
\ \"acc_norm_stderr\": 0.031700561834973086\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.4397590361445783,\n\
\ \"acc_stderr\": 0.03864139923699121,\n \"acc_norm\": 0.4397590361445783,\n\
\ \"acc_norm_stderr\": 0.03864139923699121\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.03565079670708311,\n\
\ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.03565079670708311\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.32558139534883723,\n\
\ \"mc1_stderr\": 0.016403989469907825,\n \"mc2\": 0.5022562674091439,\n\
\ \"mc2_stderr\": 0.014750233587042572\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6614048934490924,\n \"acc_stderr\": 0.01330016986584242\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4890068233510235,\n \
\ \"acc_stderr\": 0.013769155509690904\n }\n}\n```"
repo_url: https://huggingface.co/KnutJaegersberg/Deita-4b
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_13T12_13_56.610648
path:
- '**/details_harness|arc:challenge|25_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|gsm8k|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hellaswag|10_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T12-13-56.610648.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-13T12-13-56.610648.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- '**/details_harness|winogrande|5_2024-02-13T12-13-56.610648.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-13T12-13-56.610648.parquet'
- config_name: results
data_files:
- split: 2024_02_13T12_13_56.610648
path:
- results_2024-02-13T12-13-56.610648.parquet
- split: latest
path:
- results_2024-02-13T12-13-56.610648.parquet
---
# Dataset Card for Evaluation run of KnutJaegersberg/Deita-4b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [KnutJaegersberg/Deita-4b](https://huggingface.co/KnutJaegersberg/Deita-4b) 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_KnutJaegersberg__Deita-4b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-13T12:13:56.610648](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Deita-4b/blob/main/results_2024-02-13T12-13-56.610648.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": 0.5527501076415084,
"acc_stderr": 0.03417435776098714,
"acc_norm": 0.5557602717989104,
"acc_norm_stderr": 0.03486732917817813,
"mc1": 0.32558139534883723,
"mc1_stderr": 0.016403989469907825,
"mc2": 0.5022562674091439,
"mc2_stderr": 0.014750233587042572
},
"harness|arc:challenge|25": {
"acc": 0.4300341296928328,
"acc_stderr": 0.01446763155913799,
"acc_norm": 0.46075085324232085,
"acc_norm_stderr": 0.014566303676636586
},
"harness|hellaswag|10": {
"acc": 0.5262895837482573,
"acc_stderr": 0.004982879340691411,
"acc_norm": 0.7180840470025891,
"acc_norm_stderr": 0.00449013069102043
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.04793724854411022,
"acc_norm": 0.35,
"acc_norm_stderr": 0.04793724854411022
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5111111111111111,
"acc_stderr": 0.04318275491977976,
"acc_norm": 0.5111111111111111,
"acc_norm_stderr": 0.04318275491977976
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5328947368421053,
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"acc_norm": 0.5328947368421053,
"acc_norm_stderr": 0.04060127035236395
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.63,
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"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5962264150943396,
"acc_stderr": 0.03019761160019795,
"acc_norm": 0.5962264150943396,
"acc_norm_stderr": 0.03019761160019795
},
"harness|hendrycksTest-college_biology|5": {
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},
"harness|hendrycksTest-college_chemistry|5": {
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},
"harness|hendrycksTest-college_computer_science|5": {
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"acc_norm": 0.4,
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},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.46,
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},
"harness|hendrycksTest-college_medicine|5": {
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},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm": 0.3627450980392157,
"acc_norm_stderr": 0.04784060704105654
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.68,
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},
"harness|hendrycksTest-conceptual_physics|5": {
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},
"harness|hendrycksTest-econometrics|5": {
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"acc_norm": 0.3684210526315789,
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},
"harness|hendrycksTest-electrical_engineering|5": {
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"harness|hendrycksTest-elementary_mathematics|5": {
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"harness|hendrycksTest-formal_logic|5": {
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"harness|hendrycksTest-global_facts|5": {
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"harness|hendrycksTest-high_school_biology|5": {
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},
"harness|hendrycksTest-high_school_chemistry|5": {
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"harness|hendrycksTest-high_school_computer_science|5": {
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},
"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5504201680672269,
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"acc_norm_stderr": 0.03231293497137707
},
"harness|hendrycksTest-high_school_physics|5": {
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},
"harness|hendrycksTest-high_school_psychology|5": {
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},
"harness|hendrycksTest-high_school_statistics|5": {
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},
"harness|hendrycksTest-high_school_us_history|5": {
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"acc_norm": 0.7058823529411765,
"acc_norm_stderr": 0.031980016601150706
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.70042194092827,
"acc_stderr": 0.02981802474975309,
"acc_norm": 0.70042194092827,
"acc_norm_stderr": 0.02981802474975309
},
"harness|hendrycksTest-human_aging|5": {
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"harness|hendrycksTest-international_law|5": {
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"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.6388888888888888,
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"acc_norm": 0.6388888888888888,
"acc_norm_stderr": 0.04643454608906276
},
"harness|hendrycksTest-logical_fallacies|5": {
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"acc_norm": 0.6441717791411042,
"acc_norm_stderr": 0.03761521380046734
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4107142857142857,
"acc_stderr": 0.04669510663875191,
"acc_norm": 0.4107142857142857,
"acc_norm_stderr": 0.04669510663875191
},
"harness|hendrycksTest-management|5": {
"acc": 0.7281553398058253,
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"acc_norm": 0.7281553398058253,
"acc_norm_stderr": 0.044052680241409216
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.024414947304543688,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.024414947304543688
},
"harness|hendrycksTest-medical_genetics|5": {
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},
"harness|hendrycksTest-miscellaneous|5": {
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},
"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-moral_scenarios|5": {
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"acc_norm": 0.2536312849162011,
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},
"harness|hendrycksTest-nutrition|5": {
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"acc_norm_stderr": 0.02753007844711031
},
"harness|hendrycksTest-philosophy|5": {
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"acc_norm": 0.5916398713826366,
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},
"harness|hendrycksTest-prehistory|5": {
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},
"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
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"harness|hendrycksTest-professional_psychology|5": {
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"harness|hendrycksTest-public_relations|5": {
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},
"harness|hendrycksTest-security_studies|5": {
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"acc_norm": 0.6489795918367347,
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},
"harness|hendrycksTest-sociology|5": {
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"acc_norm": 0.7213930348258707,
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},
"harness|hendrycksTest-us_foreign_policy|5": {
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"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
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},
"harness|hendrycksTest-virology|5": {
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"acc_stderr": 0.03864139923699121,
"acc_norm": 0.4397590361445783,
"acc_norm_stderr": 0.03864139923699121
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.6842105263157895,
"acc_stderr": 0.03565079670708311,
"acc_norm": 0.6842105263157895,
"acc_norm_stderr": 0.03565079670708311
},
"harness|truthfulqa:mc|0": {
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"mc1_stderr": 0.016403989469907825,
"mc2": 0.5022562674091439,
"mc2_stderr": 0.014750233587042572
},
"harness|winogrande|5": {
"acc": 0.6614048934490924,
"acc_stderr": 0.01330016986584242
},
"harness|gsm8k|5": {
"acc": 0.4890068233510235,
"acc_stderr": 0.013769155509690904
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## More Information [optional]
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## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
arthurmluz/temario_data-xlsum_cstnews_1024_results | ---
dataset_info:
features:
- name: id
dtype: string
- 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: 223671
num_examples: 25
download_size: 178850
dataset_size: 223671
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "temario_data-xlsumm_cstnews_1024_results"
rouge= {'rouge1': 0.3842309401085634, 'rouge2': 0.15048098740220198, 'rougeL': 0.2290304095965295, 'rougeLsum': 0.2290304095965295}
bert= {'precision': 0.7249824571609497, 'recall': 0.6954835605621338, 'f1': 0.7095399975776673}
mover = 0.6115396799603942 |
ovior/twitter_dataset_1713216323 | ---
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: 2308952
num_examples: 7101
download_size: 1304382
dataset_size: 2308952
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Ebullioscopic/Samsung-sample-data | ---
license: apache-2.0
---
|
euclaise/MiniCoT | ---
size_categories:
- 10K<n<100K
task_categories:
- question-answering
pretty_name: MiniCoT
dataset_info:
features:
- name: rationale
dtype: string
- name: target
dtype: string
- name: source
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 57000705
num_examples: 128562
download_size: 31364563
dataset_size: 57000705
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- chain-of-thought
- cot
---
# Dataset Card for "MiniCoT"
Subset of [MegaCoT](https://huggingface.co/datasets/euclaise/MegaCoT) that excludes cos_e, and creak (since they have some lower-quality annotations). The datasets included are GSM8K, SenMaking, qasc, ROPES, Entailmentbank, MATH, feasibilityQA, TAL-SCQ5K, aqua_rat, Quartz, a filtered subset of esnli, FLUTE, and StrategyQA.
I reserve no rights to the dataset, but the original datasets were made available under various public licenses. Hence, consider each subset of this dataset to be licensed as the original dataset from where it comes was. |
maknee/league-of-legends-replays | ---
license: mit
---
|
MauroLeidi/OCT_balanced | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
0: DRUSEN
1: NORMAL
splits:
- name: train
num_bytes: 1037539349.736
num_examples: 17232
- name: test
num_bytes: 21771538.0
num_examples: 500
download_size: 1080333714
dataset_size: 1059310887.736
---
# Dataset Card for "OCT_balanced"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tasksource/VUAC | ---
dataset_info:
features:
- name: label
dtype: string
- name: expression
dtype: string
- name: dataset
dtype: string
- name: position
sequence: int32
- name: id
dtype: string
- name: context
dtype: string
- name: previous_sentence
dtype: string
- name: next_sentence
dtype: string
splits:
- name: train
num_bytes: 5671035
num_examples: 14929
- name: validation
num_bytes: 866481
num_examples: 2311
- name: test
num_bytes: 2054321
num_examples: 5873
download_size: 4444711
dataset_size: 8591837
---
# Dataset Card for "VUAC"
```
@article{steen2010method,
title={A method for linguistic metaphor identification},
author={Steen, Gerard and Dorst, Aletta G and Herrmann, J Berenike and Kaal, Anna and Krennmayr, Tina and Pasma, Trijntje},
journal={Amsterdam: Benjamins},
year={2010}
}
``` |
AdapterOcean/med_alpaca_standardized_cluster_91_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 24120737
num_examples: 11899
download_size: 12421648
dataset_size: 24120737
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_91_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hugginglearners/marriage-and-divorce-dataset | ---
license:
- cc0-1.0
kaggle_id: hosseinmousavi/marriage-and-divorce-dataset
---
# Dataset Card for Marriage and Divorce Dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://kaggle.com/datasets/hosseinmousavi/marriage-and-divorce-dataset
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This data contains 31 columns (100x31). The first 30 columns are features (inputs), namely Age Gap, Education, Economic Similarity, Social Similarities, Cultural Similarities, Social Gap, Common Interests, Religion Compatibility, No of Children from Previous Marriage, Desire to Marry, Independency, Relationship with the Spouse Family, Trading in, Engagement Time, Love, Commitment, Mental Health, The Sense of Having Children, Previous Trading, Previous Marriage, The Proportion of Common Genes, Addiction, Loyalty, Height Ratio, Good Income, Self Confidence, Relation with Non-spouse Before Marriage, Spouse Confirmed by Family, Divorce in the Family of Grade 1 and Start Socializing with the Opposite Sex Age. The 31th column is Divorce Probability (Target).
### 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
This dataset was shared by [@hosseinmousavi](https://kaggle.com/hosseinmousavi)
### Licensing Information
The license for this dataset is cc0-1.0
### Citation Information
```bibtex
[More Information Needed]
```
### Contributions
[More Information Needed] |
Zaun/Otaotakinp | ---
license: bigscience-bloom-rail-1.0
---
|
open-llm-leaderboard/details_DreadPoor__Sphinx-7B-Model_Stock | ---
pretty_name: Evaluation run of DreadPoor/Sphinx-7B-Model_Stock
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [DreadPoor/Sphinx-7B-Model_Stock](https://huggingface.co/DreadPoor/Sphinx-7B-Model_Stock)\
\ 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_DreadPoor__Sphinx-7B-Model_Stock\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-08T22:26:26.778029](https://huggingface.co/datasets/open-llm-leaderboard/details_DreadPoor__Sphinx-7B-Model_Stock/blob/main/results_2024-04-08T22-26-26.778029.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\": 0.6529923195613286,\n\
\ \"acc_stderr\": 0.031992924733670366,\n \"acc_norm\": 0.6527974725135783,\n\
\ \"acc_norm_stderr\": 0.032655475708529405,\n \"mc1\": 0.4847001223990208,\n\
\ \"mc1_stderr\": 0.017495304473187902,\n \"mc2\": 0.6512402494060415,\n\
\ \"mc2_stderr\": 0.015248257681545458\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6877133105802048,\n \"acc_stderr\": 0.013542598541688065,\n\
\ \"acc_norm\": 0.7090443686006825,\n \"acc_norm_stderr\": 0.013273077865907595\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6989643497311293,\n\
\ \"acc_stderr\": 0.004577707025031378,\n \"acc_norm\": 0.8720374427404899,\n\
\ \"acc_norm_stderr\": 0.003333654120593685\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\
\ \"acc_stderr\": 0.04135176749720386,\n \"acc_norm\": 0.6444444444444445,\n\
\ \"acc_norm_stderr\": 0.04135176749720386\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\
\ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\
\ \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \
\ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249386,\n\
\ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249386\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\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6820809248554913,\n\
\ \"acc_stderr\": 0.035506839891655796,\n \"acc_norm\": 0.6820809248554913,\n\
\ \"acc_norm_stderr\": 0.035506839891655796\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663454,\n\
\ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663454\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.8,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.8,\n\
\ \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\
\ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\
\ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n\
\ \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\
\ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4021164021164021,\n \"acc_stderr\": 0.025253032554997692,\n \"\
acc_norm\": 0.4021164021164021,\n \"acc_norm_stderr\": 0.025253032554997692\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\
\ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\
\ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7838709677419354,\n\
\ \"acc_stderr\": 0.02341529343356852,\n \"acc_norm\": 0.7838709677419354,\n\
\ \"acc_norm_stderr\": 0.02341529343356852\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n\
\ \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\"\
: 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.03192271569548301,\n\
\ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.03192271569548301\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8080808080808081,\n \"acc_stderr\": 0.028057791672989017,\n \"\
acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.028057791672989017\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033477,\n\
\ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033477\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6871794871794872,\n \"acc_stderr\": 0.023507579020645358,\n\
\ \"acc_norm\": 0.6871794871794872,\n \"acc_norm_stderr\": 0.023507579020645358\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.362962962962963,\n \"acc_stderr\": 0.02931820364520686,\n \
\ \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.02931820364520686\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6974789915966386,\n \"acc_stderr\": 0.02983796238829194,\n \
\ \"acc_norm\": 0.6974789915966386,\n \"acc_norm_stderr\": 0.02983796238829194\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\
acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8330275229357799,\n \"acc_stderr\": 0.01599015488507338,\n \"\
acc_norm\": 0.8330275229357799,\n \"acc_norm_stderr\": 0.01599015488507338\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5,\n \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n\
\ \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 0.026756401538078966,\n\
\ \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.026756401538078966\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621133,\n \
\ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621133\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.036412970813137296,\n\
\ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.036412970813137296\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\
\ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\
\ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\
\ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\
\ \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n\
\ \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8314176245210728,\n\
\ \"acc_stderr\": 0.013387895731543602,\n \"acc_norm\": 0.8314176245210728,\n\
\ \"acc_norm_stderr\": 0.013387895731543602\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7369942196531792,\n \"acc_stderr\": 0.023703099525258165,\n\
\ \"acc_norm\": 0.7369942196531792,\n \"acc_norm_stderr\": 0.023703099525258165\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4122905027932961,\n\
\ \"acc_stderr\": 0.01646320023811452,\n \"acc_norm\": 0.4122905027932961,\n\
\ \"acc_norm_stderr\": 0.01646320023811452\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7516339869281046,\n \"acc_stderr\": 0.02473998135511359,\n\
\ \"acc_norm\": 0.7516339869281046,\n \"acc_norm_stderr\": 0.02473998135511359\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\
\ \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n\
\ \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\"\
: 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\": 0.5,\n\
\ \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness|hendrycksTest-professional_law|5\"\
: {\n \"acc\": 0.46870925684485004,\n \"acc_stderr\": 0.012745204626083133,\n\
\ \"acc_norm\": 0.46870925684485004,\n \"acc_norm_stderr\": 0.012745204626083133\n\
\ },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\"\
: 0.6911764705882353,\n \"acc_stderr\": 0.02806499816704009,\n \"\
acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.02806499816704009\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6666666666666666,\n \"acc_stderr\": 0.0190709855896875,\n \
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.0190709855896875\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\
\ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\
\ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.02879518557429129,\n\
\ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.02879518557429129\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\
\ \"acc_stderr\": 0.025196929874827072,\n \"acc_norm\": 0.8507462686567164,\n\
\ \"acc_norm_stderr\": 0.025196929874827072\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\
\ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\
\ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4847001223990208,\n\
\ \"mc1_stderr\": 0.017495304473187902,\n \"mc2\": 0.6512402494060415,\n\
\ \"mc2_stderr\": 0.015248257681545458\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8303078137332282,\n \"acc_stderr\": 0.010549542647363705\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6815769522365428,\n \
\ \"acc_stderr\": 0.012832225723075404\n }\n}\n```"
repo_url: https://huggingface.co/DreadPoor/Sphinx-7B-Model_Stock
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|arc:challenge|25_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|gsm8k|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hellaswag|10_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-08T22-26-26.778029.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-08T22-26-26.778029.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- '**/details_harness|winogrande|5_2024-04-08T22-26-26.778029.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-08T22-26-26.778029.parquet'
- config_name: results
data_files:
- split: 2024_04_08T22_26_26.778029
path:
- results_2024-04-08T22-26-26.778029.parquet
- split: latest
path:
- results_2024-04-08T22-26-26.778029.parquet
---
# Dataset Card for Evaluation run of DreadPoor/Sphinx-7B-Model_Stock
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [DreadPoor/Sphinx-7B-Model_Stock](https://huggingface.co/DreadPoor/Sphinx-7B-Model_Stock) 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_DreadPoor__Sphinx-7B-Model_Stock",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-08T22:26:26.778029](https://huggingface.co/datasets/open-llm-leaderboard/details_DreadPoor__Sphinx-7B-Model_Stock/blob/main/results_2024-04-08T22-26-26.778029.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": 0.6529923195613286,
"acc_stderr": 0.031992924733670366,
"acc_norm": 0.6527974725135783,
"acc_norm_stderr": 0.032655475708529405,
"mc1": 0.4847001223990208,
"mc1_stderr": 0.017495304473187902,
"mc2": 0.6512402494060415,
"mc2_stderr": 0.015248257681545458
},
"harness|arc:challenge|25": {
"acc": 0.6877133105802048,
"acc_stderr": 0.013542598541688065,
"acc_norm": 0.7090443686006825,
"acc_norm_stderr": 0.013273077865907595
},
"harness|hellaswag|10": {
"acc": 0.6989643497311293,
"acc_stderr": 0.004577707025031378,
"acc_norm": 0.8720374427404899,
"acc_norm_stderr": 0.003333654120593685
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6444444444444445,
"acc_stderr": 0.04135176749720386,
"acc_norm": 0.6444444444444445,
"acc_norm_stderr": 0.04135176749720386
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.59,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.02815283794249386,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.02815283794249386
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7569444444444444,
"acc_stderr": 0.03586879280080341,
"acc_norm": 0.7569444444444444,
"acc_norm_stderr": 0.03586879280080341
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.27,
"acc_stderr": 0.0446196043338474,
"acc_norm": 0.27,
"acc_norm_stderr": 0.0446196043338474
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6820809248554913,
"acc_stderr": 0.035506839891655796,
"acc_norm": 0.6820809248554913,
"acc_norm_stderr": 0.035506839891655796
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.39215686274509803,
"acc_stderr": 0.048580835742663454,
"acc_norm": 0.39215686274509803,
"acc_norm_stderr": 0.048580835742663454
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.8,
"acc_stderr": 0.04020151261036845,
"acc_norm": 0.8,
"acc_norm_stderr": 0.04020151261036845
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5787234042553191,
"acc_stderr": 0.03227834510146268,
"acc_norm": 0.5787234042553191,
"acc_norm_stderr": 0.03227834510146268
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4649122807017544,
"acc_stderr": 0.046920083813689104,
"acc_norm": 0.4649122807017544,
"acc_norm_stderr": 0.046920083813689104
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5586206896551724,
"acc_stderr": 0.04137931034482757,
"acc_norm": 0.5586206896551724,
"acc_norm_stderr": 0.04137931034482757
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4021164021164021,
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"harness|hendrycksTest-machine_learning|5": {
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"harness|hendrycksTest-management|5": {
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},
"harness|hendrycksTest-marketing|5": {
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},
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},
"harness|hendrycksTest-nutrition|5": {
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},
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},
"harness|hendrycksTest-prehistory|5": {
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},
"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
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},
"harness|hendrycksTest-professional_medicine|5": {
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"acc_norm_stderr": 0.02806499816704009
},
"harness|hendrycksTest-professional_psychology|5": {
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"acc_norm_stderr": 0.0190709855896875
},
"harness|hendrycksTest-public_relations|5": {
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"acc_stderr": 0.04350271442923243,
"acc_norm": 0.7090909090909091,
"acc_norm_stderr": 0.04350271442923243
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7183673469387755,
"acc_stderr": 0.02879518557429129,
"acc_norm": 0.7183673469387755,
"acc_norm_stderr": 0.02879518557429129
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8507462686567164,
"acc_stderr": 0.025196929874827072,
"acc_norm": 0.8507462686567164,
"acc_norm_stderr": 0.025196929874827072
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
"acc_stderr": 0.0358870281282637,
"acc_norm": 0.85,
"acc_norm_stderr": 0.0358870281282637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5240963855421686,
"acc_stderr": 0.03887971849597264,
"acc_norm": 0.5240963855421686,
"acc_norm_stderr": 0.03887971849597264
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8304093567251462,
"acc_stderr": 0.02878210810540171,
"acc_norm": 0.8304093567251462,
"acc_norm_stderr": 0.02878210810540171
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4847001223990208,
"mc1_stderr": 0.017495304473187902,
"mc2": 0.6512402494060415,
"mc2_stderr": 0.015248257681545458
},
"harness|winogrande|5": {
"acc": 0.8303078137332282,
"acc_stderr": 0.010549542647363705
},
"harness|gsm8k|5": {
"acc": 0.6815769522365428,
"acc_stderr": 0.012832225723075404
}
}
```
## 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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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
[More Information Needed] |
jacobbieker/dwd | ---
license: mit
tags:
- climate
---
# Dataset Card for DWD Observations
<!-- Provide a quick summary of the dataset. -->
This dataset is a collection of historical German Weather Service (DWD) weather station observations at 10 minutely, and hourly resolutions for various parameters. The data has been
converted to Zarr and Xarray. The data was gathered using the wonderful wetterdienst package.
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
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## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
AdapterOcean/math_dataset_standardized_cluster_1_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: 11241663
num_examples: 11084
download_size: 4969226
dataset_size: 11241663
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "math_dataset_standardized_cluster_1_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
julep-ai/dfe-stacked_samsum | ---
language:
- en
license: mit
task_categories:
- feature-extraction
pretty_name: Dialog-Fact Encod
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: dialogue
dtype: string
- name: summary
dtype: string
- name: is_truncated
dtype: bool
- name: is_augmented
dtype: bool
splits:
- name: train
num_bytes: 225951776.22338164
num_examples: 336975
- name: test
num_bytes: 25105976.423639305
num_examples: 37442
- name: validation
num_bytes: 27895380.35297907
num_examples: 41602
download_size: 174858508
dataset_size: 278953133.0
---
# Dataset Card for "dfe-stacked_samsum"
This custom dataset [julep-ai/dfe-stacked_samsum](https://huggingface.co/datasets/julep-ai/dfe-stacked_samsum) was created from [stacked-summaries/stacked-samsum-1024](https://huggingface.co/datasets/stacked-summaries/stacked-samsum-1024) by:
1. Extracting summaries for corresponding dialogs to emulate "facts"
2. Then truncating the dialogs to emulate "missing information"
3. And then augmenting the dialogs using LLMs to emulate "additional information"
It is used to train our [Dialog-Fact Encoder](https://huggingface.co/julep-ai/dfe-base-en) model.
> This dataset is permissively licensed under the MIT license.
## Notebooks
The data preparation process is documented in the [notebook](https://huggingface.co/datasets/julep-ai/dfe-stacked_samsum/blob/main/data_prep.ipynb) and you can also view the [rendered pdf](https://huggingface.co/datasets/julep-ai/dfe-stacked_samsum/blob/main/data_prep.pdf). |
open-llm-leaderboard/details_DopeorNope__SOLARC-MOE-10.7Bx4 | ---
pretty_name: Evaluation run of DopeorNope/SOLARC-MOE-10.7Bx4
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [DopeorNope/SOLARC-MOE-10.7Bx4](https://huggingface.co/DopeorNope/SOLARC-MOE-10.7Bx4)\
\ 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_DopeorNope__SOLARC-MOE-10.7Bx4\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-30T04:44:29.560090](https://huggingface.co/datasets/open-llm-leaderboard/details_DopeorNope__SOLARC-MOE-10.7Bx4/blob/main/results_2023-12-30T04-44-29.560090.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\": 0.6670293129180128,\n\
\ \"acc_stderr\": 0.03161017521655265,\n \"acc_norm\": 0.6679232544824841,\n\
\ \"acc_norm_stderr\": 0.032253513481689276,\n \"mc1\": 0.5703794369645043,\n\
\ \"mc1_stderr\": 0.01732923458040909,\n \"mc2\": 0.719098916907282,\n\
\ \"mc2_stderr\": 0.015045918795928207\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6825938566552902,\n \"acc_stderr\": 0.013602239088038167,\n\
\ \"acc_norm\": 0.7098976109215017,\n \"acc_norm_stderr\": 0.013261573677520767\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7134037044413464,\n\
\ \"acc_stderr\": 0.004512471612415586,\n \"acc_norm\": 0.8842859988050189,\n\
\ \"acc_norm_stderr\": 0.003192279039468747\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-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\": 0.743421052631579,\n \"acc_stderr\": 0.0355418036802569,\n\
\ \"acc_norm\": 0.743421052631579,\n \"acc_norm_stderr\": 0.0355418036802569\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.74,\n\
\ \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n \
\ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6867924528301886,\n \"acc_stderr\": 0.028544793319055326,\n\
\ \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.028544793319055326\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\
\ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\
\ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.51,\n \"acc_stderr\": 0.05024183937956913,\n \"acc_norm\": 0.51,\n\
\ \"acc_norm_stderr\": 0.05024183937956913\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n\
\ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\
\ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6297872340425532,\n \"acc_stderr\": 0.03156564682236786,\n\
\ \"acc_norm\": 0.6297872340425532,\n \"acc_norm_stderr\": 0.03156564682236786\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6344827586206897,\n \"acc_stderr\": 0.040131241954243856,\n\
\ \"acc_norm\": 0.6344827586206897,\n \"acc_norm_stderr\": 0.040131241954243856\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.49206349206349204,\n \"acc_stderr\": 0.02574806587167328,\n \"\
acc_norm\": 0.49206349206349204,\n \"acc_norm_stderr\": 0.02574806587167328\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8225806451612904,\n \"acc_stderr\": 0.021732540689329286,\n \"\
acc_norm\": 0.8225806451612904,\n \"acc_norm_stderr\": 0.021732540689329286\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175007,\n \"\
acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175007\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\"\
: 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.806060606060606,\n \"acc_stderr\": 0.03087414513656209,\n\
\ \"acc_norm\": 0.806060606060606,\n \"acc_norm_stderr\": 0.03087414513656209\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822516,\n \"\
acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822516\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603347,\n\
\ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603347\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\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251976,\n \
\ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251976\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.726890756302521,\n \"acc_stderr\": 0.028942004040998167,\n \
\ \"acc_norm\": 0.726890756302521,\n \"acc_norm_stderr\": 0.028942004040998167\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242741,\n \"\
acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242741\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\
acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5740740740740741,\n \"acc_stderr\": 0.03372343271653062,\n \"\
acc_norm\": 0.5740740740740741,\n \"acc_norm_stderr\": 0.03372343271653062\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8480392156862745,\n \"acc_stderr\": 0.0251956584289318,\n \"acc_norm\"\
: 0.8480392156862745,\n \"acc_norm_stderr\": 0.0251956584289318\n },\n\
\ \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\":\
\ 0.8523206751054853,\n \"acc_stderr\": 0.0230943295825957,\n \"acc_norm\"\
: 0.8523206751054853,\n \"acc_norm_stderr\": 0.0230943295825957\n },\n\
\ \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\
\ \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n\
\ \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306086,\n\
\ \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306086\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.03749492448709696,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.03749492448709696\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\
\ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\
\ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.033519538795212696,\n\
\ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.033519538795212696\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\
\ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\
\ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.03586594738573974,\n\
\ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.03586594738573974\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n\
\ \"acc_stderr\": 0.0230866350868414,\n \"acc_norm\": 0.8547008547008547,\n\
\ \"acc_norm_stderr\": 0.0230866350868414\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8058748403575989,\n\
\ \"acc_stderr\": 0.014143970276657567,\n \"acc_norm\": 0.8058748403575989,\n\
\ \"acc_norm_stderr\": 0.014143970276657567\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7572254335260116,\n \"acc_stderr\": 0.023083658586984204,\n\
\ \"acc_norm\": 0.7572254335260116,\n \"acc_norm_stderr\": 0.023083658586984204\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39106145251396646,\n\
\ \"acc_stderr\": 0.016320763763808383,\n \"acc_norm\": 0.39106145251396646,\n\
\ \"acc_norm_stderr\": 0.016320763763808383\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n\
\ \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7234726688102894,\n\
\ \"acc_stderr\": 0.02540383297817961,\n \"acc_norm\": 0.7234726688102894,\n\
\ \"acc_norm_stderr\": 0.02540383297817961\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7839506172839507,\n \"acc_stderr\": 0.022899162918445806,\n\
\ \"acc_norm\": 0.7839506172839507,\n \"acc_norm_stderr\": 0.022899162918445806\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \
\ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4876140808344198,\n\
\ \"acc_stderr\": 0.012766317315473556,\n \"acc_norm\": 0.4876140808344198,\n\
\ \"acc_norm_stderr\": 0.012766317315473556\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7426470588235294,\n \"acc_stderr\": 0.02655651947004151,\n\
\ \"acc_norm\": 0.7426470588235294,\n \"acc_norm_stderr\": 0.02655651947004151\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6813725490196079,\n \"acc_stderr\": 0.018850084696468712,\n \
\ \"acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.018850084696468712\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\
\ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\
\ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.02812342933514278,\n\
\ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.02812342933514278\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\
\ \"acc_stderr\": 0.02553843336857834,\n \"acc_norm\": 0.845771144278607,\n\
\ \"acc_norm_stderr\": 0.02553843336857834\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.91,\n \"acc_stderr\": 0.028762349126466125,\n \
\ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.028762349126466125\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5843373493975904,\n\
\ \"acc_stderr\": 0.03836722176598052,\n \"acc_norm\": 0.5843373493975904,\n\
\ \"acc_norm_stderr\": 0.03836722176598052\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.783625730994152,\n \"acc_stderr\": 0.03158149539338733,\n\
\ \"acc_norm\": 0.783625730994152,\n \"acc_norm_stderr\": 0.03158149539338733\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5703794369645043,\n\
\ \"mc1_stderr\": 0.01732923458040909,\n \"mc2\": 0.719098916907282,\n\
\ \"mc2_stderr\": 0.015045918795928207\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8358326756116812,\n \"acc_stderr\": 0.01041084977522279\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.643669446550417,\n \
\ \"acc_stderr\": 0.013191685031357463\n }\n}\n```"
repo_url: https://huggingface.co/DopeorNope/SOLARC-MOE-10.7Bx4
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|arc:challenge|25_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|gsm8k|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hellaswag|10_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-30T04-44-29.560090.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-30T04-44-29.560090.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- '**/details_harness|winogrande|5_2023-12-30T04-44-29.560090.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-30T04-44-29.560090.parquet'
- config_name: results
data_files:
- split: 2023_12_30T04_44_29.560090
path:
- results_2023-12-30T04-44-29.560090.parquet
- split: latest
path:
- results_2023-12-30T04-44-29.560090.parquet
---
# Dataset Card for Evaluation run of DopeorNope/SOLARC-MOE-10.7Bx4
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [DopeorNope/SOLARC-MOE-10.7Bx4](https://huggingface.co/DopeorNope/SOLARC-MOE-10.7Bx4) 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_DopeorNope__SOLARC-MOE-10.7Bx4",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-30T04:44:29.560090](https://huggingface.co/datasets/open-llm-leaderboard/details_DopeorNope__SOLARC-MOE-10.7Bx4/blob/main/results_2023-12-30T04-44-29.560090.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": 0.6670293129180128,
"acc_stderr": 0.03161017521655265,
"acc_norm": 0.6679232544824841,
"acc_norm_stderr": 0.032253513481689276,
"mc1": 0.5703794369645043,
"mc1_stderr": 0.01732923458040909,
"mc2": 0.719098916907282,
"mc2_stderr": 0.015045918795928207
},
"harness|arc:challenge|25": {
"acc": 0.6825938566552902,
"acc_stderr": 0.013602239088038167,
"acc_norm": 0.7098976109215017,
"acc_norm_stderr": 0.013261573677520767
},
"harness|hellaswag|10": {
"acc": 0.7134037044413464,
"acc_stderr": 0.004512471612415586,
"acc_norm": 0.8842859988050189,
"acc_norm_stderr": 0.003192279039468747
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.743421052631579,
"acc_stderr": 0.0355418036802569,
"acc_norm": 0.743421052631579,
"acc_norm_stderr": 0.0355418036802569
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.74,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.74,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6867924528301886,
"acc_stderr": 0.028544793319055326,
"acc_norm": 0.6867924528301886,
"acc_norm_stderr": 0.028544793319055326
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7638888888888888,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956913,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956913
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.03599586301247077,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.03599586301247077
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.38235294117647056,
"acc_stderr": 0.04835503696107223,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107223
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6297872340425532,
"acc_stderr": 0.03156564682236786,
"acc_norm": 0.6297872340425532,
"acc_norm_stderr": 0.03156564682236786
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5,
"acc_stderr": 0.047036043419179864,
"acc_norm": 0.5,
"acc_norm_stderr": 0.047036043419179864
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6344827586206897,
"acc_stderr": 0.040131241954243856,
"acc_norm": 0.6344827586206897,
"acc_norm_stderr": 0.040131241954243856
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.49206349206349204,
"acc_stderr": 0.02574806587167328,
"acc_norm": 0.49206349206349204,
"acc_norm_stderr": 0.02574806587167328
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.044444444444444495,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.044444444444444495
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8225806451612904,
"acc_stderr": 0.021732540689329286,
"acc_norm": 0.8225806451612904,
"acc_norm_stderr": 0.021732540689329286
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5221674876847291,
"acc_stderr": 0.03514528562175007,
"acc_norm": 0.5221674876847291,
"acc_norm_stderr": 0.03514528562175007
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.806060606060606,
"acc_stderr": 0.03087414513656209,
"acc_norm": 0.806060606060606,
"acc_norm_stderr": 0.03087414513656209
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8686868686868687,
"acc_stderr": 0.024063156416822516,
"acc_norm": 0.8686868686868687,
"acc_norm_stderr": 0.024063156416822516
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9015544041450777,
"acc_stderr": 0.02150024957603347,
"acc_norm": 0.9015544041450777,
"acc_norm_stderr": 0.02150024957603347
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6615384615384615,
"acc_stderr": 0.023991500500313036,
"acc_norm": 0.6615384615384615,
"acc_norm_stderr": 0.023991500500313036
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3592592592592593,
"acc_stderr": 0.029252905927251976,
"acc_norm": 0.3592592592592593,
"acc_norm_stderr": 0.029252905927251976
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.726890756302521,
"acc_stderr": 0.028942004040998167,
"acc_norm": 0.726890756302521,
"acc_norm_stderr": 0.028942004040998167
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.36423841059602646,
"acc_stderr": 0.03929111781242741,
"acc_norm": 0.36423841059602646,
"acc_norm_stderr": 0.03929111781242741
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8477064220183487,
"acc_stderr": 0.015405084393157074,
"acc_norm": 0.8477064220183487,
"acc_norm_stderr": 0.015405084393157074
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5740740740740741,
"acc_stderr": 0.03372343271653062,
"acc_norm": 0.5740740740740741,
"acc_norm_stderr": 0.03372343271653062
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8480392156862745,
"acc_stderr": 0.0251956584289318,
"acc_norm": 0.8480392156862745,
"acc_norm_stderr": 0.0251956584289318
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8523206751054853,
"acc_stderr": 0.0230943295825957,
"acc_norm": 0.8523206751054853,
"acc_norm_stderr": 0.0230943295825957
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6771300448430493,
"acc_stderr": 0.03138147637575499,
"acc_norm": 0.6771300448430493,
"acc_norm_stderr": 0.03138147637575499
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7480916030534351,
"acc_stderr": 0.03807387116306086,
"acc_norm": 0.7480916030534351,
"acc_norm_stderr": 0.03807387116306086
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7851239669421488,
"acc_stderr": 0.03749492448709696,
"acc_norm": 0.7851239669421488,
"acc_norm_stderr": 0.03749492448709696
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8055555555555556,
"acc_stderr": 0.038260763248848646,
"acc_norm": 0.8055555555555556,
"acc_norm_stderr": 0.038260763248848646
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7607361963190185,
"acc_stderr": 0.033519538795212696,
"acc_norm": 0.7607361963190185,
"acc_norm_stderr": 0.033519538795212696
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4732142857142857,
"acc_stderr": 0.047389751192741546,
"acc_norm": 0.4732142857142857,
"acc_norm_stderr": 0.047389751192741546
},
"harness|hendrycksTest-management|5": {
"acc": 0.8446601941747572,
"acc_stderr": 0.03586594738573974,
"acc_norm": 0.8446601941747572,
"acc_norm_stderr": 0.03586594738573974
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8547008547008547,
"acc_stderr": 0.0230866350868414,
"acc_norm": 0.8547008547008547,
"acc_norm_stderr": 0.0230866350868414
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8058748403575989,
"acc_stderr": 0.014143970276657567,
"acc_norm": 0.8058748403575989,
"acc_norm_stderr": 0.014143970276657567
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7572254335260116,
"acc_stderr": 0.023083658586984204,
"acc_norm": 0.7572254335260116,
"acc_norm_stderr": 0.023083658586984204
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.39106145251396646,
"acc_stderr": 0.016320763763808383,
"acc_norm": 0.39106145251396646,
"acc_norm_stderr": 0.016320763763808383
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7581699346405228,
"acc_stderr": 0.024518195641879334,
"acc_norm": 0.7581699346405228,
"acc_norm_stderr": 0.024518195641879334
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7234726688102894,
"acc_stderr": 0.02540383297817961,
"acc_norm": 0.7234726688102894,
"acc_norm_stderr": 0.02540383297817961
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7839506172839507,
"acc_stderr": 0.022899162918445806,
"acc_norm": 0.7839506172839507,
"acc_norm_stderr": 0.022899162918445806
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.49645390070921985,
"acc_stderr": 0.02982674915328092,
"acc_norm": 0.49645390070921985,
"acc_norm_stderr": 0.02982674915328092
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4876140808344198,
"acc_stderr": 0.012766317315473556,
"acc_norm": 0.4876140808344198,
"acc_norm_stderr": 0.012766317315473556
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7426470588235294,
"acc_stderr": 0.02655651947004151,
"acc_norm": 0.7426470588235294,
"acc_norm_stderr": 0.02655651947004151
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6813725490196079,
"acc_stderr": 0.018850084696468712,
"acc_norm": 0.6813725490196079,
"acc_norm_stderr": 0.018850084696468712
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6818181818181818,
"acc_stderr": 0.04461272175910509,
"acc_norm": 0.6818181818181818,
"acc_norm_stderr": 0.04461272175910509
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7387755102040816,
"acc_stderr": 0.02812342933514278,
"acc_norm": 0.7387755102040816,
"acc_norm_stderr": 0.02812342933514278
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.845771144278607,
"acc_stderr": 0.02553843336857834,
"acc_norm": 0.845771144278607,
"acc_norm_stderr": 0.02553843336857834
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.91,
"acc_stderr": 0.028762349126466125,
"acc_norm": 0.91,
"acc_norm_stderr": 0.028762349126466125
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5843373493975904,
"acc_stderr": 0.03836722176598052,
"acc_norm": 0.5843373493975904,
"acc_norm_stderr": 0.03836722176598052
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.783625730994152,
"acc_stderr": 0.03158149539338733,
"acc_norm": 0.783625730994152,
"acc_norm_stderr": 0.03158149539338733
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5703794369645043,
"mc1_stderr": 0.01732923458040909,
"mc2": 0.719098916907282,
"mc2_stderr": 0.015045918795928207
},
"harness|winogrande|5": {
"acc": 0.8358326756116812,
"acc_stderr": 0.01041084977522279
},
"harness|gsm8k|5": {
"acc": 0.643669446550417,
"acc_stderr": 0.013191685031357463
}
}
```
## 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] |
rkramos/tfnerd | ---
language:
- tl
---
Dataset creation and annotation are detailed on our paper. If you use our dataset, please cite our work:
```
@inproceedings{10.1145/3639233.3639341,
author = {Ramos, Robin Kamille and Vergara, John Paul},
title = {TF-NERD: Tagalog Fine-grained Named Entity Recognition Dataset},
year = {2024},
isbn = {9798400709227},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3639233.3639341},
doi = {10.1145/3639233.3639341},
booktitle = {Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval},
pages = {222–227},
numpages = {6},
keywords = {Corpus Creation, Low-resource Languages, Named Entity Recognition, Transformers},
location = {<conf-loc>, <city>Seoul</city>, <country>Republic of Korea</country>, </conf-loc>},
series = {NLPIR '23}
}
``` |
hausa_voa_ner | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ha
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Hausa VOA NER Corpus
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-DATE
'8': I-DATE
config_name: hausa_voa_ner
splits:
- name: train
num_bytes: 483634
num_examples: 1015
- name: validation
num_bytes: 69673
num_examples: 146
- name: test
num_bytes: 139227
num_examples: 292
download_size: 324962
dataset_size: 692534
---
# Dataset Card for Hausa VOA NER Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.aclweb.org/anthology/2020.emnlp-main.204/
- **Repository:** [Hausa VOA NER](https://github.com/uds-lsv/transfer-distant-transformer-african/tree/master/data/hausa_ner)
- **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.204/
- **Leaderboard:**
- **Point of Contact:** [David Adelani](mailto:didelani@lsv.uni-saarland.de)
### Dataset Summary
The Hausa VOA NER is a named entity recognition (NER) dataset for Hausa language based on the [VOA Hausa news](https://www.voahausa.com/) corpus.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Hausa.
## Dataset Structure
### Data Instances
A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
{'id': '0',
'ner_tags': [B-PER, 0, 0, B-LOC, 0],
'tokens': ['Trump', 'ya', 'ce', 'Rasha', 'ma']
}
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE",
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and dates & times (DATE). (O) is used for tokens not considered part of any named entity.
### Data Splits
Training (1,014 sentences), validation (145 sentences) and test split (291 sentences)
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - Hausa.
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The dataset is based on the news domain and was crawled from [VOA Hausa news](https://www.voahausa.com/).
[More Information Needed]
#### Who are the source language producers?
The dataset was collected from VOA Hausa news. Most of the texts used in creating the Hausa VOA NER are news stories from Nigeria, Niger Republic, United States, and other parts of the world.
[More Information Needed]
### Annotations
Named entity recognition annotation
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The data was annotated by Jesujoba Alabi and David Adelani for the paper:
[Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages](https://www.aclweb.org/anthology/2020.emnlp-main.204/).
[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
The annotated data sets were developed by students of Saarland University, Saarbrücken, Germany .
### Licensing Information
The data is under the [Creative Commons Attribution 4.0 ](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@inproceedings{hedderich-etal-2020-transfer,
title = "Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on {A}frican Languages",
author = "Hedderich, Michael A. and
Adelani, David and
Zhu, Dawei and
Alabi, Jesujoba and
Markus, Udia and
Klakow, Dietrich",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.204",
doi = "10.18653/v1/2020.emnlp-main.204",
pages = "2580--2591",
}
```
### Contributions
Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset. |
itamarcard/testes | ---
license: openrail
---
|
BangumiBase/onipan | ---
license: mit
tags:
- art
size_categories:
- n<1K
---
# Bangumi Image Base of Onipan!
This is the image base of bangumi Onipan!, we detected 21 characters, 952 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 | 11 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 20 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 19 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 8 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 8 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 7 | [Download](5/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 6 | 6 | [Download](6/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 7 | 9 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 205 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 12 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 193 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 9 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 21 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 57 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 17 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 180 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 13 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 10 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 14 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 24 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 109 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
coffeelatte369/tangzi | ---
license: other
---
|
Prajapat/banking_conversion_test | ---
dataset_info:
features:
- name: client_data
dtype: string
- name: agent_data
dtype: string
splits:
- name: train
num_bytes: 46323
num_examples: 200
download_size: 25954
dataset_size: 46323
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
distil-whisper/ami-sdm | ---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
language:
- en
-pretty_name: AMI SDM
---
# Distil Whisper: AMI SDM
This is a variant of the [AMI SDM](https://huggingface.co/datasets/edinburghstr/ami) dataset, augmented to return the pseudo-labelled Whisper
Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by
labelling the input audio data with the Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2)
model with *greedy* sampling. For information on how the original dataset was curated, refer to the original
[dataset card](https://huggingface.co/datasets/edinburghstr/ami).
## Standalone Usage
First, install the latest version of the 🤗 Datasets package:
```bash
pip install --upgrade pip
pip install --upgrade datasets[audio]
```
The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset)
function:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/ami-sdm", "sdm")
# take the first sample of the validation set
sample = dataset["validation"][0]
```
It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire
dataset to disk:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/ami-sdm", "sdm", streaming=True)
# take the first sample of the validation set
sample = next(iter(dataset["validation"]))
```
## Distil Whisper Usage
To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the
[Distil Whisper repository](https://github.com/huggingface/distil-whisper#training).
## License
This dataset is licensed under cc-by-4.0.
|
radius27/scam_finetuned | ---
dataset_info:
features:
- name: prompts
dtype: string
- name: outputs
dtype: string
splits:
- name: train
num_bytes: 5317286
num_examples: 289
download_size: 2549644
dataset_size: 5317286
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AdapterOcean/python3-standardized_cluster_11 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 38569598
num_examples: 3612
download_size: 0
dataset_size: 38569598
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "python3-standardized_cluster_11"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
heliosprime/twitter_dataset_1713179781 | ---
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: 11977
num_examples: 33
download_size: 13542
dataset_size: 11977
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713179781"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/chemistry_dataset_standardized_cluster_4_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4182760
num_examples: 3029
download_size: 1880227
dataset_size: 4182760
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "chemistry_dataset_standardized_cluster_4_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
breno30/LocutorLindomarTop | ---
license: openrail
---
|
zZWipeoutZz/assassin_style | ---
license: creativeml-openrail-m
---
<h4> Usage </h4>
To use this embedding you have to download the file and put it into the "\stable-diffusion-webui\embeddings" folder
To use it in a prompt add
<em style="font-weight:600">art by assassin_style </em>
add <b>[ ]</b> around it to reduce its weight.
<h4> Included Files </h4>
<ul>
<li>6500 steps <em>Usage: art by assassin_style-6500</em></li>
<li>10,000 steps <em>Usage: art by assassin_style-10000</em> </li>
<li>15,000 steps <em>Usage: art by assassin_style </em></li>
</ul>
cheers<br>
Wipeout
<h4> Example Pictures </h4>
<table>
<tbody>
<tr>
<td><img height="100%/" width="100%" src="https://i.imgur.com/RhE7Qce.png"></td>
<td><img height="100%/" width="100%" src="https://i.imgur.com/wVOH8GU.png"></td>
<td><img height="100%/" width="100%" src="https://i.imgur.com/YaBbNNK.png"></td>
<td><img height="100%/" width="100%" src="https://i.imgur.com/63HpAf1.png"></td>
</tr>
</tbody>
</table>
<h4> prompt comparison </h4>
<em> click the image to enlarge</em>
<a href="https://i.imgur.com/nrkCPEf.jpg" target="_blank"><img height="50%" width="50%" src="https://i.imgur.com/nrkCPEf.jpg"></a>
|
lakshmikarpolam/21_classes | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 117652110.173
num_examples: 26249
download_size: 2525235673
dataset_size: 117652110.173
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mfidabel/sam-coyo-1k | ---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 914695375.841
num_examples: 1159
download_size: 913350586
dataset_size: 914695375.841
---
|
pandaresiddhi/training_bert | ---
license: apache-2.0
---
|
open-llm-leaderboard/details_jsfs11__MixtureofMerges-MoE-v2 | ---
pretty_name: Evaluation run of jsfs11/MixtureofMerges-MoE-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jsfs11/MixtureofMerges-MoE-v2](https://huggingface.co/jsfs11/MixtureofMerges-MoE-v2)\
\ 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_jsfs11__MixtureofMerges-MoE-v2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-02T00:33:54.387134](https://huggingface.co/datasets/open-llm-leaderboard/details_jsfs11__MixtureofMerges-MoE-v2/blob/main/results_2024-02-02T00-33-54.387134.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\": 0.6543220046149792,\n\
\ \"acc_stderr\": 0.032031600560374206,\n \"acc_norm\": 0.6541067503001904,\n\
\ \"acc_norm_stderr\": 0.03269555640627793,\n \"mc1\": 0.5667074663402693,\n\
\ \"mc1_stderr\": 0.017347024450107485,\n \"mc2\": 0.7091803983210125,\n\
\ \"mc2_stderr\": 0.01482201181219182\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.697098976109215,\n \"acc_stderr\": 0.013428241573185349,\n\
\ \"acc_norm\": 0.7244027303754266,\n \"acc_norm_stderr\": 0.01305716965576184\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.710017924716192,\n\
\ \"acc_stderr\": 0.004528264116475881,\n \"acc_norm\": 0.8840868352917746,\n\
\ \"acc_norm_stderr\": 0.0031946652660786025\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\
\ \"acc_stderr\": 0.041539484047423976,\n \"acc_norm\": 0.6370370370370371,\n\
\ \"acc_norm_stderr\": 0.041539484047423976\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\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\
\ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n\
\ \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n\
\ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\
\ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n\
\ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\
\ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\
\ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n\
\ \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\
\ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4312169312169312,\n \"acc_stderr\": 0.025506481698138208,\n \"\
acc_norm\": 0.4312169312169312,\n \"acc_norm_stderr\": 0.025506481698138208\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\
\ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\
\ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7935483870967742,\n \"acc_stderr\": 0.023025899617188723,\n \"\
acc_norm\": 0.7935483870967742,\n \"acc_norm_stderr\": 0.023025899617188723\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n \"\
acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\
\ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7828282828282829,\n \"acc_stderr\": 0.029376616484945633,\n \"\
acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.029376616484945633\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033456,\n\
\ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033456\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563976,\n\
\ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563976\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3296296296296296,\n \"acc_stderr\": 0.02866120111652457,\n \
\ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.02866120111652457\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977945,\n\
\ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977945\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\"\
: 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374307,\n \"\
acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374307\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5324074074074074,\n \"acc_stderr\": 0.03402801581358966,\n \"\
acc_norm\": 0.5324074074074074,\n \"acc_norm_stderr\": 0.03402801581358966\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8529411764705882,\n \"acc_stderr\": 0.024857478080250444,\n \"\
acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.024857478080250444\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290902,\n \
\ \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290902\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159465,\n\
\ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159465\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990946,\n \"\
acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990946\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\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\
\ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.04718471485219588\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\": 0.8846153846153846,\n\
\ \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n\
\ \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.822477650063857,\n\
\ \"acc_stderr\": 0.01366423099583483,\n \"acc_norm\": 0.822477650063857,\n\
\ \"acc_norm_stderr\": 0.01366423099583483\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7369942196531792,\n \"acc_stderr\": 0.023703099525258176,\n\
\ \"acc_norm\": 0.7369942196531792,\n \"acc_norm_stderr\": 0.023703099525258176\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.42793296089385474,\n\
\ \"acc_stderr\": 0.01654788799741611,\n \"acc_norm\": 0.42793296089385474,\n\
\ \"acc_norm_stderr\": 0.01654788799741611\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7254901960784313,\n \"acc_stderr\": 0.025553169991826524,\n\
\ \"acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.025553169991826524\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.729903536977492,\n\
\ \"acc_stderr\": 0.02521804037341063,\n \"acc_norm\": 0.729903536977492,\n\
\ \"acc_norm_stderr\": 0.02521804037341063\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712995,\n\
\ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712995\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422473,\n \
\ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422473\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47131681877444587,\n\
\ \"acc_stderr\": 0.012749206007657474,\n \"acc_norm\": 0.47131681877444587,\n\
\ \"acc_norm_stderr\": 0.012749206007657474\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\": 0.6813725490196079,\n \"acc_stderr\": 0.01885008469646872,\n \
\ \"acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.01885008469646872\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\
\ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\
\ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784593,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784593\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\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\
\ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\
\ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5667074663402693,\n\
\ \"mc1_stderr\": 0.017347024450107485,\n \"mc2\": 0.7091803983210125,\n\
\ \"mc2_stderr\": 0.01482201181219182\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8358326756116812,\n \"acc_stderr\": 0.010410849775222789\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6868840030326004,\n \
\ \"acc_stderr\": 0.012774285669385087\n }\n}\n```"
repo_url: https://huggingface.co/jsfs11/MixtureofMerges-MoE-v2
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_02T00_33_54.387134
path:
- '**/details_harness|arc:challenge|25_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|gsm8k|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hellaswag|10_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T00-33-54.387134.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T00-33-54.387134.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- '**/details_harness|winogrande|5_2024-02-02T00-33-54.387134.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-02T00-33-54.387134.parquet'
- config_name: results
data_files:
- split: 2024_02_02T00_33_54.387134
path:
- results_2024-02-02T00-33-54.387134.parquet
- split: latest
path:
- results_2024-02-02T00-33-54.387134.parquet
---
# Dataset Card for Evaluation run of jsfs11/MixtureofMerges-MoE-v2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [jsfs11/MixtureofMerges-MoE-v2](https://huggingface.co/jsfs11/MixtureofMerges-MoE-v2) 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_jsfs11__MixtureofMerges-MoE-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-02T00:33:54.387134](https://huggingface.co/datasets/open-llm-leaderboard/details_jsfs11__MixtureofMerges-MoE-v2/blob/main/results_2024-02-02T00-33-54.387134.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": 0.6543220046149792,
"acc_stderr": 0.032031600560374206,
"acc_norm": 0.6541067503001904,
"acc_norm_stderr": 0.03269555640627793,
"mc1": 0.5667074663402693,
"mc1_stderr": 0.017347024450107485,
"mc2": 0.7091803983210125,
"mc2_stderr": 0.01482201181219182
},
"harness|arc:challenge|25": {
"acc": 0.697098976109215,
"acc_stderr": 0.013428241573185349,
"acc_norm": 0.7244027303754266,
"acc_norm_stderr": 0.01305716965576184
},
"harness|hellaswag|10": {
"acc": 0.710017924716192,
"acc_stderr": 0.004528264116475881,
"acc_norm": 0.8840868352917746,
"acc_norm_stderr": 0.0031946652660786025
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6370370370370371,
"acc_stderr": 0.041539484047423976,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.041539484047423976
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6973684210526315,
"acc_stderr": 0.03738520676119669,
"acc_norm": 0.6973684210526315,
"acc_norm_stderr": 0.03738520676119669
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.65,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.65,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.720754716981132,
"acc_stderr": 0.027611163402399715,
"acc_norm": 0.720754716981132,
"acc_norm_stderr": 0.027611163402399715
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.03599586301247077,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.03599586301247077
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.04878608714466996,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.04878608714466996
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5829787234042553,
"acc_stderr": 0.03223276266711712,
"acc_norm": 0.5829787234042553,
"acc_norm_stderr": 0.03223276266711712
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4649122807017544,
"acc_stderr": 0.046920083813689104,
"acc_norm": 0.4649122807017544,
"acc_norm_stderr": 0.046920083813689104
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5586206896551724,
"acc_stderr": 0.04137931034482757,
"acc_norm": 0.5586206896551724,
"acc_norm_stderr": 0.04137931034482757
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4312169312169312,
"acc_stderr": 0.025506481698138208,
"acc_norm": 0.4312169312169312,
"acc_norm_stderr": 0.025506481698138208
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4603174603174603,
"acc_stderr": 0.04458029125470973,
"acc_norm": 0.4603174603174603,
"acc_norm_stderr": 0.04458029125470973
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7935483870967742,
"acc_stderr": 0.023025899617188723,
"acc_norm": 0.7935483870967742,
"acc_norm_stderr": 0.023025899617188723
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4876847290640394,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.4876847290640394,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7636363636363637,
"acc_stderr": 0.03317505930009182,
"acc_norm": 0.7636363636363637,
"acc_norm_stderr": 0.03317505930009182
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7828282828282829,
"acc_stderr": 0.029376616484945633,
"acc_norm": 0.7828282828282829,
"acc_norm_stderr": 0.029376616484945633
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9015544041450777,
"acc_stderr": 0.021500249576033456,
"acc_norm": 0.9015544041450777,
"acc_norm_stderr": 0.021500249576033456
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6641025641025641,
"acc_stderr": 0.023946724741563976,
"acc_norm": 0.6641025641025641,
"acc_norm_stderr": 0.023946724741563976
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3296296296296296,
"acc_stderr": 0.02866120111652457,
"acc_norm": 0.3296296296296296,
"acc_norm_stderr": 0.02866120111652457
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6890756302521008,
"acc_stderr": 0.030066761582977945,
"acc_norm": 0.6890756302521008,
"acc_norm_stderr": 0.030066761582977945
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3576158940397351,
"acc_stderr": 0.03913453431177258,
"acc_norm": 0.3576158940397351,
"acc_norm_stderr": 0.03913453431177258
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8458715596330275,
"acc_stderr": 0.015480826865374307,
"acc_norm": 0.8458715596330275,
"acc_norm_stderr": 0.015480826865374307
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5324074074074074,
"acc_stderr": 0.03402801581358966,
"acc_norm": 0.5324074074074074,
"acc_norm_stderr": 0.03402801581358966
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8529411764705882,
"acc_stderr": 0.024857478080250444,
"acc_norm": 0.8529411764705882,
"acc_norm_stderr": 0.024857478080250444
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8059071729957806,
"acc_stderr": 0.025744902532290902,
"acc_norm": 0.8059071729957806,
"acc_norm_stderr": 0.025744902532290902
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7938931297709924,
"acc_stderr": 0.03547771004159465,
"acc_norm": 0.7938931297709924,
"acc_norm_stderr": 0.03547771004159465
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8016528925619835,
"acc_stderr": 0.03640118271990946,
"acc_norm": 0.8016528925619835,
"acc_norm_stderr": 0.03640118271990946
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.75,
"acc_stderr": 0.04186091791394607,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04186091791394607
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.754601226993865,
"acc_stderr": 0.03380939813943354,
"acc_norm": 0.754601226993865,
"acc_norm_stderr": 0.03380939813943354
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.44642857142857145,
"acc_stderr": 0.04718471485219588,
"acc_norm": 0.44642857142857145,
"acc_norm_stderr": 0.04718471485219588
},
"harness|hendrycksTest-management|5": {
"acc": 0.8058252427184466,
"acc_stderr": 0.03916667762822584,
"acc_norm": 0.8058252427184466,
"acc_norm_stderr": 0.03916667762822584
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8846153846153846,
"acc_stderr": 0.02093019318517933,
"acc_norm": 0.8846153846153846,
"acc_norm_stderr": 0.02093019318517933
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768078,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768078
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.822477650063857,
"acc_stderr": 0.01366423099583483,
"acc_norm": 0.822477650063857,
"acc_norm_stderr": 0.01366423099583483
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7369942196531792,
"acc_stderr": 0.023703099525258176,
"acc_norm": 0.7369942196531792,
"acc_norm_stderr": 0.023703099525258176
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.42793296089385474,
"acc_stderr": 0.01654788799741611,
"acc_norm": 0.42793296089385474,
"acc_norm_stderr": 0.01654788799741611
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"harness|winogrande|5": {
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"harness|gsm8k|5": {
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}
}
```
## Dataset Details
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