datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
thejaminator/imdb_rewarded | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
---
This is the imdb dataset, https://huggingface.co/datasets/imdb
We've used a reward / sentiment model, https://huggingface.co/lvwerra/distilbert-imdb to compute the rewards of the offline data.
This is so that we can use offline RL on the data. |
AIGym/reddit-clean | ---
language:
- en
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 219359991
num_examples: 152431
download_size: 136445950
dataset_size: 219359991
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AdityaNG/commavq-trajectory | ---
license: mit
---
# CommaVQ Trajectory
<img src="media/demo.gif" >
Based on the [commavq](https://huggingface.co/datasets/commaai/commavq) dataset.
Contains images of highway driving along with the corresponding control signal (trajectory).
The trajectory has been quantized into one of 256 template trajectories.
<img src="trajectory_templates/trajectory_templates_128.png" >
This data is formatted to train the LLaVA model:
```json
[
{
"id": "4376800841",
"image": "data_0_to_2500/324a487ead6977b02c746d5117e9825a_16/841.png",
"conversations": [
{
"from": "human",
"value": "<image>\nYou are DriveLLaVA, a self-driving car. You will select the appropriate trrajectory token given the above image as context.\nYou may select one from the following templates: ,!,\",#,$,%,&,',(,),*,+,,,-,.,/,0,1,2,3,4,5,6,7,8,9,:,;,<,=,>,?,@,A,B,C,D,E,F,G,H,I,J,K,L,M,N,O,P,Q,R,S,T,U,V,W,X,Y,Z,[,],^,_,`,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,{,|,},~,¡,¢,£,¤,¥,¦,§,¨,©,ª,«,¬,®,¯,°,±,²,³,´,µ,¶,·,¸,¹,º,»,¼,½,¾,¿,À,Á,Â,Ã,Ä,Å,Æ,Ç,È,É,Ê,Ë,Ì,Í,Î,Ï,Ð,Ñ,Ò,Ó,Ô,Õ,Ö,×,Ø,Ù,Ú,Û,Ü,Ý,Þ,ß,à,á,â,ã,ä,å,æ,ç,è,é,ê,ë,ì,í,î,ï,ð,ñ,ò,ó,ô,õ,ö,÷,ø,ù,ú,û,ü,ý,þ,ÿ,Ā,ā,Ă,ă,Ą,ą,Ć,ć,Ĉ,ĉ,Ċ,ċ,Č,č,Ď,ď,Đ,đ,Ē,ē,Ĕ,ĕ,Ė,ė,Ę,ę,Ě,ě,Ĝ,ĝ,Ğ,ğ,Ġ,ġ,Ģ,ģ,Ĥ,ĥ,Ħ,ħ,Ĩ,ĩ,Ī,ī,Ĭ,ĭ,Į,į,İ,ı,IJ,ij,Ĵ,ĵ,Ķ,ķ,ĸ,Ĺ,ĺ,Ļ,ļ,Ľ,ľ,Ŀ,ŀ,Ł,ł,Ń"
},
{
"from": "gpt",
"value": ")"
}
]
}
]
```
## Getting Started
```
cd ~/Datasets/
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/AdityaNG/commavq-trajectory ~/Datasets/commavq
cd ~/Datasets/commavq
git lfs pull
unzip "*.zip"
```
|
bh8648/split_dataset_5 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: page_num
dtype: int64
splits:
- name: train
num_bytes: 775242
num_examples: 212
download_size: 383344
dataset_size: 775242
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "split_dataset_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hrangel/newsql | ---
license: creativeml-openrail-m
---
|
liuyanchen1015/MULTI_VALUE_qqp_my_me | ---
dataset_info:
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 800591
num_examples: 4654
- name: test
num_bytes: 8527694
num_examples: 48427
- name: train
num_bytes: 7426425
num_examples: 42844
download_size: 10087004
dataset_size: 16754710
---
# Dataset Card for "MULTI_VALUE_qqp_my_me"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MillionScope/millionscope | ---
license: mit
---
|
mattymchen/celeba-hq | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': female
'1': male
splits:
- name: train
num_bytes: 2731627350.0
num_examples: 28000
- name: validation
num_bytes: 197550788.0
num_examples: 2000
download_size: 2762109745
dataset_size: 2929178138.0
---
# Dataset Card for "celeba-hq"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rvpierre/insurance-qa-en | ---
dataset_info:
features:
- name: index
dtype: int64
- name: topic_en
dtype: string
- name: question_en
dtype: string
splits:
- name: train
num_bytes: 1044899
num_examples: 12888
- name: test
num_bytes: 162551
num_examples: 1999
- name: valid
num_bytes: 162498
num_examples: 1999
download_size: 126622
dataset_size: 1369948
---
# Dataset Card for "insurance-qa-en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
PRAli22/Arabic_Tweets | ---
license: apache-2.0
---
|
Nikutka/L1_scraped_korpus_wzorcowy_test | ---
dataset_info:
features:
- name: content
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 1207567
num_examples: 7372
download_size: 865883
dataset_size: 1207567
---
# Dataset Card for "L1_scraped_korpus_wzorcowy_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
deepapaikar/SC_Katz_11k | ---
license: apache-2.0
---
|
k-seungri/k_whisper_dataset | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcripts
dtype: string
splits:
- name: train
num_bytes: 7859062.290076337
num_examples: 104
- name: test
num_bytes: 972595.9236641221
num_examples: 14
- name: valid
num_bytes: 1338620.786259542
num_examples: 13
download_size: 8246363
dataset_size: 10170279.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
|
imdatta0/ultrachat_1k | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5889290.5683977585
num_examples: 1000
- name: test
num_bytes: 1472322.6420994396
num_examples: 250
download_size: 3614189
dataset_size: 7361613.210497199
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
scikit-learn/churn-prediction | ---
license: cc-by-4.0
---
Customer churn prediction dataset of a fictional telecommunication company made by IBM Sample Datasets.
Context
Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs.
Content
Each row represents a customer, each column contains customer’s attributes described on the column metadata.
The data set includes information about:
- Customers who left within the last month: the column is called Churn
- Services that each customer has signed up for: phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies
- Customer account information: how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
- Demographic info about customers: gender, age range, and if they have partners and dependents
Credits for the dataset and the card:
- [Kaggle](https://www.kaggle.com/datasets/blastchar/telco-customer-churn)
- [Latest version of the dataset by IBM Samples team](https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/11/telco-customer-churn-1113)
|
everettshen/StreetView360X | ---
license: mit
task_categories:
- text-to-image
- image-classification
- image-to-text
- image-feature-extraction
tags:
- geography
- street views
- panoramas
- equirectangular panorama
- 360 degree image
- panoramic street views
size_categories:
- 1K<n<10K
---
StreetView 360X is a dataset containing 6342 360 degree equirectangular street view images randomly sampled and downloaded from Google Street View. It is published as part of the paper "StreetView360X: A Location-Conditioned Latent Diffusion Model for Generating Equirectangular 360 Degree Street Views" (Princeton COS Senior Independent Work by [Everett Shen](https://github.com/Everett-Shen)). Images are labelled with their capture coordinates and panorama IDs. Scripts for extending the dataset (i.e. fetching additional images) can be found in the Github repo.
[Link to model](https://huggingface.co/everettshen/StreetView360X)
- "caption" folder contains captions for each image in the form of "StreetView360X [Country], StreetView360X [Continent], StreetView360X [Region]" corresponding to the image capture location
- Files in caption folder have same file names as the images they are captioning
- Image files are captioned with their Google API panorama ID and capture coordinates
- "caption_metadata.txt" contains mapping of countries to list of file names for easy fetching
- "Countries and regions summarized.txt" contains panorama counts per country/continent/region
Total: 6342 images |
Asap7772/skewlognormal_minlength | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: output
dtype: string
- name: text
dtype: string
- name: alpaca_text
dtype: string
- name: prompt
dtype: string
- name: alpaca_prompt
dtype: string
- name: y_ref
dtype: string
- name: y_1
dtype: string
- name: y_2
dtype: string
- name: y_w
dtype: string
- name: y_w_alpaca
dtype: string
- name: y_l
dtype: string
- name: y_l_alpaca
dtype: string
- name: y_w_score
dtype: float64
- name: y_l_score
dtype: float64
- name: score_diff
dtype: float64
splits:
- name: train
num_bytes: 77844991
num_examples: 19000
- name: test
num_bytes: 4082779
num_examples: 1000
download_size: 40225094
dataset_size: 81927770
---
# Dataset Card for "skewlognormal_minlength"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nlplabtdtu/val-tokenizor-ds-T5 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 885510284
num_examples: 209524
download_size: 296327037
dataset_size: 885510284
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "val-tokenizor-ds-T5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jonas/osdg_sdg_data_processed | ---
language:
- en
task_categories:
- text-classification
---
# AutoTrain Dataset for project: osdg-sdg-classifier
## Dataset Descritpion
This dataset has been pre-processed using standard python cleaning functions and further automatically processed by AutoTrain for project osdg-sdg-classifier.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "teams of technical experts elaborate and validate these plans in collaboration with the local commun[...]",
"target": 14
},
{
"text": "yet commitments to promote the cohesion of families cannot be seen in isolation from two critical el[...]",
"target": 10
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=15, names=['1', '10', '11', '12', '13', '14', '15', '2', '3', '4', '5', '6', '7', '8', '9'], 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 | 14098 |
| valid | 3533 |
|
erkam/clevr-with-depth-full-v2 | ---
dataset_info:
features:
- name: target_img
dtype: image
- name: source_img
dtype: image
- name: target_obj
sequence: int64
- name: source_obj
sequence: int64
- name: target_box
sequence:
sequence: float32
- name: source_box
sequence:
sequence: float32
- name: target_depth
dtype: image
- name: source_depth
dtype: image
- name: target_tri
sequence:
sequence: int64
- name: source_tri
sequence:
sequence: int64
- name: pos_prompt
dtype: string
- name: neg_prompt
dtype: string
splits:
- name: test
num_bytes: 43042513.0
num_examples: 300
- name: val
num_bytes: 43023361.0
num_examples: 300
- name: train
num_bytes: 200465105.0
num_examples: 1400
download_size: 283955264
dataset_size: 286530979.0
---
# Dataset Card for "clevr-with-depth-full-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Gabizu/JigsawMoreno | ---
license: openrail
---
|
tyzhu/fwv2_baseline_squad_train_10000_eval_100 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 3612366
num_examples: 10000
- name: eval_find_word
num_bytes: 35542
num_examples: 100
- name: validation
num_bytes: 35542
num_examples: 100
download_size: 2150107
dataset_size: 3683450
---
# Dataset Card for "fwv2_baseline_squad_train_10000_eval_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
316usman/thematic2c | ---
license: bsd
dataset_info:
features:
- name: text
dtype: string
- name: thematic
dtype: string
- name: sub-thematic
dtype: string
- name: country
dtype: string
- name: document_url
dtype: string
- name: source_url
dtype: string
splits:
- name: train
num_bytes: 200962592
num_examples: 249415
download_size: 61663748
dataset_size: 200962592
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/miku_darlinginthefranxx | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Miku/ミク (Darling in the FranXX)
This is the dataset of Miku/ミク (Darling in the FranXX), containing 369 images and their tags.
The core tags of this character are `twintails, red_hair, ahoge, long_hair, brown_hair, blue_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 369 | 210.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miku_darlinginthefranxx/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 369 | 210.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miku_darlinginthefranxx/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 689 | 358.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miku_darlinginthefranxx/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/miku_darlinginthefranxx',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 15 |  |  |  |  |  | military_uniform, solo, upper_body, 1boy, cosplay, male_focus, 1girl, alternate_hairstyle |
| 1 | 21 |  |  |  |  |  | 1girl, military_uniform, solo, smile |
| 2 | 5 |  |  |  |  |  | 2girls, military_uniform, solo_focus, 1girl, hair_between_eyes |
| 3 | 14 |  |  |  |  |  | pilot_suit, white_bodysuit, 1girl, medium_breasts, covered_navel, solo, closed_mouth |
| 4 | 12 |  |  |  |  |  | short_hair, 1girl, double_bun, solo, white_bikini, cleavage, medium_breasts, outdoors, single_hair_bun |
| 5 | 9 |  |  |  |  |  | 1girl, beach, breasts, navel, ocean, pink_bikini, striped_bikini, striped_clothes, open_mouth, outdoors, running, bracelet, brown_eyes, day, water, sandals, holding_hands, solo_focus |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | military_uniform | solo | upper_body | 1boy | cosplay | male_focus | 1girl | alternate_hairstyle | smile | 2girls | solo_focus | hair_between_eyes | pilot_suit | white_bodysuit | medium_breasts | covered_navel | closed_mouth | short_hair | double_bun | white_bikini | cleavage | outdoors | single_hair_bun | beach | breasts | navel | ocean | pink_bikini | striped_bikini | striped_clothes | open_mouth | running | bracelet | brown_eyes | day | water | sandals | holding_hands |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------|:-------|:-------------|:-------|:----------|:-------------|:--------|:----------------------|:--------|:---------|:-------------|:--------------------|:-------------|:-----------------|:-----------------|:----------------|:---------------|:-------------|:-------------|:---------------|:-----------|:-----------|:------------------|:--------|:----------|:--------|:--------|:--------------|:-----------------|:------------------|:-------------|:----------|:-----------|:-------------|:------|:--------|:----------|:----------------|
| 0 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 21 |  |  |  |  |  | X | X | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | | | | | | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 14 |  |  |  |  |  | | X | | | | | X | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 4 | 12 |  |  |  |  |  | | X | | | | | X | | | | | | | | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | |
| 5 | 9 |  |  |  |  |  | | | | | | | X | | | | X | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
bdsaglam/web_nlg-erx-sft-alpaca | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 12207850
num_examples: 17713
- name: dev
num_bytes: 3079286
num_examples: 4464
download_size: 5338275
dataset_size: 15287136
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
---
|
KolaGang/legal_sum | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 354554492
num_examples: 91118
download_size: 159543985
dataset_size: 354554492
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ovior/twitter_dataset_1713150312 | ---
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: 2703566
num_examples: 8388
download_size: 1522173
dataset_size: 2703566
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Mahziar/Link | ---
license: mit
---
|
Luka-Wang/COCO | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
paperswithcode_id: acronym-identification
pretty_name: Acronym Identification Dataset
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- token-classification-other-acronym-identification
train-eval-index:
- col_mapping:
labels: tags
tokens: tokens
config: default
splits:
eval_split: test
task: token-classification
task_id: entity_extraction
---
# Dataset Card for [COCO]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-scuwyh2000](https://github.com/scuwyh2000) for adding this dataset. |
CJWeiss/inabs | ---
dataset_info:
features:
- name: text
dtype: string
- name: summary
dtype: string
- name: file
dtype: string
splits:
- name: train
num_bytes: 159441006
num_examples: 5346
- name: test
num_bytes: 32277886
num_examples: 1069
- name: valid
num_bytes: 21628228
num_examples: 713
download_size: 103927432
dataset_size: 213347120
---
# Dataset Card for "inabs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_yeontaek__llama-2-70b-IA3-guanaco | ---
pretty_name: Evaluation run of yeontaek/llama-2-70b-IA3-guanaco
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [yeontaek/llama-2-70b-IA3-guanaco](https://huggingface.co/yeontaek/llama-2-70b-IA3-guanaco)\
\ 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_yeontaek__llama-2-70b-IA3-guanaco\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T01:35:02.299684](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__llama-2-70b-IA3-guanaco/blob/main/results_2023-10-23T01-35-02.299684.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\": 0.059354026845637585,\n\
\ \"em_stderr\": 0.0024197909382591906,\n \"f1\": 0.12265834731543575,\n\
\ \"f1_stderr\": 0.0026243794222964158,\n \"acc\": 0.5548770235038503,\n\
\ \"acc_stderr\": 0.011602676960733152\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.059354026845637585,\n \"em_stderr\": 0.0024197909382591906,\n\
\ \"f1\": 0.12265834731543575,\n \"f1_stderr\": 0.0026243794222964158\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.287338893100834,\n \
\ \"acc_stderr\": 0.012464677060107086\n },\n \"harness|winogrande|5\":\
\ {\n \"acc\": 0.8224151539068666,\n \"acc_stderr\": 0.01074067686135922\n\
\ }\n}\n```"
repo_url: https://huggingface.co/yeontaek/llama-2-70b-IA3-guanaco
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_18T03_44_14.521953
path:
- '**/details_harness|arc:challenge|25_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T01_35_02.299684
path:
- '**/details_harness|drop|3_2023-10-23T01-35-02.299684.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T01-35-02.299684.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T01_35_02.299684
path:
- '**/details_harness|gsm8k|5_2023-10-23T01-35-02.299684.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T01-35-02.299684.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hellaswag|10_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T03:44:14.521953.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T03:44:14.521953.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T03:44:14.521953.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T01_35_02.299684
path:
- '**/details_harness|winogrande|5_2023-10-23T01-35-02.299684.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T01-35-02.299684.parquet'
- config_name: results
data_files:
- split: 2023_08_18T03_44_14.521953
path:
- results_2023-08-18T03:44:14.521953.parquet
- split: 2023_10_23T01_35_02.299684
path:
- results_2023-10-23T01-35-02.299684.parquet
- split: latest
path:
- results_2023-10-23T01-35-02.299684.parquet
---
# Dataset Card for Evaluation run of yeontaek/llama-2-70b-IA3-guanaco
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/yeontaek/llama-2-70b-IA3-guanaco
- **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 [yeontaek/llama-2-70b-IA3-guanaco](https://huggingface.co/yeontaek/llama-2-70b-IA3-guanaco) 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_yeontaek__llama-2-70b-IA3-guanaco",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T01:35:02.299684](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__llama-2-70b-IA3-guanaco/blob/main/results_2023-10-23T01-35-02.299684.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": 0.059354026845637585,
"em_stderr": 0.0024197909382591906,
"f1": 0.12265834731543575,
"f1_stderr": 0.0026243794222964158,
"acc": 0.5548770235038503,
"acc_stderr": 0.011602676960733152
},
"harness|drop|3": {
"em": 0.059354026845637585,
"em_stderr": 0.0024197909382591906,
"f1": 0.12265834731543575,
"f1_stderr": 0.0026243794222964158
},
"harness|gsm8k|5": {
"acc": 0.287338893100834,
"acc_stderr": 0.012464677060107086
},
"harness|winogrande|5": {
"acc": 0.8224151539068666,
"acc_stderr": 0.01074067686135922
}
}
```
### 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] |
deokhk/pl_wiki_sentences_1000000 | ---
dataset_info:
features:
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 113265727
num_examples: 1000000
- name: dev
num_bytes: 112360
num_examples: 1000
download_size: 74085916
dataset_size: 113378087
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
---
|
SUFE-AIFLM-Lab/FinEval | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
- multiple-choice
- question-answering
language:
- zh
pretty_name: FinEval
size_categories:
- 1K<n<10K
viewer: false
---
<p><h1> The FinEval Dataset </h1></p>
<a name="dataset-announcement"></a>
FinEval is a collection of high-quality multiple-choice questions covering various domains such as finance, economics, accounting, and certifications. It consists of 4,661 questions spanning across 34 distinct academic subjects. To ensure a comprehensive assessment of model performance, FinEval employs various methods including zero-shot, few-shot, answer-only, and chain-of-thought prompts. Evaluating state-of-the-art large language models in both Chinese and English on FinEval reveals that only GPT-4 achieves an accuracy of 60% across different prompt settings, highlighting substantial growth potential of large language models in financial domain knowledge. Our work provides a more comprehensive benchmark for evaluating financial knowledge, utilizing simulated exam data and encompassing a wide range of large language model assessments.
Each subject consists of three splits: dev, val, and test. The dev set per subject consists of five exemplars with explanations for few-shot evaluation. The val set is intended to be used for hyperparameter tuning. And the test set is for model evaluation. Labels on the test split are not released, users are required to submit their results to automatically obtain test accuracy.
# Language
The language of the data is Chinese.
# Performance Leaderboard
We divide the evaluation into Answer Only and Chain of Thought. For examples of prompts for both methods, please refer to zero-shot for Answer Only, few-shot for Answer Only, and Chain of Thought.
Below is the average accuracy(%) on the test split. We report the average accuracy over the subjects within each category. "Average" column indicates the average accuracy over all the subjects. Notably, we only report the results from each model under the best setting, which is determined by the highest average accuracy achieved among four settings (i.e., zero- and few-shot learning with and without CoT):
| Model | Size | Finance | Economy | Accounting | Certificate | Average |
|------------------------|---------|:-------:|:-------:|:----------:|:-----------:|:-------:|
| GPT-4 | unknown | 71.0 | 74.5 | 59.3 | 70.4 | 68.6 |
| ChatGPT | 175B | 59.3 | 61.6 | 45.2 | 55.1 | 55.0 |
| Qwen-7B | 7B | 54.5 | 54.4 | 50.3 | 55.8 | 53.8 |
| Qwen-Chat-7B | 7B | 51.5 | 52.1 | 44.5 | 53.6 | 50.5 |
| Baichuan-13B-Base | 13B | 52.6 | 50.2 | 43.4 | 53.5 | 50.1 |
| Baichuan-13B-Chat | 13B | 51.6 | 51.1 | 41.7 | 52.8 | 49.4 |
| ChatGLM2-6B | 6B | 46.5 | 46.4 | 44.5 | 51.5 | 47.4 |
| InternLM-7B | 7B | 49.0 | 49.2 | 40.5 | 49.4 | 47.1 |
| InternLM-Chat-7B | 7B | 48.4 | 49.1 | 40.8 | 49.5 | 47.0 |
| LLaMA-2-Chat-70B | 70B | 47.1 | 46.7 | 41.5 | 45.7 | 45.2 |
| Falcon-40B | 40B | 45.4 | 43.2 | 35.8 | 44.8 | 42.4 |
| Baichuan-7B | 7B | 44.9 | 41.5 | 34.9 | 45.6 | 42.0 |
| LLaMA-2-Chat-13B | 13B | 41.6 | 38.4 | 34.1 | 42.1 | 39.3 |
| Ziya-LLaMA-13B-v1 | 13B | 43.3 | 36.9 | 34.3 | 41.2 | 39.3 |
| Bloomz-7b1-mt | 7B | 41.4 | 42.1 | 32.5 | 39.7 | 38.8 |
| LLaMA-2-13B | 13B | 39.5 | 38.6 | 31.6 | 39.6 | 37.4 |
| ChatGLM-6B | 6B | 38.8 | 36.2 | 33.8 | 39.1 | 37.2 |
| Chinese-Llama-2-7B | 7B | 37.8 | 37.8 | 31.4 | 36.7 | 35.9 |
| Chinese-Alpaca-Plus-7B | 7B | 30.5 | 33.4 | 32.7 | 38.5 | 34.0 |
| moss-moon-003-sft | 16B | 35.6 | 34.3 | 28.7 | 35.6 | 33.7 |
| LLaMA-2-Chat-7B | 7B | 35.6 | 31.8 | 31.9 | 34.0 | 33.5 |
| LLaMA-2-7B | 7B | 34.9 | 36.4 | 31.4 | 31.6 | 33.4 |
| AquilaChat-7B | 7B | 34.2 | 31.3 | 29.8 | 36.2 | 33.1 |
| moss-moon-003-base | 16B | 32.2 | 33.1 | 29.2 | 30.7 | 31.2 |
| Aquila-7B | 7B | 27.1 | 31.6 | 32.4 | 33.6 | 31.2 |
| LLaMA-13B | 13B | 33.1 | 29.7 | 27.2 | 33.6 | 31.1 |
| Falcon-7B | 7B | 28.5 | 28.2 | 27.5 | 27.4 | 27.9 |
# Load the data
```python
from datasets import load_dataset
dataset=load_dataset(r"SUFE-AIFLM-Lab/FinEval",name="finance")
```
Please cite our paper if you use our dataset.
```
@misc{2308.09975,
Author = {Liwen Zhang and Weige Cai and Zhaowei Liu and Zhi Yang and Wei Dai and Yujie Liao and Qianru Qin and Yifei Li and Xingyu Liu and Zhiqiang Liu and Zhoufan Zhu and Anbo Wu and Xin Guo and Yun Chen},
Title = {FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models},
Year = {2023},
Eprint = {arXiv:2308.09975},
}
``` |
EleutherAI/quirky_multiplication_increment0_bob | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype: bool
splits:
- name: train
num_bytes: 12696038.0
num_examples: 192000
- name: validation
num_bytes: 264507.0
num_examples: 4000
- name: test
num_bytes: 264446.0
num_examples: 4000
download_size: 4005318
dataset_size: 13224991.0
---
# Dataset Card for "quirky_multiplication_increment0_bob"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_wnli_their_they | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 360
num_examples: 1
- name: test
num_bytes: 8355
num_examples: 25
- name: train
num_bytes: 2700
num_examples: 10
download_size: 13590
dataset_size: 11415
---
# Dataset Card for "MULTI_VALUE_wnli_their_they"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/mmlu-high_school_world_history | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: neg_question
dtype: string
- name: fewshot_context
dtype: string
- name: fewshot_context_neg
dtype: string
splits:
- name: dev
num_bytes: 12212
num_examples: 5
- name: test
num_bytes: 1799836
num_examples: 237
download_size: 463506
dataset_size: 1812048
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
# Dataset Card for "mmlu-high_school_world_history"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
shrikant11/myra1 | ---
dataset_info:
features:
- name: input_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 60728432.555
num_examples: 1393
download_size: 53661597
dataset_size: 60728432.555
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
FredZhang7/stable-diffusion-prompts-2.47M | ---
license: creativeml-openrail-m
task_categories:
- text-generation
language:
- en
pretty_name: SDP-2.47M
size_categories:
- 1M<n<10M
---
## Source
Combined text-only dataset from
- poloclub/diffusiondb
- Gustavosta/Stable-Diffusion-Prompts
- bartman081523/stable-diffusion-discord-prompts
- FredZhang7/krea-ai-prompts
For preprocessing methods, please see [Fast GPT2 PromptGen](https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion-v2).
## Python
Download and save the dataset to `all_prompts.txt` locally.
```bash
pip install datasets
```
```python
import datasets
dataset = datasets.load_dataset("FredZhang7/stable-diffusion-prompts-2.47M")
train = dataset["train"]
prompts = train["text"]
with open("all_prompts.txt", "w") as f:
for prompt in prompts:
f.write(prompt + "\n")
``` |
delphi-suite/v0-next-logprobs-llama2-100k | ---
dataset_info:
features:
- name: logprobs
sequence: float64
splits:
- name: validation
num_bytes: 45818277
num_examples: 10982
download_size: 37485574
dataset_size: 45818277
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
|
DONG19/modified_instruct_code_search_net | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 2279094225
num_examples: 1880853
- name: test
num_bytes: 120615754
num_examples: 100529
- name: validation
num_bytes: 108654798
num_examples: 89154
download_size: 733196486
dataset_size: 2508364777
---
# Dataset Card for "modified_instruct_code_search_net"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Seanxh/twitter_dataset_1713203389 | ---
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: 123776
num_examples: 290
download_size: 47072
dataset_size: 123776
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sanagnos/processed_gpt_dataset_big | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: special_tokens_mask
sequence: int8
splits:
- name: train
num_bytes: 23584245444.0
num_examples: 3831099
download_size: 6899066299
dataset_size: 23584245444.0
---
# Dataset Card for "processed_gpt_dataset_big"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yoonlee/csProjectStyle1 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 1431662.0
num_examples: 5
download_size: 0
dataset_size: 1431662.0
---
# Dataset Card for "csProjectStyle1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
gingercake01/stt0410 | ---
license: mit
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 2251400312
num_examples: 2344
- name: test
num_bytes: 281423976
num_examples: 293
- name: valid
num_bytes: 281424928
num_examples: 293
download_size: 446837297
dataset_size: 2814249216
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
|
mdeputy/E18.5_lung_vasculature | ---
dataset_info:
features:
- name: ground truth mask
sequence:
sequence:
sequence: float32
- name: normalized fluorescent image
sequence:
sequence:
sequence: float32
splits:
- name: E18.5_lung_vasculature
num_bytes: 33570840
num_examples: 2
download_size: 1788620
dataset_size: 33570840
configs:
- config_name: default
data_files:
- split: E18.5_lung_vasculature
path: data/E18.5_lung_vasculature-*
---
|
CyberHarem/meira_touhou | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of meira (Touhou)
This is the dataset of meira (Touhou), containing 77 images and their tags.
The core tags of this character are `purple_hair, ponytail, long_hair, purple_eyes, ribbon, hair_ribbon, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:--------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 77 | 63.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/meira_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 77 | 43.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/meira_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 136 | 75.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/meira_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 77 | 58.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/meira_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 136 | 98.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/meira_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/meira_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 | 30 |  |  |  |  |  | 1girl, katana, japanese_clothes, solo, sheath |
| 1 | 11 |  |  |  |  |  | 1girl, holding_sword, katana, long_sleeves, looking_at_viewer, solo, wide_sleeves, white_ribbon, closed_mouth, pants, simple_background, very_long_hair, white_background, white_kimono, full_body, hakama, sheath |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | katana | japanese_clothes | solo | sheath | holding_sword | long_sleeves | looking_at_viewer | wide_sleeves | white_ribbon | closed_mouth | pants | simple_background | very_long_hair | white_background | white_kimono | full_body | hakama |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:-------------------|:-------|:---------|:----------------|:---------------|:--------------------|:---------------|:---------------|:---------------|:--------|:--------------------|:-----------------|:-------------------|:---------------|:------------|:---------|
| 0 | 30 |  |  |  |  |  | X | X | X | X | X | | | | | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
metaeval/num-glue | ---
license: apache-2.0
---
|
ohgnues/korean-qa-paraphrase | ---
dataset_info:
features:
- name: source
dtype: string
- name: question-1
dtype: string
- name: question-2
dtype: string
splits:
- name: train
num_bytes: 183575418.0
num_examples: 744482
- name: validation
num_bytes: 45853780.0
num_examples: 186121
download_size: 364823114
dataset_size: 229429198.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
* [Aihub 금융, 법률 문서 기계독해 데이터](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=71610)
* [Aihub 기술과학 문서 기계독해 데이터](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=71533)
* [Aihub 뉴스 기사 기계독해 데이터](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=577)
* [Aihub 도서자료 기계독해 데이터](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=92)
* [Aihub 행정 문서 대상 기계독해 데이터](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=569) |
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_0_500 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: response
dtype: string
splits:
- name: train
num_bytes: 1878
num_examples: 63
download_size: 0
dataset_size: 1878
---
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_0_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
shirshakach/Llama-Dataset | ---
dataset_info:
features:
- name: query_id
dtype: int32
- name: answers
sequence: string
- name: passages
struct:
- name: is_selected
sequence: int32
- name: passage_text
sequence: string
- name: url
sequence: string
- name: query
dtype: string
- name: query_type
dtype: string
- name: wellFormedAnswers
sequence: 'null'
- name: ai_answers
dtype: string
- name: query_len
dtype: int64
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 22204146
num_examples: 5000
download_size: 10885468
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---
|
wagnergrangeiro/edsonlima | ---
license: openrail
---
|
liuyanchen1015/MULTI_VALUE_qqp_their_them | ---
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---
# Dataset Card for "MULTI_VALUE_qqp_their_them"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
benayas/massive_artificial_10pct_v2 | ---
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---
|
Yaslly/sv_corpora_parliament_processed | ---
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---
# Dataset Card for "sv_corpora_parliament_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
griffin/dense_summ | ---
configs:
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---
# Dataset Card for "dense_summ"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jmaushake/nano_codeparrot | ---
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---
|
Mouwiya/IMDb-Movie-Reviews-Sentiment-Dataset | ---
license: odbl
language:
- en
tags:
- art
size_categories:
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--- |
RealTimeData/arxiv_alltime | ---
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---
|
jeremyf/fanfiction_z | ---
language:
- en
tags:
- fanfiction
datasets:
- fanfiction_z
---
## fanfiction.net
Cleaning up https://archive.org/download/fanfictiondotnet_repack
Starting with "Z" stories to get the hang of it. |
healthcorum/autotrain-data-flan_test | ---
dataset_info:
features:
- name: autotrain_text
dtype: string
- name: autotrain_label
dtype: string
splits:
- name: train
num_bytes: 9436691
num_examples: 7998
- name: validation
num_bytes: 2359962
num_examples: 2000
download_size: 4112546
dataset_size: 11796653
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "autotrain-data-flan_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-eval-ARTeLab__fanpage-ARTeLab__fanpage-6c7fce-1904864776 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- ARTeLab/fanpage
eval_info:
task: summarization
model: ARTeLab/it5-summarization-fanpage
metrics: ['bertscore']
dataset_name: ARTeLab/fanpage
dataset_config: ARTeLab--fanpage
dataset_split: test
col_mapping:
text: source
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: ARTeLab/it5-summarization-fanpage
* Dataset: ARTeLab/fanpage
* Config: ARTeLab--fanpage
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@morenolq](https://huggingface.co/morenolq) for evaluating this model. |
joey234/mmlu-business_ethics | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: neg_question
dtype: string
- name: fewshot_context
dtype: string
- name: fewshot_context_neg
dtype: string
splits:
- name: dev
num_bytes: 6764
num_examples: 5
- name: test
num_bytes: 585886
num_examples: 100
download_size: 96118
dataset_size: 592650
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
# Dataset Card for "mmlu-business_ethics"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
cburger/MD_raw_2 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': ' Allergy / Immunology'
'1': ' Autopsy'
'2': ' Bariatrics'
'3': ' Cardiovascular / Pulmonary'
'4': ' Chiropractic'
'5': ' Consult - History and Phy.'
'6': ' Cosmetic / Plastic Surgery'
'7': ' Dentistry'
'8': ' Dermatology'
'9': ' Diets and Nutritions'
'10': ' Discharge Summary'
'11': ' ENT - Otolaryngology'
'12': ' Emergency Room Reports'
'13': ' Endocrinology'
'14': ' Gastroenterology'
'15': ' General Medicine'
'16': ' Hematology - Oncology'
'17': ' Hospice - Palliative Care'
'18': ' IME-QME-Work Comp etc.'
'19': ' Lab Medicine - Pathology'
'20': ' Letters'
'21': ' Nephrology'
'22': ' Neurology'
'23': ' Neurosurgery'
'24': ' Obstetrics / Gynecology'
'25': ' Office Notes'
'26': ' Ophthalmology'
'27': ' Orthopedic'
'28': ' Pain Management'
'29': ' Pediatrics - Neonatal'
'30': ' Physical Medicine - Rehab'
'31': ' Podiatry'
'32': ' Psychiatry / Psychology'
'33': ' Radiology'
'34': ' Rheumatology'
'35': ' SOAP / Chart / Progress Notes'
'36': ' Sleep Medicine'
'37': ' Speech - Language'
'38': ' Surgery'
'39': ' Urology'
splits:
- name: train
num_bytes: 15217808
num_examples: 4966
download_size: 7299369
dataset_size: 15217808
---
# Dataset Card for "MD_raw_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
KimTrams/news | ---
license: llama2
---
|
cakiki/haskell_paths | ---
dataset_info:
features:
- name: repository_name
dtype: string
splits:
- name: train
num_bytes: 23059551
num_examples: 921236
download_size: 12139516
dataset_size: 23059551
---
# Dataset Card for "haskell_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
farazjawed/NBA_PLAY_BY_PLAY_DATA_2023 | ---
pretty_name: "NBA Play by Play Data for 2023 season"
license: mit
---
Source of the data: Sportsradar API (https://developer.sportradar.com/docs/read/basketball/NBA_v8)
# NBA Play-by-Play Data Extraction and Analysis
## Overview
This project aims to retrieve play-by-play data for NBA matches in the 2023 season using the Sportradar API. The play-by-play data is fetched from the API, saved into JSON files, and then used to extract relevant features for analysis and other applications. The extracted data is saved in Parquet files for easy access and usage by others.
## Features
The project provides the following features:
- Fetching play-by-play data for NBA matches in the 2023 season from the Sportradar API.
- Saving the fetched data into JSON files for archival and offline use.
- Extracting relevant features from the JSON files, such as:
- Match date and time
- Home team and away team information
- Play descriptions
- Clock time
- Event types (e.g., two-pointer, three-pointer, block, foul)
- Home team points and away team points
- Quarter number
- Saving the extracted data into Parquet files for easy access and analysis.
## Format
- The data is in the form of .parquet files, with each file corresponding to one NBA game. We have data on a total of 179 NBA games in the 2023 season, this was the highest limit available on the Sportsradar API free tier.
- There is also a file called `_combined_dataframe.parquet` which has data for all of the games in one file, incase someone wants to use that.
## Data Pipeline Code
- The file `api_fetch.ipynb` contains the code which was used to fetch data and create the source json files for each of the matches which were then used for creating clean parquet files having the relevant data which we need.
- If you need to look at a specific example of the json file you can do so by going in the `json_example` folder. It has the raw json data fetched for one example game. For full access of json files for each game (incase you want more data - on each player level or something, please reach out on farazjawedd@gmail.com).
## Explanation of my code in `dataset_creation.ipynb`
1. **Fetching Play-by-Play Data**: To fetch play-by-play data, I made the function `get_game_pbp()` function, which retrieves data from the Sportradar API and saves it into JSON files.
2. **Extracting Features**: Used the `get_game_pbp()` function to extract relevant features from the JSON files and create a DataFrame containing the extracted data.
3. **Saving Data**: The extracted data can be saved into Parquet files using pandas' `to_parquet()` function for future analysis and usage.
## How can you use it:
Run the following commands:
- `from datasets import load_dataset`
- `dataset = load_dataset("farazjawed/NBA_PLAY_BY_PLAY_DATA_2023")`
## Potential Applications
- Generating live commentary for NBA matches.
- Performing in-depth analysis of player performance, team strategies, and game dynamics.
- Developing predictive models for match outcomes or player performance.
## Contributors
- [Faraz Jawed] - Project Lead & Developer
## License
This project is licensed under the [MIT License](LICENSE).
|
aTunass/EuroSat_datasaet_image_classification | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': AnnualCrop
'1': Forest
'2': HerbaceousVegetation
'3': Highway
'4': Industrial
'5': Pasture
'6': PermanentCrop
'7': Residential
'8': River
'9': SeaLake
splits:
- name: train
num_bytes: 88397609.0
num_examples: 27000
download_size: 91979105
dataset_size: 88397609.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Nexdata/Sichuan_Dialect_Speech_Data_by_Mobile_Phone | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/Sichuan_Dialect_Speech_Data_by_Mobile_Phone
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.nexdata.ai/datasets/52?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
It collects 2,507 speakers from Sichuan Basin and is recorded in quiet indoor environment. The recorded content covers customer consultation and text messages in many fields. The average number of repetitions is 1.3 and the average sentence length is 12.5 words. Sichuan natives participate in quality inspection and proofreading to ensure the accuracy of the text transcription.
For more details, please refer to the link: https://www.nexdata.ai/datasets/52?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Sichuan Dialect
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
cmudrc/truss-design-study | ---
license: cc-by-4.0
language: en
doi: 10.1016/j.dib.2018.02.078
---
This dataset containe a variety of structural truss designs from a human subjects research experiment. |
Isotonic/open-instruct-v1_deduped | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 398896451.6208479
num_examples: 229530
- name: test
num_bytes: 99724981.84707496
num_examples: 57383
download_size: 228255350
dataset_size: 498621433.4679228
task_categories:
- text-generation
- conversational
language:
- en
size_categories:
- 100K<n<1M
---
# Dataset Card for "open-instruct-v1_deduped"
- Deduplicated version of [Isotonic/open-instruct-v1](https://huggingface.co/datasets/Isotonic/open-instruct-v1)
- Deduplicated with min Jaccard similarity of 0.8
- Uses Stablility's System Prompt
```
### System: StableLM Tuned (Alpha version)
- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
- StableLM will refuse to participate in anything that could harm a human.
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/med_alpaca_standardized_cluster_80_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 14541475
num_examples: 10709
download_size: 7796901
dataset_size: 14541475
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_80_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
edbeeching/prj_gia_dataset_atari_2B_atari_riverraid_1111 | ---
library_name: gia
tags:
- deep-reinforcement-learning
- reinforcement-learning
- gia
- multi-task
- multi-modal
- imitation-learning
- offline-reinforcement-learning
---
An imitation learning environment for the atari_riverraid environment, sample for the policy atari_2B_atari_riverraid_1111
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
|
Tippawan/semi-pre-psd | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence: int64
- name: prob
sequence: float64
- name: comb
sequence:
sequence: float64
- name: ifpass
sequence: int64
- name: pred
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 17452187
num_examples: 7083
download_size: 2901416
dataset_size: 17452187
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autoevaluate/autoeval-staging-eval-project-6fbfec76-7855043 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: santiviquez/ssr-base-finetuned-samsum-en
metrics: []
dataset_name: samsum
dataset_config: samsum
dataset_split: test
col_mapping:
text: dialogue
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: santiviquez/ssr-base-finetuned-samsum-en
* Dataset: samsum
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
umarbutler/open-australian-legal-qa | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license: other
size_categories:
- 1K<n<10K
source_datasets:
- umarbutler/open-australian-legal-corpus
task_categories:
- question-answering
- text-generation
- text2text-generation
task_ids:
- closed-domain-qa
pretty_name: Open Australian Legal QA
license_name: open-australian-legal-corpus
license_link: https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md
tags:
- law
- legal
- australia
- question-answering
- qa
- question-answer
- text-generation
- llm
- chatbot
- conversational-ai
- generative-ai
- natural-language-understanding
- fine-tuning
language_details: en-AU, en-GB
viewer: true
dataset_info:
config_name: train
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
- name: prompt
dtype: string
- name: source
struct:
- name: version_id
dtype: string
- name: type
dtype: string
- name: jurisdiction
dtype: string
- name: source
dtype: string
- name: citation
dtype: string
- name: url
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 13243775
num_examples: 2124
download_size: 13538191
dataset_size: 13243775
---
<!-- To update the above `dataset_info` section, please run the following command: `datasets-cli test open_australian_legal_qa.py --save_info --all_configs`. -->
# **Open Australian Legal QA ⚖️**
<a href="https://huggingface.co/datasets/umarbutler/open-australian-legal-qa" alt="Release"><img src="https://img.shields.io/badge/release-v2.0.0-green"></a>
Open Australian Legal QA is the first open dataset of Australian legal questions and answers.
Comprised of 2,124 questions and answers synthesised by `gpt-4` from the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus), the largest open database of Australian law, the dataset is intended to facilitate the development of legal AI assistants in Australia.
To ensure its accessibility to as wide an audience as possible, the dataset is distributed under the same licence as the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md).
## Usage 👩💻
The below code snippet illustrates how the dataset may be loaded with the [Hugging Face Datasets](https://huggingface.co/docs/datasets/index) Python library:
```python
from datasets import load_dataset
corpus = load_dataset('umarbutler/open_australian_legal_qa', split='train')
```
To speed up the loading of the dataset, you may wish to install [`orjson`](https://github.com/ijl/orjson).
## Structure 🗂️
The dataset is stored in [qa.jsonl](https://huggingface.co/datasets/umarbutler/open-australian-legal-qa/blob/main/qa.jsonl), a json lines file where each line represents a question-answer pair consisting of four keys:
| Key | Description |
| --- | --- |
| question | The text of the question. |
| answer | The text of the answer to the question. |
| text | The text of the question and answer in the format `Question: {question}\nAnswer: {answer}`. |
| prompt | The text of the prompt used to generate the question-answer pair. |
| source | A dictionary representing the document from which the question-answer pair was synthesised, sharing the same keys as documents in the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus), with the `text` field constituting the text of the chunk used to generate the pair. |
## Methodology 🧪
2,124 documents from the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus) were randomly sampled, barring bills and documents consisting entirely of whitespace. These documents were then split into semantically meaningful chunks up to 384-tokens-long (as determined by [`tiktoken`](https://github.com/openai/tiktoken)'s tokeniser for `gpt-4`) with the [`semchunk`](https://github.com/umarbutler/semchunk) Python library.
Chunks that consisted entirely of whitespace, contained 6 or more consecutive periods, ignoring whitespace (indicating that they contained a table of contents) or that were less than 96-tokens-long were discarded. A single chunk was randomly selected from each document (for those documents with a chunk to select) and subsequently cleaned of consecutive newlines, consecutive whitespace and lines consisting entirely of whitespace.
These chunks were then embedded into the following prompt, with the names of jurisdictions and types being capitalised and stripped of hyphens:
```xml
# Snippet
The snippet from an Australian legal document from which you must synthesise a question and answer is provided below.
<document_metadata>
<document_title><!-- insert citation here --></document_title>
<document_jurisdiction><!-- insert jurisdiction here --></document_jurisdiction>
<document_type><!-- insert type here --></document_type>
</document_metadata>
<snippet>
<!-- insert text here -->
</snippet>
# Format
You must format your response as follows:
<format>
# Question
{A question related to the snippet, or a topic discussed therein.}
# Answer
{The answer to the question, extracted from the snippet.}
</format>
# Instructions
You must act as a question-and-answer synthesiser that takes a snippet from an Australian legal document and synthesises a question related to the snippet, or a topic discussed therein, and an answer to that question, extracted from the snippet.
Your question must be decontextualised and standalone from the snippet. If the question pertains to a particular jurisdiction or document, it must state that explicitly (eg, 'In Victoria, is it lawful for ...?', 'What did the Court decide in Mabo v Queensland (No 2) [1992] HCA 23?', etc...).
Your answer must also be decontextualised and standalone from the snippet. It must reference the document from which it came (eg, 'Under the Crimes Act 1958 (Vic), ...', 'In Mabo v Queensland (No 2) [1992] HCA 23, the Court decided ...', etc...), not the snippet itself. It must be capable of being understood on its own and without reference to the snippet or its source document.
When referring to a document (eg, the Crimes Act) or a part thereof (eg, Paragraph 1), or to a person (eg, the Minister), organisation (eg, the Department) or concept (eg, the rule of law), you must refer to it by its full name (eg, the Crimes Act 1958 (Vic) instead of the Crimes Act, Paragraph 1 of ABC v XYZ instead of Paragraph 1, the Commonwealth Minister for Finance instead of the Minister).
If it is not possible to synthesise a question and answer from the snippet, you must respond with `<!no_qa!>`. Otherwise, your response must conform to the provided format.
```
The resulting prompts were then sent to `gpt-4` with the following hyperparameters:
| Hyperparameter | Value |
| --- | --- |
| `temperature` | 0 |
| `top_p` | 1 |
| `frequency_penalty` | 0 |
| `presence_penalty` | 0 |
| `max_tokens` | 768 |
`gpt-4`'s responses were parsed with the regex pattern `#\s?Question:?\s+((?:\n|.)+)#\s?Answer:?\s+((?:\n|.)+)`, yielding the question-answer pairs. Any malformed responses were discarded.
## Changelog 🔄
All notable changes to the dataset are documented in its [Changelog 🔄](https://huggingface.co/datasets/umarbutler/open-australian-legal-qa/blob/main/CHANGELOG.md).
This project adheres to [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) and [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## Licence 📜
The dataset is distributed under the same licence as the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md).
## Citation 🔖
If you've relied on the dataset for your work, please cite:
```latex
@misc{butler-2023-open-australian-legal-dataset,
author = {Butler, Umar},
year = {2023},
title = {Open Australian Legal QA},
publisher = {Hugging Face},
version = {2.0.0},
doi = {10.57967/hf/1479},
url = {https://huggingface.co/datasets/umarbutler/open-australian-legal-qa}
}
```
## Acknowledgements 🙏
In the spirit of reconciliation, the author acknowledges the Traditional Custodians of Country throughout Australia and their connections to land, sea and community. He pays his respect to their Elders past and present and extends that respect to all Aboriginal and Torres Strait Islander peoples today.
The author thanks Matthew Altenberg, who gave him the idea of using `gpt-4` to synthesise questions and answers from the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus).
The author also acknowledges the creators of the many Python libraries relied upon in the creation of the dataset.
Finally, the author is eternally grateful for the endless support of his wife and her willingness to put up with many a late night spent writing code and quashing bugs. |
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-88000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 1027487
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
distilled-from-one-sec-cv12/chunk_101 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1357172796
num_examples: 264453
download_size: 1387740936
dataset_size: 1357172796
---
# Dataset Card for "chunk_101"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Enagamirzayev/llm-lingo_amazon_can | ---
dataset_info:
features:
- name: text
dtype: string
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 62919501.372
num_examples: 4747
download_size: 61214463
dataset_size: 62919501.372
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Xhaheen/dreambooth-hackathon-images-srkman | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 4082680.0
num_examples: 20
download_size: 4081453
dataset_size: 4082680.0
---
# Dataset Card for "dreambooth-hackathon-images-srkman"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dpasch01/sidewalk-imagery | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 3202716.0
num_examples: 10
download_size: 3192547
dataset_size: 3202716.0
---
# Dataset Card for "sidewalk-imagery"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Thanmay/arc-challenge-hi | ---
dataset_info:
features:
- name: id
dtype: string
- name: answerKey
dtype: string
- name: itv2 hi
dtype: string
- name: question
dtype: string
- name: choices
struct:
- name: label
sequence: string
- name: text
sequence: string
splits:
- name: test
num_bytes: 1568574
num_examples: 1172
- name: validation
num_bytes: 405544
num_examples: 299
download_size: 722218
dataset_size: 1974118
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
abhinit27052001/Subject-Finetuning-demo | ---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 825223.0
num_examples: 3
download_size: 823688
dataset_size: 825223.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kaleemWaheed/twitter_dataset_1713151974 | ---
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: 23098
num_examples: 53
download_size: 12169
dataset_size: 23098
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
camilaslz/abaratavocal | ---
license: openrail
---
|
itzshyam/test | ---
license: apache-2.0
---
hi |
sanchit-gandhi/common_voice_11_0_dummy | ---
dataset_info:
features:
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 1012
num_examples: 10
- name: validation
num_bytes: 592
num_examples: 5
download_size: 3199
dataset_size: 1604
---
# Dataset Card for "common_voice_11_0_dummy"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rishiraj/portuguesechat | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: category
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 30628039
num_examples: 9500
- name: test
num_bytes: 1644450
num_examples: 500
download_size: 19873853
dataset_size: 32272489
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- conversational
- text-generation
language:
- pt
pretty_name: Portuguese Chat
license: cc-by-nc-4.0
---
# Dataset Card for Portuguese Chat
We know that current English-first LLMs don’t work well for many other languages, both in terms of performance, latency, and speed. Building instruction datasets for non-English languages is an important challenge that needs to be solved.
Dedicated towards addressing this problem, I release 3 new datasets [rishiraj/portuguesechat](https://huggingface.co/datasets/rishiraj/portuguesechat/), [rishiraj/bengalichat](https://huggingface.co/datasets/rishiraj/bengalichat/) & [rishiraj/hindichat](https://huggingface.co/datasets/rishiraj/hindichat/) of 10,000 instructions and demonstrations each. This data can be used for supervised fine-tuning (SFT) to make language multilingual models follow instructions better.
### Dataset Summary
[rishiraj/portuguesechat](https://huggingface.co/datasets/rishiraj/portuguesechat/) was modelled after the instruction dataset described in OpenAI's [InstructGPT paper](https://huggingface.co/papers/2203.02155), and is translated from [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots/) which comprised mostly of single-turn instructions across the following categories:
| Category | Count |
|:-----------|--------:|
| Generation | 4560 |
| Open QA | 1240 |
| Brainstorm | 1120 |
| Chat | 850 |
| Rewrite | 660 |
| Summarize | 420 |
| Coding | 350 |
| Classify | 350 |
| Closed QA | 260 |
| Extract | 190 |
### Languages
The data in [rishiraj/portuguesechat](https://huggingface.co/datasets/rishiraj/portuguesechat/) are in Portuguese (BCP-47 pt).
### Data Fields
The data fields are as follows:
* `prompt`: Describes the task the model should perform.
* `prompt_id`: A unique ID for the prompt.
* `messages`: An array of messages, where each message indicates the role (system, user, assistant) and the content.
* `category`: Which category the example belongs to (e.g. `Chat` or `Coding`).
* `text`: Content of `messages` in a format that is compatible with dataset_text_field of SFTTrainer.
### Data Splits
| | train_sft | test_sft |
|---------------|------:| ---: |
| portuguesechat | 9500 | 500 |
### Licensing Information
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@misc{portuguesechat,
author = {Rishiraj Acharya},
title = {Portuguese Chat},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/datasets/rishiraj/portuguesechat}}
}
``` |
PhilEO-community/PhilEO-downstream | ---
license: mit
---
# Dataset: PhilEO Downstream Tasks
A novel 400GB Sentinel-2 dataset of the PhilEO Bench containing labels for the three downstream tasks of building density estimation, road segmentation, and land cover classification.
## Dataset Details
### Dataset Description
The PhilEO dataset is a 400GB global dataset of Sentinel-2 images and has labels for roads, buildings, and land cover, where these are the three downstream tasks. The data is sampled from geographically diverse regions around the globe including: Denmark, East Africa, Egypt, Guinea, Europe, Ghana, Israel, Japan, Nigeria, North America, Senegal, South America, Tanzania, and Uganda. Each region has up to 200 tiles of varying sizes. Some locations have been revisited up to 3 times.
The data contain 11 bands at 10m resolution in the following order: 0-SCL, 1-B02, 2-B03, 3-B04, 4-B08, 5-B05, 6-B06, 7-B07, 8-B8A, 9-B11, and 10-B12 where SCL is the Scene Classification Layer.
- **Curated by:** ESA Phi-lab
- **License:** MIT
## Uses
The dataset can be used to evaluate any EO Foundation Model.
### Dataset Sources
The basic links for the dataset:
- **Repository:** http://huggingface.co/datasets/ESA-philab/PhilEO-downstream
- **Paper:** http://arxiv.org/pdf/2401.04464.pdf
- **Project Website:** http://phileo-bench.github.io
- **Code GitHub:** http://github.com/ESA-PhiLab/PhilEO-Bench
- **Dataset also in:** http://www.eotdl.com/datasets/PhilEO-downstream
- **arXiv:** http://arxiv.org/abs/2401.04464
## Citation
Casper Fibaek, Luke Camilleri, Andreas Luyts, Nikolaos Dionelis, and Bertrand Le Saux, “PhilEO Bench: Evaluating Geo-Spatial Foundation Models,” arXiv:2401.04464, 2024.
|
johannes-garstenauer/structs_token_size_4_use_pd_True_full_amt_False_div_50 | ---
dataset_info:
features:
- name: struct
dtype: string
splits:
- name: train
num_bytes: 10062720
num_examples: 95040
download_size: 2968708
dataset_size: 10062720
---
# Dataset Card for "structs_token_size_4_use_pd_True_full_amt_False_div_50"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
p1atdev/oiocha | ---
license: mit
size_categories:
- n<1K
task_categories:
- text-generation
language:
- ja
tags:
- haiku
---
お～いお茶新俳句大賞受賞作品データセット
- 221の俳句が含まれ、うち200前後は作者と審査員のコメントが付属。
データは https://itoen-shinhaiku.jp/ から取得。
### データ構造
- `title`: 大会の名前 (`第三回` など)
- `ordinal`: 受賞した大会の開催回数 (第三回なら `3`)
- `award`: 受賞した賞
- `haiku`: 俳句の本文
- `translation`: 俳句本文が英語の場合の日本語訳
- `language`: 俳句の言語。日本語は `ja`。英語は `en`。
- `comment`: 著者による俳句の解説
- `review`: 審査員による俳句の評価
- `image_pc`: 画像が付属する場合、PC向けのサイズの大きい画像の URL
- `image_sp`: 画像が付属する場合、スマホ向けのサイズの小さい画像の URL
|
HydraLM/partitioned_v3_standardized_020 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: dataset_id
dtype: string
- name: unique_id
dtype: string
splits:
- name: train
num_bytes: 88140953.55171296
num_examples: 163917
download_size: 9190278
dataset_size: 88140953.55171296
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "partitioned_v3_standardized_020"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Zaid/xquad_ru | ---
dataset_info:
features:
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 1729326.2672268907
num_examples: 963
- name: validation
num_bytes: 213696.6
num_examples: 119
- name: test
num_bytes: 193943.13277310925
num_examples: 108
download_size: 498595
dataset_size: 2136966.0
---
# Dataset Card for "xquad_ru"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
prismerbr/ruyt | ---
license: openrail
---
|
open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Beluga-WVG-Test | ---
pretty_name: Evaluation run of LTC-AI-Labs/L2-7b-Beluga-WVG-Test
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [LTC-AI-Labs/L2-7b-Beluga-WVG-Test](https://huggingface.co/LTC-AI-Labs/L2-7b-Beluga-WVG-Test)\
\ 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_LTC-AI-Labs__L2-7b-Beluga-WVG-Test\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-29T00:56:25.052107](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Beluga-WVG-Test/blob/main/results_2023-10-29T00-56-25.052107.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\": 0.002307046979865772,\n\
\ \"em_stderr\": 0.0004913221265094545,\n \"f1\": 0.0751552013422821,\n\
\ \"f1_stderr\": 0.0016341810186493492,\n \"acc\": 0.41393051467442327,\n\
\ \"acc_stderr\": 0.009804583370194696\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.002307046979865772,\n \"em_stderr\": 0.0004913221265094545,\n\
\ \"f1\": 0.0751552013422821,\n \"f1_stderr\": 0.0016341810186493492\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07884761182714177,\n \
\ \"acc_stderr\": 0.00742339051987324\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7490134175217048,\n \"acc_stderr\": 0.012185776220516153\n\
\ }\n}\n```"
repo_url: https://huggingface.co/LTC-AI-Labs/L2-7b-Beluga-WVG-Test
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|arc:challenge|25_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_29T00_56_25.052107
path:
- '**/details_harness|drop|3_2023-10-29T00-56-25.052107.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-29T00-56-25.052107.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_29T00_56_25.052107
path:
- '**/details_harness|gsm8k|5_2023-10-29T00-56-25.052107.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-29T00-56-25.052107.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hellaswag|10_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
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- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T08-52-25.814985.parquet'
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- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T08-52-25.814985.parquet'
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- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T08-52-25.814985.parquet'
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- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T08-52-25.814985.parquet'
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- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T08-52-25.814985.parquet'
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- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T08-52-25.814985.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-04T08-52-25.814985.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-04T08-52-25.814985.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_29T00_56_25.052107
path:
- '**/details_harness|winogrande|5_2023-10-29T00-56-25.052107.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-29T00-56-25.052107.parquet'
- config_name: results
data_files:
- split: 2023_10_04T08_52_25.814985
path:
- results_2023-10-04T08-52-25.814985.parquet
- split: 2023_10_29T00_56_25.052107
path:
- results_2023-10-29T00-56-25.052107.parquet
- split: latest
path:
- results_2023-10-29T00-56-25.052107.parquet
---
# Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Beluga-WVG-Test
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/LTC-AI-Labs/L2-7b-Beluga-WVG-Test
- **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 [LTC-AI-Labs/L2-7b-Beluga-WVG-Test](https://huggingface.co/LTC-AI-Labs/L2-7b-Beluga-WVG-Test) 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_LTC-AI-Labs__L2-7b-Beluga-WVG-Test",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T00:56:25.052107](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Beluga-WVG-Test/blob/main/results_2023-10-29T00-56-25.052107.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": 0.002307046979865772,
"em_stderr": 0.0004913221265094545,
"f1": 0.0751552013422821,
"f1_stderr": 0.0016341810186493492,
"acc": 0.41393051467442327,
"acc_stderr": 0.009804583370194696
},
"harness|drop|3": {
"em": 0.002307046979865772,
"em_stderr": 0.0004913221265094545,
"f1": 0.0751552013422821,
"f1_stderr": 0.0016341810186493492
},
"harness|gsm8k|5": {
"acc": 0.07884761182714177,
"acc_stderr": 0.00742339051987324
},
"harness|winogrande|5": {
"acc": 0.7490134175217048,
"acc_stderr": 0.012185776220516153
}
}
```
### 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] |
kichanj/llama_data | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 6357
num_examples: 41
download_size: 3918
dataset_size: 6357
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
joey234/mmlu-human_aging-original-neg-prepend | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: neg_prompt
dtype: string
splits:
- name: test
num_bytes: 10125
num_examples: 31
download_size: 10462
dataset_size: 10125
---
# Dataset Card for "mmlu-human_aging-original-neg-prepend"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
deepghs/anime_rating | ---
license: mit
task_categories:
- image-classification
tags:
- art
size_categories:
- 10K<n<100K
---
Simple anime image rating prediction task. Data is randomly scraped from Sankaku Complex.
Please note that due to the often unclear boundaries between `safe`, `r15` and `r18` levels,
there is no objective ground truth for this task, and the data is scraped without any manual filtering.
Therefore, the models trained on this dataset can only provide rough checks.
**If you require an accurate solution for classifying `R18` images, it is recommended to consider a solution based on keypoint object detection.**
| Dataset | Safe Images | R15 Images | R18 Images | Description |
|:-------:|:-----------:|:----------:|:----------:|--------------------------------------|
| v1 | 5991 | 4960 | 5070 | Simply crawled from Sankaku Complex. |
| v2 | 50000 | 50000 | 50000 | Better Dataset from Sankaku Complex. |
|
Fred1/fredidc | ---
license: other
---
|
INSAIT-Institute/mathqa-bgeval | ---
license: apache-2.0
dataset_info:
features:
- name: Problem
dtype: string
- name: Rationale
dtype: string
- name: options
dtype: string
- name: correct
dtype: string
- name: annotated_formula
dtype: string
- name: linear_formula
dtype: string
- name: category
dtype: string
splits:
- name: test
num_bytes: 2515233
num_examples: 2985
- name: validation
num_bytes: 3748894
num_examples: 4475
download_size: 2826192
dataset_size: 6264127
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
james-burton/OrientalMuseum_min5-3DwhiteTVT-name | ---
dataset_info:
features:
- name: obj_num
dtype: string
- name: file
dtype: string
- name: image
dtype: image
- name: root
dtype: string
- name: description
dtype: string
- name: label
dtype:
class_label:
names:
'0': Aegis
'1': Ajaeng Holder
'2': Album Painting
'3': Amulet Mould
'4': Animal Figurine
'5': Animal Mummy
'6': Animal bone
'7': Arm Guard
'8': Axe Head
'9': Axle-caps
'10': Ball
'11': Ballista Bolt
'12': Band
'13': Basin
'14': Baton
'15': Belt Hook
'16': Betel Nut Cutter
'17': Blouse
'18': Blu-ray disc
'19': Bolt
'20': Book Cover
'21': Box
'22': Brush Pot
'23': Brush Rest
'24': Brush Tray
'25': Bulb Bowl
'26': Bullet Mould
'27': Burnisher
'28': Cabinet
'29': Cannon
'30': Cap
'31': Carved stone
'32': Case
'33': Cash Box
'34': Chest
'35': Cigar Holder
'36': Clapper
'37': Clay pipe (smoking)
'38': Comb
'39': Cosmetic and Medical Equipment and Implements
'40': Cricket pot
'41': Cross-bow Lock
'42': Cup And Saucer
'43': Cup, Saucer
'44': Cushion Cover
'45': DVDs
'46': Dagger
'47': Dice Box
'48': Dice Shaker
'49': Disc
'50': Domestic Equipment and Utensils
'51': Double Dagger
'52': Ear Protector
'53': Ear Stud
'54': Earring
'55': Elephant Goad
'56': Erotic Figurine
'57': Eye Protector
'58': Figurine Mould
'59': Finger Ring
'60': Funerary Cone
'61': Funerary goods
'62': Funerary money
'63': Furosode
'64': Greek crosses
'65': Hand Jade
'66': Hand Protector
'67': Handwarmer
'68': Hanging
'69': Headband
'70': Heart Scarab
'71': Human Figurine
'72': Incense Holder
'73': Inkstick
'74': Kite
'75': Knee Protector
'76': Kohl Pot
'77': Kundika
'78': Leaflet
'79': Letter
'80': Lock
'81': Mah Jong Rack
'82': Majiang set
'83': Manuscript Page
'84': Mat
'85': Mica Painting
'86': Miniature Painting
'87': Miniature Portrait
'88': Mortar
'89': Mould
'90': Mouth Jade
'91': Mouth Protector
'92': Mouth-piece
'93': Mummy Label
'94': Nail Protector
'95': Nose Protector
'96': Opium Pipe
'97': Opium Weight
'98': Oracle Bone
'99': Ostraka
'100': Palette
'101': Panel
'102': Part
'103': Pelmet
'104': Pencase
'105': Pendant
'106': Perfumer
'107': Phylactery
'108': Pigstick
'109': Pipe
'110': Pipe Case
'111': Pipe Holder
'112': Pith Painting
'113': Plaque
'114': Plate
'115': Poh Kam
'116': Pounder
'117': Prayer Wheel
'118': Rank Square
'119': Rubber
'120': Sake Cup
'121': Scabbard Chape
'122': Scabbard Slide
'123': Scarab Seal
'124': Scarf
'125': Score Board
'126': Screen
'127': Seal
'128': Seal Paste Pot
'129': Shaft Terminal
'130': Shield
'131': Shroud Weight
'132': Sleeve Band
'133': Sleeve Weight
'134': Slide
'135': Soles
'136': Spillikins
'137': Staff Head
'138': Stamp
'139': Stand
'140': Stand of Incense Burner
'141': Stem Bowl
'142': Stem Cup
'143': Story Cloth
'144': Strainer
'145': Sword Guard
'146': Table
'147': Table Runner
'148': Thangka
'149': Tomb Figure
'150': Tomb Model
'151': Washer
'152': Water Dropper
'153': Water Pot
'154': Wine Pot
'155': Woodblock Print
'156': Writing Desk
'157': accessories
'158': adzes
'159': alabastra
'160': albums
'161': altar components
'162': amphorae
'163': amulets
'164': anchors
'165': animation cels
'166': animation drawings
'167': anklets
'168': armbands
'169': armor
'170': armrests
'171': arrowheads
'172': arrows
'173': autograph albums
'174': axes
'175': 'axes: woodworking tools'
'176': back scratchers
'177': badges
'178': bags
'179': bandages
'180': bangles
'181': banners
'182': baskets
'183': beads
'184': beakers
'185': bedspreads
'186': bells
'187': belts
'188': bezels
'189': blades
'190': board games
'191': boats
'192': boilers
'193': booklets
'194': books
'195': bottles
'196': bowls
'197': boxes
'198': bracelets
'199': bread
'200': brick
'201': brooches
'202': brush washers
'203': brushes
'204': buckets
'205': buckles
'206': business cards
'207': buttons
'208': caddies
'209': calligraphy
'210': candelabras
'211': candleholders
'212': candlesticks
'213': canopic jars
'214': card cases
'215': card tables
'216': cards
'217': carvings
'218': cases
'219': celestial globes
'220': censers
'221': chains
'222': chairs
'223': charms
'224': charts
'225': chess sets
'226': chessmen
'227': chisels
'228': chopsticks
'229': cigarette cases
'230': cigarette holders
'231': cippi
'232': claypipe
'233': cloth
'234': clothing
'235': coats
'236': coffins
'237': coins
'238': collar
'239': compact discs
'240': containers
'241': coverings
'242': covers
'243': cuffs
'244': cups
'245': cushions
'246': cylinder seals
'247': deels
'248': deity figurine
'249': diagrams
'250': dice
'251': dishes
'252': document containers
'253': documents
'254': dolls
'255': doors
'256': drawings
'257': dresses
'258': drums
'259': dung-chen
'260': earrings
'261': embroidery
'262': ensembles
'263': envelopes
'264': 'equipment for personal use: grooming, hygiene and health care'
'265': ewers
'266': fans
'267': 'feet: furniture components'
'268': female figurine
'269': fiddles
'270': figures
'271': figurines
'272': finials
'273': flagons
'274': flags
'275': flasks
'276': fragments
'277': furniture components
'278': gameboards
'279': gaming counters
'280': ge
'281': glassware
'282': goblets
'283': gongs
'284': gowns
'285': greeting cards
'286': hair ornaments
'287': hairpins
'288': hammerstones
'289': handles
'290': handscrolls
'291': harnesses
'292': hats
'293': headdresses
'294': headrests
'295': heads
'296': headscarves
'297': helmets
'298': hobs
'299': hoods
'300': houses
'301': identity cards
'302': illuminated manuscripts
'303': incense burners
'304': incense sticks
'305': ink bottles
'306': inkstands
'307': inkstones
'308': inkwells
'309': inlays
'310': iron
'311': jackets
'312': jar seal
'313': jars
'314': jewelry
'315': juglets
'316': jugs
'317': keys
'318': kimonos
'319': knives
'320': ladles
'321': lamps
'322': lanterns
'323': lanyards
'324': leatherwork
'325': lids
'326': loom weights
'327': maces
'328': manuscripts
'329': maps
'330': masks
'331': medals
'332': miniatures
'333': mirrors
'334': models
'335': money
'336': mounts
'337': mugs
'338': mummies
'339': musical instruments
'340': nails
'341': necklaces
'342': needles
'343': netsukes
'344': nozzles
'345': obelisks
'346': obis
'347': oboes
'348': oil lamps
'349': ornaments
'350': pages
'351': paintings
'352': paper money
'353': paperweights
'354': papyrus
'355': passports
'356': pectorals
'357': pendants
'358': pestles
'359': petticoats
'360': photograph albums
'361': photographs
'362': pictures
'363': pins
'364': pipes
'365': pitchers
'366': playing card boxes
'367': playing cards
'368': plinths
'369': plumb bobs
'370': plume holders
'371': poker
'372': pommels
'373': postage stamps
'374': postcards
'375': posters
'376': pots
'377': pottery
'378': prayers
'379': printing blocks
'380': printing plates
'381': prints
'382': punch bowls
'383': puppets
'384': purses
'385': puzzles
'386': pyxides
'387': quilts
'388': razors
'389': reliefs
'390': rifles
'391': rings
'392': robes
'393': roofing tile
'394': rose bowls
'395': rubbings
'396': rugs
'397': rulers
'398': sandals
'399': saris
'400': sarongs
'401': sashes
'402': sauceboats
'403': saucers
'404': saws
'405': scabbards
'406': scaraboids
'407': scarabs
'408': scepters
'409': scissors
'410': scrolls
'411': sculpture
'412': seed
'413': seppa
'414': shadow puppets
'415': shawls
'416': shears
'417': shell
'418': shelves
'419': sherds
'420': shields
'421': shoes
'422': shrines
'423': sistra
'424': situlae
'425': sketches
'426': skewers
'427': skirts
'428': snuff bottles
'429': socks
'430': spatulas
'431': spearheads
'432': spears
'433': spittoons
'434': spoons
'435': statues
'436': statuettes
'437': steelyards
'438': stelae
'439': sticks
'440': stirrup jars
'441': stools
'442': stoppers
'443': straps
'444': studs
'445': styluses
'446': sugar bowls
'447': swagger sticks
'448': swords
'449': tablets
'450': tacks
'451': talismans
'452': tallies
'453': tangrams
'454': tankards
'455': tea bowls
'456': tea caddies
'457': tea kettles
'458': teacups
'459': teapots
'460': telephones
'461': ties
'462': tiles
'463': toggles
'464': toilet caskets
'465': tools
'466': toys
'467': trays
'468': trophies
'469': trousers
'470': tubes
'471': tureens
'472': tweezers
'473': typewriters
'474': underwear
'475': unidentified
'476': urinals
'477': ushabti
'478': utensils
'479': vases
'480': veils
'481': vessels
'482': waistcoats
'483': watches
'484': weight
'485': weights
'486': whetstones
'487': whistles
'488': whorls
'489': wood blocks
'490': writing boards
- name: other_name
dtype: string
- name: material
dtype: string
- name: production.period
dtype: string
- name: production.place
dtype: string
- name: new_root
dtype: string
splits:
- name: validation
num_bytes: 168066265.86
num_examples: 5436
- name: test
num_bytes: 159879306.456
num_examples: 5436
- name: train
num_bytes: 3338040187.5
num_examples: 115500
download_size: 3336057140
dataset_size: 3665985759.816
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- split: train
path: data/train-*
---
|
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