datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
JM-Lee/Understanding | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: answer
dtype: string
- name: generated
dtype: string
- name: understanding
dtype: string
splits:
- name: train
num_bytes: 1759284
num_examples: 744
download_size: 478005
dataset_size: 1759284
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Star3073/Interview_Data | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 87901359
num_examples: 68075
- name: valid
num_bytes: 9045971
num_examples: 8026
download_size: 47540084
dataset_size: 96947330
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
---
|
hooman34/fashionpedia | ---
license: unknown
---
|
celta/carla | ---
license: other
---
|
zolak/twitter_dataset_50_1713203424 | ---
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: 3994233
num_examples: 9861
download_size: 2012745
dataset_size: 3994233
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autoevaluate/autoeval-staging-eval-project-xsum-9818ea4b-12975767 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: sshleifer/distilbart-cnn-12-6
metrics: []
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
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: sshleifer/distilbart-cnn-12-6
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@grapplerulrich](https://huggingface.co/grapplerulrich) for evaluating this model. |
CyberHarem/tove_nikke | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of tove/トーブ/托比/토브 (Nikke: Goddess of Victory)
This is the dataset of tove/トーブ/托比/토브 (Nikke: Goddess of Victory), containing 31 images and their tags.
The core tags of this character are `blonde_hair, long_hair, blue_eyes, braid, breasts, bangs, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 31 | 45.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tove_nikke/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 31 | 22.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tove_nikke/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 82 | 50.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tove_nikke/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 31 | 38.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tove_nikke/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 82 | 76.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tove_nikke/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/tove_nikke',
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 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, solo, ass, blush, hood, looking_back, smile, skin_tight, black_jacket, from_behind, open_mouth, orange_bodysuit, white_background |
| 1 | 5 |  |  |  |  |  | 1girl, blush, looking_at_viewer, smile, solo, covered_navel, simple_background, white_background, multicolored_bodysuit, one_eye_closed, orange_bodysuit, skin_tight, black_jacket, full_body, high_heels, long_sleeves, medium_breasts, open_jacket |
| 2 | 8 |  |  |  |  |  | 1girl, blush, 1boy, hetero, mosaic_censoring, ass, penis, completely_nude, open_mouth, pussy, solo_focus, sweat, vaginal, hair_between_eyes, looking_at_viewer, anus, closed_eyes, cum, nipples, sex_from_behind, straddling |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | ass | blush | hood | looking_back | smile | skin_tight | black_jacket | from_behind | open_mouth | orange_bodysuit | white_background | covered_navel | simple_background | multicolored_bodysuit | one_eye_closed | full_body | high_heels | long_sleeves | medium_breasts | open_jacket | 1boy | hetero | mosaic_censoring | penis | completely_nude | pussy | solo_focus | sweat | vaginal | hair_between_eyes | anus | closed_eyes | cum | nipples | sex_from_behind | straddling |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:------|:--------|:-------|:---------------|:--------|:-------------|:---------------|:--------------|:-------------|:------------------|:-------------------|:----------------|:--------------------|:------------------------|:-----------------|:------------|:-------------|:---------------|:-----------------|:--------------|:-------|:---------|:-------------------|:--------|:------------------|:--------|:-------------|:--------|:----------|:--------------------|:-------|:--------------|:------|:----------|:------------------|:-------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | | X | | | X | X | X | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | X | | X | X | | | | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
pccl-org/formal-logic-simple-order-new-objects-bigger-1000 | ---
dataset_info:
features:
- name: greater_than
dtype: string
- name: less_than
dtype: string
- name: correct_example
sequence: string
- name: incorrect_example
sequence: string
- name: distance
dtype: int64
- name: index
dtype: int64
splits:
- name: train
num_bytes: 69843087
num_examples: 499500
download_size: 20572653
dataset_size: 69843087
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "formal-logic-simple-order-new-objects-bigger-1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nuphantom/l1 | ---
license: other
---
|
Foxe/test | ---
license: openrail
---
|
joemmile/Lia | ---
license: cc
---
|
hasangoni/Electron_microscopy_dataset | ---
task_categories:
- image-segmentation
language:
- en
tags:
- microscopy
- EPFL
- image segmentation
pretty_name: electron microscopy patch image
size_categories:
- 10K<n<100K
---
The dataset:
- Is a patch from the existing dataset available at https://www.epfl.ch/labs/cvlab/data/data-em/.
- Contains patches of size (256, 256).
- Removes any patches with empty masks to ensure quality.
- Has the same license applied as the original dataset.
- Please refer to the license for information on allowed usage.
- If you have any questions or concerns about the dataset, please do not hesitate to contact me. |
open-spaced-repetition/fsrs-dataset | ---
license: mit
---
|
Black4cosmos/final_dataset_for_finetuning_llama_2_model | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: test
num_bytes: 252379.7531687792
num_examples: 450
- name: train
num_bytes: 557621
num_examples: 1000
download_size: 229273
dataset_size: 810000.7531687792
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
Maiia/mcphrasy_test_skill_tok_embed | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: query_pos
dtype: int64
- name: phrase
dtype: string
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 9817826669
num_examples: 3001935
download_size: 10979706470
dataset_size: 9817826669
---
# Dataset Card for "mcphrasy_test_skill_tok_embed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
goup/medicaid | ---
license: apache-2.0
task_categories:
- table-question-answering
language:
- en
size_categories:
- 1K<n<10K
--- |
samitizerxu/kelp_rgbagg_swin_nir | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 803819359.25
num_examples: 5635
- name: test
num_bytes: 204072964.5
num_examples: 1426
download_size: 1007587469
dataset_size: 1007892323.75
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
tuple_ie | ---
annotations_creators:
- found
language_creators:
- machine-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: tupleinf-open-ie-dataset
pretty_name: TupleInf Open IE
tags:
- open-information-extraction
dataset_info:
- config_name: all
features:
- name: sentence
dtype: string
- name: tuples
sequence:
- name: score
dtype: float32
- name: tuple_text
dtype: string
- name: context
dtype: string
- name: arg1
dtype: string
- name: rel
dtype: string
- name: arg2s
sequence: string
splits:
- name: train
num_bytes: 115621096
num_examples: 267719
download_size: 18026102
dataset_size: 115621096
- config_name: 4th_grade
features:
- name: sentence
dtype: string
- name: tuples
sequence:
- name: score
dtype: float32
- name: tuple_text
dtype: string
- name: context
dtype: string
- name: arg1
dtype: string
- name: rel
dtype: string
- name: arg2s
sequence: string
splits:
- name: train
num_bytes: 65363445
num_examples: 158910
download_size: 18026102
dataset_size: 65363445
- config_name: 8th_grade
features:
- name: sentence
dtype: string
- name: tuples
sequence:
- name: score
dtype: float32
- name: tuple_text
dtype: string
- name: context
dtype: string
- name: arg1
dtype: string
- name: rel
dtype: string
- name: arg2s
sequence: string
splits:
- name: train
num_bytes: 50257651
num_examples: 108809
download_size: 18026102
dataset_size: 50257651
---
# Dataset Card for TupleInf Open IE
## 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:** [Tuple IE Homepage](https://allenai.org/data/tuple-ie)
- **Repository:**
- **Paper:** [Answering Complex Questions Using Open Information Extraction](https://www.semanticscholar.org/paper/Answering-Complex-Questions-Using-Open-Information-Khot-Sabharwal/0ff595f0645a3e25a2f37145768985b10ead0509)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The TupleInf Open IE dataset contains Open IE tuples extracted from 263K sentences that were used by the solver in “Answering Complex Questions Using Open Information Extraction” (referred as Tuple KB, T). These sentences were collected from a large Web corpus using training questions from 4th and 8th grade as queries. This dataset contains 156K sentences collected for 4th grade questions and 107K sentences for 8th grade questions. Each sentence is followed by the Open IE v4 tuples using their simple format.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English, collected from a large Web corpus using training questions from 4th and 8th grade as queries.
## Dataset Structure
### Data Instances
This dataset contains setences with corresponding relation tuples extracted from each sentence. Each instance should contain a sentence and followed by the [Open IE v4](https://github.com/allenai/openie-standalone) tuples using their *simple format*.
An example of an instance:
```JSON
{
"sentence": "0.04593 kg Used a triple beam balance to mass a golf ball.",
"tuples": {
"score": 0.8999999761581421,
"tuple_text": "(0.04593 kg; Used; a triple beam balance; to mass a golf ball)",
"context": "",
"arg1": "0.04593 kg",
"rel": "Used",
"arg2s": ["a triple beam balance", "to mass a golf ball"],
}
}
```
### Data Fields
- `sentence`: the input text/sentence.
- `tuples`: the extracted relation tuples from the sentence.
- `score`: the confident score for each tuple.
- `tuple_text`: the relationship representation text of the extraction, in the *simple format* of [Open IE v4](https://github.com/allenai/openie-standalone).
- `context`: an optional representation of the context for this extraction. Defaults to `""` if there's no context.
- `arg1`: the first argument in the relationship.
- `rel`: the relation.
- `arg2s`: a sequence of the 2nd arguments in the realtionship.
### Data Splits
| name | train|
|-----------|-----:|
| all |267719|
| 4th_grade |158910|
| 8th_grade |108809|
## 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
```bibtex
@article{Khot2017AnsweringCQ,
title={Answering Complex Questions Using Open Information Extraction},
author={Tushar Khot and A. Sabharwal and Peter Clark},
journal={ArXiv},
year={2017},
volume={abs/1704.05572}
}
```
### Contributions
Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset. |
correll/armbench-segmentation-mix-object-tote | ---
dataset_info:
features:
- name: rgb
dtype: image
- name: mask
dtype: image
splits:
- name: train
num_bytes: 13895663120.768
num_examples: 30992
download_size: 12376280750
dataset_size: 13895663120.768
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
---
This is data from the Amazon Armbench dataset (https://armbench.s3.amazonaws.com/index.html).
|
heliosprime/twitter_dataset_1713059029 | ---
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: 11736
num_examples: 26
download_size: 10063
dataset_size: 11736
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713059029"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pipi00pipi/smotrich_he | ---
license: openrail
---
|
alx-ai/noggles_inversion | ---
license: cc0-1.0
---
|
open-llm-leaderboard/details_FlagAlpha__Llama2-Chinese-7b-Chat | ---
pretty_name: Evaluation run of FlagAlpha/Llama2-Chinese-7b-Chat
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [FlagAlpha/Llama2-Chinese-7b-Chat](https://huggingface.co/FlagAlpha/Llama2-Chinese-7b-Chat)\
\ 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_FlagAlpha__Llama2-Chinese-7b-Chat\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-24T18:22:20.160130](https://huggingface.co/datasets/open-llm-leaderboard/details_FlagAlpha__Llama2-Chinese-7b-Chat/blob/main/results_2023-10-24T18-22-20.160130.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\": 0.276006711409396,\n\
\ \"em_stderr\": 0.004577904649189297,\n \"f1\": 0.3353460570469806,\n\
\ \"f1_stderr\": 0.004529633421686287,\n \"acc\": 0.4115316008576012,\n\
\ \"acc_stderr\": 0.009887124096052392\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.276006711409396,\n \"em_stderr\": 0.004577904649189297,\n\
\ \"f1\": 0.3353460570469806,\n \"f1_stderr\": 0.004529633421686287\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0803639120545868,\n \
\ \"acc_stderr\": 0.007488258573239077\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7426992896606156,\n \"acc_stderr\": 0.01228598961886571\n\
\ }\n}\n```"
repo_url: https://huggingface.co/FlagAlpha/Llama2-Chinese-7b-Chat
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|arc:challenge|25_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_24T18_22_20.160130
path:
- '**/details_harness|drop|3_2023-10-24T18-22-20.160130.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-24T18-22-20.160130.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_24T18_22_20.160130
path:
- '**/details_harness|gsm8k|5_2023-10-24T18-22-20.160130.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-24T18-22-20.160130.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hellaswag|10_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-55-21.985751.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-01T14-55-21.985751.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-01T14-55-21.985751.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_24T18_22_20.160130
path:
- '**/details_harness|winogrande|5_2023-10-24T18-22-20.160130.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-24T18-22-20.160130.parquet'
- config_name: results
data_files:
- split: 2023_10_01T14_55_21.985751
path:
- results_2023-10-01T14-55-21.985751.parquet
- split: 2023_10_24T18_22_20.160130
path:
- results_2023-10-24T18-22-20.160130.parquet
- split: latest
path:
- results_2023-10-24T18-22-20.160130.parquet
---
# Dataset Card for Evaluation run of FlagAlpha/Llama2-Chinese-7b-Chat
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/FlagAlpha/Llama2-Chinese-7b-Chat
- **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 [FlagAlpha/Llama2-Chinese-7b-Chat](https://huggingface.co/FlagAlpha/Llama2-Chinese-7b-Chat) 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_FlagAlpha__Llama2-Chinese-7b-Chat",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T18:22:20.160130](https://huggingface.co/datasets/open-llm-leaderboard/details_FlagAlpha__Llama2-Chinese-7b-Chat/blob/main/results_2023-10-24T18-22-20.160130.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": 0.276006711409396,
"em_stderr": 0.004577904649189297,
"f1": 0.3353460570469806,
"f1_stderr": 0.004529633421686287,
"acc": 0.4115316008576012,
"acc_stderr": 0.009887124096052392
},
"harness|drop|3": {
"em": 0.276006711409396,
"em_stderr": 0.004577904649189297,
"f1": 0.3353460570469806,
"f1_stderr": 0.004529633421686287
},
"harness|gsm8k|5": {
"acc": 0.0803639120545868,
"acc_stderr": 0.007488258573239077
},
"harness|winogrande|5": {
"acc": 0.7426992896606156,
"acc_stderr": 0.01228598961886571
}
}
```
### 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] |
Deojoandco/reward_model_anthropic_8 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: output
sequence: string
- name: toxicity
sequence: float64
- name: severe_toxicity
sequence: float64
- name: obscene
sequence: float64
- name: identity_attack
sequence: float64
- name: insult
sequence: float64
- name: threat
sequence: float64
- name: sexual_explicit
sequence: float64
- name: mean_toxity_value
dtype: float64
- name: max_toxity_value
dtype: float64
- name: min_toxity_value
dtype: float64
- name: sd_toxity_value
dtype: float64
- name: median_toxity_value
dtype: float64
- name: median_output
dtype: string
- name: toxic
dtype: bool
- name: regard
list:
list:
- name: label
dtype: string
- name: score
dtype: float64
- name: regard_neutral
dtype: float64
- name: regard_positive
dtype: float64
- name: regard_other
dtype: float64
- name: regard_negative
dtype: float64
- name: bias_matches
dtype: string
splits:
- name: test
num_bytes: 25267747
num_examples: 8552
download_size: 15240877
dataset_size: 25267747
---
# Dataset Card for "reward_model_anthropic_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/thite_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of thite (Fire Emblem)
This is the dataset of thite (Fire Emblem), containing 72 images and their tags.
The core tags of this character are `blue_hair, blue_eyes, short_hair, bangs, headband, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 72 | 81.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/thite_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 72 | 54.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/thite_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 139 | 97.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/thite_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 72 | 74.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/thite_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 139 | 125.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/thite_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/thite_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, armor, fingerless_gloves, pegasus_knight_uniform_(fire_emblem), skirt, solo, spear, thighhighs, thigh_boots, belt |
| 1 | 6 |  |  |  |  |  | 1girl, bare_shoulders, hair_flower, solo, strapless_dress, white_dress, blue_flower, detached_sleeves, medium_breasts, rose, smile, wedding_dress, feathers, official_alternate_costume, simple_background, upper_body, blush, cleavage, detached_collar, holding, white_background |
| 2 | 9 |  |  |  |  |  | 1girl, detached_collar, feather_trim, medium_breasts, wedding_dress, white_dress, white_footwear, bare_shoulders, full_body, shiny_hair, simple_background, smile, strapless_dress, solo, white_background, hair_flower, skirt_hold, holding, looking_away, collarbone, high_heels, looking_at_viewer |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | armor | fingerless_gloves | pegasus_knight_uniform_(fire_emblem) | skirt | solo | spear | thighhighs | thigh_boots | belt | bare_shoulders | hair_flower | strapless_dress | white_dress | blue_flower | detached_sleeves | medium_breasts | rose | smile | wedding_dress | feathers | official_alternate_costume | simple_background | upper_body | blush | cleavage | detached_collar | holding | white_background | feather_trim | white_footwear | full_body | shiny_hair | skirt_hold | looking_away | collarbone | high_heels | looking_at_viewer |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:---------------------------------------|:--------|:-------|:--------|:-------------|:--------------|:-------|:-----------------|:--------------|:------------------|:--------------|:--------------|:-------------------|:-----------------|:-------|:--------|:----------------|:-----------|:-----------------------------|:--------------------|:-------------|:--------|:-----------|:------------------|:----------|:-------------------|:---------------|:-----------------|:------------|:-------------|:-------------|:---------------|:-------------|:-------------|:--------------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | | | | | X | | | | | X | X | X | X | | | X | | X | X | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X |
|
freshpearYoon/v3_train_free_concat_17 | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 3842445592
num_examples: 2500
download_size: 1680221242
dataset_size: 3842445592
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
gintokimmco/mings | ---
license: llama2
---
|
Nestor95/ME | ---
license: openrail
---
|
pphuc25/cv13-train-vectorized | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: input_length
dtype: int64
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 273530247.93
num_examples: 1671
download_size: 253957905
dataset_size: 273530247.93
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "cv13-train-vectorized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ChristophSchuhmann/emotions | ---
license: apache-2.0
---
|
peterpull/MediatorBot | ---
license: creativeml-openrail-m
---
|
Shubh8434/kingcouty | ---
license: apache-2.0
---
|
AlekseyKorshuk/gpteacher-role-play-chatml | ---
dataset_info:
features:
- name: conversation
list:
- name: content
dtype: string
- name: do_train
dtype: bool
- name: role
dtype: string
splits:
- name: train
num_bytes: 6168190
num_examples: 9111
download_size: 0
dataset_size: 6168190
---
# Dataset Card for "gpteacher-role-play-chatml"
Data preprocessing pipeline: https://github.com/AlekseyKorshuk/chat-data-pipeline |
AdapterOcean/biology_dataset_standardized_cluster_4 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 46464229
num_examples: 4217
download_size: 0
dataset_size: 46464229
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "biology_dataset_standardized_cluster_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Ambrosiussen/flower-dataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 11753553.0
num_examples: 300
download_size: 11742671
dataset_size: 11753553.0
---
# Dataset Card for "flower-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
susnato/plant_disease_detection_processed | ---
license: cc-by-4.0
task_categories:
- object-detection
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int64
- name: height
dtype: int64
- name: objects
struct:
- name: area
sequence: int64
- name: bbox
sequence:
sequence: int64
- name: category
sequence: int64
- name: pixel_values
sequence:
sequence:
sequence: float32
- name: pixel_mask
sequence:
sequence: int64
- name: labels
struct:
- name: area
sequence: float32
- name: boxes
sequence:
sequence: float32
- name: class_labels
sequence: int64
- name: image_id
sequence: int64
- name: iscrowd
sequence: int64
- name: orig_size
sequence: int64
- name: size
sequence: int64
splits:
- name: train
num_bytes: 27853534555.06
num_examples: 2110
- name: test
num_bytes: 2810816579.0
num_examples: 214
download_size: 5331925364
dataset_size: 30664351134.06
---
This Dataset is created from processing the files from this GitHub repository : [PlantDoc-Object-Detection-Dataset](https://github.com/pratikkayal/PlantDoc-Object-Detection-Dataset/tree/master)
Citation
BibTeX:
```
@inproceedings{10.1145/3371158.3371196,
author = {Singh, Davinder and Jain, Naman and Jain, Pranjali and Kayal, Pratik and Kumawat, Sudhakar and Batra, Nipun},
title = {PlantDoc: A Dataset for Visual Plant Disease Detection},
year = {2020},
isbn = {9781450377386},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3371158.3371196},
doi = {10.1145/3371158.3371196},
booktitle = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD},
pages = {249–253},
numpages = {5},
keywords = {Deep Learning, Object Detection, Image Classification},
location = {Hyderabad, India},
series = {CoDS COMAD 2020}
}
``` |
automated-research-group/llama2_7b_chat-boolq-results_jacksee | ---
dataset_info:
config_name: '{''do_sample''=False, ''beams''=1}'
features:
- name: id
dtype: string
- name: prediction
dtype: string
- name: bool_accuracy
dtype: bool
splits:
- name: train
num_bytes: 503592
num_examples: 3270
download_size: 265378
dataset_size: 503592
configs:
- config_name: '{''do_sample''=False, ''beams''=1}'
data_files:
- split: train
path: '{''do_sample''=False, ''beams''=1}/train-*'
---
# Dataset Card for "llama2_7b_chat-boolq-results_jacksee"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ovior/twitter_dataset_1713188811 | ---
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: 2514498
num_examples: 7438
download_size: 1444662
dataset_size: 2514498
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
alisson40889/chapeu | ---
license: openrail
---
|
shetumohanto/doctor_qa_bangla | ---
license: apache-2.0
---
|
another-symato/vnexpress-dedup | ---
dataset_info:
features:
- name: content
dtype: string
splits:
- name: train
num_bytes: 2015062877
num_examples: 633823
download_size: 1071825960
dataset_size: 2015062877
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
rjds0207/Betinho | ---
license: openrail
---
|
polinaeterna/push_to_hub_config_none_be56a8b | ---
dataset_info:
features:
- name: x
dtype: int64
- name: y
dtype: int64
splits:
- name: train
num_bytes: 48
num_examples: 3
download_size: 950
dataset_size: 48
configs_kwargs:
config_name: default
data_dir: default
---
# Dataset Card for "push_to_hub_config_none_be56a8b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_digitous__Javalion-R | ---
pretty_name: Evaluation run of digitous/Javalion-R
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [digitous/Javalion-R](https://huggingface.co/digitous/Javalion-R) 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_digitous__Javalion-R\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-12T21:07:25.804829](https://huggingface.co/datasets/open-llm-leaderboard/details_digitous__Javalion-R/blob/main/results_2023-10-12T21-07-25.804829.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0010486577181208054,\n\
\ \"em_stderr\": 0.0003314581465219256,\n \"f1\": 0.04845847315436258,\n\
\ \"f1_stderr\": 0.0011637240305010866,\n \"acc\": 0.34041837679282755,\n\
\ \"acc_stderr\": 0.008896821469599773\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.0003314581465219256,\n\
\ \"f1\": 0.04845847315436258,\n \"f1_stderr\": 0.0011637240305010866\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.026535253980288095,\n \
\ \"acc_stderr\": 0.004427045987265169\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.654301499605367,\n \"acc_stderr\": 0.013366596951934376\n\
\ }\n}\n```"
repo_url: https://huggingface.co/digitous/Javalion-R
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_07_19T14_00_54.512853
path:
- '**/details_harness|arc:challenge|25_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_12T21_07_25.804829
path:
- '**/details_harness|drop|3_2023-10-12T21-07-25.804829.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-12T21-07-25.804829.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_12T21_07_25.804829
path:
- '**/details_harness|gsm8k|5_2023-10-12T21-07-25.804829.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-12T21-07-25.804829.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hellaswag|10_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:00:54.512853.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T14:00:54.512853.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T14:00:54.512853.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_12T21_07_25.804829
path:
- '**/details_harness|winogrande|5_2023-10-12T21-07-25.804829.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-12T21-07-25.804829.parquet'
- config_name: results
data_files:
- split: 2023_07_19T14_00_54.512853
path:
- results_2023-07-19T14:00:54.512853.parquet
- split: 2023_10_12T21_07_25.804829
path:
- results_2023-10-12T21-07-25.804829.parquet
- split: latest
path:
- results_2023-10-12T21-07-25.804829.parquet
---
# Dataset Card for Evaluation run of digitous/Javalion-R
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/digitous/Javalion-R
- **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 [digitous/Javalion-R](https://huggingface.co/digitous/Javalion-R) 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_digitous__Javalion-R",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-12T21:07:25.804829](https://huggingface.co/datasets/open-llm-leaderboard/details_digitous__Javalion-R/blob/main/results_2023-10-12T21-07-25.804829.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0010486577181208054,
"em_stderr": 0.0003314581465219256,
"f1": 0.04845847315436258,
"f1_stderr": 0.0011637240305010866,
"acc": 0.34041837679282755,
"acc_stderr": 0.008896821469599773
},
"harness|drop|3": {
"em": 0.0010486577181208054,
"em_stderr": 0.0003314581465219256,
"f1": 0.04845847315436258,
"f1_stderr": 0.0011637240305010866
},
"harness|gsm8k|5": {
"acc": 0.026535253980288095,
"acc_stderr": 0.004427045987265169
},
"harness|winogrande|5": {
"acc": 0.654301499605367,
"acc_stderr": 0.013366596951934376
}
}
```
### 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] |
AntaFluorescent/man_in_armor | ---
license: cc0-1.0
size_categories:
- n<1K
---
Regularization dataset with photorealistic men in fantasy armor for small-scale finetunes/LoRAs.
Produced with various Stable Diffusion derivatives
Body horrors and extreme crops were hand pruned, though some were left
Prompts were cycled for a variety of poses and environments and to reduce full frontal static portraits and 'sameface' (still suffers from it, though).
Work in progress |
tyzhu/squad_qa_wrong_rare_v5_full_recite_full_passage | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: answer
dtype: string
- name: context_id
dtype: string
- name: correct_id
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 9247079
num_examples: 5070
- name: validation
num_bytes: 587391
num_examples: 300
download_size: 1847562
dataset_size: 9834470
---
# Dataset Card for "squad_qa_wrong_rare_v5_full_recite_full_passage"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
justahandsomeboy/recipedia_1 | ---
license: mit
---
|
gigaword | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|gigaword_2003
task_categories:
- summarization
task_ids: []
pretty_name: Gigaword
tags:
- headline-generation
dataset_info:
features:
- name: document
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 915246340
num_examples: 3803957
- name: validation
num_bytes: 45766944
num_examples: 189651
- name: test
num_bytes: 450774
num_examples: 1951
download_size: 578402958
dataset_size: 961464058
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
eval_split: test
col_mapping:
document: text
summary: target
metrics:
- type: rouge
name: Rouge
---
# Dataset Card for Gigaword
## 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
- **Repository:** [Gigaword repository](https://github.com/harvardnlp/sent-summary)
- **Leaderboard:** [Gigaword leaderboard](https://paperswithcode.com/sota/text-summarization-on-gigaword)
- **Paper:** [A Neural Attention Model for Abstractive Sentence Summarization](https://arxiv.org/abs/1509.00685)
- **Point of Contact:** [Alexander Rush](mailto:arush@cornell.edu)
- **Size of downloaded dataset files:** 578.41 MB
- **Size of the generated dataset:** 962.96 MB
- **Total amount of disk used:** 1.54 GB
### Dataset Summary
Headline-generation on a corpus of article pairs from Gigaword consisting of
around 4 million articles. Use the 'org_data' provided by
https://github.com/microsoft/unilm/ which is identical to
https://github.com/harvardnlp/sent-summary but with better format.
### Supported Tasks and Leaderboards
- `summarization`: This dataset can be used for Summarization, where given a dicument, the goal is to predict its summery. The model performance is evaluated using the [ROUGE](https://huggingface.co/metrics/rouge) metric. The leaderboard for this task is available [here](https://paperswithcode.com/sota/text-summarization-on-gigaword).
### Languages
English.
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{
'document': "australia 's current account deficit shrunk by a record #.## billion dollars -lrb- #.## billion us -rrb- in the june quarter due to soaring commodity prices , figures released monday showed .",
'summary': 'australian current account deficit narrows sharply'
}
```
### Data Fields
The data fields are the same among all splits.
- `document`: a `string` feature.
- `summary`: a `string` feature.
### Data Splits
| name | train |validation|test|
|-------|------:|---------:|---:|
|default|3803957| 189651|1951|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
From the paper:
> For our training set, we pair the headline of each article with its first sentence to create an inputsummary pair. While the model could in theory be trained on any pair, Gigaword contains many spurious headline-article pairs. We therefore prune training based on the following heuristic filters: (1) Are there no non-stop-words in common? (2) Does the title contain a byline or other extraneous editing marks? (3) Does the title have a question mark or colon? After applying these filters, the training set consists of roughly J = 4 million title-article pairs. We apply a minimal preprocessing step using PTB tokenization, lower-casing, replacing all digit characters with #, and replacing of word types seen less than 5 times with UNK. We also remove all articles from the time-period of the DUC evaluation. release.
The complete input training vocabulary consists of 119 million word tokens and 110K unique word types with an average sentence size of 31.3 words. The headline vocabulary consists of 31 million tokens and 69K word types with the average title of length 8.3 words (note that this is significantly shorter than the DUC summaries). On average there are 4.6 overlapping word types between the headline and the input; although only 2.6 in the
first 75-characters of the input.
#### Who are the source language producers?
From the paper:
> For training data for both tasks, we utilize the annotated Gigaword data set (Graff et al., 2003; Napoles et al., 2012), which consists of standard Gigaword, preprocessed with Stanford CoreNLP tools (Manning et al., 2014).
### Annotations
#### Annotation process
Annotations are inherited from the annotatated Gigaword data set.
Additional information from the paper:
> Our model only uses annotations for tokenization and sentence separation, although several of the baselines use parsing and tagging as well.
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```bibtex
@article{graff2003english,
title={English gigaword},
author={Graff, David and Kong, Junbo and Chen, Ke and Maeda, Kazuaki},
journal={Linguistic Data Consortium, Philadelphia},
volume={4},
number={1},
pages={34},
year={2003}
}
@article{Rush_2015,
title={A Neural Attention Model for Abstractive Sentence Summarization},
url={http://dx.doi.org/10.18653/v1/D15-1044},
DOI={10.18653/v1/d15-1044},
journal={Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
publisher={Association for Computational Linguistics},
author={Rush, Alexander M. and Chopra, Sumit and Weston, Jason},
year={2015}
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
Bluebomber182/Judy-Hopps-WAV-Dataset | ---
license: unknown
---
|
BEE-spoke-data/falcon-refinedweb-100k_en-long | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1748631587.0
num_examples: 100000
download_size: 1035546649
dataset_size: 1748631587.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
source_datasets: tiiuae/falcon-refinedweb
language:
- en
license: odc-by
task_categories:
- text-generation
---
# BEE-spoke-data/falcon-refinedweb-100k_en-long
A sample from [falcon-refinedweb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb):
- more than 2048 & less than 16384 gpt4 tiktoken tokens
- `en` only (via fasttext-langdetect)
- 100k samples
|
zrthxn/HNC_Mini | ---
license: mit
language:
- en
pretty_name: hnc-mini
task_categories:
- sentence-similarity
task_ids:
- semantic-similarity-classification
---
# HNC_Mini
Contains 306,084 samples collected from the following datasets.
- QQP_triplets
- HC3
- sentence-compression
|
JuanKO/T5_summarization_RLAIF | ---
license: apache-2.0
dataset_info:
features:
- name: prompt
dtype: string
- name: summary_1
dtype: string
- name: summary_2
dtype: string
splits:
- name: train
num_bytes: 1697095
num_examples: 1000
download_size: 906302
dataset_size: 1697095
---
|
Nexdata/104320_Images_Korean_and_Hindi_OCR_Data_in_Natural_Scenes | ---
license: cc-by-nc-nd-4.0
---
## Description
104,320 Images - Korean and Hindi OCR Data in Natural Scenes. The collecting scenes of this dataset include packaging, posters, tickets, reminders, menus, building signs, etc.. The data diversity includes multiple scenes, multiple shooting angles and multiple light conditions. For annotation, line-level polygon bounding box (or tetragon bounding box, rectangle bounding box) annotation, transcription and text attributes (language type) for the texts; vertical-level polygon bounding box (or tetragon bounding box, rectangle bounding box) annotation, transcription and text attributes (language type) for the text. The dataset can be used for Korean and Hindi OCR tasks in natural scenes.
For more details, please refer to the link: https://www.nexdata.ai/dataset/1254?source=Huggingface
## Data size
76,861 images of Korean, 555,913 bounding boxes; 27,459 images of Hindi, 200,453 bounding boxes
## Collecting environment
including packaging, posters, tickets, reminders, menus, building signs, etc.
## Data diversity
multiple natural scenes, multiple shooting angles, multiple light conditions
## Device
cellphone
## Collecting angle
looking up angle, looking down angle, eye-level angle
## Language distribution
Korean, Hindi, English (a few)
## Data format
the image data format is .jpg, the annotation file format is .json
## Bounding box shape distribution
315,822 tetragon bounding boxes and 240,091 polygon bounding boxes of Korean; 780 tetragon bounding boxes, 199,671 polygon bounding boxes and 2 rectangle bounding boxes of Hindi
## Annotation content
line-level polygon bounding box (or tetragon bounding box, rectangle bounding box) annotation, transcription and text attributes (language type) for the texts; vertical-level polygon bounding box (or tetragon bounding box, rectangle bounding box) annotation, transcription and text attributes (language type) for the text
## Accuracy
The error bound of each vertex of a bounding box is within 5 pixels, which is a qualified annotation, the accuracy of bounding boxes is not less than 95%; The texts transcription accuracy is not less than 95%.
# Licensing Information
Commercial License
|
CHEN0312/fyefu | ---
license: apache-2.0
---
|
argilla/twitter-coronavirus | ---
language:
- en
license:
- unknown
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- sentiment-analysis
dataset_info:
features:
- name: text
dtype: string
- name: inputs
struct:
- name: text
dtype: string
- name: prediction
list:
- name: label
dtype: string
- name: score
dtype: float64
- name: prediction_agent
dtype: string
- name: annotation
dtype: 'null'
- name: annotation_agent
dtype: 'null'
- name: multi_label
dtype: bool
- name: explanation
dtype: 'null'
- name: id
dtype: string
- name: metadata
struct:
- name: location
dtype: string
- name: screen_name
dtype: int64
- name: split
dtype: string
- name: user_name
dtype: int64
- name: status
dtype: string
- name: event_timestamp
dtype: timestamp[us]
- name: metrics
struct:
- name: text_length
dtype: int64
splits:
- name: train
num_bytes: 25394534
num_examples: 44955
download_size: 15712627
dataset_size: 25394534
---
# Dataset Card for "twitter-coronavirus"
## Dataset Description
- **Homepage:** Kaggle Challenge
- **Repository:** https://www.kaggle.com/datasets/datatattle/covid-19-nlp-text-classification
- **Paper:** N.A.
- **Leaderboard:** N.A.
- **Point of Contact:** N.A.
### Dataset Summary
Perform Text Classification on the data. The tweets have been pulled from Twitter and manual tagging has been done then.
The names and usernames have been given codes to avoid any privacy concerns.
Columns:
1) Location
2) Tweet At
3) Original Tweet
4) Label
- Extremely Negative
- Negative
- Neutral
- Positive
- Extremely Positive
### Languages
english
### Citation Information
https://www.kaggle.com/datasets/datatattle/covid-19-nlp-text-classification
### Contributions
Thanks to [@davidberenstein1957](https://github.com/davidberenstein1957) for adding this dataset. |
ragu8/hello_dataset | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4560.0
num_examples: 80
download_size: 1128
dataset_size: 4560.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
markkerzner/cool_new_dataset | ---
dataset_info:
features:
- name: name
dtype: string
- name: description
dtype: string
- name: ad
dtype: string
splits:
- name: train
num_bytes: 3099
num_examples: 5
download_size: 7195
dataset_size: 3099
---
# Dataset Card for "cool_new_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Arthuerwang/finetune_lora_pikachu_5 | ---
dataset_info:
features:
- name: image
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 608
num_examples: 5
download_size: 2038
dataset_size: 608
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
lethanhphatit/testing | ---
license: openrail
---
|
erfanzar/Flan-GPT4 | ---
dataset_info:
features:
- name: response
dtype: string
- name: instruction
dtype: string
- name: system
dtype: string
- name: toxin_prompt
dtype: string
- name: llama_prompt
dtype: string
splits:
- name: train
num_bytes: 4093492977
num_examples: 724248
download_size: 2266772484
dataset_size: 4093492977
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Flan-GPT4 Dataset
## Overview
The Flan-GPT4 dataset is a collection of prompts and responses designed for training and evaluating language generation models. It contains various features such as response, instruction, system, toxin_prompt, and llama_prompt, each with a data type of string.
Edited and customized from `SlimOrca-Flan`
## Dataset Information
- **Features:**
- response (string)
- instruction (string)
- system (string)
- toxin_prompt (string)
- llama_prompt (string)
- **Splits:**
- Train:
- Number of examples: 724,248
- Size: 4,093,492,977 bytes
## Intended Use
This dataset is intended for training and evaluating language generation models, particularly those focused on natural language processing and text generation tasks.
|
Gopal1853/trainingandtest | ---
task_categories:
- translation
language:
- en
- ru
pretty_name: r
size_categories:
- 1K<n<10K
--- |
lexlms/lex_files_preprocessed | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- extended
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
pretty_name: LexFiles
configs:
- eu_legislation
- eu_court_cases
- uk_legislation
- uk_court_cases
- us_legislation
- us_court_cases
- us_contracts
- canadian_legislation
- canadian_court_cases
- indian_court_cases
---
# Dataset Card for "LexFiles"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Specifications](#supported-tasks-and-leaderboards)
## Dataset Description
- **Homepage:** https://github.com/coastalcph/lexlms
- **Repository:** https://github.com/coastalcph/lexlms
- **Paper:** https://arxiv.org/abs/xxx
- **Point of Contact:** [Ilias Chalkidis](mailto:ilias.chalkidis@di.ku.dk)
### Dataset Summary
**Disclaimer: This is a pre-proccessed version of the LexFiles corpus (https://huggingface.co/datasets/lexlms/lexfiles), where documents are pre-split in chunks of 512 tokens.**
The LeXFiles is a new diverse English multinational legal corpus that we created including 11 distinct sub-corpora that cover legislation and case law from 6 primarily English-speaking legal systems (EU, CoE, Canada, US, UK, India).
The corpus contains approx. 19 billion tokens. In comparison, the "Pile of Law" corpus released by Hendersons et al. (2022) comprises 32 billion in total, where the majority (26/30) of sub-corpora come from the United States of America (USA), hence the corpus as a whole is biased towards the US legal system in general, and the federal or state jurisdiction in particular, to a significant extent.
### Dataset Specifications
| Corpus | Corpus alias | Documents | Tokens | Pct. | Sampl. (a=0.5) | Sampl. (a=0.2) |
|-----------------------------------|----------------------|-----------|--------|--------|----------------|----------------|
| EU Legislation | `eu-legislation` | 93.7K | 233.7M | 1.2% | 5.0% | 8.0% |
| EU Court Decisions | `eu-court-cases` | 29.8K | 178.5M | 0.9% | 4.3% | 7.6% |
| ECtHR Decisions | `ecthr-cases` | 12.5K | 78.5M | 0.4% | 2.9% | 6.5% |
| UK Legislation | `uk-legislation` | 52.5K | 143.6M | 0.7% | 3.9% | 7.3% |
| UK Court Decisions | `uk-court-cases` | 47K | 368.4M | 1.9% | 6.2% | 8.8% |
| Indian Court Decisions | `indian-court-cases` | 34.8K | 111.6M | 0.6% | 3.4% | 6.9% |
| Canadian Legislation | `canadian-legislation` | 6K | 33.5M | 0.2% | 1.9% | 5.5% |
| Canadian Court Decisions | `canadian-court-cases` | 11.3K | 33.1M | 0.2% | 1.8% | 5.4% |
| U.S. Court Decisions [1] | `court-listener` | 4.6M | 11.4B | 59.2% | 34.7% | 17.5% |
| U.S. Legislation | `us-legislation` | 518 | 1.4B | 7.4% | 12.3% | 11.5% |
| U.S. Contracts | `us-contracts` | 622K | 5.3B | 27.3% | 23.6% | 15.0% |
| Total | `lexlms/lexfiles` | 5.8M | 18.8B | 100% | 100% | 100% |
[1] We consider only U.S. Court Decisions from 1965 onwards (cf. post Civil Rights Act), as a hard threshold for cases relying on severely out-dated and in many cases harmful law standards. The rest of the corpora include more recent documents.
[2] Sampling (Sampl.) ratios are computed following the exponential sampling introduced by Lample et al. (2019).
Additional corpora not considered for pre-training, since they do not represent factual legal knowledge.
| Corpus | Corpus alias | Documents | Tokens |
|----------------------------------------|------------------------|-----------|--------|
| Legal web pages from C4 | `legal-c4` | 284K | 340M |
### Citation
[*Ilias Chalkidis\*, Nicolas Garneau\*, Catalina E.C. Goanta, Daniel Martin Katz, and Anders Søgaard.*
*LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development.*
*2022. In the Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics. Toronto, Canada.*](https://aclanthology.org/xxx/)
```
@inproceedings{chalkidis-garneau-etal-2023-lexlms,
title = {{LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development}},
author = "Chalkidis*, Ilias and
Garneau*, Nicolas and
Goanta, Catalina and
Katz, Daniel Martin and
Søgaard, Anders",
booktitle = "Proceedings of the 61h Annual Meeting of the Association for Computational Linguistics",
month = june,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/xxx",
}
``` |
distilled-from-one-sec-cv12/chunk_143 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1146026920
num_examples: 223310
download_size: 1172281960
dataset_size: 1146026920
---
# Dataset Card for "chunk_143"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
google/dreambooth | ---
configs:
- config_name: default
data_files:
- split: train
path: "dataset/backpack/*.jpg"
- config_name: backpack
data_files:
- split: train
path: "dataset/backpack/*.jpg"
- config_name: backpack_dog
data_files:
- split: train
path: "dataset/backpack_dog/*.jpg"
- config_name: bear_plushie
data_files:
- split: train
path: "dataset/bear_plushie/*.jpg"
- config_name: berry_bowl
data_files:
- split: train
path: "dataset/berry_bowl/*.jpg"
- config_name: can
data_files:
- split: train
path: "dataset/can/*.jpg"
- config_name: candle
data_files:
- split: train
path: "dataset/candle/*.jpg"
- config_name: cat
data_files:
- split: train
path: "dataset/cat/*.jpg"
- config_name: cat2
data_files:
- split: train
path: "dataset/cat2/*.jpg"
- config_name: clock
data_files:
- split: train
path: "dataset/clock/*.jpg"
- config_name: colorful_sneaker
data_files:
- split: train
path: "dataset/colorful_sneaker/*.jpg"
- config_name: dog
data_files:
- split: train
path: "dataset/dog/*.jpg"
- config_name: dog2
data_files:
- split: train
path: "dataset/dog2/*.jpg"
- config_name: dog3
data_files:
- split: train
path: "dataset/dog3/*.jpg"
- config_name: dog5
data_files:
- split: train
path: "dataset/dog5/*.jpg"
- config_name: dog6
data_files:
- split: train
path: "dataset/dog6/*.jpg"
- config_name: dog7
data_files:
- split: train
path: "dataset/dog7/*.jpg"
- config_name: dog8
data_files:
- split: train
path: "dataset/dog8/*.jpg"
- config_name: duck_toy
data_files:
- split: train
path: "dataset/duck_toy/*.jpg"
- config_name: fancy_boot
data_files:
- split: train
path: "dataset/fancy_boot/*.jpg"
- config_name: grey_sloth_plushie
data_files:
- split: train
path: "dataset/grey_sloth_plushie/*.jpg"
- config_name: monster_toy
data_files:
- split: train
path: "dataset/monster_toy/*.jpg"
- config_name: pink_sunglasses
data_files:
- split: train
path: "dataset/pink_sunglasses/*.jpg"
- config_name: poop_emoji
data_files:
- split: train
path: "dataset/poop_emoji/*.jpg"
- config_name: rc_car
data_files:
- split: train
path: "dataset/rc_car/*.jpg"
- config_name: red_cartoon
data_files:
- split: train
path: "dataset/red_cartoon/*.jpg"
- config_name: robot_toy
data_files:
- split: train
path: "dataset/robot_toy/*.jpg"
- config_name: shiny_sneaker
data_files:
- split: train
path: "dataset/shiny_sneaker/*.jpg"
- config_name: teapot
data_files:
- split: train
path: "dataset/teapot/*.jpg"
- config_name: vase
data_files:
- split: train
path: "dataset/vase/*.jpg"
- config_name: wolf_plushie
data_files:
- split: train
path: "dataset/wolf_plushie/*.jpg"
license: cc-by-4.0
---
# Dataset Card for "dreambooth"
## Dataset of the Google paper DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
The dataset includes 30 subjects of 15 different classes. 9 out of these subjects are live subjects (dogs and cats) and 21 are objects. The dataset contains a variable number of images per subject (4-6). Images of the subjects are usually captured in different conditions, environments and under different angles.
We include a file dataset/prompts\_and\_classes.txt which contains all of the prompts used in the paper for live subjects and objects, as well as the class name used for the subjects.
The images have either been captured by the paper authors, or sourced from www.unsplash.com
The dataset/references\_and\_licenses.txt file contains a list of all the reference links to the images in www.unsplash.com - and attribution to the photographer, along with the license of the image.
### [project page](https://dreambooth.github.io/) | [arxiv](https://arxiv.org/abs/2208.12242)
## Academic Citation
If you use this work please cite:
```
@inproceedings{ruiz2023dreambooth,
title={Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation},
author={Ruiz, Nataniel and Li, Yuanzhen and Jampani, Varun and Pritch, Yael and Rubinstein, Michael and Aberman, Kfir},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2023}
}
```
## Disclaimer
This is not an officially supported Google product. |
BangumiBase/koitouso | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Koi To Uso
This is the image base of bangumi Koi to Uso, we detected 22 characters, 1550 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 549 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 28 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 17 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 11 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 12 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 140 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 11 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 10 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 173 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 46 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 25 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 12 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 25 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 25 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 47 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 235 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 17 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 13 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 8 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 7 | [Download](19/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 20 | 28 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 111 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
chentong00/propositionizer-wiki-data | ---
license: apache-2.0
task_categories:
- text2text-generation
size_categories:
- 10K<n<100K
---
## Dataset Summary
This is the training data of the model `Propositionizer-wiki`. We prompt GPT-4 to decompose a Wikipedia paragraph into a list of propositions.
We propose this training data to explore the concept of propositions as retrieval units. The propositions are defined as follows:
1. Each proposition should correspond to a distinct piece of meaning in the text, where the composition of all propositions would represent the semantics of the entire text.
2. A proposition should be *minimal*, i.e. it cannot be further split into separate propositions.
3. A proposition should be *contextualized and self-contained* ([Choi et al. 2021](https://aclanthology.org/2021.tacl-1.27/)). A proposition should include all the necessary context from the text (e.g. coreference) to interpret its meaning.
Check out more details in the paper.
## Dataset Structure
Here we provide details about the structure of the dataset.
* `sources` represents a Wikipedia paragraph. It is always in the format of "Title: {title}. Section: {section}. {content}". The title will not be empty, but the section can be empty.
* `targets` are a list of propositions in a JSON-formatted string.
Example:
```
{
"sources": "Title: Leaning Tower of Pisa. Section: . Prior to restoration work performed between 1990 and 2001, the tower leaned at an angle of 5.5 degrees, but the tower now leans at about 3.99 degrees. This means the top of the Leaning Tower of Pisa is displaced horizontally 3.9 meters (12 ft 10 in) from the center."
"targets": "[\"Prior to restoration work performed between 1990 and 2001, the Leaning Tower of Pisa leaned at an angle of 5.5 degrees.\", \"The Leaning Tower of Pisa now leans at about 3.99 degrees.\", \"The top of the Leaning Tower of Pisa is displaced horizontally 3.9 meters (12 ft 10 in) from the center.\"]"
}
```
## Citation
```
``` |
gyataro/cdacm-models | ---
license: gpl-3.0
---
|
nz/100_v2_rlhf | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 27978220.857103713
num_examples: 110183
- name: test
num_bytes: 3108804.061910828
num_examples: 12243
download_size: 17880457
dataset_size: 31087024.91901454
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
CICLAB-Comillas/calls_10k_v1 | ---
license: mit
task_categories:
- summarization
- text2text-generation
language:
- es
tags:
- phone_calls
pretty_name: PhoneCallsSum
size_categories:
- 1K<n<10K
--- |
tpremoli/CelebA-attrs-160k | ---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: 5_o_Clock_Shadow
dtype: int64
- name: Arched_Eyebrows
dtype: int64
- name: Attractive
dtype: int64
- name: Bags_Under_Eyes
dtype: int64
- name: Bald
dtype: int64
- name: Bangs
dtype: int64
- name: Big_Lips
dtype: int64
- name: Big_Nose
dtype: int64
- name: Black_Hair
dtype: int64
- name: Blond_Hair
dtype: int64
- name: Blurry
dtype: int64
- name: Brown_Hair
dtype: int64
- name: Bushy_Eyebrows
dtype: int64
- name: Chubby
dtype: int64
- name: Double_Chin
dtype: int64
- name: Eyeglasses
dtype: int64
- name: Goatee
dtype: int64
- name: Gray_Hair
dtype: int64
- name: Heavy_Makeup
dtype: int64
- name: High_Cheekbones
dtype: int64
- name: Male
dtype: int64
- name: Mouth_Slightly_Open
dtype: int64
- name: Mustache
dtype: int64
- name: Narrow_Eyes
dtype: int64
- name: No_Beard
dtype: int64
- name: Oval_Face
dtype: int64
- name: Pale_Skin
dtype: int64
- name: Pointy_Nose
dtype: int64
- name: Receding_Hairline
dtype: int64
- name: Rosy_Cheeks
dtype: int64
- name: Sideburns
dtype: int64
- name: Smiling
dtype: int64
- name: Straight_Hair
dtype: int64
- name: Wavy_Hair
dtype: int64
- name: Wearing_Earrings
dtype: int64
- name: Wearing_Hat
dtype: int64
- name: Wearing_Lipstick
dtype: int64
- name: Wearing_Necklace
dtype: int64
- name: Wearing_Necktie
dtype: int64
- name: Young
dtype: int64
- name: prompt_string
dtype: string
splits:
- name: train
num_bytes: 1190947307.632
num_examples: 159999
- name: validation
num_bytes: 146307394.663
num_examples: 19621
- name: test
num_bytes: 146901649.777
num_examples: 19527
download_size: 1400976910
dataset_size: 1484156352.072
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# CelebA-128x128
CelebA with attrs at 128x128 resolution.
## Dataset Information
The attributes are binary attributes. The dataset is already split into train/test/validation sets.
This dataset has been reduced so there's 160k train samples.
## Citation
```bibtex
@inproceedings{liu2015faceattributes,
title = {Deep Learning Face Attributes in the Wild},
author = {Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},
booktitle = {Proceedings of International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}
``` |
AlekseyKorshuk/lmeh-chai-davinci-vs-lit | ---
dataset_info:
features:
- name: davinci
dtype: string
- name: lit
dtype: string
- name: prompt
dtype: string
- name: api_prompt
dtype: string
splits:
- name: test
num_bytes: 402675309
num_examples: 10000
download_size: 200661267
dataset_size: 402675309
---
# Dataset Card for "lmeh-chai-davinci-vs-lit"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bdsaglam/musique-jerx-sft-mt-ms-openai | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 92531
num_examples: 40
download_size: 35558
dataset_size: 92531
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AdapterOcean/GPTeacher_roleplay_standardized_unified | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
splits:
- name: train
num_bytes: 1511672
num_examples: 1922
download_size: 929720
dataset_size: 1511672
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "GPTeacher_roleplay_standardized_unified"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ibivibiv/alpaca_lamini13 | ---
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 56354008
num_examples: 129281
download_size: 36384500
dataset_size: 56354008
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CAiRE/prosocial-dialog-pes_Arab | ---
dataset_info:
features:
- name: context
dtype: string
- name: response
dtype: string
- name: rots
sequence: string
- name: safety_label
dtype: string
- name: safety_annotations
sequence: string
- name: safety_annotation_reasons
sequence: string
- name: source
dtype: string
- name: etc
dtype: string
- name: dialogue_id
dtype: int64
- name: response_id
dtype: int64
- name: episode_done
dtype: bool
- name: mt_context
dtype: string
splits:
- name: train
num_bytes: 82362818
num_examples: 120236
- name: validation
num_bytes: 13981263
num_examples: 20416
- name: test
num_bytes: 17102874
num_examples: 25029
download_size: 51753430
dataset_size: 113446955
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
SarcasmNet/sarcasm | ---
license: apache-2.0
task_categories:
- token-classification
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset Card for Sarcasm Detection Dataset
## Dataset Details
### Dataset Description
The Sarcasm Detection Dataset is designed for identifying instances of sarcasm in text. The dataset aims to address difficulties in sarcasm detection due to the subjective and contextual nature of language.
## Uses
### Direct Use
The dataset can be used for training machine learning models to detect sarcasm in text, which has applications in sentiment analysis, social media monitoring, and natural language understanding tasks.
## Dataset Structure
The dataset consists of text examples labeled as sarcastic or non-sarcastic. Each example is accompanied by metadata indicating sarcasm markers and linguistic patterns.
## Dataset Creation
### Curation Rationale
The dataset was curated to provide a diverse collection of sarcastic and non-sarcastic text examples, aiming to capture the complexities of sarcasm in natural language.
### Source Data
#### Data Collection and Processing
The data collection process involved sourcing text samples from various sources, including social media, online forums, and news articles. Each sample was manually annotated as sarcastic or non-sarcastic by human annotators.
### Annotations [optional]
#### Annotation process
Annotations were performed by human annotators who were provided with guidelines for identifying sarcasm in text. Interannotator agreement was measured to ensure consistency in labeling.
## Bias, Risks, and Limitations
The dataset may contain biases inherent in the selection and annotation process, including cultural biases and subjective interpretations of sarcasm.
### Recommendations
Users are advised to consider the limitations of the dataset when training and evaluating sarcasm detection models.
## Citation [optional]
Khodak, M., Saunshi, N., & Vodrahalli, K. (2018). A Large Self-Annotated Corpus for Sarcasm. In LREC 2018 (pp. 1-6).
Rahman M O, Hossain M S, Junaid T S, et al. Predicting prices of stock market using gated recurrent units (GRUs) neural networks[J]. Int. J. Comput. Sci. Netw. Secur, 2019, 19(1): 213-222.
Yu Y, Si X, Hu C, et al. A review of recurrent neural networks: LSTM cells and network architectures[J]. Neural computation, 2019, 31(7): 1235-1270.
Gole, M., Nwadiugwu, W. P., & Miranskyy, A. (2023). On Sarcasm Detection with OpenAI GPT-based Models.
B. Sonare, J. H. Dewan, S. D. Thepade, V. Dadape, T. Gadge and A. Gavali, "Detecting Sarcasm in Reddit Comments: A Comparative Analysis," 2023 4th International Conference for Emerging Technology (INCET), Belgaum, India, 2023, pp. 1-6, doi: 10.1109/INCET57972.2023.10170613.
|
autoevaluate/autoeval-eval-squad-plain_text-07b8d6-1707959801 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: 21iridescent/distilroberta-base-finetuned-squad2-lwt
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: 21iridescent/distilroberta-base-finetuned-squad2-lwt
* Dataset: squad
* Config: plain_text
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@crazymageqi@gmail.com](https://huggingface.co/crazymageqi@gmail.com) for evaluating this model. |
mfidabel/sam-coyo-2.5k | ---
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2299967269.632
num_examples: 2736
download_size: 2357202624
dataset_size: 2299967269.632
---
# Dataset Card for "sam-coyo-2.5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ccccrrrr/github-issues | ---
dataset_info:
features:
- name: url
dtype: string
- name: repository_url
dtype: string
- name: labels_url
dtype: string
- name: comments_url
dtype: string
- name: events_url
dtype: string
- name: html_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
dtype: string
- name: user
struct:
- name: login
dtype: string
- name: id
dtype: int64
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dtype: string
- name: gravatar_id
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
- name: site_admin
dtype: bool
- name: labels
list:
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dtype: int64
- name: node_id
dtype: string
- name: url
dtype: string
- name: name
dtype: string
- name: color
dtype: string
- name: default
dtype: bool
- name: description
dtype: string
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dtype: string
- name: locked
dtype: bool
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struct:
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dtype: string
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dtype: int64
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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- name: assignees
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dtype: int64
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dtype: string
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- name: milestone
struct:
- name: url
dtype: string
- name: html_url
dtype: string
- name: labels_url
dtype: string
- name: id
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sequence: string
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- name: closed_at
dtype: timestamp[s]
- name: author_association
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- name: active_lock_reason
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- name: draft
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- name: pull_request
struct:
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- name: html_url
dtype: string
- name: diff_url
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- name: body
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- name: reactions
struct:
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- name: total_count
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- name: '+1'
dtype: int64
- name: '-1'
dtype: int64
- name: laugh
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- name: hooray
dtype: int64
- name: confused
dtype: int64
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dtype: int64
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dtype: int64
- name: eyes
dtype: int64
- name: timeline_url
dtype: string
- name: performed_via_github_app
dtype: 'null'
- name: state_reason
dtype: string
- name: is_pull_request
dtype: bool
splits:
- name: train
num_bytes: 20688393
num_examples: 2500
download_size: 6077452
dataset_size: 20688393
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
peldrak/coastal2 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 1098506769.894
num_examples: 6594
- name: test
num_bytes: 173113819.0
num_examples: 827
download_size: 1414219519
dataset_size: 1271620588.894
---
# Dataset Card for "coastal2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
anonym-repos/Calc-ape210k_selftrain_experiment_negative | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: question_chinese
dtype: string
- name: chain
dtype: string
- name: result
dtype: string
- name: result_float
dtype: float64
- name: equation
dtype: string
- name: model_checkpoint
dtype: string
- name: prediction
dtype: string
splits:
- name: train
num_bytes: 43185012
num_examples: 48194
download_size: 12438720
dataset_size: 43185012
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Calc-ape210k_selftrain_experiment_prompted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
fahamu/ioi | ---
license: mit
---
# Dataset Release: Indirect Object Identification
`mecha_ioi` is a pair of datasets tailored for the Indirect Object Identification task, where sentences are generated from the following set of templates:
- BABA
```
baba_templates = [
"Then, {B} and {A} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {B} and {A} had a lot of fun at the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {B} and {A} were working at the {PLACE}. {B} decided to give a {OBJECT} to {A}",
"Then, {B} and {A} were thinking about going to the {PLACE}. {B} wanted to give a {OBJECT} to {A}",
"Then, {B} and {A} had a long argument, and afterwards {B} said to {A}",
"After {B} and {A} went to the {PLACE}, {B} gave a {OBJECT} to {A}",
"When {B} and {A} got a {OBJECT} at the {PLACE}, {B} decided to give it to {A}",
"When {B} and {A} got a {OBJECT} at the {PLACE}, {B} decided to give the {OBJECT} to {A}",
"While {B} and {A} were working at the {PLACE}, {B} gave a {OBJECT} to {A}",
"While {B} and {A} were commuting to the {PLACE}, {B} gave a {OBJECT} to {A}",
"After the lunch, {B} and {A} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Afterwards, {B} and {A} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {B} and {A} had a long argument. Afterwards {B} said to {A}",
"The {PLACE} {B} and {A} went to had a {OBJECT}. {B} gave it to {A}",
"Friends {B} and {A} found a {OBJECT} at the {PLACE}. {B} gave it to {A}",
]
```
- ABBA
```
abba_templates = [
"Then, {A} and {B} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {A} and {B} had a lot of fun at the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {A} and {B} were working at the {PLACE}. {B} decided to give a {OBJECT} to {A}",
"Then, {A} and {B} were thinking about going to the {PLACE}. {B} wanted to give a {OBJECT} to {A}",
"Then, {A} and {B} had a long argument, and afterwards {B} said to {A}",
"After {A} and {B} went to the {PLACE}, {B} gave a {OBJECT} to {A}",
"When {A} and {B} got a {OBJECT} at the {PLACE}, {B} decided to give it to {A}",
"When {A} and {B} got a {OBJECT} at the {PLACE}, {B} decided to give the {OBJECT} to {A}",
"While {A} and {B} were working at the {PLACE}, {B} gave a {OBJECT} to {A}",
"While {A} and {B} were commuting to the {PLACE}, {B} gave a {OBJECT} to {A}",
"After the lunch, {A} and {B} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Afterwards, {A} and {B} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {A} and {B} had a long argument. Afterwards {B} said to {A}",
"The {PLACE} {A} and {B} went to had a {OBJECT}. {B} gave it to {A}",
"Friends {A} and {B} found a {OBJECT} at the {PLACE}. {B} gave it to {A}",
]
```
The purpose of this dataset is to facilitate interpretability research, inspired by the paper
_Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small_, from Redwood Research. We are not affiliated with Redwood Research,
and release this dataset to contribute to the collective research effort behind understanding how Transformer language models perform this task.
### BibTex
```
@misc {fahamu_2022,
author = { {Brian Muhia} },
title = { ioi (Revision 223da8b) },
year = 2022,
url = { https://huggingface.co/datasets/fahamu/ioi },
doi = { 10.57967/hf/0142 },
publisher = { Hugging Face }
}
``` |
ShoukanLabs/OpenNiji-170001_205000 | ---
dataset_info:
features:
- name: image
dtype: image
- name: url
dtype: string
- name: prompt
dtype: string
- name: style
dtype: string
splits:
- name: train
num_bytes: 58707875109.132
num_examples: 34996
download_size: 54716614668
dataset_size: 58707875109.132
---
# Dataset Card for "OpenNiji-170001_205000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MicPie/unpredictable_cluster13 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cluster13
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-cluster13" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
liuyanchen1015/MULTI_VALUE_sst2_analytic_superlative | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 5886
num_examples: 37
- name: test
num_bytes: 11294
num_examples: 78
- name: train
num_bytes: 170624
num_examples: 1695
download_size: 86561
dataset_size: 187804
---
# Dataset Card for "MULTI_VALUE_sst2_analytic_superlative"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
huggingface/autotrain-data-autotrain-g8rnq-42bx0-1 | Invalid username or password. |
kevin50jiang/bank-churn-synthetic | ---
license: cc-by-sa-4.0
---
Collated dataset for LLM training on the dataset for https://www.kaggle.com/competitions/playground-series-s4e1/data |
liuyanchen1015/MULTI_VALUE_sst2_possessives_belong | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 49105
num_examples: 306
- name: test
num_bytes: 96799
num_examples: 604
- name: train
num_bytes: 1413836
num_examples: 11532
download_size: 890156
dataset_size: 1559740
---
# Dataset Card for "MULTI_VALUE_sst2_possessives_belong"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
automated-research-group/llama2_7b_chat-hellaswag_0_label | ---
dataset_info:
features:
- name: id
dtype: string
- name: request
dtype: string
- name: response
dtype: string
- name: input_perplexity
dtype: float64
- name: input_likelihood
dtype: float64
- name: output_perplexity
dtype: float64
- name: output_likelihood
dtype: float64
splits:
- name: validation
num_bytes: 11385236
num_examples: 10042
download_size: 5370207
dataset_size: 11385236
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
|
izou3/Test_MaskFormer | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 31994532.174
num_examples: 1647
- name: validation
num_bytes: 3068731.0
num_examples: 158
download_size: 31916319
dataset_size: 35063263.173999995
---
# Dataset Card for "Test_MaskFormer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
openlifescienceai/mmlu_college_biology | ---
dataset_info:
features:
- name: subject_name
dtype: string
- name: data
struct:
- name: Correct Answer
dtype: string
- name: Correct Option
dtype: string
- name: Options
struct:
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: Question
dtype: string
- name: id
dtype: string
splits:
- name: test
num_bytes: 62958
num_examples: 144
- name: validation
num_bytes: 6295
num_examples: 16
- name: dev
num_bytes: 1948
num_examples: 5
download_size: 68830
dataset_size: 71201
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: validation
path: data/validation-*
- split: dev
path: data/dev-*
---
|
alkzzz/palui | ---
license: cc-by-4.0
---
|
TinyPixel/elm-2 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2577268
num_examples: 1073
download_size: 1393304
dataset_size: 2577268
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "elm-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Ramos-Ramos/nllb-eng-tgl-12k | ---
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- eng_Latn
- tgl_Latn
- name: laser_score
dtype: float32
- name: source_sentence_lid
dtype: float32
- name: target_sentence_lid
dtype: float32
- name: source_sentence_source
dtype: string
- name: source_sentence_url
dtype: string
- name: target_sentence_source
dtype: string
- name: target_sentence_url
dtype: string
splits:
- name: train
num_bytes: 5795415
num_examples: 12000
download_size: 2811921
dataset_size: 5795415
---
# Dataset Card for "nllb-eng-tgl-12k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
valurank/PoliticalBias | ---
license:
- other
language:
- en
multilinguality:
- monolingual
task_categories:
- classification
task_ids:
- classification
---
# Dataset Card for PoliticalBias
## Table of Contents
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Source Data](#source-data)
## Dataset Description
roughly 8200 articles written by the website’s editors, each article covering one topic with 3 links that describe the same piece of news from different angles (usually one from the right, one from the left, and one from the center)
## Languages
The text in the dataset is in English
## Dataset Structure
The dataset consists of four columns namely Left, Right, Center, and Main URL
## Source Data
The dataset is scrapped from http://allsides.com/
|
DigirentEnterprise/Translate_all_mixed_dataset | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input
dtype: string
- name: ouput
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 1543490120
num_examples: 3370045
download_size: 950032312
dataset_size: 1543490120
---
# Dataset Card for "Translate_all_mixed_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Rexhaif/laion-2b-en-very-unsafe | ---
dataset_info:
features:
- name: URL
dtype: string
- name: TEXT
dtype: string
- name: WIDTH
dtype: int32
- name: HEIGHT
dtype: int32
- name: similarity
dtype: float64
- name: hash
dtype: int64
- name: punsafe
dtype: float32
- name: pwatermark
dtype: float32
splits:
- name: train
num_bytes: 6799407448
num_examples: 34607134
download_size: 5322013902
dataset_size: 6799407448
---
# Dataset Card for "laion-2b-en-very-unsafe"
A version of laion5b dataset(en subset) with strictly `unsafe` images.
Dataset was filtered to retain only examples with `punsafe` present and > 0.9.
However, due to the way nsfw detector was train, there is a significant amount of false postives.
There is, likely, more false positives than real unsafe images. |
andrinho1010/coringa | ---
license: openrail
---
|
BioBlast3r/Train-01-Maxx | ---
license: unknown
---
|
ravel365artur/teste | ---
license: openrail
---
|
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