datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
distilled-one-sec-cv12-each-chunk-uniq/chunk_185 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 748748536.0
num_examples: 145898
download_size: 766085530
dataset_size: 748748536.0
---
# Dataset Card for "chunk_185"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ziozzang/deepl-trans-ID-KO | ---
task_categories:
- translation
language:
- ko
- id
---
This dataset is some wikipedia article with DeepL translation, auto-aggregated.
# String/Corpus pairs
From ID/Indonesian to KO/Korean.
# Quality Filtering
- Stripping whole HTML tags.
- removed references and annotation mark.
- Filtered by string length.
---
The strings/corpus are aggregated from wikipedia(pt) using DeepL translated.
whole data collected by Jioh L. Jung<ziozzang@gmail.com>
license: mit
--- |
chronbmm/sanskrit-stemming-tagging-pali-long | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: unsandhied
dtype: string
splits:
- name: train
num_bytes: 525248615
num_examples: 1655728
- name: validation
num_bytes: 1858678
num_examples: 3051
- name: test
num_bytes: 1924834
num_examples: 3137
- name: test_long_500
num_bytes: 302454
num_examples: 500
- name: validation_long_500
num_bytes: 311042
num_examples: 500
download_size: 184746128
dataset_size: 529645623
---
# Dataset Card for "sanskrit-stemming-tagging-pali-long"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/be64ca95 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 182
num_examples: 10
download_size: 1331
dataset_size: 182
---
# Dataset Card for "be64ca95"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
KEEPYs/titou | ---
license: openrail
---
|
irds/wikiclir_fr | ---
pretty_name: '`wikiclir/fr`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `wikiclir/fr`
The `wikiclir/fr` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/wikiclir#wikiclir/fr).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=1,894,397
- `queries` (i.e., topics); count=1,089,179
- `qrels`: (relevance assessments); count=5,137,366
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/wikiclir_fr', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'text': ...}
queries = load_dataset('irds/wikiclir_fr', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/wikiclir_fr', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{sasaki-etal-2018-cross,
title = "Cross-Lingual Learning-to-Rank with Shared Representations",
author = "Sasaki, Shota and
Sun, Shuo and
Schamoni, Shigehiko and
Duh, Kevin and
Inui, Kentaro",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2073",
doi = "10.18653/v1/N18-2073",
pages = "458--463"
}
```
|
manucm/1 | ---
license: other
---
|
twodgirl/kimiko_v3 | ---
language:
- en
---
All conversations are made up by Mistral 7B. The indices come from the [Kimiko dataset](https://huggingface.co/datasets/Chat-Error/Kimiko_v3-v0.1). |
2HW/llama2_dobe_01 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3338910
num_examples: 6440
download_size: 752557
dataset_size: 3338910
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AdapterOcean/med_alpaca_standardized_cluster_50_std | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 7133138
num_examples: 17928
download_size: 2600516
dataset_size: 7133138
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_50_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
adityarra07/test_ds_noise2 | ---
dataset_info:
features:
- name: audio
struct:
- name: array
sequence: float32
- name: path
dtype: 'null'
- name: sampling_rate
dtype: int64
- name: transcription
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 228091121.30052426
num_examples: 1000
download_size: 230405299
dataset_size: 228091121.30052426
---
# Dataset Card for "test_ds_noise2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Against61/output_test | ---
license: other
---
|
autoevaluate/autoeval-staging-eval-project-03e83e3b-2528-4e84-b075-34edd28549da-5755 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: autoevaluate/natural-language-inference
metrics: []
dataset_name: glue
dataset_config: mrpc
dataset_split: validation
col_mapping:
text1: sentence1
text2: sentence2
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: autoevaluate/natural-language-inference
* Dataset: glue
* Config: mrpc
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
huggingartists/oxxxymiron | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/oxxxymiron"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 2.070318 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/57ecbbdaf70c671be2d8b7bd39112db0.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/oxxxymiron">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Oxxxymiron</div>
<a href="https://genius.com/artists/oxxxymiron">
<div style="text-align: center; font-size: 14px;">@oxxxymiron</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/oxxxymiron).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/oxxxymiron")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|210| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/oxxxymiron")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
NightMachinery/ImageNet1K-val-indexed | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
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- name: id
dtype: int64
splits:
- name: train
num_bytes: 6633504145.375
num_examples: 49101
download_size: 6622641479
dataset_size: 6633504145.375
---
# Dataset Card for "ImageNet1K-val-indexed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
cahya/instructions-es | ---
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 10931526.154626371
num_examples: 22947
- name: test
num_bytes: 287734.42268681433
num_examples: 604
- name: validation
num_bytes: 287734.42268681433
num_examples: 604
download_size: 6659541
dataset_size: 11506995.0
---
# Dataset Card for "instructions-es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ohhhchank3/tlcn | ---
size_categories:
- 10K<n<100K
--- |
and-effect/MDK_taxonomy | ---
language:
- de
license: cc0-1.0
size_categories:
- n<1K
---
# Dataset Card for MDK_taxonomy
## Dataset Description / Summary
This dataset was created as part of the [Bertelsmann Foundation](https://www.bertelsmann-stiftung.de/de/startseite)
[Musterdatenkatalog]("https://www.bertelsmann-stiftung.de/de/unsere-projekte/smart-country/musterdatenkatalog") project. See the project on GitHub [here](https://github.com/bertelsmannstift/Musterdatenkatalog-V4).
The MDK provides an overview of Open Data in municipalities in Germany.
This data contains the taxonomy created by and-effect as part of the project.
The taxonomy adheres to the SKOS standard and is available as RDF and JSON-LD.
There are two levels to the taxonomy: 'Thema' (Topic, first level) and 'Bezeichnung' (Label, second level).
The taxonomy contains 25 elements on the first level and 241 elements on the second level.
### Languages
German, some information translated to English
## Dataset Structure
### Data Fields
Contains information for each concept such as 'skos:prefLabel' (in german and english), 'skos:definition' in german and optionally matches to other concepts.
## Dataset Creation
The RDF and JSON-LD file are created with the help of '2023-05-17_MDK_taxonomy_info.csv' which contains all the information about the taxonomy and its concepts.
## Additional Information
### Licensing Information
CC0
### Contributions
The taxonomy was based on previous work by the Bertelsmann Stiftung and together with and-effect revised. |
noobmaster29/Verified-Camel-zh | ---
license: apache-2.0
task_categories:
- conversational
- question-answering
- text-generation
language:
- en
- zh
tags:
- Physics
- Chemistry
- Math
- Biology
- Culture
- Logic
size_categories:
- n<1K
---
This is a direct Chinese translation using GPT4 of the Verified-Camel dataset. I hope you find it useful.
https://huggingface.co/datasets/LDJnr/Verified-Camel
Citation:
```
@article{daniele2023amplify-instruct,
title={Amplify-Instruct: Synthetically Generated Diverse Multi-turn Conversations for Effecient LLM Training.},
author={Daniele, Luigi and Suphavadeeprasit},
journal={arXiv preprint arXiv:(comming soon)},
year={2023}
}
``` |
ctang/gpt_util_eval_llama2 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response_a
dtype: string
- name: response_b
dtype: string
- name: more_pleasant
dtype: string
splits:
- name: train
num_bytes: 1653
num_examples: 10
download_size: 3774
dataset_size: 1653
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
yuchen0187/pcmask | ---
dataset_info:
features:
- name: xyz
sequence:
sequence: float32
- name: rgb
sequence:
sequence: uint8
- name: mask
sequence:
sequence: bool
splits:
- name: test
num_bytes: 1369365892
num_examples: 5348
- name: partnet
num_bytes: 3850276066
num_examples: 15383
- name: shapenet
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num_examples: 20860
- name: fusion360
num_bytes: 8408989552
num_examples: 35858
- name: scanobjectnn
num_bytes: 982688978
num_examples: 2902
- name: scannet_scale_0
num_bytes: 372642986
num_examples: 1513
- name: scannet_scale_1
num_bytes: 1454217230
num_examples: 6052
- name: partnet_mobility
num_bytes: 635042984
num_examples: 2290
- name: partnet_mobility_aug1
num_bytes: 634966490
num_examples: 2290
- name: shapenet_new
num_bytes: 4958407478
num_examples: 19735
- name: scannet_new
num_bytes: 324334058
num_examples: 1201
download_size: 16313033591
dataset_size: 27950055826
configs:
- config_name: default
data_files:
- split: partnet
path: data/partnet-*
- split: shapenet
path: data/shapenet-*
- split: test
path: data/test-*
- split: fusion360
path: data/fusion360-*
- split: scanobjectnn
path: data/scanobjectnn-*
- split: scannet_scale_0
path: data/scannet_scale_0-*
- split: scannet_scale_1
path: data/scannet_scale_1-*
- split: partnet_mobility
path: data/partnet_mobility-*
- split: partnet_mobility_aug1
path: data/partnet_mobility_aug1-*
- split: shapenet_new
path: data/shapenet_new-*
- split: scannet_new
path: data/scannet_new-*
---
|
AISE-TUDelft/PY150k | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: index
dtype: int64
- name: input
dtype: string
- name: gt
dtype: string
- name: hash
dtype: int64
- name: full_line
dtype: string
splits:
- name: train
num_bytes: 662931126
num_examples: 95000
- name: dev
num_bytes: 41218084
num_examples: 5000
- name: test
num_bytes: 343336086
num_examples: 50000
download_size: 277005224
dataset_size: 1047485296
license: cc0-1.0
tags:
- code
pretty_name: PY150 Line Completion Dataset
size_categories:
- 100K<n<1M
---
# Dataset Card for "PY150k"
## Dataset Summary
Code Completion dataset created from the code available in [CodeXGlue](https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/CodeCompletion-line).
|
thercyl/HD | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: float64
- name: Ticker
dtype: string
- name: Year
dtype: string
- name: Text
dtype: string
- name: Embedding
dtype: string
splits:
- name: train
num_bytes: 57668542
num_examples: 1656
download_size: 33638115
dataset_size: 57668542
---
# Dataset Card for "HD"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pharaouk/cortex_beta | ---
dataset_info:
features:
- name: prompts
dtype: string
- name: responses
dtype: string
splits:
- name: train
num_bytes: 23398376
num_examples: 9923
download_size: 11618983
dataset_size: 23398376
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
VedCodes/esy_shr | ---
task_categories:
- text-generation
language:
- en
tags:
- medical
pretty_name: boyyyzz
size_categories:
- n<1K
--- |
minimario/apps_partial_sorted_0_200 | ---
dataset_info:
features:
- name: problem
dtype: string
- name: code
dtype: string
- name: label
dtype: int64
- name: full_sample
dtype: string
- name: where_from
dtype: string
splits:
- name: train
num_bytes: 100164623
num_examples: 80215
download_size: 3452557
dataset_size: 100164623
---
# Dataset Card for "apps_partial_sorted_0_200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/mmlu-high_school_computer_science-neg-answer | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: neg_answer
dtype: string
splits:
- name: test
num_bytes: 50115
num_examples: 100
download_size: 31373
dataset_size: 50115
---
# Dataset Card for "mmlu-high_school_computer_science-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kgr123/quality_counter_500_4_simple | ---
dataset_info:
features:
- name: context
dtype: string
- name: word
dtype: string
- name: claim
dtype: string
- name: label
dtype: int64
splits:
- name: test
num_bytes: 2727654
num_examples: 1747
- name: train
num_bytes: 2728865
num_examples: 1777
- name: validation
num_bytes: 2826634
num_examples: 1807
download_size: 2047104
dataset_size: 8283153
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
harpreetsahota/vectordb_trend_analysis | ---
dataset_info:
features:
- name: provider
dtype: string
- name: package_name
dtype: string
- name: 'last_day_downloads (Reference Date: 2024-04-07)'
dtype: int64
- name: last_week_downloads
dtype: int64
- name: last_month_downloads
dtype: int64
- name: last_180days_downloads
dtype: int64
- name: total_downloads (pepy.tech)
dtype: int64
splits:
- name: train
num_bytes: 1990
num_examples: 26
download_size: 5660
dataset_size: 1990
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
neil-code/autotrain-data-summarization | ---
language:
- en
task_categories:
- summarization
---
# AutoTrain Dataset for project: summarization
## Dataset Description
This dataset has been automatically processed by AutoTrain for project summarization.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"feat_id": "train_0",
"text": "#Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. Why are you here today?\n#Person2#: I found it would be a good idea to get a check-up.\n#Person1#: Yes, well, you haven't had one for 5 years. You should have one every year.\n#Person2#: I know. I figure as long as there is nothing wrong, why go see the doctor?\n#Person1#: Well, the best way to avoid serious illnesses is to find out about them early. So try to come at least once a year for your own good.\n#Person2#: Ok.\n#Person1#: Let me see here. Your eyes and ears look fine. Take a deep breath, please. Do you smoke, Mr. Smith?\n#Person2#: Yes.\n#Person1#: Smoking is the leading cause of lung cancer and heart disease, you know. You really should quit.\n#Person2#: I've tried hundreds of times, but I just can't seem to kick the habit.\n#Person1#: Well, we have classes and some medications that might help. I'll give you more information before you leave.\n#Person2#: Ok, thanks doctor.",
"target": "Mr. Smith's getting a check-up, and Doctor Hawkins advises him to have one every year. Hawkins'll give some information about their classes and medications to help Mr. Smith quit smoking.",
"feat_topic": "get a check-up"
},
{
"feat_id": "train_1",
"text": "#Person1#: Hello Mrs. Parker, how have you been?\n#Person2#: Hello Dr. Peters. Just fine thank you. Ricky and I are here for his vaccines.\n#Person1#: Very well. Let's see, according to his vaccination record, Ricky has received his Polio, Tetanus and Hepatitis B shots. He is 14 months old, so he is due for Hepatitis A, Chickenpox and Measles shots.\n#Person2#: What about Rubella and Mumps?\n#Person1#: Well, I can only give him these for now, and after a couple of weeks I can administer the rest.\n#Person2#: OK, great. Doctor, I think I also may need a Tetanus booster. Last time I got it was maybe fifteen years ago!\n#Person1#: We will check our records and I'll have the nurse administer and the booster as well. Now, please hold Ricky's arm tight, this may sting a little.",
"target": "Mrs Parker takes Ricky for his vaccines. Dr. Peters checks the record and then gives Ricky a vaccine.",
"feat_topic": "vaccines"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"feat_id": "Value(dtype='string', id=None)",
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)",
"feat_topic": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1999 |
| valid | 499 |
|
arattinger/noto-emoji-captions | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 77868555.5
num_examples: 3468
download_size: 77424588
dataset_size: 77868555.5
annotations_creators:
- machine-generated
language:
- en
multilinguality:
- monolingual
pretty_name: 'Pokémon BLIP captions'
---
# Dataset Card for Noto Emoji Captions
BLIP generated captions for Noto emojis.
The dataset was captioned with the [pre-trained BLIP model](https://github.com/salesforce/BLIP).
It contains a list of ´image´ and ´text´ keys with the images being 512x512. |
myradeng/diffusion_db_5k_val_v1 | ---
dataset_info:
features:
- name: image
dtype: image
- name: prompt
dtype: string
- name: seed
dtype: uint32
- name: step
dtype: uint16
- name: cfg
dtype: float32
- name: sampler
dtype: string
- name: width
dtype: uint16
- name: height
dtype: uint16
- name: user_name
dtype: string
- name: timestamp
dtype: timestamp[us, tz=UTC]
- name: image_nsfw
dtype: float32
- name: prompt_nsfw
dtype: float32
splits:
- name: train
num_bytes: 519559992.6
num_examples: 1000
download_size: 519441334
dataset_size: 519559992.6
---
# Dataset Card for "diffusion_db_5k_val_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vwxyzjn/openhermes-dev__mistralai_Mistral-7B-Instruct-v0.1__1707331096 | ---
dataset_info:
features:
- name: model
dtype: 'null'
- name: category
dtype: string
- name: language
dtype: string
- name: custom_instruction
dtype: bool
- name: id
dtype: string
- name: topic
dtype: string
- name: avatarUrl
dtype: 'null'
- name: idx
dtype: 'null'
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: weight
dtype: 'null'
- name: system_prompt
dtype: string
- name: source
dtype: string
- name: model_name
dtype: string
- name: skip_prompt_formatting
dtype: bool
- name: title
dtype: string
- name: hash
dtype: 'null'
- name: views
dtype: 'null'
- name: prompt
dtype: string
- name: token_length
dtype: int64
- name: candidate0
list:
- name: content
dtype: string
- name: role
dtype: string
- name: candidate1
list:
- name: content
dtype: string
- name: role
dtype: string
- name: candidate0_policy
dtype: string
- name: candidate1_policy
dtype: string
- name: candidate0_score
dtype: float64
- name: candidate1_score
dtype: float64
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: chosen_policy
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected_policy
dtype: string
splits:
- name: train_prefs
num_bytes: 1204323.2874251497
num_examples: 87
download_size: 606169
dataset_size: 1204323.2874251497
configs:
- config_name: default
data_files:
- split: train_prefs
path: data/train_prefs-*
---
|
DLIlab/webarena_paraphrased_instruction_web_web | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: generated_task
sequence: string
- name: target_web_name
dtype: string
- name: given_task
dtype: string
- name: annotation_id
dtype: string
splits:
- name: train
num_bytes: 104975
num_examples: 73
download_size: 51033
dataset_size: 104975
---
In this case, given_tasks are made through the following steps.
1. make vectorDB with webname and webdescription in mind2Web.
2. for each website in WebArena, retrieve relevant websites in mind2web, top_4, with a threshold 0.36(L2 distance)
3. every instructions in retrieved websites(in mind2web) are used as give_task.
---
dataset_info:
features:
- name: annotation_id
dtype: string
- name: given_task
dtype: string
- name: target_web_name
dtype: string
- name: generated_task
sequence: string
splits:
- name: train
num_bytes: 115053
num_examples: 146
download_size: 51880
dataset_size: 115053
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_malhajar__Platypus2-70B-instruct-4bit-gptq | ---
pretty_name: Evaluation run of malhajar/Platypus2-70B-instruct-4bit-gptq
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [malhajar/Platypus2-70B-instruct-4bit-gptq](https://huggingface.co/malhajar/Platypus2-70B-instruct-4bit-gptq)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the 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_malhajar__Platypus2-70B-instruct-4bit-gptq\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-08-26T12:30:11.519673](https://huggingface.co/datasets/open-llm-leaderboard/details_malhajar__Platypus2-70B-instruct-4bit-gptq/blob/main/results_2023-08-26T12%3A30%3A11.519673.json)\
\ (note that their might be results for other tasks in the repos if successive evals\
\ didn't cover the same tasks. You find each in the results and the \"latest\" split\
\ for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.23568332946534118,\n\
\ \"acc_stderr\": 0.030875990616634128,\n \"acc_norm\": 0.23665349264658486,\n\
\ \"acc_norm_stderr\": 0.030890666475037305,\n \"mc1\": 0.2460220318237454,\n\
\ \"mc1_stderr\": 0.015077219200662574,\n \"mc2\": 0.4955854635237609,\n\
\ \"mc2_stderr\": 0.01695340721579618\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.2363481228668942,\n \"acc_stderr\": 0.012414960524301829,\n\
\ \"acc_norm\": 0.2901023890784983,\n \"acc_norm_stderr\": 0.01326157367752077\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2560246962756423,\n\
\ \"acc_stderr\": 0.004355436696716298,\n \"acc_norm\": 0.25951005775741887,\n\
\ \"acc_norm_stderr\": 0.0043746991892848605\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \
\ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.18518518518518517,\n\
\ \"acc_stderr\": 0.03355677216313142,\n \"acc_norm\": 0.18518518518518517,\n\
\ \"acc_norm_stderr\": 0.03355677216313142\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.21710526315789475,\n \"acc_stderr\": 0.033550453048829226,\n\
\ \"acc_norm\": 0.21710526315789475,\n \"acc_norm_stderr\": 0.033550453048829226\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.29,\n\
\ \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \
\ \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.21132075471698114,\n \"acc_stderr\": 0.025125766484827845,\n\
\ \"acc_norm\": 0.21132075471698114,\n \"acc_norm_stderr\": 0.025125766484827845\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.24305555555555555,\n\
\ \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.24305555555555555,\n\
\ \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \
\ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n\
\ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.25,\n\
\ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.21965317919075145,\n\
\ \"acc_stderr\": 0.031568093627031744,\n \"acc_norm\": 0.21965317919075145,\n\
\ \"acc_norm_stderr\": 0.031568093627031744\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808778,\n\
\ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808778\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n\
\ \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n\
\ \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2719298245614035,\n\
\ \"acc_stderr\": 0.041857744240220575,\n \"acc_norm\": 0.2719298245614035,\n\
\ \"acc_norm_stderr\": 0.041857744240220575\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2482758620689655,\n \"acc_stderr\": 0.036001056927277716,\n\
\ \"acc_norm\": 0.2482758620689655,\n \"acc_norm_stderr\": 0.036001056927277716\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.23809523809523808,\n \"acc_stderr\": 0.021935878081184763,\n \"\
acc_norm\": 0.23809523809523808,\n \"acc_norm_stderr\": 0.021935878081184763\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.19047619047619047,\n\
\ \"acc_stderr\": 0.035122074123020534,\n \"acc_norm\": 0.19047619047619047,\n\
\ \"acc_norm_stderr\": 0.035122074123020534\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \
\ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.19032258064516128,\n \"acc_stderr\": 0.022331707611823088,\n \"\
acc_norm\": 0.19032258064516128,\n \"acc_norm_stderr\": 0.022331707611823088\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.18226600985221675,\n \"acc_stderr\": 0.02716334085964515,\n \"\
acc_norm\": 0.18226600985221675,\n \"acc_norm_stderr\": 0.02716334085964515\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421255,\n \"acc_norm\"\
: 0.28,\n \"acc_norm_stderr\": 0.045126085985421255\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"\
acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.20207253886010362,\n \"acc_stderr\": 0.02897908979429673,\n\
\ \"acc_norm\": 0.20207253886010362,\n \"acc_norm_stderr\": 0.02897908979429673\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.2,\n \"acc_stderr\": 0.020280805062535722,\n \"acc_norm\"\
: 0.2,\n \"acc_norm_stderr\": 0.020280805062535722\n },\n \"harness|hendrycksTest-high_school_mathematics|5\"\
: {\n \"acc\": 0.24444444444444444,\n \"acc_stderr\": 0.02620276653465215,\n\
\ \"acc_norm\": 0.24444444444444444,\n \"acc_norm_stderr\": 0.02620276653465215\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n\
\ \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.1986754966887417,\n \"acc_stderr\": 0.032578473844367774,\n \"\
acc_norm\": 0.1986754966887417,\n \"acc_norm_stderr\": 0.032578473844367774\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.1981651376146789,\n \"acc_stderr\": 0.017090573804217878,\n \"\
acc_norm\": 0.1981651376146789,\n \"acc_norm_stderr\": 0.017090573804217878\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.12037037037037036,\n \"acc_stderr\": 0.02219169594400172,\n \"\
acc_norm\": 0.12037037037037036,\n \"acc_norm_stderr\": 0.02219169594400172\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\
\ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.2742616033755274,\n \"acc_stderr\": 0.02904133351059804,\n\
\ \"acc_norm\": 0.2742616033755274,\n \"acc_norm_stderr\": 0.02904133351059804\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.31390134529147984,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\
\ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.2644628099173554,\n \"acc_stderr\": 0.04026187527591207,\n \"\
acc_norm\": 0.2644628099173554,\n \"acc_norm_stderr\": 0.04026187527591207\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n\
\ \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n\
\ \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.2331288343558282,\n \"acc_stderr\": 0.03322015795776741,\n\
\ \"acc_norm\": 0.2331288343558282,\n \"acc_norm_stderr\": 0.03322015795776741\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.33035714285714285,\n\
\ \"acc_stderr\": 0.04464285714285712,\n \"acc_norm\": 0.33035714285714285,\n\
\ \"acc_norm_stderr\": 0.04464285714285712\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\
\ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2905982905982906,\n\
\ \"acc_stderr\": 0.02974504857267404,\n \"acc_norm\": 0.2905982905982906,\n\
\ \"acc_norm_stderr\": 0.02974504857267404\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2388250319284802,\n\
\ \"acc_stderr\": 0.015246803197398691,\n \"acc_norm\": 0.2388250319284802,\n\
\ \"acc_norm_stderr\": 0.015246803197398691\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.2543352601156069,\n \"acc_stderr\": 0.023445826276545546,\n\
\ \"acc_norm\": 0.2543352601156069,\n \"acc_norm_stderr\": 0.023445826276545546\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24916201117318434,\n\
\ \"acc_stderr\": 0.014465893829859923,\n \"acc_norm\": 0.24916201117318434,\n\
\ \"acc_norm_stderr\": 0.014465893829859923\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.23202614379084968,\n \"acc_stderr\": 0.02417084087934101,\n\
\ \"acc_norm\": 0.23202614379084968,\n \"acc_norm_stderr\": 0.02417084087934101\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.1864951768488746,\n\
\ \"acc_stderr\": 0.02212243977248077,\n \"acc_norm\": 0.1864951768488746,\n\
\ \"acc_norm_stderr\": 0.02212243977248077\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.2654320987654321,\n \"acc_stderr\": 0.024569223600460845,\n\
\ \"acc_norm\": 0.2654320987654321,\n \"acc_norm_stderr\": 0.024569223600460845\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.20921985815602837,\n \"acc_stderr\": 0.02426476943998847,\n \
\ \"acc_norm\": 0.20921985815602837,\n \"acc_norm_stderr\": 0.02426476943998847\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n\
\ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\
\ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.16911764705882354,\n \"acc_stderr\": 0.022770868010112997,\n\
\ \"acc_norm\": 0.16911764705882354,\n \"acc_norm_stderr\": 0.022770868010112997\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2565359477124183,\n \"acc_stderr\": 0.017667841612378984,\n \
\ \"acc_norm\": 0.2565359477124183,\n \"acc_norm_stderr\": 0.017667841612378984\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.20909090909090908,\n\
\ \"acc_stderr\": 0.03895091015724136,\n \"acc_norm\": 0.20909090909090908,\n\
\ \"acc_norm_stderr\": 0.03895091015724136\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.20816326530612245,\n \"acc_stderr\": 0.025991117672813292,\n\
\ \"acc_norm\": 0.20816326530612245,\n \"acc_norm_stderr\": 0.025991117672813292\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.24875621890547264,\n\
\ \"acc_stderr\": 0.030567675938916707,\n \"acc_norm\": 0.24875621890547264,\n\
\ \"acc_norm_stderr\": 0.030567675938916707\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.28313253012048195,\n\
\ \"acc_stderr\": 0.03507295431370518,\n \"acc_norm\": 0.28313253012048195,\n\
\ \"acc_norm_stderr\": 0.03507295431370518\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.3216374269005848,\n \"acc_stderr\": 0.03582529442573122,\n\
\ \"acc_norm\": 0.3216374269005848,\n \"acc_norm_stderr\": 0.03582529442573122\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2460220318237454,\n\
\ \"mc1_stderr\": 0.015077219200662574,\n \"mc2\": 0.4955854635237609,\n\
\ \"mc2_stderr\": 0.01695340721579618\n }\n}\n```"
repo_url: https://huggingface.co/malhajar/Platypus2-70B-instruct-4bit-gptq
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|arc:challenge|25_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hellaswag|10_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T12:30:11.519673.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T12:30:11.519673.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-26T12:30:11.519673.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-26T12:30:11.519673.parquet'
- config_name: results
data_files:
- split: 2023_08_26T12_30_11.519673
path:
- results_2023-08-26T12:30:11.519673.parquet
- split: latest
path:
- results_2023-08-26T12:30:11.519673.parquet
---
# Dataset Card for Evaluation run of malhajar/Platypus2-70B-instruct-4bit-gptq
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/malhajar/Platypus2-70B-instruct-4bit-gptq
- **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 [malhajar/Platypus2-70B-instruct-4bit-gptq](https://huggingface.co/malhajar/Platypus2-70B-instruct-4bit-gptq) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_malhajar__Platypus2-70B-instruct-4bit-gptq",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-26T12:30:11.519673](https://huggingface.co/datasets/open-llm-leaderboard/details_malhajar__Platypus2-70B-instruct-4bit-gptq/blob/main/results_2023-08-26T12%3A30%3A11.519673.json) (note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
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"acc_stderr": 0.030875990616634128,
"acc_norm": 0.23665349264658486,
"acc_norm_stderr": 0.030890666475037305,
"mc1": 0.2460220318237454,
"mc1_stderr": 0.015077219200662574,
"mc2": 0.4955854635237609,
"mc2_stderr": 0.01695340721579618
},
"harness|arc:challenge|25": {
"acc": 0.2363481228668942,
"acc_stderr": 0.012414960524301829,
"acc_norm": 0.2901023890784983,
"acc_norm_stderr": 0.01326157367752077
},
"harness|hellaswag|10": {
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"acc_stderr": 0.004355436696716298,
"acc_norm": 0.25951005775741887,
"acc_norm_stderr": 0.0043746991892848605
},
"harness|hendrycksTest-abstract_algebra|5": {
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"acc_norm": 0.22,
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},
"harness|hendrycksTest-anatomy|5": {
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"acc_norm": 0.18518518518518517,
"acc_norm_stderr": 0.03355677216313142
},
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},
"harness|hendrycksTest-business_ethics|5": {
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"acc_norm": 0.29,
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},
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},
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},
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},
"harness|hendrycksTest-college_computer_science|5": {
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},
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},
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},
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
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"harness|hendrycksTest-high_school_world_history|5": {
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},
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"harness|hendrycksTest-marketing|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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},
"harness|hendrycksTest-virology|5": {
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"acc_norm": 0.28313253012048195,
"acc_norm_stderr": 0.03507295431370518
},
"harness|hendrycksTest-world_religions|5": {
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"acc_stderr": 0.03582529442573122,
"acc_norm": 0.3216374269005848,
"acc_norm_stderr": 0.03582529442573122
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2460220318237454,
"mc1_stderr": 0.015077219200662574,
"mc2": 0.4955854635237609,
"mc2_stderr": 0.01695340721579618
}
}
```
### 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] |
snowfly/verification_guide_query_list | ---
dataset_info:
features:
- name: 待验证query
dtype: string
- name: 可回答query的CSCO推荐data_id
dtype: string
splits:
- name: train
num_bytes: 7890
num_examples: 60
download_size: 6420
dataset_size: 7890
---
# Dataset Card for "verification_guide_query_list"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
webjunkie/ds | ---
license: apache-2.0
---
|
M-AI-C/en-tafsir-maarif | ---
dataset_info:
features:
- name: ayah
dtype: int64
- name: sorah
dtype: int64
- name: sentence
dtype: string
- name: en-tafsir-maarif-html
dtype: string
- name: en-tafsir-maarif-text
dtype: string
splits:
- name: train
num_bytes: 24431167
num_examples: 6235
download_size: 13388809
dataset_size: 24431167
---
# Dataset Card for "en-tafsir-maarif"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Neekey/test_datase | ---
license: mit
---
|
saibo/bookcorpus_small_compact_256_meta | ---
dataset_info:
features:
- name: text
dtype: string
- name: concept_with_offset
dtype: string
- name: cid_arrangement
sequence: int32
- name: schema_lengths
sequence: int64
- name: topic_entity_mask
sequence: int64
- name: text_lengths
sequence: int64
splits:
- name: train
num_bytes: 213919213
num_examples: 6104
download_size: 45654115
dataset_size: 213919213
---
# Dataset Card for "bookcorpus_small_compact_256_meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ZHENGRAN/code_ujb_testgenissue | ---
dataset_info:
features:
- name: function
dtype: string
- name: testmethod
dtype: string
- name: location_fixed
dtype: string
- name: end_buggy
dtype: int64
- name: location
dtype: string
- name: function_name
dtype: string
- name: source_buggy
dtype: string
- name: prompt_complete
dtype: string
- name: end_fixed
dtype: int64
- name: comment
dtype: string
- name: bug_id
dtype: string
- name: start_fixed
dtype: int64
- name: location_buggy
dtype: string
- name: source_dir
dtype: string
- name: prompt_chat
dtype: string
- name: start_buggy
dtype: int64
- name: classes_modified
sequence: string
- name: task_id
dtype: string
- name: function_signature
dtype: string
- name: prompt_complete_without_signature
dtype: string
- name: project
dtype: string
- name: indent
dtype: string
- name: source_fixed
dtype: string
splits:
- name: train
num_bytes: 33813808
num_examples: 451
download_size: 8570988
dataset_size: 33813808
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
chaoscodes/xp3 | ---
license: apache-2.0
---
|
CyberHarem/rei_nikke | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of rei/ライ/莱伊/라이 (Nikke: Goddess of Victory)
This is the dataset of rei/ライ/莱伊/라이 (Nikke: Goddess of Victory), containing 14 images and their tags.
The core tags of this character are `hat, long_hair, purple_hair, ahoge, bangs, blue_headwear, beret, bow, one_side_up, purple_headwear, red_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 14 | 18.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rei_nikke/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 14 | 11.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rei_nikke/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 31 | 22.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rei_nikke/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 14 | 16.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rei_nikke/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 31 | 34.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rei_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/rei_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 | 14 |  |  |  |  |  | blush, long_sleeves, open_mouth, 1girl, bag, jacket, solo, looking_at_viewer, bird, plaid_skirt, pleated_skirt, standing, white_shirt, black_footwear, collared_shirt, shoes, stuffed_animal, white_socks, :d, multiple_girls, school_uniform |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | blush | long_sleeves | open_mouth | 1girl | bag | jacket | solo | looking_at_viewer | bird | plaid_skirt | pleated_skirt | standing | white_shirt | black_footwear | collared_shirt | shoes | stuffed_animal | white_socks | :d | multiple_girls | school_uniform |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------------|:--------|:------|:---------|:-------|:--------------------|:-------|:--------------|:----------------|:-----------|:--------------|:-----------------|:-----------------|:--------|:-----------------|:--------------|:-----|:-----------------|:-----------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
air_dialogue | ---
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- conversational
- dialogue-generation
- dialogue-modeling
- language-modeling
- masked-language-modeling
pretty_name: AirDialogue
dataset_info:
- config_name: air_dialogue_data
features:
- name: action
struct:
- name: status
dtype: string
- name: name
dtype: string
- name: flight
sequence: int32
- name: intent
struct:
- name: return_month
dtype: string
- name: return_day
dtype: string
- name: max_price
dtype: int32
- name: departure_airport
dtype: string
- name: max_connections
dtype: int32
- name: departure_day
dtype: string
- name: goal
dtype: string
- name: departure_month
dtype: string
- name: name
dtype: string
- name: return_airport
dtype: string
- name: timestamps
sequence: int64
- name: dialogue
sequence: string
- name: expected_action
struct:
- name: status
dtype: string
- name: name
dtype: string
- name: flight
sequence: int32
- name: search_info
list:
- name: button_name
dtype: string
- name: field_name
dtype: string
- name: field_value
dtype: string
- name: timestmamp
dtype: int64
- name: correct_sample
dtype: bool_
splits:
- name: train
num_bytes: 353718365
num_examples: 321459
- name: validation
num_bytes: 44441818
num_examples: 40363
download_size: 141766743
dataset_size: 398160183
- config_name: air_dialogue_kb
features:
- name: kb
list:
- name: airline
dtype: string
- name: class
dtype: string
- name: departure_airport
dtype: string
- name: departure_day
dtype: string
- name: departure_month
dtype: string
- name: departure_time_num
dtype: int32
- name: flight_number
dtype: int32
- name: num_connections
dtype: int32
- name: price
dtype: int32
- name: return_airport
dtype: string
- name: return_day
dtype: string
- name: return_month
dtype: string
- name: return_time_num
dtype: int32
- name: reservation
dtype: int32
splits:
- name: train
num_bytes: 782590970
num_examples: 321459
- name: validation
num_bytes: 98269609
num_examples: 40363
download_size: 57883938
dataset_size: 880860579
configs:
- config_name: air_dialogue_data
data_files:
- split: train
path: air_dialogue_data/train-*
- split: validation
path: air_dialogue_data/validation-*
default: true
- config_name: air_dialogue_kb
data_files:
- split: train
path: air_dialogue_kb/train-*
- split: validation
path: air_dialogue_kb/validation-*
---
# Dataset Card for air_dialogue
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59
- **Repository:** https://github.com/google/airdialogue
- **Paper:** https://aclanthology.org/D18-1419/
- **Leaderboard:** https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59
- **Point of Contact:** [AirDialogue-Google](mailto:airdialogue@gmail.com)
- **Point of Contact:** [Wei Wei](mailto:wewei@google.com)
### Dataset Summary
AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions.
News in v1.3:
- We have included the test split of the AirDialogue dataset.
- We have included the meta context for OOD2 in the original AirDialogue paper.
### Supported Tasks and Leaderboards
We use perplexity and BLEU score to evaluate the quality of the language generated by the model. We also compare the dialogue state generated by the model s and the ground truth state s0. Two categories of the metrics are used: exact match scores and scaled scores
The inference competition & leaderboard can be found here:
https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59
### Languages
The text in the dataset is in English. The BCP 47 code is `en`
## Dataset Structure
### Data Instances
The data is provided in two set of files. The first one has the dialogues (`air_dialogue_data`) and the knowledge-base (`air_dialogue_kb`)
BuilderConfig: `air_dialogue_data`
```
{"action": {"status": "book", "name": "Emily Edwards", "flight": [1027]}, "intent": {"return_month": "June", "return_day": "14", "max_price": 200, "departure_airport": "DFW", "return_time": "afternoon", "max_connections": 1, "departure_day": "12", "goal": "book", "departure_month": "June", "name": "Emily Edwards", "return_airport": "IAD"}, "timestamps": [1519233239, 1519233244, 1519233249, 1519233252, 1519233333, 1519233374, 1519233392, 1519233416, 1519233443, 1519233448, 1519233464, 1519233513, 1519233525, 1519233540, 1519233626, 1519233628, 1519233638], "dialogue": ["customer: Hello.", "agent: Hello.", "customer: My name is Emily Edwards.", "agent: How may I help you out?", "customer: I need some help in my flight ticket reservation to attend a convocation meeting, can you please help me?", "agent: Sure, I will help you out. May I know your travelling dates please?", "customer: Thank you and my dates are 06/12 and back on 06/14.", "agent: Can I know your airport codes?", "customer: The airport codes are from DFW to IAD.", "agent: Ok, please wait a moment.", "customer: Sure.", "agent: There is a flight with connection 1 and price 200, can I proceed with this flight?", "customer: Yes, do proceed with booking.", "agent: Ok, your ticket has been booked.", "customer: Thank you for your assistance in my flight ticket reservation.", "agent: Thank you for choosing us.", "customer: You are welcome."], "expected_action": {"status": "book", "name": "Emily Edwards", "flight": [1027]}, "correct_sample": true}
```
BuilderConfig: `air_dialogue_kb`
```
{"kb": [{"return_airport": "DTW", "airline": "Spirit", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1000, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 2, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Frontier", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1001, "departure_month": "June", "departure_time_num": 0, "class": "business", "return_time_num": 15, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 500}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1002, "departure_month": "June", "departure_time_num": 0, "class": "business", "return_time_num": 13, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 600}, {"return_airport": "IAD", "airline": "Hawaiian", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1003, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 5, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "AA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1004, "departure_month": "June", "departure_time_num": 9, "class": "economy", "return_time_num": 11, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "IAD", "airline": "AA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1005, "departure_month": "June", "departure_time_num": 3, "class": "economy", "return_time_num": 17, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Frontier", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1006, "departure_month": "June", "departure_time_num": 10, "class": "economy", "return_time_num": 10, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "IAD", "airline": "UA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1007, "departure_month": "June", "departure_time_num": 14, "class": "economy", "return_time_num": 20, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "AA", "departure_day": "13", "departure_airport": "DTW", "flight_number": 1008, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 8, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 400}, {"return_airport": "DFW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1009, "departure_month": "June", "departure_time_num": 18, "class": "economy", "return_time_num": 6, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "Frontier", "departure_day": "13", "departure_airport": "DTW", "flight_number": 1010, "departure_month": "June", "departure_time_num": 4, "class": "economy", "return_time_num": 2, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Southwest", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1011, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 22, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 100}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "11", "departure_airport": "DFW", "flight_number": 1012, "departure_month": "June", "departure_time_num": 13, "class": "economy", "return_time_num": 22, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Southwest", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1013, "departure_month": "June", "departure_time_num": 16, "class": "economy", "return_time_num": 13, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1014, "departure_month": "June", "departure_time_num": 0, "class": "economy", "return_time_num": 8, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Southwest", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1015, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 1, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 300}, {"return_airport": "DTW", "airline": "UA", "departure_day": "11", "departure_airport": "DFW", "flight_number": 1016, "departure_month": "June", "departure_time_num": 10, "class": "economy", "return_time_num": 4, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 200}, {"return_airport": "DFW", "airline": "AA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1017, "departure_month": "June", "departure_time_num": 14, "class": "economy", "return_time_num": 23, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 400}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1018, "departure_month": "June", "departure_time_num": 3, "class": "economy", "return_time_num": 1, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Hawaiian", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1019, "departure_month": "June", "departure_time_num": 7, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1020, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 200}, {"return_airport": "IAD", "airline": "Delta", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1021, "departure_month": "June", "departure_time_num": 11, "class": "business", "return_time_num": 8, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 1000}, {"return_airport": "IAD", "airline": "JetBlue", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1022, "departure_month": "June", "departure_time_num": 4, "class": "economy", "return_time_num": 14, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 200}, {"return_airport": "IAD", "airline": "Frontier", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1023, "departure_month": "June", "departure_time_num": 19, "class": "economy", "return_time_num": 23, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "UA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1024, "departure_month": "June", "departure_time_num": 11, "class": "economy", "return_time_num": 19, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Hawaiian", "departure_day": "11", "departure_airport": "IAD", "flight_number": 1025, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 10, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "UA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1026, "departure_month": "June", "departure_time_num": 0, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 300}, {"return_airport": "IAD", "airline": "Delta", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1027, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 15, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "IAD", "airline": "Southwest", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1028, "departure_month": "June", "departure_time_num": 23, "class": "economy", "return_time_num": 13, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Spirit", "departure_day": "11", "departure_airport": "DTW", "flight_number": 1029, "departure_month": "June", "departure_time_num": 22, "class": "business", "return_time_num": 4, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 800}], "reservation": 0}
```
### Data Fields
BuilderConfig: `air_dialogue_data`:
Provides for customer context, dialogue states and environment
key name | Description |
|---|---|
|'search_action' | search action performed by customer |
|'action' | Action taken by the agent |
|'intent' | Intents from the conversation |
|'timestamps' | Timestamp for each of the dialogues |
|'dialogue' | Dialogue recorded between agent & customer |
|'expected_action' | Expected action from agent (human-annotated)|
|'correct_sample' | whether action performed by agent was same as expected_action |
BuilderConfig: `air_dialogue_kb`:
Provides for the Agent Context _ca_ = (_db_, _r_ )
key name | Description |
|---|---|
|'kb' | Available flights in the database |
|'reservation' | whether customer has an existing reservation|
### Data Splits
Data is split into Train/Dev & Test in the ration of 80%, 10% and 10%
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail.
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
No personal and sensitive information is stored
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[AirDialogue team](mailto:airdialogue@gmail.com)
For issues regarding HuggingFace Dataset Hub implementation [Aakash Gupta](mailto:aakashg80@gmail.com)
### Licensing Information
cc-by-nc-4.0
### Citation Information
```bibtex
@inproceedings{wei-etal-2018-airdialogue,
title = "{A}ir{D}ialogue: An Environment for Goal-Oriented Dialogue Research",
author = "Wei, Wei and
Le, Quoc and
Dai, Andrew and
Li, Jia",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1419",
doi = "10.18653/v1/D18-1419",
pages = "3844--3854",
abstract = "Recent progress in dialogue generation has inspired a number of studies on dialogue systems that are capable of accomplishing tasks through natural language interactions. A promising direction among these studies is the use of reinforcement learning techniques, such as self-play, for training dialogue agents. However, current datasets are limited in size, and the environment for training agents and evaluating progress is relatively unsophisticated. We present AirDialogue, a large dataset that contains 301,427 goal-oriented conversations. To collect this dataset, we create a context-generator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail. Our experimental results indicate that state-of-the-art dialogue models can only achieve a score of 0.17 while humans can reach a score of 0.91, which suggests significant opportunities for future improvement.",
}
```
### Contributions
Thanks to [@skyprince999](https://github.com/skyprince999) for adding this dataset.
|
open-llm-leaderboard/details_OpenLemur__lemur-70b-chat-v1 | ---
pretty_name: Evaluation run of OpenLemur/lemur-70b-chat-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [OpenLemur/lemur-70b-chat-v1](https://huggingface.co/OpenLemur/lemur-70b-chat-v1)\
\ 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_OpenLemur__lemur-70b-chat-v1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T13:31:04.707005](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenLemur__lemur-70b-chat-v1/blob/main/results_2023-09-17T13-31-04.707005.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\": 0.006711409395973154,\n\
\ \"em_stderr\": 0.0008361500895152445,\n \"f1\": 0.0739702181208053,\n\
\ \"f1_stderr\": 0.001585201628872726,\n \"acc\": 0.5850941225115532,\n\
\ \"acc_stderr\": 0.01201805791264202\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.006711409395973154,\n \"em_stderr\": 0.0008361500895152445,\n\
\ \"f1\": 0.0739702181208053,\n \"f1_stderr\": 0.001585201628872726\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.35329795299469297,\n \
\ \"acc_stderr\": 0.013166337192115683\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8168902920284136,\n \"acc_stderr\": 0.010869778633168358\n\
\ }\n}\n```"
repo_url: https://huggingface.co/OpenLemur/lemur-70b-chat-v1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|arc:challenge|25_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T13_31_04.707005
path:
- '**/details_harness|drop|3_2023-09-17T13-31-04.707005.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T13-31-04.707005.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T13_31_04.707005
path:
- '**/details_harness|gsm8k|5_2023-09-17T13-31-04.707005.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T13-31-04.707005.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hellaswag|10_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T04:11:57.870589.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_24T04_11_57.870589
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-24T04:11:57.870589.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-24T04:11:57.870589.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T13_31_04.707005
path:
- '**/details_harness|winogrande|5_2023-09-17T13-31-04.707005.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T13-31-04.707005.parquet'
- config_name: results
data_files:
- split: 2023_09_17T13_31_04.707005
path:
- results_2023-09-17T13-31-04.707005.parquet
- split: latest
path:
- results_2023-09-17T13-31-04.707005.parquet
---
# Dataset Card for Evaluation run of OpenLemur/lemur-70b-chat-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/OpenLemur/lemur-70b-chat-v1
- **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 [OpenLemur/lemur-70b-chat-v1](https://huggingface.co/OpenLemur/lemur-70b-chat-v1) 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_OpenLemur__lemur-70b-chat-v1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T13:31:04.707005](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenLemur__lemur-70b-chat-v1/blob/main/results_2023-09-17T13-31-04.707005.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": 0.006711409395973154,
"em_stderr": 0.0008361500895152445,
"f1": 0.0739702181208053,
"f1_stderr": 0.001585201628872726,
"acc": 0.5850941225115532,
"acc_stderr": 0.01201805791264202
},
"harness|drop|3": {
"em": 0.006711409395973154,
"em_stderr": 0.0008361500895152445,
"f1": 0.0739702181208053,
"f1_stderr": 0.001585201628872726
},
"harness|gsm8k|5": {
"acc": 0.35329795299469297,
"acc_stderr": 0.013166337192115683
},
"harness|winogrande|5": {
"acc": 0.8168902920284136,
"acc_stderr": 0.010869778633168358
}
}
```
### 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] |
open-llm-leaderboard/details_Changgil__K2S3-Mistral-7b-v1.46 | ---
pretty_name: Evaluation run of Changgil/K2S3-Mistral-7b-v1.46
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Changgil/K2S3-Mistral-7b-v1.46](https://huggingface.co/Changgil/K2S3-Mistral-7b-v1.46)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Changgil__K2S3-Mistral-7b-v1.46\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-08T07:26:46.778850](https://huggingface.co/datasets/open-llm-leaderboard/details_Changgil__K2S3-Mistral-7b-v1.46/blob/main/results_2024-04-08T07-26-46.778850.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6267592703097891,\n\
\ \"acc_stderr\": 0.032306235635994135,\n \"acc_norm\": 0.6312457601112512,\n\
\ \"acc_norm_stderr\": 0.0329595615269791,\n \"mc1\": 0.3598531211750306,\n\
\ \"mc1_stderr\": 0.016801860466677157,\n \"mc2\": 0.5181608703310118,\n\
\ \"mc2_stderr\": 0.015007220671761173\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5691126279863481,\n \"acc_stderr\": 0.014471133392642466,\n\
\ \"acc_norm\": 0.6075085324232082,\n \"acc_norm_stderr\": 0.014269634635670731\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6340370444134634,\n\
\ \"acc_stderr\": 0.0048071469251620555,\n \"acc_norm\": 0.8348934475204143,\n\
\ \"acc_norm_stderr\": 0.0037051790292873276\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\
\ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\
\ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.618421052631579,\n \"acc_stderr\": 0.03953173377749194,\n\
\ \"acc_norm\": 0.618421052631579,\n \"acc_norm_stderr\": 0.03953173377749194\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\
\ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \
\ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.02794321998933714,\n\
\ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.02794321998933714\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7222222222222222,\n\
\ \"acc_stderr\": 0.037455547914624555,\n \"acc_norm\": 0.7222222222222222,\n\
\ \"acc_norm_stderr\": 0.037455547914624555\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\
\ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.53,\n\
\ \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \
\ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6184971098265896,\n\
\ \"acc_stderr\": 0.03703851193099521,\n \"acc_norm\": 0.6184971098265896,\n\
\ \"acc_norm_stderr\": 0.03703851193099521\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.04576665403207763,\n\
\ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.04576665403207763\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.8,\n\
\ \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.0325005368436584,\n\
\ \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.0325005368436584\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\
\ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\
\ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\
\ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.36772486772486773,\n \"acc_stderr\": 0.024833839825562424,\n \"\
acc_norm\": 0.36772486772486773,\n \"acc_norm_stderr\": 0.024833839825562424\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\
\ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\
\ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7548387096774194,\n\
\ \"acc_stderr\": 0.024472243840895518,\n \"acc_norm\": 0.7548387096774194,\n\
\ \"acc_norm_stderr\": 0.024472243840895518\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.47783251231527096,\n \"acc_stderr\": 0.03514528562175007,\n\
\ \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.03514528562175007\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.03453131801885417,\n\
\ \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.03453131801885417\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.797979797979798,\n \"acc_stderr\": 0.02860620428922987,\n \"acc_norm\"\
: 0.797979797979798,\n \"acc_norm_stderr\": 0.02860620428922987\n },\n\
\ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \
\ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.02463978909770944,\n\
\ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.02463978909770944\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6333333333333333,\n \"acc_stderr\": 0.02443301646605246,\n \
\ \"acc_norm\": 0.6333333333333333,\n \"acc_norm_stderr\": 0.02443301646605246\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3148148148148148,\n \"acc_stderr\": 0.028317533496066485,\n \
\ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.028317533496066485\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.03038835355188679,\n \
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.03038835355188679\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"\
acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8220183486238533,\n \"acc_stderr\": 0.016399436366612917,\n \"\
acc_norm\": 0.8220183486238533,\n \"acc_norm_stderr\": 0.016399436366612917\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5,\n \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n\
\ \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n\
\ \"acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7552742616033755,\n \"acc_stderr\": 0.027985699387036423,\n \
\ \"acc_norm\": 0.7552742616033755,\n \"acc_norm_stderr\": 0.027985699387036423\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\
\ \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n\
\ \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.03641297081313729,\n\
\ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.03641297081313729\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8264462809917356,\n \"acc_stderr\": 0.0345727283691767,\n \"acc_norm\"\
: 0.8264462809917356,\n \"acc_norm_stderr\": 0.0345727283691767\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7484662576687117,\n \"acc_stderr\": 0.034089978868575295,\n\
\ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.034089978868575295\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\
\ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\
\ \"acc_stderr\": 0.022509033937077826,\n \"acc_norm\": 0.8632478632478633,\n\
\ \"acc_norm_stderr\": 0.022509033937077826\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8135376756066411,\n\
\ \"acc_stderr\": 0.013927751372001506,\n \"acc_norm\": 0.8135376756066411,\n\
\ \"acc_norm_stderr\": 0.013927751372001506\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7109826589595376,\n \"acc_stderr\": 0.02440517393578323,\n\
\ \"acc_norm\": 0.7109826589595376,\n \"acc_norm_stderr\": 0.02440517393578323\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.31508379888268156,\n\
\ \"acc_stderr\": 0.015536850852473636,\n \"acc_norm\": 0.31508379888268156,\n\
\ \"acc_norm_stderr\": 0.015536850852473636\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.025261691219729474,\n\
\ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.025261691219729474\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\
\ \"acc_stderr\": 0.026003301117885142,\n \"acc_norm\": 0.7009646302250804,\n\
\ \"acc_norm_stderr\": 0.026003301117885142\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7191358024691358,\n \"acc_stderr\": 0.025006469755799208,\n\
\ \"acc_norm\": 0.7191358024691358,\n \"acc_norm_stderr\": 0.025006469755799208\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \
\ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4491525423728814,\n\
\ \"acc_stderr\": 0.012704030518851488,\n \"acc_norm\": 0.4491525423728814,\n\
\ \"acc_norm_stderr\": 0.012704030518851488\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6397058823529411,\n \"acc_stderr\": 0.02916312857067073,\n\
\ \"acc_norm\": 0.6397058823529411,\n \"acc_norm_stderr\": 0.02916312857067073\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6486928104575164,\n \"acc_stderr\": 0.01931267606578655,\n \
\ \"acc_norm\": 0.6486928104575164,\n \"acc_norm_stderr\": 0.01931267606578655\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\
\ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\
\ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6979591836734694,\n \"acc_stderr\": 0.0293936093198798,\n\
\ \"acc_norm\": 0.6979591836734694,\n \"acc_norm_stderr\": 0.0293936093198798\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8557213930348259,\n\
\ \"acc_stderr\": 0.02484575321230604,\n \"acc_norm\": 0.8557213930348259,\n\
\ \"acc_norm_stderr\": 0.02484575321230604\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.027966785859160893,\n\
\ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.027966785859160893\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3598531211750306,\n\
\ \"mc1_stderr\": 0.016801860466677157,\n \"mc2\": 0.5181608703310118,\n\
\ \"mc2_stderr\": 0.015007220671761173\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8042620363062352,\n \"acc_stderr\": 0.011151145042218329\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.42380591357088704,\n \
\ \"acc_stderr\": 0.01361163200881037\n }\n}\n```"
repo_url: https://huggingface.co/Changgil/K2S3-Mistral-7b-v1.46
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|arc:challenge|25_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|gsm8k|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hellaswag|10_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-08T07-26-46.778850.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-08T07-26-46.778850.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- '**/details_harness|winogrande|5_2024-04-08T07-26-46.778850.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-08T07-26-46.778850.parquet'
- config_name: results
data_files:
- split: 2024_04_08T07_26_46.778850
path:
- results_2024-04-08T07-26-46.778850.parquet
- split: latest
path:
- results_2024-04-08T07-26-46.778850.parquet
---
# Dataset Card for Evaluation run of Changgil/K2S3-Mistral-7b-v1.46
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Changgil/K2S3-Mistral-7b-v1.46](https://huggingface.co/Changgil/K2S3-Mistral-7b-v1.46) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Changgil__K2S3-Mistral-7b-v1.46",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-08T07:26:46.778850](https://huggingface.co/datasets/open-llm-leaderboard/details_Changgil__K2S3-Mistral-7b-v1.46/blob/main/results_2024-04-08T07-26-46.778850.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6267592703097891,
"acc_stderr": 0.032306235635994135,
"acc_norm": 0.6312457601112512,
"acc_norm_stderr": 0.0329595615269791,
"mc1": 0.3598531211750306,
"mc1_stderr": 0.016801860466677157,
"mc2": 0.5181608703310118,
"mc2_stderr": 0.015007220671761173
},
"harness|arc:challenge|25": {
"acc": 0.5691126279863481,
"acc_stderr": 0.014471133392642466,
"acc_norm": 0.6075085324232082,
"acc_norm_stderr": 0.014269634635670731
},
"harness|hellaswag|10": {
"acc": 0.6340370444134634,
"acc_stderr": 0.0048071469251620555,
"acc_norm": 0.8348934475204143,
"acc_norm_stderr": 0.0037051790292873276
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6370370370370371,
"acc_stderr": 0.04153948404742398,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.04153948404742398
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.618421052631579,
"acc_stderr": 0.03953173377749194,
"acc_norm": 0.618421052631579,
"acc_norm_stderr": 0.03953173377749194
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7094339622641509,
"acc_stderr": 0.02794321998933714,
"acc_norm": 0.7094339622641509,
"acc_norm_stderr": 0.02794321998933714
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.037455547914624555,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.037455547914624555
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.4,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6184971098265896,
"acc_stderr": 0.03703851193099521,
"acc_norm": 0.6184971098265896,
"acc_norm_stderr": 0.03703851193099521
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.30392156862745096,
"acc_stderr": 0.04576665403207763,
"acc_norm": 0.30392156862745096,
"acc_norm_stderr": 0.04576665403207763
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.8,
"acc_stderr": 0.04020151261036846,
"acc_norm": 0.8,
"acc_norm_stderr": 0.04020151261036846
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5531914893617021,
"acc_stderr": 0.0325005368436584,
"acc_norm": 0.5531914893617021,
"acc_norm_stderr": 0.0325005368436584
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4824561403508772,
"acc_stderr": 0.04700708033551038,
"acc_norm": 0.4824561403508772,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5448275862068965,
"acc_stderr": 0.04149886942192117,
"acc_norm": 0.5448275862068965,
"acc_norm_stderr": 0.04149886942192117
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.36772486772486773,
"acc_stderr": 0.024833839825562424,
"acc_norm": 0.36772486772486773,
"acc_norm_stderr": 0.024833839825562424
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4603174603174603,
"acc_stderr": 0.04458029125470973,
"acc_norm": 0.4603174603174603,
"acc_norm_stderr": 0.04458029125470973
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7548387096774194,
"acc_stderr": 0.024472243840895518,
"acc_norm": 0.7548387096774194,
"acc_norm_stderr": 0.024472243840895518
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.47783251231527096,
"acc_stderr": 0.03514528562175007,
"acc_norm": 0.47783251231527096,
"acc_norm_stderr": 0.03514528562175007
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7333333333333333,
"acc_stderr": 0.03453131801885417,
"acc_norm": 0.7333333333333333,
"acc_norm_stderr": 0.03453131801885417
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.797979797979798,
"acc_stderr": 0.02860620428922987,
"acc_norm": 0.797979797979798,
"acc_norm_stderr": 0.02860620428922987
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8652849740932642,
"acc_stderr": 0.02463978909770944,
"acc_norm": 0.8652849740932642,
"acc_norm_stderr": 0.02463978909770944
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6333333333333333,
"acc_stderr": 0.02443301646605246,
"acc_norm": 0.6333333333333333,
"acc_norm_stderr": 0.02443301646605246
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3148148148148148,
"acc_stderr": 0.028317533496066485,
"acc_norm": 0.3148148148148148,
"acc_norm_stderr": 0.028317533496066485
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6764705882352942,
"acc_stderr": 0.03038835355188679,
"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.03038835355188679
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2980132450331126,
"acc_stderr": 0.037345356767871984,
"acc_norm": 0.2980132450331126,
"acc_norm_stderr": 0.037345356767871984
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8220183486238533,
"acc_stderr": 0.016399436366612917,
"acc_norm": 0.8220183486238533,
"acc_norm_stderr": 0.016399436366612917
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5,
"acc_stderr": 0.034099716973523674,
"acc_norm": 0.5,
"acc_norm_stderr": 0.034099716973523674
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7892156862745098,
"acc_stderr": 0.028626547912437406,
"acc_norm": 0.7892156862745098,
"acc_norm_stderr": 0.028626547912437406
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7552742616033755,
"acc_stderr": 0.027985699387036423,
"acc_norm": 0.7552742616033755,
"acc_norm_stderr": 0.027985699387036423
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6771300448430493,
"acc_stderr": 0.031381476375754995,
"acc_norm": 0.6771300448430493,
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"acc": 0.7786259541984732,
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},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8264462809917356,
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"acc": 0.7685185185185185,
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},
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"acc": 0.7484662576687117,
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"acc_norm_stderr": 0.034089978868575295
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"acc_norm_stderr": 0.04697113923010212
},
"harness|hendrycksTest-management|5": {
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"harness|hendrycksTest-marketing|5": {
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"harness|hendrycksTest-medical_genetics|5": {
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"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7109826589595376,
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"acc_norm_stderr": 0.02440517393578323
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"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.31508379888268156,
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"acc_norm": 0.31508379888268156,
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-philosophy|5": {
"acc": 0.7009646302250804,
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"harness|hendrycksTest-prehistory|5": {
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"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.49645390070921985,
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"acc_norm": 0.49645390070921985,
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"acc": 0.4491525423728814,
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"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6397058823529411,
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"acc_norm_stderr": 0.02916312857067073
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"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6486928104575164,
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"harness|hendrycksTest-public_relations|5": {
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"harness|hendrycksTest-security_studies|5": {
"acc": 0.6979591836734694,
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"acc_norm_stderr": 0.0293936093198798
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8557213930348259,
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"acc_norm": 0.8557213930348259,
"acc_norm_stderr": 0.02484575321230604
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.84,
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"acc_norm": 0.84,
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},
"harness|hendrycksTest-virology|5": {
"acc": 0.5542168674698795,
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"acc_norm": 0.5542168674698795,
"acc_norm_stderr": 0.03869543323472101
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8421052631578947,
"acc_stderr": 0.027966785859160893,
"acc_norm": 0.8421052631578947,
"acc_norm_stderr": 0.027966785859160893
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3598531211750306,
"mc1_stderr": 0.016801860466677157,
"mc2": 0.5181608703310118,
"mc2_stderr": 0.015007220671761173
},
"harness|winogrande|5": {
"acc": 0.8042620363062352,
"acc_stderr": 0.011151145042218329
},
"harness|gsm8k|5": {
"acc": 0.42380591357088704,
"acc_stderr": 0.01361163200881037
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
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[More Information Needed]
### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
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### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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#### Who are the source data producers?
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### Annotations [optional]
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#### Annotation process
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#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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### Recommendations
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## Citation [optional]
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## Glossary [optional]
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pbevan11/image_gen_ocr_evaluation_data | ---
license: apache-2.0
---
# image_gen_ocr_eval
**Author:** Peter J. Bevan
**Date:** 15/12/23
**github:** [https://github.com/pbevan1/image-gen-spelling-eval](https://github.com/pbevan1/image-gen-spelling-eval)
---
*Table 1: Normalised Levenshtein similarity scores between instructed text and text present in image (as identified by OCR)*
| Model | object | signage | natural | long | Overall |
| --- | --- | --- | --- | --- | --- |
| DALLE3 | 0.62 | 0.62 | 0.62 | 0.58 | 0.61 |
| DeepFloydIF | 0.57 | 0.56 | 0.66 | 0.39 | 0.54 |
| DALLE2 | 0.44 | 0.35 | 0.42 | 0.22 | 0.36 |
| SDXL | 0.3 | 0.33 | 0.4 | 0.21 | 0.31 |
| SD | 0.28 | 0.26 | 0.32 | 0.22 | 0.27 |
| PlayGroundV2 | 0.19 | 0.23 | 0.17 | 0.2 | 0.2 |
| Wuerstchen | 0.14 | 0.19 | 0.19 | 0.19 | 0.18 |
| Kandinsky | 0.13 | 0.2 | 0.18 | 0.17 | 0.17 |
---
This is a POC that calculates the normalised Levenshtein similarity between prompted text and the text present in the generated image (as recognised by OCR).
To us this to create a metric, we create a dataset of prompts, each instructing to include some text in the image. We also provide a column for ground truth generated text which contains only the instructed text. The below scorer is then run on the generated images, comparing the target text with the actual text, outputting a score. The scores are then averaged to give a benchmark score. A score of 1 indicates a perfect match to the text.
You can find the dataset at https://huggingface.co/datasets/pbevan11/image_gen_ocr_evaluation_data
Since this metric solely looks at text within the generated images and not image quality as a whole, this metric should be used alongside other benchmarks such as those in https://karine-h.github.io/T2I-CompBench/.
---
```
@misc {peter_j._bevan_2024,
author = { {Peter J. Bevan} },
title = { image_gen_ocr_evaluation_data (Revision 6182779) },
year = 2024,
url = { https://huggingface.co/datasets/pbevan11/image_gen_ocr_evaluation_data },
doi = { 10.57967/hf/1944 },
publisher = { Hugging Face }
}
``` |
CyberHarem/focalors_genshin | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of focalors_genshin
This is the dataset of focalors_genshin, containing 200 images and their tags.
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)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 464 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 464 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 464 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 464 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
alisson40889/friza | ---
license: openrail
---
|
mteb/sts22-crosslingual-sts | ---
language:
- ar
- de
- en
- es
- fr
- it
- pl
- ru
- tr
- zh
---
Scores in this dataset have been inverted to be from least to most similar!
The scores in the original STS22 task were from most to least similar. |
vitruv/report_simplify | ---
dataset_info:
features:
- name: name
sequence: string
- name: time
sequence: int64
- name: score
sequence: int64
- name: time_mean
dtype: float64
- name: score_mean
dtype: float64
- name: time_median
dtype: int64
- name: score_median
dtype: int64
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 5260965
num_examples: 2900
- name: val
num_bytes: 181435
num_examples: 100
- name: test
num_bytes: 141592
num_examples: 100
download_size: 976397
dataset_size: 5583992
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
|
bn22/dolphin-50k-mini | ---
license: apache-2.0
---
|
SuryaKrishna02/aya-telugu-jokes | ---
annotations_creators:
- expert-generated
language:
- te
language_creators:
- expert-generated
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: Telugu Jokes
size_categories:
- n<1K
source_datasets:
- original
tags:
- jokes
- humor
- fun conversations
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Summary
`aya-telugu-jokes` is an open source dataset of instruct-style records generated by webscraping a Telugu Jokes website. This was created as part of [Aya Open Science Initiative](https://sites.google.com/cohere.com/aya-en/home) from Cohere For AI.
This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Apache 2.0](https://opensource.org/license/apache-2-0) License.
Supported Tasks:
- Training LLMs
- Synthetic Data Generation
- Data Augmentation
Languages: Telugu Version: 1.0
# Dataset Overview
`aya-telugu-jokes` is a corpus of more than 900 records generated by webscraping of the Telugu Jokes website. This Dataset can be used for the following task:
- Given the title of a funny conversation, generate a funny conversation based on the title.
# Intended Uses
While immediately valuable for instruction fine tuning large language models, as a corpus of instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods. For example, prompt-completions could be submitted as few-shot examples to a large open language model to generate additional funny conversations and their titles.
# Dataset
## Load with Datasets
To load this dataset with Datasets, you'll just need to install Datasets as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset('SuryaKrishna02/aya-telugu-jokes')
```
## Purpose of Collection
Telugu is a low-resource language where there no funny conversation generation instruct-style dataset to the best of my knowledge. This was created as a part of [Aya Open Science Initiative](https://sites.google.com/cohere.com/aya-en/home) from Cohere For AI to make sure Telugu is well represented in the space of AI/ML. Unlike other datasets that are limited to non-commercial use, this dataset can be used, modified, and extended for any purpose, including academic or commercial applications.
## Sources
- **Andhrajyothi Website**: Performed webscraping from [Andhrajyothi Website](https://lit.andhrajyothy.com/jokes/) which is a website consisting of funny conversations. Next, performed some pre-processing of the data like removing unwanted characters from the scraped data. Finally, converted the scraped data into Instruct-style prompts and completions.
## Data Fields
- `inputs` : Prompt or input to the language model.
- `targets` : Completion or output of the language model.
- `template_id` : Id of the template used in `inputs` and `targets`.
- `template_lang`: ISO code of the language used in the `inputs` and `targets` where *tel* refers to Telugu.
## Templates
For the creation of instruct-style prompts and completions from the scraped data, the following one template category with 14 different variations were used:
1. Given the title of a funny conversation, generate a funny conversation based on the title.
| template_id | inputs | targets |
|-------------|--------|---------|
| 1 | ```{{Title}} అనే శీర్షిక తో జోక్ ఇవ్వు``` | ```శీర్షిక: {{Title}}\n\n{{Funny Conversation}}``` |
| 2 | ```{{Title}} అనే టైటిల్ తో జోక్ ఇవ్వు``` | ```శీర్షిక: {{Title}}\n\n{{Funny Conversation}}``` |
| 3 | ```ఒక హాస్య సంభాషణ ఇవ్వు మరియు దాని యొక్క శీర్షిక {{Title}} ఉండే లాగా ఇవ్వు.``` | ```శీర్షిక: {{Title}}\n\n{{Funny Conversation}}``` |
| 4 | ```ఒక చిన్న హాస్య సన్నివేశం ఇవ్వు మరియు దాని యొక్క శీర్షిక {{Title}} ఉండే లాగా ఇవ్వు.``` | ```శీర్షిక: {{Title}}\n\n{{Funny Conversation}}``` |
| 5 | ```ఒక చమత్కారమయిన సంభాషణ ఇవ్వు మరియు దాని యొక్క శీర్షిక {{Title}} ఉండే లాగా ఇవ్వు.``` | ```శీర్షిక: {{Title}}\n\n{{Funny Conversation}}``` |
| 6 | ```ఒక చిన్న చమత్కారమయిన సన్నివేశం ఇవ్వు మరియు దాని యొక్క శీర్షిక {{Title}} ఉండే లాగా ఇవ్వు.``` | ```శీర్షిక: {{Title}}\n\n{{Funny Conversation}}``` |
| 7 | ```ఒక తమాషా అయినా సంభాషణ ఇవ్వు మరియు దాని యొక్క శీర్షిక {{Title}} ఉండే లాగా ఇవ్వు.``` | ```శీర్షిక: {{Title}}\n\n{{Funny Conversation}}``` |
| 8 | ```ఒక చిన్న తమాషా అయినా సన్నివేశం ఇవ్వు మరియు దాని యొక్క శీర్షిక {{Title}} ఉండే లాగా ఇవ్వు.``` | ```శీర్షిక: {{Title}}\n\n{{Funny Conversation}}``` |
| 9 | ```ఒక హాస్య సంభాషణ ఇవ్వు మరియు దాని యొక్క టైటిల్ {{Title}} ఉండే లాగా ఇవ్వు.``` | ```శీర్షిక: {{Title}}\n\n{{Funny Conversation}}``` |
| 10 | ```ఒక చిన్న హాస్య సన్నివేశం ఇవ్వు మరియు దాని యొక్క టైటిల్ {{Title}} ఉండే లాగా ఇవ్వు.``` | ```శీర్షిక: {{Title}}\n\n{{Funny Conversation}}``` |
| 11 | ```ఒక చమత్కారమయిన సంభాషణ ఇవ్వు మరియు దాని యొక్క టైటిల్ {{Title}} ఉండే లాగా ఇవ్వు.``` | ```శీర్షిక: {{Title}}\n\n{{Funny Conversation}}``` |
| 12 | ```ఒక చిన్న చమత్కారమయిన సన్నివేశం ఇవ్వు మరియు దాని యొక్క టైటిల్ {{Title}} ఉండే లాగా ఇవ్వు.``` | ```శీర్షిక: {{Title}}\n\n{{Funny Conversation}}``` |
| 13 | ```ఒక తమాషా అయినా సంభాషణ ఇవ్వు మరియు దాని యొక్క టైటిల్ {{Title}} ఉండే లాగా ఇవ్వు.``` | ```శీర్షిక: {{Title}}\n\n{{Funny Conversation}}``` |
| 14 | ```ఒక చిన్న తమాషా అయినా సన్నివేశం ఇవ్వు మరియు దాని యొక్క టైటిల్ {{Title}} ఉండే లాగా ఇవ్వు.``` | ```శీర్షిక: {{Title}}\n\n{{Funny Conversation}}``` |
## Personal or Sensitive Data
This dataset contains public information. To our knowledge, there are no private person’s personal identifiers or sensitive information.
## Language
Telugu
# Known Limitations
- The Dataset is scraped from the Jokes Website and the contents of this dataset may reflect the bias, factual errors, inappropriate and sensitive matters.
- Although there is utmost care taken to keep the dataset as monolingual, there might be some records that may contain English Language along with Telugu.
# Contributors
[SuryaKrishna02](https://github.com/SuryaKrishna02) and [Desik98](https://github.com/desik1998) |
Intel/neural-chat-dataset-v1-1 | ---
license: apache-2.0
---
Here is a collective list of instruction dataset used for Neural Chat fine-tuning. The total number of instruction samples and tokens are about 1.1M and 326M respectively.
| Type | Language | Dataset | Number |
|--| ---- |--------|----|
| HC3 | en | [HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3) | 24K |
| dolly | en | [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) | 15K |
| alpaca-zh | zh | [tigerbot-alpaca-zh-0.5m](https://huggingface.co/datasets/TigerResearch/tigerbot-alpaca-zh-0.5m) | 500K |
| alpaca-en | en | [TigerResearch/tigerbot-alpaca-en-50k](https://huggingface.co/datasets/TigerResearch/tigerbot-alpaca-en-50k) | 50K |
| math | en | [tigerbot-gsm-8k-en](https://huggingface.co/datasets/TigerResearch/tigerbot-gsm-8k-en) | 8K |
| general | en | [tigerbot-stackexchange-qa-en-0.5m](https://huggingface.co/datasets/TigerResearch/tigerbot-stackexchange-qa-en-0.5m) | 500K |
The collective dataset has been validated on multiple LLMs (such as MPT, LLama) by the NeuralChat team (Kaokao Lv, Wenxin Zhang, Xuhui Ren, and Haihao Shen) from Intel/SATG/AIA/AIPT. Thanks to [Hello-SimpleAI](https://huggingface.co/Hello-SimpleAI), [databricks](https://huggingface.co/databricks), [TigerResearch/TigerBot](https://github.com/TigerResearch/TigerBot) for releasing the open-source instruction dataset.
|
atluzz/tuntun_faq_guanaco_compr | ---
license: apache-2.0
---
|
PavolPragoData/testSet | ---
language:
- en
size_categories:
- n<1K
---
Q: What is the general workflow suggested for using the GitHub client?
A: The general workflow for using the GitHub client is referred to as the "GitHub Flow," which involves committing to a branch and syncing up with a remote repository regularly.
Q: How does branch management differ between the GitHub clients on macOS and Windows?
A: On macOS, branch management in the GitHub client involves a button at the top of the window for creating a new branch. On Windows, creating a branch is done by typing the new branch’s name in the branch-switching widget.
Q: What is the main way to interact with other repositories over the network in the GitHub client?
A: The main way to interact with other repositories over the network in the GitHub client is through the “Sync” feature, which internally uses a combination of Git operations such as pull, push, fetch, merge, and rebase.
Q: What happens when you click the Sync button in the GitHub client?
A: When the Sync button is clicked, it first performs a git pull --rebase, and if that fails due to a merge conflict, it falls back to git pull --no-rebase. Then, it executes git push. |
wolfbr950/dataset2 | ---
license: apache-2.0
---
|
Ahmad-Sarmad-Ali/SHEAKESPEAR-QNA | ---
license: mit
---
|
autoevaluate/autoeval-staging-eval-project-samsum-0c672345-10275367 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: facebook/bart-large-cnn
metrics: []
dataset_name: samsum
dataset_config: samsum
dataset_split: train
col_mapping:
text: dialogue
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-cnn
* Dataset: samsum
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ikadebi](https://huggingface.co/ikadebi) for evaluating this model. |
bond005/sberdevices_golos_100h_farfield | ---
pretty_name: Golos
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- ru
license:
- other
multilinguality:
- monolingual
paperswithcode_id: golos
size_categories:
- 10K<n<100k
source_datasets:
- extended
task_categories:
- automatic-speech-recognition
- audio-classification
---
# Dataset Card for sberdevices_golos_100h_farfield
## 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:** [Golos ASR corpus](https://www.openslr.org/114)
- **Repository:** [Golos dataset](https://github.com/sberdevices/golos)
- **Paper:** [Golos: Russian Dataset for Speech Research](https://arxiv.org/pdf/2106.10161.pdf)
- **Leaderboard:** [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
- **Point of Contact:** [Nikolay Karpov](mailto:karpnv@gmail.com)
### Dataset Summary
Sberdevices Golos is a corpus of approximately 1200 hours of 16kHz Russian speech from crowd (reading speech) and farfield (communication with smart devices) domains, prepared by SberDevices Team (Alexander Denisenko, Angelina Kovalenko, Fedor Minkin, and Nikolay Karpov). The data is derived from the crowd-sourcing platform, and has been manually annotated.
Authors divide all dataset into train and test subsets. The training subset includes approximately 1000 hours. For experiments with a limited number of records, authors identified training subsets of shorter length: 100 hours, 10 hours, 1 hour, 10 minutes.
This dataset is a simpler version of the above mentioned Golos:
- it includes the farfield domain only (without any sound from the crowd domain);
- validation split is built on the 10-hour training subset;
- training split corresponds to the 100-hour training subset without sounds from the 10-hour training subset;
- test split is a full original test split.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER.
### Languages
The audio is in Russian.
## Dataset Structure
### Data Instances
A typical data point comprises the audio data, usually called `audio` and its transcription, called `transcription`. Any additional information about the speaker and the passage which contains the transcription is not provided.
```
{'audio': {'path': None,
'array': array([ 1.22070312e-04, 1.22070312e-04, 9.15527344e-05, ...,
6.10351562e-05, 6.10351562e-05, 3.05175781e-05]), dtype=float64),
'sampling_rate': 16000},
'transcription': 'джой источники истории турции'}
```
### Data Fields
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- transcription: the transcription of the audio file.
### Data Splits
This dataset is a simpler version of the original Golos:
- it includes the farfield domain only (without any sound from the crowd domain);
- validation split is built on the 10-hour training subset;
- training split corresponds to the 100-hour training subset without sounds from the 10-hour training subset;
- test split is a full original test split.
| | Train | Validation | Test |
| ----- | ------ | ---------- | ----- |
| examples | 9570 | 933 | 1916 |
| hours | 10.3h | 1.0h | 1.4h |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
All recorded audio files were manually annotated on the crowd-sourcing platform.
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The dataset was initially created by Alexander Denisenko, Angelina Kovalenko, Fedor Minkin, and Nikolay Karpov.
### Licensing Information
[Public license with attribution and conditions reserved](https://github.com/sberdevices/golos/blob/master/license/en_us.pdf)
### Citation Information
```
@misc{karpov2021golos,
author = {Karpov, Nikolay and Denisenko, Alexander and Minkin, Fedor},
title = {Golos: Russian Dataset for Speech Research},
publisher = {arXiv},
year = {2021},
url = {https://arxiv.org/abs/2106.10161}
}
```
### Contributions
Thanks to [@bond005](https://github.com/bond005) for adding this dataset.
|
ovior/twitter_dataset_1713127382 | ---
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: 2248910
num_examples: 7113
download_size: 1260609
dataset_size: 2248910
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
vwxyzjn/ultrachat_200k_filtered_1707919115 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: query_reference_response
list:
- name: content
dtype: string
- name: role
dtype: string
- name: query_reference_response_token
sequence: int64
- name: query_reference_response_token_len
dtype: int64
- name: query
list:
- name: content
dtype: string
- name: role
dtype: string
- name: query_token
sequence: int64
- name: query_token_len
dtype: int64
- name: reference_response
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: reference_response_token
sequence: int64
- name: reference_response_token_len
dtype: int64
splits:
- name: test_gen
num_bytes: 30484069
num_examples: 1000
- name: test_sft
num_bytes: 39592502
num_examples: 1000
- name: train_gen
num_bytes: 29613744
num_examples: 1000
- name: train_sft
num_bytes: 39521233
num_examples: 1000
download_size: 50859072
dataset_size: 139211548
---
# Dataset Card for "ultrachat_200k_filtered_1707919115"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
davidfant/natural-questions-chunk-19 | ---
dataset_info:
features:
- name: id
dtype: string
- name: document
struct:
- name: html
dtype: string
- name: title
dtype: string
- name: tokens
sequence:
- name: end_byte
dtype: int64
- name: is_html
dtype: bool
- name: start_byte
dtype: int64
- name: token
dtype: string
- name: url
dtype: string
- name: question
struct:
- name: text
dtype: string
- name: tokens
sequence: string
- name: long_answer_candidates
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: top_level
dtype: bool
- name: annotations
sequence:
- name: id
dtype: string
- name: long_answer
struct:
- name: candidate_index
dtype: int64
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: short_answers
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: text
dtype: string
- name: yes_no_answer
dtype:
class_label:
names:
'0': 'NO'
'1': 'YES'
splits:
- name: train
num_bytes: 4658446669
num_examples: 10000
download_size: 1809697228
dataset_size: 4658446669
---
# Dataset Card for "natural-questions-chunk-19"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
GokhanAI/Synthetic | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 15850263.160279274
num_examples: 24354
- name: test
num_bytes: 1301655.8397207255
num_examples: 2000
download_size: 5685426
dataset_size: 17151919.0
---
# Dataset Card for "AGENT_V3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
KIND-Dataset/Open-ended_Questions_dialectal_data | ---
license: cc-by-4.0
task_categories:
- question-answering
language:
- ar
size_categories:
- n<1K
---
### Dataset Summary
A collection of open-ended questions that was provided to the data marathon competitors to populate KIND dataset. It was designed to elicit longer responses cultural and context-rich sentences.
For more details, please check the paper
[The KIND Dataset: A Social Collaboration Approach for Nuanced Dialect Data Collection](https://aclanthology.org/2024.eacl-srw.3/)
### Citation Information
```
@inproceedings{yamani-etal-2024-kind,
title = "The {KIND} Dataset: A Social Collaboration Approach for Nuanced Dialect Data Collection",
author = "Yamani, Asma and
Alziyady, Raghad and
AlYami, Reem and
Albelali, Salma and
Albelali, Leina and
Almulhim, Jawharah and
Alsulami, Amjad and
Alfarraj, Motaz and
Al-Zaidy, Rabeah",
editor = "Falk, Neele and
Papi, Sara and
Zhang, Mike",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-srw.3",
pages = "32--43",
}
``` |
CyberHarem/miura_yumiko_yahariorenoseishunlovecomewamachigatteiru | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Miura Yumiko (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
This is the dataset of Miura Yumiko (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 190 images and their tags.
The core tags of this character are `long_hair, blonde_hair, green_eyes, drill_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 190 | 100.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miura_yumiko_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 190 | 88.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miura_yumiko_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 372 | 167.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miura_yumiko_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 190 | 100.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miura_yumiko_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 372 | 184.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miura_yumiko_yahariorenoseishunlovecomewamachigatteiru/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/miura_yumiko_yahariorenoseishunlovecomewamachigatteiru',
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 | 8 |  |  |  |  |  | 1girl, solo, from_side, profile, bangs, blurry_background, indoors, closed_mouth, holding, open_mouth, reading, school_uniform, upper_body |
| 1 | 7 |  |  |  |  |  | 1girl, solo, earrings, profile, school_uniform, from_side, upper_body, black_jacket, ribbon |
| 2 | 7 |  |  |  |  |  | 1girl, blazer, indoors, school_uniform, solo, sitting, chair, classroom, school_bag, school_desk, black_jacket, collared_shirt, long_sleeves, open_mouth, plaid, red_ribbon, skirt, smartphone, white_shirt |
| 3 | 23 |  |  |  |  |  | 1girl, solo, sobu_high_school_uniform, ribbon, blazer, black_jacket, shirt, upper_body |
| 4 | 5 |  |  |  |  |  | 1girl, crossed_arms, indoors, school_uniform, solo, sweater_vest, earrings, long_sleeves, school_desk, white_shirt, chair, classroom, looking_at_viewer, sitting, closed_mouth, collared_shirt, open_mouth |
| 5 | 5 |  |  |  |  |  | 1girl, cloud, day, earrings, tree, outdoors, school_uniform, solo, blue_sky, crossed_arms, ribbon, anime_coloring, blazer, chain-link_fence, frown, shirt |
| 6 | 6 |  |  |  |  |  | 1girl, collarbone, upper_body, fur_trim, solo, striped_shirt, blurry, orange_jacket, looking_at_viewer |
| 7 | 12 |  |  |  |  |  | 1girl, tennis_racket, tennis_uniform, solo, chain-link_fence, outdoors, skirt |
| 8 | 6 |  |  |  |  |  | 1girl, earrings, off_shoulder, short_sleeves, bare_shoulders, closed_mouth, red_dress, bangs, crossed_arms, breasts, solo |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | from_side | profile | bangs | blurry_background | indoors | closed_mouth | holding | open_mouth | reading | school_uniform | upper_body | earrings | black_jacket | ribbon | blazer | sitting | chair | classroom | school_bag | school_desk | collared_shirt | long_sleeves | plaid | red_ribbon | skirt | smartphone | white_shirt | sobu_high_school_uniform | shirt | crossed_arms | sweater_vest | looking_at_viewer | cloud | day | tree | outdoors | blue_sky | anime_coloring | chain-link_fence | frown | collarbone | fur_trim | striped_shirt | blurry | orange_jacket | tennis_racket | tennis_uniform | off_shoulder | short_sleeves | bare_shoulders | red_dress | breasts |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:------------|:----------|:--------|:--------------------|:----------|:---------------|:----------|:-------------|:----------|:-----------------|:-------------|:-----------|:---------------|:---------|:---------|:----------|:--------|:------------|:-------------|:--------------|:-----------------|:---------------|:--------|:-------------|:--------|:-------------|:--------------|:---------------------------|:--------|:---------------|:---------------|:--------------------|:--------|:------|:-------|:-----------|:-----------|:-----------------|:-------------------|:--------|:-------------|:-----------|:----------------|:---------|:----------------|:----------------|:-----------------|:---------------|:----------------|:-----------------|:------------|:----------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | X | | | | | X | | | X | | X | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 23 |  |  |  |  |  | X | X | | | | | | | | | | | X | | X | X | X | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | X | | | | | X | X | | X | | X | | X | | | | X | X | X | | X | X | X | | | | | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | X | | | | | | | | | | X | | X | | X | X | | | | | | | | | | | | | | X | X | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | X | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | X | | | | | | | |
| 7 | 12 |  |  |  |  |  | X | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | X | | | X | | | | | | | X | X | | | | | |
| 8 | 6 |  |  |  |  |  | X | X | | | X | | | X | | | | | | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X |
|
AshtonIsNotHere/nlp_pp_code_dataset | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2126529.0
num_examples: 1463
- name: test
num_bytes: 528817.0
num_examples: 258
download_size: 948983
dataset_size: 2655346.0
---
# Dataset Card for "nlp_pp_code_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zense-raaga-ai/raaga_dataset_v2 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: RaagaNumber
dtype: int64
splits:
- name: train
num_bytes: 22119715558.812
num_examples: 86746
download_size: 29873744194
dataset_size: 22119715558.812
---
# Dataset Card for "raaga_dataset_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zooxufpb/NSText2SQL_1_column | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 405208506
num_examples: 289288
download_size: 0
dataset_size: 405208506
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "NSText2SQL_1_column"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
RUCAIBox/Style-Transfer | ---
language:
- en
multilinguality:
- monolingual
task_categories:
- other
task_ids: []
tags:
- style-transfer
---
This is the text style transfer datasets collected by TextBox, including:
- GYAFC Entertainment & Music (gyafc_em).
- GYAFC Family & Relationships (gyafc_fr).
The detail and leaderboard of each dataset can be found in [TextBox page](https://github.com/RUCAIBox/TextBox#dataset). |
hztttian/vits-simple-api | ---
license: apache-2.0
---
|
heliosprime/twitter_dataset_1713011915 | ---
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: 12269
num_examples: 27
download_size: 10776
dataset_size: 12269
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713011915"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ZhaofengWu/transparency-data | ---
license: apache-2.0
---
Datasets used in our paper (https://arxiv.org/abs/2210.07468)
```bibtex
@inproceedings{wu-etal-2022-continued,
title = "Transparency Helps Reveal When Language Models Learn Meaning",
author = "Zhaofeng Wu and William Merrill and Hao Peng and Iz Beltagy and Noah A. Smith",
url = {https://arxiv.org/abs/2210.07468},
publisher = {arXiv},
year = {2022},
doi = {10.48550/ARXIV.2210.07468},
}
```
Please see the "Files and versions" tab for the data.
|
open-llm-leaderboard/details_abideen__PhigRange-2.7B-slerp | ---
pretty_name: Evaluation run of abideen/PhigRange-2.7B-slerp
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [abideen/PhigRange-2.7B-slerp](https://huggingface.co/abideen/PhigRange-2.7B-slerp)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_abideen__PhigRange-2.7B-slerp\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-04T21:20:07.760171](https://huggingface.co/datasets/open-llm-leaderboard/details_abideen__PhigRange-2.7B-slerp/blob/main/results_2024-04-04T21-20-07.760171.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5767941428995237,\n\
\ \"acc_stderr\": 0.03387553112768988,\n \"acc_norm\": 0.5768839451218056,\n\
\ \"acc_norm_stderr\": 0.034576125147848885,\n \"mc1\": 0.3769889840881273,\n\
\ \"mc1_stderr\": 0.016965517578930354,\n \"mc2\": 0.5316928803057882,\n\
\ \"mc2_stderr\": 0.015529301409234428\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5921501706484642,\n \"acc_stderr\": 0.014361097288449703,\n\
\ \"acc_norm\": 0.6168941979522184,\n \"acc_norm_stderr\": 0.014206472661672874\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5813582951603267,\n\
\ \"acc_stderr\": 0.004923281841828515,\n \"acc_norm\": 0.7663811989643498,\n\
\ \"acc_norm_stderr\": 0.004222676709104567\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4740740740740741,\n\
\ \"acc_stderr\": 0.04313531696750574,\n \"acc_norm\": 0.4740740740740741,\n\
\ \"acc_norm_stderr\": 0.04313531696750574\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5855263157894737,\n \"acc_stderr\": 0.04008973785779206,\n\
\ \"acc_norm\": 0.5855263157894737,\n \"acc_norm_stderr\": 0.04008973785779206\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\
\ \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \
\ \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.5924528301886792,\n \"acc_stderr\": 0.030242233800854494,\n\
\ \"acc_norm\": 0.5924528301886792,\n \"acc_norm_stderr\": 0.030242233800854494\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6180555555555556,\n\
\ \"acc_stderr\": 0.040629907841466674,\n \"acc_norm\": 0.6180555555555556,\n\
\ \"acc_norm_stderr\": 0.040629907841466674\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.45,\n \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.45,\n\
\ \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.04960449637488584,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.04960449637488584\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5895953757225434,\n\
\ \"acc_stderr\": 0.03750757044895537,\n \"acc_norm\": 0.5895953757225434,\n\
\ \"acc_norm_stderr\": 0.03750757044895537\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.047551296160629475,\n\
\ \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.047551296160629475\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n\
\ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.46808510638297873,\n \"acc_stderr\": 0.03261936918467382,\n\
\ \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.03261936918467382\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.38596491228070173,\n\
\ \"acc_stderr\": 0.04579639422070434,\n \"acc_norm\": 0.38596491228070173,\n\
\ \"acc_norm_stderr\": 0.04579639422070434\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.4896551724137931,\n \"acc_stderr\": 0.04165774775728763,\n\
\ \"acc_norm\": 0.4896551724137931,\n \"acc_norm_stderr\": 0.04165774775728763\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406772,\n \"\
acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406772\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\
\ \"acc_stderr\": 0.04375888492727061,\n \"acc_norm\": 0.3968253968253968,\n\
\ \"acc_norm_stderr\": 0.04375888492727061\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6612903225806451,\n\
\ \"acc_stderr\": 0.026923446059302837,\n \"acc_norm\": 0.6612903225806451,\n\
\ \"acc_norm_stderr\": 0.026923446059302837\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4433497536945813,\n \"acc_stderr\": 0.03495334582162934,\n\
\ \"acc_norm\": 0.4433497536945813,\n \"acc_norm_stderr\": 0.03495334582162934\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\
: 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.696969696969697,\n \"acc_stderr\": 0.035886248000917075,\n\
\ \"acc_norm\": 0.696969696969697,\n \"acc_norm_stderr\": 0.035886248000917075\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7222222222222222,\n \"acc_stderr\": 0.03191178226713547,\n \"\
acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.03191178226713547\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7772020725388601,\n \"acc_stderr\": 0.03003114797764154,\n\
\ \"acc_norm\": 0.7772020725388601,\n \"acc_norm_stderr\": 0.03003114797764154\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5769230769230769,\n \"acc_stderr\": 0.025049197876042338,\n\
\ \"acc_norm\": 0.5769230769230769,\n \"acc_norm_stderr\": 0.025049197876042338\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948503,\n \
\ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948503\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6134453781512605,\n \"acc_stderr\": 0.03163145807552379,\n \
\ \"acc_norm\": 0.6134453781512605,\n \"acc_norm_stderr\": 0.03163145807552379\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.4105960264900662,\n \"acc_stderr\": 0.04016689594849928,\n \"\
acc_norm\": 0.4105960264900662,\n \"acc_norm_stderr\": 0.04016689594849928\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8128440366972477,\n \"acc_stderr\": 0.016722684526200148,\n \"\
acc_norm\": 0.8128440366972477,\n \"acc_norm_stderr\": 0.016722684526200148\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4537037037037037,\n \"acc_stderr\": 0.03395322726375797,\n \"\
acc_norm\": 0.4537037037037037,\n \"acc_norm_stderr\": 0.03395322726375797\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.6666666666666666,\n \"acc_stderr\": 0.03308611113236434,\n \"\
acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.03308611113236434\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7341772151898734,\n \"acc_stderr\": 0.02875679962965834,\n \
\ \"acc_norm\": 0.7341772151898734,\n \"acc_norm_stderr\": 0.02875679962965834\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6367713004484304,\n\
\ \"acc_stderr\": 0.032277904428505,\n \"acc_norm\": 0.6367713004484304,\n\
\ \"acc_norm_stderr\": 0.032277904428505\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6946564885496184,\n \"acc_stderr\": 0.040393149787245605,\n\
\ \"acc_norm\": 0.6946564885496184,\n \"acc_norm_stderr\": 0.040393149787245605\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.743801652892562,\n \"acc_stderr\": 0.03984979653302871,\n \"acc_norm\"\
: 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302871\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7129629629629629,\n\
\ \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.7129629629629629,\n\
\ \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\
\ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4107142857142857,\n\
\ \"acc_stderr\": 0.04669510663875191,\n \"acc_norm\": 0.4107142857142857,\n\
\ \"acc_norm_stderr\": 0.04669510663875191\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\
\ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8162393162393162,\n\
\ \"acc_stderr\": 0.025372139671722933,\n \"acc_norm\": 0.8162393162393162,\n\
\ \"acc_norm_stderr\": 0.025372139671722933\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.70242656449553,\n\
\ \"acc_stderr\": 0.016349111912909425,\n \"acc_norm\": 0.70242656449553,\n\
\ \"acc_norm_stderr\": 0.016349111912909425\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6705202312138728,\n \"acc_stderr\": 0.025305258131879702,\n\
\ \"acc_norm\": 0.6705202312138728,\n \"acc_norm_stderr\": 0.025305258131879702\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2782122905027933,\n\
\ \"acc_stderr\": 0.014987325439963544,\n \"acc_norm\": 0.2782122905027933,\n\
\ \"acc_norm_stderr\": 0.014987325439963544\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5915032679738562,\n \"acc_stderr\": 0.028146405993096358,\n\
\ \"acc_norm\": 0.5915032679738562,\n \"acc_norm_stderr\": 0.028146405993096358\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6366559485530546,\n\
\ \"acc_stderr\": 0.02731684767419271,\n \"acc_norm\": 0.6366559485530546,\n\
\ \"acc_norm_stderr\": 0.02731684767419271\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6388888888888888,\n \"acc_stderr\": 0.026725868809100793,\n\
\ \"acc_norm\": 0.6388888888888888,\n \"acc_norm_stderr\": 0.026725868809100793\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.43617021276595747,\n \"acc_stderr\": 0.02958345203628407,\n \
\ \"acc_norm\": 0.43617021276595747,\n \"acc_norm_stderr\": 0.02958345203628407\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4152542372881356,\n\
\ \"acc_stderr\": 0.012585471793400659,\n \"acc_norm\": 0.4152542372881356,\n\
\ \"acc_norm_stderr\": 0.012585471793400659\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5183823529411765,\n \"acc_stderr\": 0.030352303395351964,\n\
\ \"acc_norm\": 0.5183823529411765,\n \"acc_norm_stderr\": 0.030352303395351964\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5310457516339869,\n \"acc_stderr\": 0.02018880445636189,\n \
\ \"acc_norm\": 0.5310457516339869,\n \"acc_norm_stderr\": 0.02018880445636189\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\
\ \"acc_stderr\": 0.04582004841505416,\n \"acc_norm\": 0.6454545454545455,\n\
\ \"acc_norm_stderr\": 0.04582004841505416\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6612244897959184,\n \"acc_stderr\": 0.030299506562154185,\n\
\ \"acc_norm\": 0.6612244897959184,\n \"acc_norm_stderr\": 0.030299506562154185\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.736318407960199,\n\
\ \"acc_stderr\": 0.031157150869355575,\n \"acc_norm\": 0.736318407960199,\n\
\ \"acc_norm_stderr\": 0.031157150869355575\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4578313253012048,\n\
\ \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.4578313253012048,\n\
\ \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.695906432748538,\n \"acc_stderr\": 0.0352821125824523,\n\
\ \"acc_norm\": 0.695906432748538,\n \"acc_norm_stderr\": 0.0352821125824523\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3769889840881273,\n\
\ \"mc1_stderr\": 0.016965517578930354,\n \"mc2\": 0.5316928803057882,\n\
\ \"mc2_stderr\": 0.015529301409234428\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.755327545382794,\n \"acc_stderr\": 0.012082125654159738\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6027293404094011,\n \
\ \"acc_stderr\": 0.013478659652337794\n }\n}\n```"
repo_url: https://huggingface.co/abideen/PhigRange-2.7B-slerp
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|arc:challenge|25_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|gsm8k|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hellaswag|10_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-04T21-20-07.760171.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-04T21-20-07.760171.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- '**/details_harness|winogrande|5_2024-04-04T21-20-07.760171.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-04T21-20-07.760171.parquet'
- config_name: results
data_files:
- split: 2024_04_04T21_20_07.760171
path:
- results_2024-04-04T21-20-07.760171.parquet
- split: latest
path:
- results_2024-04-04T21-20-07.760171.parquet
---
# Dataset Card for Evaluation run of abideen/PhigRange-2.7B-slerp
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [abideen/PhigRange-2.7B-slerp](https://huggingface.co/abideen/PhigRange-2.7B-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_abideen__PhigRange-2.7B-slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-04T21:20:07.760171](https://huggingface.co/datasets/open-llm-leaderboard/details_abideen__PhigRange-2.7B-slerp/blob/main/results_2024-04-04T21-20-07.760171.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.5767941428995237,
"acc_stderr": 0.03387553112768988,
"acc_norm": 0.5768839451218056,
"acc_norm_stderr": 0.034576125147848885,
"mc1": 0.3769889840881273,
"mc1_stderr": 0.016965517578930354,
"mc2": 0.5316928803057882,
"mc2_stderr": 0.015529301409234428
},
"harness|arc:challenge|25": {
"acc": 0.5921501706484642,
"acc_stderr": 0.014361097288449703,
"acc_norm": 0.6168941979522184,
"acc_norm_stderr": 0.014206472661672874
},
"harness|hellaswag|10": {
"acc": 0.5813582951603267,
"acc_stderr": 0.004923281841828515,
"acc_norm": 0.7663811989643498,
"acc_norm_stderr": 0.004222676709104567
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4740740740740741,
"acc_stderr": 0.04313531696750574,
"acc_norm": 0.4740740740740741,
"acc_norm_stderr": 0.04313531696750574
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5855263157894737,
"acc_stderr": 0.04008973785779206,
"acc_norm": 0.5855263157894737,
"acc_norm_stderr": 0.04008973785779206
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5924528301886792,
"acc_stderr": 0.030242233800854494,
"acc_norm": 0.5924528301886792,
"acc_norm_stderr": 0.030242233800854494
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6180555555555556,
"acc_stderr": 0.040629907841466674,
"acc_norm": 0.6180555555555556,
"acc_norm_stderr": 0.040629907841466674
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.45,
"acc_stderr": 0.04999999999999999,
"acc_norm": 0.45,
"acc_norm_stderr": 0.04999999999999999
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.42,
"acc_stderr": 0.04960449637488584,
"acc_norm": 0.42,
"acc_norm_stderr": 0.04960449637488584
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5895953757225434,
"acc_stderr": 0.03750757044895537,
"acc_norm": 0.5895953757225434,
"acc_norm_stderr": 0.03750757044895537
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.35294117647058826,
"acc_stderr": 0.047551296160629475,
"acc_norm": 0.35294117647058826,
"acc_norm_stderr": 0.047551296160629475
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.46808510638297873,
"acc_stderr": 0.03261936918467382,
"acc_norm": 0.46808510638297873,
"acc_norm_stderr": 0.03261936918467382
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.38596491228070173,
"acc_stderr": 0.04579639422070434,
"acc_norm": 0.38596491228070173,
"acc_norm_stderr": 0.04579639422070434
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.4896551724137931,
"acc_stderr": 0.04165774775728763,
"acc_norm": 0.4896551724137931,
"acc_norm_stderr": 0.04165774775728763
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42328042328042326,
"acc_stderr": 0.025446365634406772,
"acc_norm": 0.42328042328042326,
"acc_norm_stderr": 0.025446365634406772
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3968253968253968,
"acc_stderr": 0.04375888492727061,
"acc_norm": 0.3968253968253968,
"acc_norm_stderr": 0.04375888492727061
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695236,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695236
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6612903225806451,
"acc_stderr": 0.026923446059302837,
"acc_norm": 0.6612903225806451,
"acc_norm_stderr": 0.026923446059302837
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4433497536945813,
"acc_stderr": 0.03495334582162934,
"acc_norm": 0.4433497536945813,
"acc_norm_stderr": 0.03495334582162934
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695237,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695237
},
"harness|hendrycksTest-high_school_european_history|5": {
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"harness|gsm8k|5": {
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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#### Who are the source data producers?
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
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#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
[More Information Needed] |
jijay/instructpix2pix-demov2 | ---
dataset_info:
features:
- name: input_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 4872706.0
num_examples: 4
download_size: 4873592
dataset_size: 4872706.0
---
# Dataset Card for "instructpix2pix-demov2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
PocketDoc/text-splitter-alpaca | ---
task_categories:
- text-generation
language:
- en
---
https://huggingface.co/datasets/mhenrichsen/context-aware-splits-english |
jhoerr-livefront/livefront-illustrations | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 1380652.0
num_examples: 29
download_size: 1372836
dataset_size: 1380652.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Kelvinloh/lady | ---
license: mit
---
|
0x7o/GCRL-q | ---
dataset_info:
features:
- name: type
dtype: string
- name: theme
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: quality
dtype: float64
- name: id
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 6519238
num_examples: 3975
download_size: 3383601
dataset_size: 6519238
---
# Dataset Card for "GCRL-q"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ZiAngGu/scannet_3dbox_v2 | ---
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2869658129.643
num_examples: 33421
download_size: 2788454287
dataset_size: 2869658129.643
---
# Dataset Card for "scannet_3dbox_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
GalaktischeGurke/full_dataset_1510_lines_invoice_contract_mail_GPT3.5_test | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 6455.2913907284765
num_examples: 2
download_size: 12763
dataset_size: 6455.2913907284765
---
# Dataset Card for "full_dataset_1510_lines_invoice_contract_mail_GPT3.5_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Pravincoder/Indian_traffic_law_QA | ---
license: bigcode-openrail-m
task_categories:
- question-answering
language:
- en
tags:
- traffic_rules
- law
- Indian_traffic_rules
size_categories:
- n<1K
---
# Dataset Card for Indian Traffic Rules
### Dataset Summary
This DataSet is curated to Train or fine-tuning LLMs on basic questions on Indian traffic rules.
### Licensing Information :- bigcode-openrail-m
|
roa7n/patched_test_p_40_f_ATCaseOTCase_m1_predictions | ---
dataset_info:
features:
- name: id
dtype: string
- name: sequence_str
dtype: string
- name: label
dtype: int64
- name: m1_preds
dtype: float32
splits:
- name: train
num_bytes: 48235460
num_examples: 130287
download_size: 4618804
dataset_size: 48235460
---
# Dataset Card for "patched_test_p_40_f_ATCaseOTCase_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/nagato_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of nagato/長門/长门 (Azur Lane)
This is the dataset of nagato/長門/长门 (Azur Lane), containing 500 images and their tags.
The core tags of this character are `animal_ears, fox_ears, black_hair, long_hair, bangs, animal_ear_fluff, yellow_eyes, hair_ornament, blunt_bangs, very_long_hair, brown_eyes, 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 | 500 | 679.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagato_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 397.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagato_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1222 | 849.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagato_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 603.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagato_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1222 | 1.15 GiB | [Download](https://huggingface.co/datasets/CyberHarem/nagato_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/nagato_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, hair_bow, hairband, looking_at_viewer, solo, twintails, white_dress, bare_shoulders, blush, flower, mini_crown, ribbon, wings, frilled_dress, official_alternate_costume, strapless_dress, choker, sidelocks, sitting |
| 1 | 5 |  |  |  |  |  | 1girl, bare_shoulders, detached_sleeves, looking_at_viewer, solo, strapless_dress, necklace, sidelocks, simple_background, white_background, red_dress, upper_body, blush, choker, collarbone, open_mouth, parted_lips, wide_sleeves |
| 2 | 16 |  |  |  |  |  | 1girl, bare_shoulders, collarbone, red_dress, solo, strapless_dress, detached_sleeves, blush, white_sleeves, twitter_username, closed_mouth, long_sleeves, looking_at_viewer, simple_background, upper_body, smile, wide_sleeves, pleated_dress, white_background |
| 3 | 10 |  |  |  |  |  | 1girl, bare_shoulders, fox_mask, looking_at_viewer, mask_on_head, necklace, red_panties, side-tie_panties, solo, white_thighhighs, blush, navel, red_bra, see-through, camisole, strap_slip, simple_background, white_background, ass_visible_through_thighs, collarbone, ribbon-trimmed_legwear |
| 4 | 5 |  |  |  |  |  | 1girl, bare_shoulders, blush, chick, fox_mask, looking_at_viewer, mask_on_head, necklace, red_panties, ribbon-trimmed_legwear, see-through, side-tie_panties, solo, strap_slip, white_thighhighs, camisole, daruma_doll, no_shoes, sanshoku_dango, tatami, collarbone, flower, jingle_bell, red_bra, small_breasts, wariza, ass, ball, knees_up, navel, smile, tail, thighs, wind_chime |
| 5 | 16 |  |  |  |  |  | 1girl, blush, solo, wataboushi, bare_shoulders, looking_at_viewer, uchikake, detached_sleeves, wide_sleeves, white_kimono, dress, smile, ribbon-trimmed_sleeves, x_hair_ornament, hood_up, long_sleeves, jingle_bell, sitting, closed_mouth, ears_through_headwear |
| 6 | 11 |  |  |  |  |  | 1girl, hair_flower, looking_at_viewer, solo, bare_shoulders, off_shoulder, official_alternate_costume, fox_tail, lantern, necklace, sitting, multiple_tails, blush, full_body, pink_kimono, small_breasts |
| 7 | 6 |  |  |  |  |  | :d, bare_shoulders, blush, detached_sleeves, long_sleeves, open_mouth, sleeveless_kimono, white_kimono, 1girl, headpiece, solo, twitter_username, white_sleeves, wide_sleeves, jingle_bell, simple_background, upper_body, ^_^, chibi, hands_up, ribbon_trim, short_hair, white_background |
| 8 | 5 |  |  |  |  |  | 1girl, alternate_costume, fox_girl, fox_tail, looking_at_viewer, signature, solo, blush, contemporary, sidelocks, casual, long_sleeves, school_uniform, skirt, blurry_background, bokeh, bow, holding, parted_lips, pink_cardigan, smile |
| 9 | 7 |  |  |  |  |  | 1girl, alternate_costume, looking_at_viewer, sidelocks, solo, blue_sky, cloudy_sky, ocean, outdoors, blue_one-piece_swimsuit, day, signature, alternate_hairstyle, depth_of_field, horizon, ass, fox_girl, hair_bow, hair_ribbon, holding, looking_back, old_school_swimsuit, parted_lips, ponytail |
| 10 | 6 |  |  |  |  |  | 1girl, alternate_costume, looking_at_viewer, petals, sidelocks, solo, dress, flower, signature, casual, collarbone, fox_girl, bare_shoulders, contemporary, fox_tail, parted_lips, simple_background, spaghetti_strap, white_background |
| 11 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, short_sleeves, sidelocks, solo, dress, fox_girl, signature, simple_background, enmaided, fox_tail, maid_apron, frills, parted_lips, white_background, blush, holding, paw_pose |
| 12 | 5 |  |  |  |  |  | 1girl, ass, looking_at_viewer, small_breasts, solo, blush, fox_tail, from_behind, looking_back, open_mouth, red_bikini, simple_background, white_background, butt_crack, covering_breasts, fox_girl, from_side, nude, short_hair, sideboob, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | hair_bow | hairband | looking_at_viewer | solo | twintails | white_dress | bare_shoulders | blush | flower | mini_crown | ribbon | wings | frilled_dress | official_alternate_costume | strapless_dress | choker | sidelocks | sitting | detached_sleeves | necklace | simple_background | white_background | red_dress | upper_body | collarbone | open_mouth | parted_lips | wide_sleeves | white_sleeves | twitter_username | closed_mouth | long_sleeves | smile | pleated_dress | fox_mask | mask_on_head | red_panties | side-tie_panties | white_thighhighs | navel | red_bra | see-through | camisole | strap_slip | ass_visible_through_thighs | ribbon-trimmed_legwear | chick | daruma_doll | no_shoes | sanshoku_dango | tatami | jingle_bell | small_breasts | wariza | ass | ball | knees_up | tail | thighs | wind_chime | wataboushi | uchikake | white_kimono | dress | ribbon-trimmed_sleeves | x_hair_ornament | hood_up | ears_through_headwear | hair_flower | off_shoulder | fox_tail | lantern | multiple_tails | full_body | pink_kimono | :d | sleeveless_kimono | headpiece | ^_^ | chibi | hands_up | ribbon_trim | short_hair | alternate_costume | fox_girl | signature | contemporary | casual | school_uniform | skirt | blurry_background | bokeh | bow | holding | pink_cardigan | blue_sky | cloudy_sky | ocean | outdoors | blue_one-piece_swimsuit | day | alternate_hairstyle | depth_of_field | horizon | hair_ribbon | looking_back | old_school_swimsuit | ponytail | petals | spaghetti_strap | short_sleeves | enmaided | maid_apron | frills | paw_pose | from_behind | red_bikini | butt_crack | covering_breasts | from_side | nude | sideboob |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-----------|:-----------|:--------------------|:-------|:------------|:--------------|:-----------------|:--------|:---------|:-------------|:---------|:--------|:----------------|:-----------------------------|:------------------|:---------|:------------|:----------|:-------------------|:-----------|:--------------------|:-------------------|:------------|:-------------|:-------------|:-------------|:--------------|:---------------|:----------------|:-------------------|:---------------|:---------------|:--------|:----------------|:-----------|:---------------|:--------------|:-------------------|:-------------------|:--------|:----------|:--------------|:-----------|:-------------|:-----------------------------|:-------------------------|:--------|:--------------|:-----------|:-----------------|:---------|:--------------|:----------------|:---------|:------|:-------|:-----------|:-------|:---------|:-------------|:-------------|:-----------|:---------------|:--------|:-------------------------|:------------------|:----------|:------------------------|:--------------|:---------------|:-----------|:----------|:-----------------|:------------|:--------------|:-----|:--------------------|:------------|:------|:--------|:-----------|:--------------|:-------------|:--------------------|:-----------|:------------|:---------------|:---------|:-----------------|:--------|:--------------------|:--------|:------|:----------|:----------------|:-----------|:-------------|:--------|:-----------|:--------------------------|:------|:----------------------|:-----------------|:----------|:--------------|:---------------|:----------------------|:-----------|:---------|:------------------|:----------------|:-----------|:-------------|:---------|:-----------|:--------------|:-------------|:-------------|:-------------------|:------------|:-------|:-----------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | | | X | X | | | X | X | | | | | | | X | X | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 16 |  |  |  |  |  | X | | | X | X | | | X | X | | | | | | | X | | | | X | | X | X | X | X | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 10 |  |  |  |  |  | X | | | X | X | | | X | X | | | | | | | | | | | | X | X | X | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | | X | X | | | X | X | X | | | | | | | | | | | X | | | | | X | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 16 |  |  |  |  |  | X | | | X | X | | | X | X | | | | | | | | | | X | X | | | | | | | | | X | | | X | X | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 11 |  |  |  |  |  | X | | | X | X | | | X | X | | | | | | X | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 6 |  |  |  |  |  | X | | | | X | | | X | X | | | | | | | | | | | X | | X | X | | X | | X | | X | X | X | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 5 |  |  |  |  |  | X | | | X | X | | | | X | | | | | | | | | X | | | | | | | | | | X | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 7 |  |  |  |  |  | X | X | | X | X | | | | | | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 10 | 6 |  |  |  |  |  | X | | | X | X | | | X | | X | | | | | | | | X | | | | X | X | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | |
| 11 | 7 |  |  |  |  |  | X | | | X | X | | | | X | | | | | | | | | X | | | | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | X | X | | | | | | | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | |
| 12 | 5 |  |  |  |  |  | X | | | X | X | | | | X | | | | | | | | | | | | | X | X | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | X | X | X | X | X | X | X |
|
simbolo-ai/burmese-hate-speech-small | ---
license: gpl
language:
- my
---
### What is Hate Speech?
Hate speech is any toxic communication used to attack individuals or groups directly, especially based on the characteristics (but not limited to): physical deficiency, mental deficiency, moral deficiency, age, ethnicity, race, national origin, caste, religion, disability, serious disease, sex, gender, gender identity, gender reassignment, sexual orientation, and immigration status.
### Disclaimer
The dataset may contain toxic data like using rude words, and these are not aligned with the definition of hate speech data mentioned above.
### Contributors:
Main Contributor: Sa Phyo Thu Htet
Other Contributors: Ei Thandar Aung, Naing Linn Phyo, Yang Ni Linn Lat, Chaw Su Thwe Thiha Nyein, Hnin Aye Thant
Data Collectors: Sa Phyo Thu Htet, Students from Simbolo, Club Members of Data Science and Machine Learning Club, University of Technology, Yatanarpon Cyber City, Myanmar
### Cite As:
@misc{burmese-hate-speech-small,
author = {{Sa Phyo Thu Htet, Data Science and ML Club, UTYCC}},
title = {burmese-hate-speech-small},
url = {https://huggingface.co/datasets/simbolo-ai/burmese-hate-speech-data},
urldate = {2024-3-1},
date = {2024-3-1}
} |
Mlxa/java_methods | ---
license: apache-2.0
---
|
LxYxvv/ChinaDaily_EN_ZH | ---
license: mit
---
|
HydraLM/GPTeacher-General-Instruct_standardized | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
splits:
- name: train
num_bytes: 62696353
num_examples: 267780
download_size: 0
dataset_size: 62696353
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "GPTeacher-General-Instruct_standardized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_openagi-project__OpenAGI-7B-v0.2 | ---
pretty_name: Evaluation run of openagi-project/OpenAGI-7B-v0.2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [openagi-project/OpenAGI-7B-v0.2](https://huggingface.co/openagi-project/OpenAGI-7B-v0.2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_openagi-project__OpenAGI-7B-v0.2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-01T18:03:01.560923](https://huggingface.co/datasets/open-llm-leaderboard/details_openagi-project__OpenAGI-7B-v0.2/blob/main/results_2024-02-01T18-03-01.560923.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.632889617920343,\n\
\ \"acc_stderr\": 0.03254482538442493,\n \"acc_norm\": 0.63500329056049,\n\
\ \"acc_norm_stderr\": 0.0331984716026879,\n \"mc1\": 0.5593635250917993,\n\
\ \"mc1_stderr\": 0.01737969755543745,\n \"mc2\": 0.7204002538063481,\n\
\ \"mc2_stderr\": 0.015000816890913878\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6621160409556314,\n \"acc_stderr\": 0.013822047922283512,\n\
\ \"acc_norm\": 0.6851535836177475,\n \"acc_norm_stderr\": 0.013572657703084948\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6982672774347739,\n\
\ \"acc_stderr\": 0.004580718115992504,\n \"acc_norm\": 0.8602867954590719,\n\
\ \"acc_norm_stderr\": 0.003459806991389835\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.562962962962963,\n\
\ \"acc_stderr\": 0.04284958639753401,\n \"acc_norm\": 0.562962962962963,\n\
\ \"acc_norm_stderr\": 0.04284958639753401\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6578947368421053,\n \"acc_stderr\": 0.03860731599316092,\n\
\ \"acc_norm\": 0.6578947368421053,\n \"acc_norm_stderr\": 0.03860731599316092\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\
\ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \
\ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.028152837942493864,\n\
\ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.028152837942493864\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\
: {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6184971098265896,\n\
\ \"acc_stderr\": 0.037038511930995215,\n \"acc_norm\": 0.6184971098265896,\n\
\ \"acc_norm_stderr\": 0.037038511930995215\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287533,\n\
\ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287533\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.044084400227680794\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.032469569197899575,\n\
\ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.032469569197899575\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\
\ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\
\ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878151,\n\
\ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878151\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3994708994708995,\n \"acc_stderr\": 0.025225450284067884,\n \"\
acc_norm\": 0.3994708994708995,\n \"acc_norm_stderr\": 0.025225450284067884\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\
\ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\
\ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7258064516129032,\n\
\ \"acc_stderr\": 0.0253781399708852,\n \"acc_norm\": 0.7258064516129032,\n\
\ \"acc_norm_stderr\": 0.0253781399708852\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n\
\ \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\
: 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7777777777777778,\n \"acc_stderr\": 0.029620227874790486,\n \"\
acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.029620227874790486\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758723,\n\
\ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758723\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6076923076923076,\n \"acc_stderr\": 0.02475600038213095,\n \
\ \"acc_norm\": 0.6076923076923076,\n \"acc_norm_stderr\": 0.02475600038213095\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.337037037037037,\n \"acc_stderr\": 0.028820884666253255,\n \
\ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.028820884666253255\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.03058869701378364,\n \
\ \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.03058869701378364\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\
acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.818348623853211,\n \"acc_stderr\": 0.016530617409266857,\n \"\
acc_norm\": 0.818348623853211,\n \"acc_norm_stderr\": 0.016530617409266857\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.803921568627451,\n \"acc_stderr\": 0.027865942286639325,\n \"\
acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.027865942286639325\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7848101265822784,\n \"acc_stderr\": 0.02675082699467617,\n \
\ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.02675082699467617\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n\
\ \"acc_stderr\": 0.03149384670994131,\n \"acc_norm\": 0.672645739910314,\n\
\ \"acc_norm_stderr\": 0.03149384670994131\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\
\ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8264462809917356,\n \"acc_stderr\": 0.03457272836917669,\n \"\
acc_norm\": 0.8264462809917356,\n \"acc_norm_stderr\": 0.03457272836917669\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7484662576687117,\n \"acc_stderr\": 0.034089978868575295,\n\
\ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.034089978868575295\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\
\ \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n\
\ \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\
\ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8931623931623932,\n\
\ \"acc_stderr\": 0.020237149008990925,\n \"acc_norm\": 0.8931623931623932,\n\
\ \"acc_norm_stderr\": 0.020237149008990925\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8212005108556832,\n\
\ \"acc_stderr\": 0.013702643715368983,\n \"acc_norm\": 0.8212005108556832,\n\
\ \"acc_norm_stderr\": 0.013702643715368983\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7109826589595376,\n \"acc_stderr\": 0.02440517393578323,\n\
\ \"acc_norm\": 0.7109826589595376,\n \"acc_norm_stderr\": 0.02440517393578323\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3776536312849162,\n\
\ \"acc_stderr\": 0.01621414875213663,\n \"acc_norm\": 0.3776536312849162,\n\
\ \"acc_norm_stderr\": 0.01621414875213663\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6993464052287581,\n \"acc_stderr\": 0.02625605383571896,\n\
\ \"acc_norm\": 0.6993464052287581,\n \"acc_norm_stderr\": 0.02625605383571896\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6945337620578779,\n\
\ \"acc_stderr\": 0.026160584450140446,\n \"acc_norm\": 0.6945337620578779,\n\
\ \"acc_norm_stderr\": 0.026160584450140446\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7129629629629629,\n \"acc_stderr\": 0.025171041915309684,\n\
\ \"acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.025171041915309684\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45827900912646674,\n\
\ \"acc_stderr\": 0.01272570165695364,\n \"acc_norm\": 0.45827900912646674,\n\
\ \"acc_norm_stderr\": 0.01272570165695364\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.029029422815681397,\n\
\ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.029029422815681397\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6584967320261438,\n \"acc_stderr\": 0.019184639328092487,\n \
\ \"acc_norm\": 0.6584967320261438,\n \"acc_norm_stderr\": 0.019184639328092487\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\
\ \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.7181818181818181,\n\
\ \"acc_norm_stderr\": 0.043091187099464585\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128438,\n\
\ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128438\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7860696517412935,\n\
\ \"acc_stderr\": 0.02899690969332891,\n \"acc_norm\": 0.7860696517412935,\n\
\ \"acc_norm_stderr\": 0.02899690969332891\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\
\ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\
\ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.02753912288906145,\n\
\ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.02753912288906145\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5593635250917993,\n\
\ \"mc1_stderr\": 0.01737969755543745,\n \"mc2\": 0.7204002538063481,\n\
\ \"mc2_stderr\": 0.015000816890913878\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7916337805840569,\n \"acc_stderr\": 0.011414554399987736\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5344958301743745,\n \
\ \"acc_stderr\": 0.013739668147545913\n }\n}\n```"
repo_url: https://huggingface.co/openagi-project/OpenAGI-7B-v0.2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|arc:challenge|25_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|gsm8k|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hellaswag|10_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T18-03-01.560923.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-01T18-03-01.560923.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- '**/details_harness|winogrande|5_2024-02-01T18-03-01.560923.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-01T18-03-01.560923.parquet'
- config_name: results
data_files:
- split: 2024_02_01T18_03_01.560923
path:
- results_2024-02-01T18-03-01.560923.parquet
- split: latest
path:
- results_2024-02-01T18-03-01.560923.parquet
---
# Dataset Card for Evaluation run of openagi-project/OpenAGI-7B-v0.2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [openagi-project/OpenAGI-7B-v0.2](https://huggingface.co/openagi-project/OpenAGI-7B-v0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_openagi-project__OpenAGI-7B-v0.2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-01T18:03:01.560923](https://huggingface.co/datasets/open-llm-leaderboard/details_openagi-project__OpenAGI-7B-v0.2/blob/main/results_2024-02-01T18-03-01.560923.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.632889617920343,
"acc_stderr": 0.03254482538442493,
"acc_norm": 0.63500329056049,
"acc_norm_stderr": 0.0331984716026879,
"mc1": 0.5593635250917993,
"mc1_stderr": 0.01737969755543745,
"mc2": 0.7204002538063481,
"mc2_stderr": 0.015000816890913878
},
"harness|arc:challenge|25": {
"acc": 0.6621160409556314,
"acc_stderr": 0.013822047922283512,
"acc_norm": 0.6851535836177475,
"acc_norm_stderr": 0.013572657703084948
},
"harness|hellaswag|10": {
"acc": 0.6982672774347739,
"acc_stderr": 0.004580718115992504,
"acc_norm": 0.8602867954590719,
"acc_norm_stderr": 0.003459806991389835
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.562962962962963,
"acc_stderr": 0.04284958639753401,
"acc_norm": 0.562962962962963,
"acc_norm_stderr": 0.04284958639753401
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6578947368421053,
"acc_stderr": 0.03860731599316092,
"acc_norm": 0.6578947368421053,
"acc_norm_stderr": 0.03860731599316092
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.028152837942493864,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.028152837942493864
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.75,
"acc_stderr": 0.03621034121889507,
"acc_norm": 0.75,
"acc_norm_stderr": 0.03621034121889507
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6184971098265896,
"acc_stderr": 0.037038511930995215,
"acc_norm": 0.6184971098265896,
"acc_norm_stderr": 0.037038511930995215
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.43137254901960786,
"acc_stderr": 0.04928099597287533,
"acc_norm": 0.43137254901960786,
"acc_norm_stderr": 0.04928099597287533
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.044084400227680794,
"acc_norm": 0.74,
"acc_norm_stderr": 0.044084400227680794
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5574468085106383,
"acc_stderr": 0.032469569197899575,
"acc_norm": 0.5574468085106383,
"acc_norm_stderr": 0.032469569197899575
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4473684210526316,
"acc_stderr": 0.04677473004491199,
"acc_norm": 0.4473684210526316,
"acc_norm_stderr": 0.04677473004491199
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5517241379310345,
"acc_stderr": 0.04144311810878151,
"acc_norm": 0.5517241379310345,
"acc_norm_stderr": 0.04144311810878151
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3994708994708995,
"acc_stderr": 0.025225450284067884,
"acc_norm": 0.3994708994708995,
"acc_norm_stderr": 0.025225450284067884
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4365079365079365,
"acc_stderr": 0.04435932892851466,
"acc_norm": 0.4365079365079365,
"acc_norm_stderr": 0.04435932892851466
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7258064516129032,
"acc_stderr": 0.0253781399708852,
"acc_norm": 0.7258064516129032,
"acc_norm_stderr": 0.0253781399708852
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5073891625615764,
"acc_stderr": 0.035176035403610105,
"acc_norm": 0.5073891625615764,
"acc_norm_stderr": 0.035176035403610105
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695237,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695237
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7818181818181819,
"acc_stderr": 0.03225078108306289,
"acc_norm": 0.7818181818181819,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.029620227874790486,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.029620227874790486
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8704663212435233,
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"acc_norm": 0.8704663212435233,
"acc_norm_stderr": 0.024233532297758723
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6076923076923076,
"acc_stderr": 0.02475600038213095,
"acc_norm": 0.6076923076923076,
"acc_norm_stderr": 0.02475600038213095
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.337037037037037,
"acc_stderr": 0.028820884666253255,
"acc_norm": 0.337037037037037,
"acc_norm_stderr": 0.028820884666253255
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6680672268907563,
"acc_stderr": 0.03058869701378364,
"acc_norm": 0.6680672268907563,
"acc_norm_stderr": 0.03058869701378364
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3576158940397351,
"acc_stderr": 0.03913453431177258,
"acc_norm": 0.3576158940397351,
"acc_norm_stderr": 0.03913453431177258
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.818348623853211,
"acc_stderr": 0.016530617409266857,
"acc_norm": 0.818348623853211,
"acc_norm_stderr": 0.016530617409266857
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.49537037037037035,
"acc_stderr": 0.03409825519163572,
"acc_norm": 0.49537037037037035,
"acc_norm_stderr": 0.03409825519163572
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.803921568627451,
"acc_stderr": 0.027865942286639325,
"acc_norm": 0.803921568627451,
"acc_norm_stderr": 0.027865942286639325
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7848101265822784,
"acc_stderr": 0.02675082699467617,
"acc_norm": 0.7848101265822784,
"acc_norm_stderr": 0.02675082699467617
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.672645739910314,
"acc_stderr": 0.03149384670994131,
"acc_norm": 0.672645739910314,
"acc_norm_stderr": 0.03149384670994131
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7862595419847328,
"acc_stderr": 0.0359546161177469,
"acc_norm": 0.7862595419847328,
"acc_norm_stderr": 0.0359546161177469
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8264462809917356,
"acc_stderr": 0.03457272836917669,
"acc_norm": 0.8264462809917356,
"acc_norm_stderr": 0.03457272836917669
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.040191074725573483,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.040191074725573483
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7484662576687117,
"acc_stderr": 0.034089978868575295,
"acc_norm": 0.7484662576687117,
"acc_norm_stderr": 0.034089978868575295
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.45535714285714285,
"acc_stderr": 0.047268355537191,
"acc_norm": 0.45535714285714285,
"acc_norm_stderr": 0.047268355537191
},
"harness|hendrycksTest-management|5": {
"acc": 0.7281553398058253,
"acc_stderr": 0.044052680241409216,
"acc_norm": 0.7281553398058253,
"acc_norm_stderr": 0.044052680241409216
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8931623931623932,
"acc_stderr": 0.020237149008990925,
"acc_norm": 0.8931623931623932,
"acc_norm_stderr": 0.020237149008990925
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8212005108556832,
"acc_stderr": 0.013702643715368983,
"acc_norm": 0.8212005108556832,
"acc_norm_stderr": 0.013702643715368983
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7109826589595376,
"acc_stderr": 0.02440517393578323,
"acc_norm": 0.7109826589595376,
"acc_norm_stderr": 0.02440517393578323
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3776536312849162,
"acc_stderr": 0.01621414875213663,
"acc_norm": 0.3776536312849162,
"acc_norm_stderr": 0.01621414875213663
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6993464052287581,
"acc_stderr": 0.02625605383571896,
"acc_norm": 0.6993464052287581,
"acc_norm_stderr": 0.02625605383571896
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6945337620578779,
"acc_stderr": 0.026160584450140446,
"acc_norm": 0.6945337620578779,
"acc_norm_stderr": 0.026160584450140446
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7129629629629629,
"acc_stderr": 0.025171041915309684,
"acc_norm": 0.7129629629629629,
"acc_norm_stderr": 0.025171041915309684
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4929078014184397,
"acc_stderr": 0.02982449855912901,
"acc_norm": 0.4929078014184397,
"acc_norm_stderr": 0.02982449855912901
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.45827900912646674,
"acc_stderr": 0.01272570165695364,
"acc_norm": 0.45827900912646674,
"acc_norm_stderr": 0.01272570165695364
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6470588235294118,
"acc_stderr": 0.029029422815681397,
"acc_norm": 0.6470588235294118,
"acc_norm_stderr": 0.029029422815681397
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6584967320261438,
"acc_stderr": 0.019184639328092487,
"acc_norm": 0.6584967320261438,
"acc_norm_stderr": 0.019184639328092487
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7181818181818181,
"acc_stderr": 0.043091187099464585,
"acc_norm": 0.7181818181818181,
"acc_norm_stderr": 0.043091187099464585
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.726530612244898,
"acc_stderr": 0.028535560337128438,
"acc_norm": 0.726530612244898,
"acc_norm_stderr": 0.028535560337128438
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7860696517412935,
"acc_stderr": 0.02899690969332891,
"acc_norm": 0.7860696517412935,
"acc_norm_stderr": 0.02899690969332891
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.84,
"acc_stderr": 0.03684529491774708,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774708
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5180722891566265,
"acc_stderr": 0.03889951252827216,
"acc_norm": 0.5180722891566265,
"acc_norm_stderr": 0.03889951252827216
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.847953216374269,
"acc_stderr": 0.02753912288906145,
"acc_norm": 0.847953216374269,
"acc_norm_stderr": 0.02753912288906145
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5593635250917993,
"mc1_stderr": 0.01737969755543745,
"mc2": 0.7204002538063481,
"mc2_stderr": 0.015000816890913878
},
"harness|winogrande|5": {
"acc": 0.7916337805840569,
"acc_stderr": 0.011414554399987736
},
"harness|gsm8k|5": {
"acc": 0.5344958301743745,
"acc_stderr": 0.013739668147545913
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
Yehor/ukrainian-tts-oleksa | ---
license: apache-2.0
task_categories:
- text-to-speech
language:
- uk
---
# 🇺🇦 Open Source Ukrainian Text-to-Speech dataset named OLEKSA
Join Ukrainian community - https://t.me/speech_synthesis_uk
More details about this dataset - https://github.com/egorsmkv/ukrainian-tts-datasets/tree/main/oleksa
# Voice OLEKSA (male)
The voice of: https://twitter.com/CHOBUDA
## Features
- Quality: high
- Duration: 6h
- Audio formats: OPUS
- Text format: JSONL (a `metadata.jsonl` file)
- Frequency: 48000 Hz |
result-kand2-sdxl-wuerst-karlo/4e08d540 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 207
num_examples: 10
download_size: 1373
dataset_size: 207
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "4e08d540"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-94000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 653970
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
banloada/banloda | ---
license: other
---
|
open-llm-leaderboard/details_doas__test5 | ---
pretty_name: Evaluation run of doas/test5
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [doas/test5](https://huggingface.co/doas/test5) 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_doas__test5\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-25T05:43:32.139729](https://huggingface.co/datasets/open-llm-leaderboard/details_doas__test5/blob/main/results_2023-09-25T05-43-32.139729.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\": 0.0,\n \"\
em_stderr\": 0.0,\n \"f1\": 4.5092281879194636e-05,\n \"f1_stderr\"\
: 2.699913059109046e-05,\n \"acc\": 0.2632202052091555,\n \"acc_stderr\"\
: 0.007016411937203614\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n\
\ \"em_stderr\": 0.0,\n \"f1\": 4.5092281879194636e-05,\n \"\
f1_stderr\": 2.699913059109046e-05\n },\n \"harness|gsm8k|5\": {\n \
\ \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.526440410418311,\n \"acc_stderr\": 0.014032823874407229\n\
\ }\n}\n```"
repo_url: https://huggingface.co/doas/test5
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|arc:challenge|25_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_25T05_43_32.139729
path:
- '**/details_harness|drop|3_2023-09-25T05-43-32.139729.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-25T05-43-32.139729.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_25T05_43_32.139729
path:
- '**/details_harness|gsm8k|5_2023-09-25T05-43-32.139729.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-25T05-43-32.139729.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hellaswag|10_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T10:59:05.636787.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_23T10_59_05.636787
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-23T10:59:05.636787.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-23T10:59:05.636787.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_25T05_43_32.139729
path:
- '**/details_harness|winogrande|5_2023-09-25T05-43-32.139729.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-25T05-43-32.139729.parquet'
- config_name: results
data_files:
- split: 2023_09_25T05_43_32.139729
path:
- results_2023-09-25T05-43-32.139729.parquet
- split: latest
path:
- results_2023-09-25T05-43-32.139729.parquet
---
# Dataset Card for Evaluation run of doas/test5
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/doas/test5
- **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 [doas/test5](https://huggingface.co/doas/test5) 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_doas__test5",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-25T05:43:32.139729](https://huggingface.co/datasets/open-llm-leaderboard/details_doas__test5/blob/main/results_2023-09-25T05-43-32.139729.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": 0.0,
"em_stderr": 0.0,
"f1": 4.5092281879194636e-05,
"f1_stderr": 2.699913059109046e-05,
"acc": 0.2632202052091555,
"acc_stderr": 0.007016411937203614
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 4.5092281879194636e-05,
"f1_stderr": 2.699913059109046e-05
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.526440410418311,
"acc_stderr": 0.014032823874407229
}
}
```
### 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] |
David19930/test_david_v1 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 91266
num_examples: 590
download_size: 9428
dataset_size: 91266
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
SergeyKarpenko1/autotrain-data-nlp | ---
language:
- en
---
# AutoTrain Dataset for project: nlp
## Dataset Description
This dataset has been automatically processed by AutoTrain for project nlp.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"context": "\u0417\u0434\u0440\u0430\u0432\u0441\u0442\u0432\u0443\u0439\u0442\u0435 \ud83e\udd1d \u041d\u0430 \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u044c\u043d\u043e\u043c \u0433\u0435\u043b\u0435, \u0432 \u043a\u043e\u0442\u043e\u0440\u043e\u043c \u043f\u043e\u0434\u043e\u0431\u0440\u0430\u043d\u044b \u0438\u043d\u0433\u0440\u0435\u0434\u0438\u0435\u043d\u0442\u044b \u043e\u0442\u0432\u0435\u0447\u0430\u044e\u0449\u0438\u0435 \u0437\u0430 \u043f\u043e\u0434\u0442\u044f\u0436\u043a\u0443, \u0430\u043d\u0442\u0438\u0446\u0435\u043b\u043b\u044e\u043b\u0438\u0442\u043d\u044b\u0439 \u044d\u0444\u0444\u0435\u043a\u0442 \u0438 \u0436\u0438\u0440\u043e\u0437\u0436\u0438\u0433\u0430\u043d\u0438\u0435 ! \u0412\u044b\u043f\u043e\u043b\u043d\u044f\u0442\u044c\u0441\u044f \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0451\u043d\u043d\u044b\u0435 \u0442\u0435\u0445\u043d\u0438\u043a\u0438 \u043c\u0430\u0441\u0441\u0430\u0436\u043d\u044b\u0435, \u043f\u043e \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0451\u043d\u043d\u044b\u043c \u043b\u0438\u043d\u0438\u044f\u043c, \u043f\u0438\u043b\u0438\u043d\u0433 \u0441\u043d\u0430\u0447\u0430\u043b\u0430, \u043f\u043e\u0442\u043e\u043c \u0432\u0431\u0438\u0432\u0430\u043d\u0438\u0435 \u0413\u0435\u043b\u044f \u0432 \u043f\u043e\u0440\u044b \u0438 \u043e\u0431\u0435\u0440\u0442\u044b\u0432\u0430\u043d\u0438\u0435 \u0432 \u0438\u043d\u0444\u0440\u0430\u043a\u0440\u0430\u0441\u043d\u043e\u0435 \u043e\u0434\u0435\u044f\u043b\u043e! \u041d\u0435 \u0431\u043e\u043b\u044c\u043d\u043e)",
"question": "\u0417\u0434\u0440\u0430\u0432\u0441\u0442\u0432\u0443\u0439\u0442\u0435, \u0440\u0430\u0441\u0441\u043a\u0430\u0436\u0438\u0442\u0435 \u043f\u0440\u043e \u043f\u0440\u043e\u0446\u0435\u0434\u0443\u0440\u0443 \u043a\u0430\u0440\u0430\u043c\u0435\u043b\u044c\u043d\u0430\u044f \u043b\u0438\u043f\u0430\u043a\u0441\u0430\u0446\u0438\u044f , \u043a\u0430\u043a \u044d\u0442\u043e \u0434\u0435\u043b\u0430\u0435\u0442\u0441\u044f , \u0431\u043e\u043b\u044c\u043d\u043e \u044d\u0442\u043e ?",
"answers.text": [
"\u0417\u0434\u0440\u0430\u0432\u0441\u0442\u0432\u0443\u0439\u0442\u0435 \ud83e\udd1d \u041d\u0430 \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u044c\u043d\u043e\u043c \u0433\u0435\u043b\u0435, \u0432 \u043a\u043e\u0442\u043e\u0440\u043e\u043c \u043f\u043e\u0434\u043e\u0431\u0440\u0430\u043d\u044b \u0438\u043d\u0433\u0440\u0435\u0434\u0438\u0435\u043d\u0442\u044b \u043e\u0442\u0432\u0435\u0447\u0430\u044e\u0449\u0438\u0435 \u0437\u0430 \u043f\u043e\u0434\u0442\u044f\u0436\u043a\u0443, \u0430\u043d\u0442\u0438\u0446\u0435\u043b\u043b\u044e\u043b\u0438\u0442\u043d\u044b\u0439 \u044d\u0444\u0444\u0435\u043a\u0442 \u0438 \u0436\u0438\u0440\u043e\u0437\u0436\u0438\u0433\u0430\u043d\u0438\u0435 ! \u0412\u044b\u043f\u043e\u043b\u043d\u044f\u0442\u044c\u0441\u044f \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0451\u043d\u043d\u044b\u0435 \u0442\u0435\u0445\u043d\u0438\u043a\u0438 \u043c\u0430\u0441\u0441\u0430\u0436\u043d\u044b\u0435, \u043f\u043e \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0451\u043d\u043d\u044b\u043c \u043b\u0438\u043d\u0438\u044f\u043c, \u043f\u0438\u043b\u0438\u043d\u0433 \u0441\u043d\u0430\u0447\u0430\u043b\u0430, \u043f\u043e\u0442\u043e\u043c \u0432\u0431\u0438\u0432\u0430\u043d\u0438\u0435 \u0413\u0435\u043b\u044f \u0432 \u043f\u043e\u0440\u044b \u0438 \u043e\u0431\u0435\u0440\u0442\u044b\u0432\u0430\u043d\u0438\u0435 \u0432 \u0438\u043d\u0444\u0440\u0430\u043a\u0440\u0430\u0441\u043d\u043e\u0435 \u043e\u0434\u0435\u044f\u043b\u043e! \u041d\u0435 \u0431\u043e\u043b\u044c\u043d\u043e)"
],
"answers.answer_start": [
-1
]
},
{
"context": "\u0417\u0434\u0440\u0430\u0432\u0441\u0442\u0432\u0443\u0439\u0442\u0435 \ud83e\udd1d \u0414\u0430, \u043d\u0430 \u0447\u0442\u043e \u0445\u043e\u0442\u0438\u0442\u0435?) \u0441 \u043a\u0430\u043a\u043e\u0433\u043e \u0447\u0438\u0441\u043b\u0430? \u0412 \u043a\u0430\u043a\u043e\u0435 \u0432\u0440\u0435\u043c\u044f \u0443\u0434\u043e\u0431\u043d\u043e?",
"question": "\u0410 \u043c\u043e\u0436\u043d\u043e \u043d\u0430 \u044f\u043d\u0432\u0430\u0440\u044c \u0443\u0436\u0435 \u0437\u0430\u043f\u0438\u0441\u0430\u0442\u044c\u0441\u044f ?",
"answers.text": [
"\u0417\u0434\u0440\u0430\u0432\u0441\u0442\u0432\u0443\u0439\u0442\u0435 \ud83e\udd1d \u0414\u0430, \u043d\u0430 \u0447\u0442\u043e \u0445\u043e\u0442\u0438\u0442\u0435?) \u0441 \u043a\u0430\u043a\u043e\u0433\u043e \u0447\u0438\u0441\u043b\u0430? \u0412 \u043a\u0430\u043a\u043e\u0435 \u0432\u0440\u0435\u043c\u044f \u0443\u0434\u043e\u0431\u043d\u043e?"
],
"answers.answer_start": [
-1
]
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"context": "Value(dtype='string', id=None)",
"question": "Value(dtype='string', id=None)",
"answers.text": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"answers.answer_start": "Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 248 |
| valid | 63 |
|
adleme94/borges | ---
license: apache-2.0
task_categories:
- text-generation
language:
- es
size_categories:
- n<1K
--- |
gguichard/coref_dataset | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 595904106.6677296
num_examples: 101287
- name: test
num_bytes: 31363993.332270347
num_examples: 5331
download_size: 94532509
dataset_size: 627268100.0
---
# Dataset Card for "coref_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vikp/code_search_net_filtered_34k | ---
license: cc-by-4.0
dataset_info:
features:
- name: code
dtype: string
- name: signature
dtype: string
- name: docstring
dtype: string
- name: loss_without_docstring
dtype: float64
- name: loss_with_docstring
dtype: float64
- name: factor
dtype: float64
- name: rendered
dtype: string
- name: quality_prob
dtype: float64
- name: learning_prob
dtype: float64
splits:
- name: train
num_bytes: 59688305.816243604
num_examples: 34488
download_size: 30704027
dataset_size: 59688305.816243604
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Filtered version of code search net python subset, with filtering based on perplexity with/without docstring, learning value/quality classifiers, and manual filtering.
Original data with perplexity filtering is from [here](https://huggingface.co/datasets/bjoernp/code_search_net_python_processed_400k), with credit to bjoernp. |
jxu9001/conll_v3 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: tokens
sequence: string
- name: tags
sequence: int64
splits:
- name: train
num_bytes: 3445822
num_examples: 14041
- name: validation
num_bytes: 866541
num_examples: 3250
- name: test
num_bytes: 784956
num_examples: 3453
download_size: 1247438
dataset_size: 5097319
---
# Dataset Card for "conll_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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