datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
dapooni/ds_translate_en_bsl_bart | ---
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 473449
num_examples: 3303
download_size: 176787
dataset_size: 473449
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
aai530-group6/pmdata | ---
license: cc-by-4.0
language:
- en
tags:
- health
pretty_name: pmdata
---
# PMData Dataset
## About Dataset
Paper: <https://dl.acm.org/doi/10.1145/3339825.3394926>
In this dataset, we present the PMData dataset that aims to combine traditional lifelogging with sports activity logging. Such a dataset enables the development of several interesting analysis applications, e.g., where additional sports data can be used to predict and analyze everyday developments like a person's weight and sleep patterns, and where traditional lifelog data can be used in a sports context to predict an athletes performance. In this respect, we have used the Fitbit Versa 2 smartwatch wristband, the PMSys sports logging app a and Google forms for the data collection, and PMData contains logging data for 5 months from 16 persons. Our initial experiments show that such analyzes are possible, but there are still large rooms for improvements.
### Dataset Details
The structure of the main folder:
```text
[Main folder]
├── p01
├── p02
├── ...
├── p16
└── participant-overview.xlsx
```
Each participant's folder (pXX) contains:
- `fitbit` [folder]
- `calories.json`: Shows how many calories the person have burned the last minute.
- `distance.json`: Gives the distance moved per minute. Distance seems to be in centimeters.
- `exercise.json`: Describes each activity in more detail. It contains the date with start and stop time, time in different activity levels, type of activity and various performance metrics depending a bit on type of exercise, e.g., for running, it contains distance, time, steps, calories, speed and pace.
- `heart_rate.json`: Shows the number of heart beats per minute (bpm) at a given time.
- `lightly_active_minutes.json`: Sums up the number of lightly active minutes per day.
- `moderately_active_minutes.json`: Sums up the number of moderately active minutes per day.
- `resting_heart_rate.json`: Gives the resting heart rate per day.
- `sedentary_minutes.json`: Sums up the number of sedentary minutes per day.
- `sleep_score.csv`: Helps understand the sleep each night so you can see trends in the sleep patterns. It contains an overall 0-100 score made up from composition, revitalization and duration scores, the number of deep sleep minutes, the resting heart rate and a restlessness score.
- `sleep.json`: A per sleep breakdown of the sleep into periods of light, deep, rem sleeps and time awake.
- `steps.json`: Displays the number of steps per minute.
- `time_in_heart_rate_zones.json`: Gives the number of minutes in different heart rate zones. Using the common formula of 220 minus your age, Fitbit will calculate your maximum heart rate and then create three target heart rate zones fat burn (50 to 69 percent of your max heart rate), cardio (70 to 84 percent of your max heart rate), and peak (85 to 100 percent of your max heart rate) - based off that number.
- `very_active_minutes.json`: Sums up the number of very active minutes per day.
- `googledocs` [folder]
- `reporting.csv`: Contains one line per report including the date reported for, a timestamp of the report submission time, the eaten meals (breakfast, lunch, dinner and evening meal), the participants weight this day, the number of glasses drunk, and whether one has consumed alcohol.
- `pmsys` [folder]
- `injury.csv`: Shows injuries with a time and date and corresponding injury locations and a minor and major severity.
- `srpe.csv`: Contains a training session’s end-time, type of activity, the perceived exertion (RPE), and the duration in the number of minutes. This is, for example, used to calculate the sessions training load or sRPE (RPE×duration).
- `wellness.csv`: Includes parameters like time and date, fatigue, mood, readiness, sleep duration (number of hours), sleep quality, soreness (and soreness area), and stress. Fatigue, sleep qual-ity, soreness, stress, and mood all have a 1-5 scale. The score 3 is normal, and 1-2 are scores below normal and 4-5 are scores above normal. Sleep length is just a measure of how long the sleep was in hours, and readiness (scale 0-10) is an overall subjective measure of how ready are you to exercise, i.e., 0 means not ready at all and 10 indicates that you cannot feel any better and are ready for anything!
- `food-images.zip`: Participants 1, 3 and 5 have taken pictures of everything they have eaten except water during 2 months (February and March). There are food images included in this .zip file, and information about day and time is given in the image header. The participants used their own mobile cameras to collect the images (Iphone 6s, Iphone X and Iphone XS). The standard export function of the MacOS Photos software with full quality was used to export the images.
### Term of use
The license for the PMData dataset is Attribution-NonCommercial 4.0 International. More information can be found here: <https://creativecommons.org/licenses/by-nc/4.0/legalcode>
### Citation
```bibtex
@inproceedings{10.1145/3339825.3394926,
address = {New York, NY, USA},
author = {Thambawita, Vajira and Hicks, Steven Alexander and Borgli, Hanna and Stensland, H\r{a}kon Kvale and Jha, Debesh and Svensen, Martin Kristoffer and Pettersen, Svein-Arne and Johansen, Dag and Johansen, H\r{a}vard Dagenborg and Pettersen, Susann Dahl and Nordvang, Simon and Pedersen, Sigurd and Gjerdrum, Anders and Gr\o{}nli, Tor-Morten and Fredriksen, Per Morten and Eg, Ragnhild and Hansen, Kjeld and Fagernes, Siri and Claudi, Christine and Bi\o{}rn-Hansen, Andreas and Nguyen, Duc Tien Dang and Kupka, Tomas and Hammer, Hugo Lewi and Jain, Ramesh and Riegler, Michael Alexander and Halvorsen, P\r{a}l},
booktitle = {Proceedings of the 11th ACM Multimedia Systems Conference},
doi = {10.1145/3339825.3394926},
isbn = {9781450368452},
keywords = {sports logging, questionnaires, food pictures, neural networks, multimedia dataset, sensor data, machine learning},
location = {Istanbul, Turkey},
numpages = {6},
pages = {231-236},
publisher = {Association for Computing Machinery},
series = {MMSys '20},
title = {PMData: A Sports Logging Dataset},
url = {https://doi.org/10.1145/3339825.3394926},
year = {2020},
}
```
|
celinelee/ny_state_math | ---
dataset_info:
features:
- name: file_source
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 30626
num_examples: 102
download_size: 18549
dataset_size: 30626
---
# Dataset Card for "ny_state_math"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Indic-LLM-Labs/C4-Kn | ---
dataset_info:
features:
- name: text
dtype: string
- name: timestamp
dtype: timestamp[s]
- name: url
dtype: string
splits:
- name: train
num_bytes: 7772502793
num_examples: 1056849
- name: validation
num_bytes: 7579027
num_examples: 1039
download_size: 3033462453
dataset_size: 7780081820
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
This is a filtered version of the [C4](https://huggingface.co/datasets/allenai/c4) dataset only containing samples of Kannada language.
The dataset contains total of 1056849 training and 1039 validation samples.
### Data Sample:
```python
{'text': 'ಹಳ್ಳಿಯ ‘ಬೋಲ್ಟ್\u200c’ಗಳನ್ನು ಗುರುತಿಸಿ | Prajavani\nಪ್ರಜಾವಾಣಿ ವಾರ್ತೆ Updated: 18 ಫೆಬ್ರವರಿ 2020, 01:30 IST\nಉಡುಪಿಯ ಐಕಳದಲ್ಲಿ ಇತ್ತೀಚೆಗೆ ನಡೆದ ಕಂಬಳದ ಓಟದಲ್ಲಿ ಶ್ರೀನಿವಾಸ ಗೌಡ ಎಂಬುವರು ವಿಶ್ವದ ವೇಗದ ಓಟಗಾರ ಉಸೇನ್ ಬೋಲ್ಟ್ ಅವರಿಗಿಂತಲೂ ವೇಗವಾಗಿ ಓಡಿ ಗುರಿ ತಲುಪಿದ್ದು, ಸಾರ್ವಜನಿಕರ ಮೆಚ್ಚುಗೆಗೆ ಪಾತ್ರರಾಗಿದ್ದಾರೆ. ಗ್ರಾಮೀಣ ಪ್ರದೇಶ\nಗಳಲ್ಲಿ ಇರುವ ಇಂತಹ ಓಟಗಾರರು ಮತ್ತು ಆಟಗಾರರು ಎಲೆಮರೆಯ ಕಾಯಿಯಂತೆ ತಮ್ಮ ಪಾಡಿಗೆ ತಾವು ಬೆಳೆಯುತ್ತಿರುತ್ತಾರೆ. ಶಾಲಾ- ಕಾಲೇಜುಗಳಲ್ಲಿ ಓದುತ್ತಿರುವವರಿಗೆ ಮುಂದೆ ಬರಲು ಸ್ವಲ್ಪಮಟ್ಟಿಗಾದರೂ ಅವಕಾಶ ಇರುತ್ತದೆ. ಅವಿದ್ಯಾವಂತರಿಗೆ ಅದೂ ಇಲ್ಲ.\nಇನ್ನು ಕ್ರೀಡಾಕೂಟಗಳಿಗೆ.......',
'timestamp': datetime.datetime(2020, 4, 1, 16, 50, 10),
'url': 'https://www.prajavani.net/op-ed/readers-letter/need-more-publicity-to-kambala-sports-706114.html'}
```
### Use with Datasets:
```python
from datasets import load_dataset
ds = load_dataset("Indic-LLM-Labs/C4-Kn")
```
|
kooshan/Islamic.Sufi.Religion_Books | ---
license: other
license_name: bcj2
license_link: LICENSE
---
|
appvoid/noisy-textbook-5k | ---
dataset_info:
features:
- name: id
dtype: large_string
- name: prompt
dtype: large_string
- name: textbook
dtype: large_string
- name: question
dtype: large_string
- name: response
dtype: large_string
- name: text
dtype: string
splits:
- name: train
num_bytes: 76894068
num_examples: 5000
download_size: 34441931
dataset_size: 76894068
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
TrainingDataPro/pose_estimation | ---
license: cc-by-nc-nd-4.0
task_categories:
- image-classification
language:
- en
tags:
- code
- finance
dataset_info:
features:
- name: image_id
dtype: uint32
- name: image
dtype: image
- name: mask
dtype: image
- name: shapes
dtype: string
splits:
- name: train
num_bytes: 142645152
num_examples: 29
download_size: 137240523
dataset_size: 142645152
---
# Pose Estimation
The dataset is primarly intended to dentify and predict the positions of major joints of a human body in an image. It consists of people's photographs with body part labeled with keypoints.
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market/pose-estimation-annotation?utm_source=huggingface&utm_medium=cpc&utm_campaign=pose_estimation) to discuss your requirements, learn about the price and buy the dataset.
# Data Format
Each image from `EP` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the key points. For each point, the x and y coordinates are provided, and there is a `Presumed_Location` attribute, indicating whether the point is presumed or accurately defined.
# Example of XML file structure
.png?generation=1684358333663868&alt=media)
# Labeled body parts
Each keypoint is ordered and corresponds to the concrete part of the body:
0. **Nose**
1. **Neck**
2. **Right shoulder**
3. **Right elbow**
4. **Right wrist**
5. **Left shoulder**
6. **Left elbow**
7. **Left wrist**
8. **Right hip**
9. **Right knee**
10. **Right foot**
11. **Left hip**
12. **Left knee**
13. **Left foot**
14. **Right eye**
15. **Left eye**
16. **Right ear**
17. **Left ear**
# Keypoint annotation is made in accordance with your requirements.
## [**TrainingData**](https://trainingdata.pro/data-market/pose-estimation-annotation?utm_source=huggingface&utm_medium=cpc&utm_campaign=pose_estimation) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets** |
Passpass119/Emoji | ---
license: mit
---
|
amin-nejad/EuroSat | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': AnnualCrop
'1': Forest
'2': HerbaceousVegetation
'3': Highway
'4': Industrial
'5': Pasture
'6': PermanentCrop
'7': Residential
'8': River
'9': SeaLake
splits:
- name: train
num_bytes: 83171379.6
num_examples: 24300
download_size: 82782583
dataset_size: 83171379.6
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "EuroSat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Databasesprojec/FinStmts_ConsUncons_English_EU_Predict_part_2 | ---
dataset_info:
features:
- name: label
dtype: int64
- name: id
dtype: string
- name: language
dtype: string
- name: text
dtype: string
- name: __index_level_0__
dtype: int64
splits:
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num_bytes: 2421578650
num_examples: 10885
download_size: 1159607309
dataset_size: 2421578650
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ted_hrlr | ---
annotations_creators:
- crowdsourced
language:
- az
- be
- en
- es
- fr
- gl
- he
- it
- pt
- ru
- tr
language_creators:
- expert-generated
license:
- cc-by-nc-nd-4.0
multilinguality:
- translation
pretty_name: TEDHrlr
size_categories:
- 1M<n<10M
source_datasets:
- extended|ted_talks_iwslt
task_categories:
- translation
task_ids: []
paperswithcode_id: null
dataset_info:
- config_name: az_to_en
features:
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dtype:
translation:
languages:
- az
- en
splits:
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- name: validation
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num_examples: 672
download_size: 131005909
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download_size: 131005909
dataset_size: 40466400
---
# Dataset Card for "ted_hrlr"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/neulab/word-embeddings-for-nmt
- **Paper:** [When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?](https://aclanthology.org/N18-2084/)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 1.83 GB
- **Size of the generated dataset:** 281.66 MB
- **Total amount of disk used:** 2.12 GB
### Dataset Summary
Data sets derived from TED talk transcripts for comparing similar language pairs
where one is high resource and the other is low resource.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### az_to_en
- **Size of downloaded dataset files:** 131.01 MB
- **Size of the generated dataset:** 1.53 MB
- **Total amount of disk used:** 132.54 MB
An example of 'train' looks as follows.
```
{
"translation": {
"az": "zəhmət olmasa , sizə xitab edən sözlər eşidəndə əlinizi qaldırın .",
"en": "please raise your hand if something applies to you ."
}
}
```
#### aztr_to_en
- **Size of downloaded dataset files:** 131.01 MB
- **Size of the generated dataset:** 40.14 MB
- **Total amount of disk used:** 171.15 MB
An example of 'train' looks as follows.
```
{
"translation": {
"az_tr": "zəhmət olmasa , sizə xitab edən sözlər eşidəndə əlinizi qaldırın .",
"en": "please raise your hand if something applies to you ."
}
}
```
#### be_to_en
- **Size of downloaded dataset files:** 131.01 MB
- **Size of the generated dataset:** 1.43 MB
- **Total amount of disk used:** 132.42 MB
An example of 'train' looks as follows.
```
{
"translation": {
"be": "zəhmət olmasa , sizə xitab edən sözlər eşidəndə əlinizi qaldırın .",
"en": "please raise your hand if something applies to you ."
}
}
```
#### beru_to_en
- **Size of downloaded dataset files:** 131.01 MB
- **Size of the generated dataset:** 60.20 MB
- **Total amount of disk used:** 191.21 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"translation": "{\"be_ru\": \"11 yaşımdaydım . səhərin birində , evimizdəki sevinc səslərinə oyandığım indiki kimi yadımdadır .\", \"en\": \"when i was..."
}
```
#### es_to_pt
- **Size of downloaded dataset files:** 131.01 MB
- **Size of the generated dataset:** 9.13 MB
- **Total amount of disk used:** 140.14 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"translation": "{\"es\": \"11 yaşımdaydım . səhərin birində , evimizdəki sevinc səslərinə oyandığım indiki kimi yadımdadır .\", \"pt\": \"when i was 11..."
}
```
### Data Fields
The data fields are the same among all splits.
#### az_to_en
- `translation`: a multilingual `string` variable, with possible languages including `az`, `en`.
#### aztr_to_en
- `translation`: a multilingual `string` variable, with possible languages including `az_tr`, `en`.
#### be_to_en
- `translation`: a multilingual `string` variable, with possible languages including `be`, `en`.
#### beru_to_en
- `translation`: a multilingual `string` variable, with possible languages including `be_ru`, `en`.
#### es_to_pt
- `translation`: a multilingual `string` variable, with possible languages including `es`, `pt`.
### Data Splits
| name |train |validation|test|
|----------|-----:|---------:|---:|
|az_to_en | 5947| 672| 904|
|aztr_to_en|188397| 672| 904|
|be_to_en | 4510| 249| 665|
|beru_to_en|212615| 249| 665|
|es_to_pt | 44939| 1017|1764|
## 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{qi-etal-2018-pre,
title = "When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?",
author = "Qi, Ye and
Sachan, Devendra and
Felix, Matthieu and
Padmanabhan, Sarguna and
Neubig, Graham",
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/N18-2084",
doi = "10.18653/v1/N18-2084",
pages = "529--535",
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. |
ag2428/gsm8k_response_templated | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 4060351
num_examples: 7473
download_size: 2318978
dataset_size: 4060351
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
james-burton/books_price_prediction | ---
dataset_info:
features:
- name: Synopsis
dtype: string
- name: Price
dtype: float64
- name: label
dtype: float64
- name: text
dtype: string
- name: scaled_label
dtype: float64
splits:
- name: train
num_bytes: 10391810
num_examples: 5051
- name: validation
num_bytes: 1158470
num_examples: 562
- name: test
num_bytes: 1284512
num_examples: 624
download_size: 8407666
dataset_size: 12834792
---
# Dataset Card for "books_price_prediction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Mysen/controlnet | ---
license: apache-2.0
---
|
TrainingDataPro/spinal-cord-dataset | ---
license: cc-by-nc-nd-4.0
task_categories:
- image-classification
- image-to-image
- image-segmentation
- object-detection
language:
- en
tags:
- medical
- biology
- code
---
# Spine MRI Dataset, Anomaly Detection & Segmentation
The dataset consists of .dcm files containing **MRI scans of the spine** of the person with several dystrophic changes, such as osteochondrosis, spondyloarthrosis, hemangioma, physiological lordosis smoothed, osteophytes and aggravated defects. The images are **labeled** by the doctors and accompanied by **report** in PDF-format.
The dataset includes 9 studies, made from the different angles which provide a comprehensive understanding of a several dystrophic changes and useful in training spine anomaly classification algorithms. Each scan includes detailed imaging of the spine, including the *vertebrae, discs, nerves, and surrounding tissues*.
### MRI study angles in the dataset
# 💴 For Commercial Usage: Full version of the dataset includes 20,000 spine studies of people with different conditions, leave a request on **[TrainingData](https://trainingdata.pro/data-market/spine-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=spinal-cord-dataset)** to buy the dataset
### Types of diseases and conditions in the full dataset:
- Degeneration of discs
- Osteophytes
- Osteochondrosis
- Hemangioma
- Disk extrusion
- Spondylitis
- **AND MANY OTHER CONDITIONS**
Researchers and healthcare professionals can use this dataset to study spinal conditions and disorders, such as herniated discs, spinal stenosis, scoliosis, and fractures. The dataset can also be used to develop and evaluate new imaging techniques, computer algorithms for image analysis, and artificial intelligence models for automated diagnosis.
# 💴 Buy the Dataset: This is just an example of the data. Leave a request on [https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/spine-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=spinal-cord-dataset) to discuss your requirements, learn about the price and buy the dataset
# Content
### The dataset includes:
- **ST000001**: includes subfolders with 9 studies. Each study includes MRI-scans in **.dcm and .jpg formats**,
- **DICOMDIR**: includes information about the patient's condition and links to access files,
- **Spine_MRI_2.pdf**: includes medical report, provided by the radiologist,
- **.csv file**: includes id of the studies and the number of files
### Medical reports include the following data:
- Patient's **demographic information**,
- **Description** of the case,
- Preliminary **diagnosis**,
- **Recommendations** on the further actions
*All patients consented to the publication of data*
# Medical data might be collected in accordance with your requirements.
## [TrainingData](https://trainingdata.pro/data-market/spine-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=spinal-cord-dataset) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **<https://www.kaggle.com/trainingdatapro/datasets>**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
*keywords: mri spine scans, spinal imaging, radiology dataset, neuroimaging, medical imaging data, image segmentation, lumbar spine mri, thoracic spine mri, cervical spine mri, spine anatomy, spinal cord mri, orthopedic imaging, radiologist dataset, mri scan analysis, spine mri dataset, machine learning medical imaging, spinal abnormalities, image classification, neural network spine scans, mri data analysis, deep learning medical imaging, mri image processing, spine tumor detection, spine injury diagnosis, mri image segmentation, spine mri classification, artificial intelligence in radiology, spine abnormalities detection, spine pathology analysis, mri feature extraction.* |
disi-unibo-nlp/medmcqa-MedGENIE | ---
dataset_info:
features:
- name: id_question
dtype: string
- name: question
dtype: string
- name: target
dtype: string
- name: answers
sequence: string
- name: ctxs
list:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1117793366
num_examples: 182822
- name: validation
num_bytes: 27887738
num_examples: 4183
download_size: 720926987
dataset_size: 1145681104
size_categories:
- 100K<n<1M
---
# Dataset Card for "medmcqa-MedGENIE"
## Dataset Description
The data is a part of the MedGENIE collection of medical datasets augmented with artificial contexts generated by [PMC-LLaMA-13B](https://huggingface.co/axiong/PMC_LLaMA_13B). Specifically, up to 5 artificial contexts were generated for each question in [MedMCQA](https://huggingface.co/datasets/medmcqa), employing a multi-view approach to encompass various perspectives associated with the given question.
The dataset has been used to train [MedGENIE-fid-flan-t5-base-medmcqa](https://huggingface.co/disi-unibo-nlp/MedGENIE-fid-flan-t5-base-medmcqa) allowing it to achieve performance levels comparable to state-of-the-art (SOTA) larger models on both MedMCQA and MMLU-Medical benchmarks.
## Dataset Structure
The dataset has two splits, suitable for:
* Training *question-answering* models, including *fusion-in-decoder* architectures.
* Augmenting your LLMs during inference with generated contexts rather than retrived chunks.
* Augmening your knolwedge base of factual documents with generated contexts for standard RAG pipeline.
The number of examples per split is:
- **train:** 182822 samples
- **validation:** 4183 samples
The dataset is stored in parquet format with each entry using the following schema:
```
{
"id_question": "45258d3d-b974-44dd-a161-c3fccbdadd88",
"question": "Which of the following is not true for myelinated nerve fibers:\nA. Impulse through myelinated fibers is slower than non-myelinated fibers\nB. Membrane currents are generated at nodes of Ranvier\nC. Saltatory conduction of impulses is seen\nD. Local anesthesia is effective only when the nerve is not covered by myelin sheath",
"target": "A",
"answers": [
"A"
],
"ctxs": [
{
"text": "The myelin sheath of myelinated nerve fibers is a covering that acts as insulation and increases the rate of conduction. Therefore, impulse through myelinated fibers is faster than non-myelinated fibers. \nUnderstanding these differences in structure and function between these two types of nerve cells helps us appreciate..."
},
{
"text": "The myelin sheath is a protective layer that wraps around the nerve fibers and helps to speed up the transmission of signals along nerves. Each fiber has its own insulating cover, known as a Schwann cell membrane, which forms the intermediate substance (or endoneurium) between the perineurium and epineurium sheaths..."
},
{
"text": "This question focuses on the differences between myelinated and non-myelinated nerve fibers. Myelinated fibers are wrapped in a sheath that helps them transmit action potentials more rapidly. The impulse jumps from one node to the next at a rate of 2-3 meters/second. In contrast, non-myelinated fibers do not have this..."
},
{
"text": "Myelinated nerve fibers are those that have a sheath of myelin surrounding them. In the central nervous system, they are called tracts and can be up to 4 inches (10 cm) in length; in contrast, the peripheral nervous system has much smaller fiber bundles called a fascicle. The myelin is produced by Schwann cells which..."
},
{
"text": "Myelinated nerve fibers\u00a0are those that have a fatty, protective sheath called a myelin coating, which is formed from the membranes of Schwann cells. Some sensory and motor fibers in the peripheral nervous system are unmyelinated (i.e., they lack a myelin covering).\nBecause they conduct impulses more rapidly than..."
}
]
}
```
## Augmenting LLMs during inference
Augmenting *state-of-the-art* LLMs with generated contexts from both **medmcqa-MedGENIE** and [medqa-MedGENIE](https://huggingface.co/datasets/disi-unibo-nlp/medqa-MedGENIE) demonstrated a remarkable performance boost. For a given question, all relevant contexts are concatenated and passed within the context window of the LLM.
| Model | Learning|medqa-5-opt-MedGENIE |Accuracy |
|------|------|-----|-----|
| LLaMA-2-chat (7B)|2-shot | NO|35.0 |
| LLaMA-2-chat (7B)| 2-shot|YES |44.1 **(+ 9.1)** |
| Zephyr-β (7B)|2-shot|NO | 43.4 |
| Zephyr-β (7B)|2-shot| YES |50.5 **(+ 7.1)** |
## Evaluation for RAG
To assess the effectiveness of using our generated contexts for RAG pipeline, we augment the [MedWiki](https://huggingface.co/datasets/VOD-LM/medwiki) dataset with a smaller portion of artificially generated chunks derived from **medmcqa-MedGENIE**.
| MedWiki chunks | Artificial chunks | Rerank | LLaMA-2-chat (7B) | mistral-instruct (7B) | Zephyr-β (7B) |
|------|-----|----------------|-------------------|-----------------------|---------------------|
| 4.5M | - | NO | 35.8 | 44.2 | 47.0 |
| 4.5M | 96K | NO | 40.9 **(+ 5.1)** | 47.4 **(+ 3.2)** | 50.1 **(+3.1)** |
| 4.5M | 2M | NO | 43.1 **(+ 7.3)** | 48.6 **(+ 4.4)** | 50.9 **(+3.9)** |
| 4.5M | - | YES | 37.3 | 44.3 | 47.1 |
| 4.5M | 96K | YES | 41.8 **(+4.5)** | 48.1 **(+3.8)** | 50.3 **(+3.2)** |
| 4.5M | 2M | YES | 43.7 **(+6.4)** | 49.8 **(+5.5)** | 50.7 **(+3.6)** |
## Citation
If you find this dataset is useful in your work, please cite it with:
```
@misc{frisoni2024generate,
title={To Generate or to Retrieve? On the Effectiveness of Artificial Contexts for Medical Open-Domain Question Answering},
author={Giacomo Frisoni and Alessio Cocchieri and Alex Presepi and Gianluca Moro and Zaiqiao Meng},
year={2024},
eprint={2403.01924},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
RealTimeData/wikitext_alltime | ---
dataset_info:
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data_files:
- split: train
path: 2022-11/train-*
- config_name: 2022-12
data_files:
- split: train
path: 2022-12/train-*
- config_name: 2023-01
data_files:
- split: train
path: 2023-01/train-*
- config_name: 2023-02
data_files:
- split: train
path: 2023-02/train-*
- config_name: 2023-03
data_files:
- split: train
path: 2023-03/train-*
- config_name: 2023-04
data_files:
- split: train
path: 2023-04/train-*
- config_name: 2023-05
data_files:
- split: train
path: 2023-05/train-*
- config_name: 2023-06
data_files:
- split: train
path: 2023-06/train-*
- config_name: 2023-07
data_files:
- split: train
path: 2023-07/train-*
- config_name: 2023-08
data_files:
- split: train
path: 2023-08/train-*
- config_name: 2023-09
data_files:
- split: train
path: 2023-09/train-*
- config_name: 2023-10
data_files:
- split: train
path: 2023-10/train-*
- config_name: 2023-11
data_files:
- split: train
path: 2023-11/train-*
- config_name: 2024-01
data_files:
- split: train
path: 2024-01/train-*
- config_name: 2024-02
data_files:
- split: train
path: 2024-02/train-*
- config_name: 2024-03
data_files:
- split: train
path: 2024-03/train-*
--- |
open-llm-leaderboard/details_remyxai__localmentor_25K_3epochs_tinyllama | ---
pretty_name: Evaluation run of remyxai/localmentor_25K_3epochs_tinyllama
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [remyxai/localmentor_25K_3epochs_tinyllama](https://huggingface.co/remyxai/localmentor_25K_3epochs_tinyllama)\
\ 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_remyxai__localmentor_25K_3epochs_tinyllama\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-07T22:25:15.681205](https://huggingface.co/datasets/open-llm-leaderboard/details_remyxai__localmentor_25K_3epochs_tinyllama/blob/main/results_2024-01-07T22-25-15.681205.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\": 0.2554930079258233,\n\
\ \"acc_stderr\": 0.030536114632474777,\n \"acc_norm\": 0.2566564564092015,\n\
\ \"acc_norm_stderr\": 0.03129471436685104,\n \"mc1\": 0.2141982864137087,\n\
\ \"mc1_stderr\": 0.014362148155690469,\n \"mc2\": 0.3606525365860081,\n\
\ \"mc2_stderr\": 0.013646263392146925\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.31399317406143346,\n \"acc_stderr\": 0.013562691224726295,\n\
\ \"acc_norm\": 0.34215017064846415,\n \"acc_norm_stderr\": 0.013864152159177275\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.44542919737104164,\n\
\ \"acc_stderr\": 0.004959973514772512,\n \"acc_norm\": 0.5901214897430791,\n\
\ \"acc_norm_stderr\": 0.004908059353503847\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.17037037037037037,\n\
\ \"acc_stderr\": 0.03247781185995594,\n \"acc_norm\": 0.17037037037037037,\n\
\ \"acc_norm_stderr\": 0.03247781185995594\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.1513157894736842,\n \"acc_stderr\": 0.029162631596843975,\n\
\ \"acc_norm\": 0.1513157894736842,\n \"acc_norm_stderr\": 0.029162631596843975\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.24,\n\
\ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.24,\n \
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.27169811320754716,\n \"acc_stderr\": 0.02737770662467071,\n\
\ \"acc_norm\": 0.27169811320754716,\n \"acc_norm_stderr\": 0.02737770662467071\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2361111111111111,\n\
\ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.2361111111111111,\n\
\ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.17,\n \"acc_stderr\": 0.0377525168068637,\n \
\ \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\
\ \"harness|hendrycksTest-college_computer_science|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_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\": 0.1791907514450867,\n\
\ \"acc_stderr\": 0.029242513059063287,\n \"acc_norm\": 0.1791907514450867,\n\
\ \"acc_norm_stderr\": 0.029242513059063287\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.04158307533083286,\n\
\ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.04158307533083286\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.2,\n \"acc_stderr\": 0.040201512610368445,\n \"acc_norm\": 0.2,\n\
\ \"acc_norm_stderr\": 0.040201512610368445\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.2553191489361702,\n \"acc_stderr\": 0.02850485647051419,\n\
\ \"acc_norm\": 0.2553191489361702,\n \"acc_norm_stderr\": 0.02850485647051419\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n\
\ \"acc_stderr\": 0.040969851398436716,\n \"acc_norm\": 0.2543859649122807,\n\
\ \"acc_norm_stderr\": 0.040969851398436716\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.1793103448275862,\n \"acc_stderr\": 0.03196766433373186,\n\
\ \"acc_norm\": 0.1793103448275862,\n \"acc_norm_stderr\": 0.03196766433373186\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.25396825396825395,\n \"acc_stderr\": 0.02241804289111395,\n \"\
acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.02241804289111395\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.24603174603174602,\n\
\ \"acc_stderr\": 0.03852273364924316,\n \"acc_norm\": 0.24603174603174602,\n\
\ \"acc_norm_stderr\": 0.03852273364924316\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.23225806451612904,\n \"acc_stderr\": 0.024022256130308235,\n \"\
acc_norm\": 0.23225806451612904,\n \"acc_norm_stderr\": 0.024022256130308235\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.16748768472906403,\n \"acc_stderr\": 0.0262730860475354,\n \"\
acc_norm\": 0.16748768472906403,\n \"acc_norm_stderr\": 0.0262730860475354\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\"\
: 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.2606060606060606,\n \"acc_stderr\": 0.03427743175816524,\n\
\ \"acc_norm\": 0.2606060606060606,\n \"acc_norm_stderr\": 0.03427743175816524\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.18181818181818182,\n \"acc_stderr\": 0.027479603010538787,\n \"\
acc_norm\": 0.18181818181818182,\n \"acc_norm_stderr\": 0.027479603010538787\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.22797927461139897,\n \"acc_stderr\": 0.030276909945178263,\n\
\ \"acc_norm\": 0.22797927461139897,\n \"acc_norm_stderr\": 0.030276909945178263\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.2717948717948718,\n \"acc_stderr\": 0.022556551010132368,\n\
\ \"acc_norm\": 0.2717948717948718,\n \"acc_norm_stderr\": 0.022556551010132368\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.22962962962962963,\n \"acc_stderr\": 0.025644108639267627,\n \
\ \"acc_norm\": 0.22962962962962963,\n \"acc_norm_stderr\": 0.025644108639267627\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.23109243697478993,\n \"acc_stderr\": 0.027381406927868973,\n\
\ \"acc_norm\": 0.23109243697478993,\n \"acc_norm_stderr\": 0.027381406927868973\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2119205298013245,\n \"acc_stderr\": 0.033367670865679766,\n \"\
acc_norm\": 0.2119205298013245,\n \"acc_norm_stderr\": 0.033367670865679766\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.22201834862385322,\n \"acc_stderr\": 0.017818849564796634,\n \"\
acc_norm\": 0.22201834862385322,\n \"acc_norm_stderr\": 0.017818849564796634\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4675925925925926,\n \"acc_stderr\": 0.03402801581358966,\n \"\
acc_norm\": 0.4675925925925926,\n \"acc_norm_stderr\": 0.03402801581358966\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.2549019607843137,\n \"acc_stderr\": 0.030587591351604246,\n \"\
acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.030587591351604246\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.28270042194092826,\n \"acc_stderr\": 0.029312814153955914,\n \
\ \"acc_norm\": 0.28270042194092826,\n \"acc_norm_stderr\": 0.029312814153955914\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.34977578475336324,\n\
\ \"acc_stderr\": 0.03200736719484503,\n \"acc_norm\": 0.34977578475336324,\n\
\ \"acc_norm_stderr\": 0.03200736719484503\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\":\
\ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\
acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.2777777777777778,\n\
\ \"acc_stderr\": 0.043300437496507437,\n \"acc_norm\": 0.2777777777777778,\n\
\ \"acc_norm_stderr\": 0.043300437496507437\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.3006134969325153,\n \"acc_stderr\": 0.03602511318806771,\n\
\ \"acc_norm\": 0.3006134969325153,\n \"acc_norm_stderr\": 0.03602511318806771\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3392857142857143,\n\
\ \"acc_stderr\": 0.04493949068613539,\n \"acc_norm\": 0.3392857142857143,\n\
\ \"acc_norm_stderr\": 0.04493949068613539\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\": 0.26495726495726496,\n\
\ \"acc_stderr\": 0.028911208802749472,\n \"acc_norm\": 0.26495726495726496,\n\
\ \"acc_norm_stderr\": 0.028911208802749472\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.23627075351213284,\n\
\ \"acc_stderr\": 0.015190473717037484,\n \"acc_norm\": 0.23627075351213284,\n\
\ \"acc_norm_stderr\": 0.015190473717037484\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.24566473988439305,\n \"acc_stderr\": 0.023176298203992012,\n\
\ \"acc_norm\": 0.24566473988439305,\n \"acc_norm_stderr\": 0.023176298203992012\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\
\ \"acc_stderr\": 0.014242630070574877,\n \"acc_norm\": 0.23798882681564246,\n\
\ \"acc_norm_stderr\": 0.014242630070574877\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.22875816993464052,\n \"acc_stderr\": 0.024051029739912255,\n\
\ \"acc_norm\": 0.22875816993464052,\n \"acc_norm_stderr\": 0.024051029739912255\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.3183279742765273,\n\
\ \"acc_stderr\": 0.026457225067811032,\n \"acc_norm\": 0.3183279742765273,\n\
\ \"acc_norm_stderr\": 0.026457225067811032\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.2716049382716049,\n \"acc_stderr\": 0.024748624490537365,\n\
\ \"acc_norm\": 0.2716049382716049,\n \"acc_norm_stderr\": 0.024748624490537365\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.22695035460992907,\n \"acc_stderr\": 0.024987106365642983,\n \
\ \"acc_norm\": 0.22695035460992907,\n \"acc_norm_stderr\": 0.024987106365642983\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2470664928292047,\n\
\ \"acc_stderr\": 0.011015752255279341,\n \"acc_norm\": 0.2470664928292047,\n\
\ \"acc_norm_stderr\": 0.011015752255279341\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.3492647058823529,\n \"acc_stderr\": 0.028959755196824866,\n\
\ \"acc_norm\": 0.3492647058823529,\n \"acc_norm_stderr\": 0.028959755196824866\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2630718954248366,\n \"acc_stderr\": 0.017812676542320657,\n \
\ \"acc_norm\": 0.2630718954248366,\n \"acc_norm_stderr\": 0.017812676542320657\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.19090909090909092,\n\
\ \"acc_stderr\": 0.03764425585984927,\n \"acc_norm\": 0.19090909090909092,\n\
\ \"acc_norm_stderr\": 0.03764425585984927\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.1673469387755102,\n \"acc_stderr\": 0.023897144768914524,\n\
\ \"acc_norm\": 0.1673469387755102,\n \"acc_norm_stderr\": 0.023897144768914524\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.24378109452736318,\n\
\ \"acc_stderr\": 0.030360490154014652,\n \"acc_norm\": 0.24378109452736318,\n\
\ \"acc_norm_stderr\": 0.030360490154014652\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \
\ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3072289156626506,\n\
\ \"acc_stderr\": 0.03591566797824662,\n \"acc_norm\": 0.3072289156626506,\n\
\ \"acc_norm_stderr\": 0.03591566797824662\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.27485380116959063,\n \"acc_stderr\": 0.034240429246915824,\n\
\ \"acc_norm\": 0.27485380116959063,\n \"acc_norm_stderr\": 0.034240429246915824\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2141982864137087,\n\
\ \"mc1_stderr\": 0.014362148155690469,\n \"mc2\": 0.3606525365860081,\n\
\ \"mc2_stderr\": 0.013646263392146925\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6045777426992897,\n \"acc_stderr\": 0.013741678387545347\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.01061410159211524,\n \
\ \"acc_stderr\": 0.002822713322387704\n }\n}\n```"
repo_url: https://huggingface.co/remyxai/localmentor_25K_3epochs_tinyllama
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_01_07T22_25_15.681205
path:
- '**/details_harness|arc:challenge|25_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|gsm8k|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hellaswag|10_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-07T22-25-15.681205.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-07T22-25-15.681205.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- '**/details_harness|winogrande|5_2024-01-07T22-25-15.681205.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-07T22-25-15.681205.parquet'
- config_name: results
data_files:
- split: 2024_01_07T22_25_15.681205
path:
- results_2024-01-07T22-25-15.681205.parquet
- split: latest
path:
- results_2024-01-07T22-25-15.681205.parquet
---
# Dataset Card for Evaluation run of remyxai/localmentor_25K_3epochs_tinyllama
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [remyxai/localmentor_25K_3epochs_tinyllama](https://huggingface.co/remyxai/localmentor_25K_3epochs_tinyllama) 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_remyxai__localmentor_25K_3epochs_tinyllama",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-07T22:25:15.681205](https://huggingface.co/datasets/open-llm-leaderboard/details_remyxai__localmentor_25K_3epochs_tinyllama/blob/main/results_2024-01-07T22-25-15.681205.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": 0.2554930079258233,
"acc_stderr": 0.030536114632474777,
"acc_norm": 0.2566564564092015,
"acc_norm_stderr": 0.03129471436685104,
"mc1": 0.2141982864137087,
"mc1_stderr": 0.014362148155690469,
"mc2": 0.3606525365860081,
"mc2_stderr": 0.013646263392146925
},
"harness|arc:challenge|25": {
"acc": 0.31399317406143346,
"acc_stderr": 0.013562691224726295,
"acc_norm": 0.34215017064846415,
"acc_norm_stderr": 0.013864152159177275
},
"harness|hellaswag|10": {
"acc": 0.44542919737104164,
"acc_stderr": 0.004959973514772512,
"acc_norm": 0.5901214897430791,
"acc_norm_stderr": 0.004908059353503847
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.17037037037037037,
"acc_stderr": 0.03247781185995594,
"acc_norm": 0.17037037037037037,
"acc_norm_stderr": 0.03247781185995594
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.1513157894736842,
"acc_stderr": 0.029162631596843975,
"acc_norm": 0.1513157894736842,
"acc_norm_stderr": 0.029162631596843975
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.27169811320754716,
"acc_stderr": 0.02737770662467071,
"acc_norm": 0.27169811320754716,
"acc_norm_stderr": 0.02737770662467071
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2361111111111111,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.2361111111111111,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.17,
"acc_stderr": 0.0377525168068637,
"acc_norm": 0.17,
"acc_norm_stderr": 0.0377525168068637
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"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": 0.1791907514450867,
"acc_stderr": 0.029242513059063287,
"acc_norm": 0.1791907514450867,
"acc_norm_stderr": 0.029242513059063287
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.22549019607843138,
"acc_stderr": 0.04158307533083286,
"acc_norm": 0.22549019607843138,
"acc_norm_stderr": 0.04158307533083286
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.2,
"acc_stderr": 0.040201512610368445,
"acc_norm": 0.2,
"acc_norm_stderr": 0.040201512610368445
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.2553191489361702,
"acc_stderr": 0.02850485647051419,
"acc_norm": 0.2553191489361702,
"acc_norm_stderr": 0.02850485647051419
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2543859649122807,
"acc_stderr": 0.040969851398436716,
"acc_norm": 0.2543859649122807,
"acc_norm_stderr": 0.040969851398436716
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.1793103448275862,
"acc_stderr": 0.03196766433373186,
"acc_norm": 0.1793103448275862,
"acc_norm_stderr": 0.03196766433373186
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.25396825396825395,
"acc_stderr": 0.02241804289111395,
"acc_norm": 0.25396825396825395,
"acc_norm_stderr": 0.02241804289111395
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.24603174603174602,
"acc_stderr": 0.03852273364924316,
"acc_norm": 0.24603174603174602,
"acc_norm_stderr": 0.03852273364924316
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.28,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.28,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.23225806451612904,
"acc_stderr": 0.024022256130308235,
"acc_norm": 0.23225806451612904,
"acc_norm_stderr": 0.024022256130308235
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.16748768472906403,
"acc_stderr": 0.0262730860475354,
"acc_norm": 0.16748768472906403,
"acc_norm_stderr": 0.0262730860475354
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.2606060606060606,
"acc_stderr": 0.03427743175816524,
"acc_norm": 0.2606060606060606,
"acc_norm_stderr": 0.03427743175816524
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.18181818181818182,
"acc_stderr": 0.027479603010538787,
"acc_norm": 0.18181818181818182,
"acc_norm_stderr": 0.027479603010538787
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.22797927461139897,
"acc_stderr": 0.030276909945178263,
"acc_norm": 0.22797927461139897,
"acc_norm_stderr": 0.030276909945178263
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.2717948717948718,
"acc_stderr": 0.022556551010132368,
"acc_norm": 0.2717948717948718,
"acc_norm_stderr": 0.022556551010132368
},
"harness|hendrycksTest-high_school_mathematics|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]
- **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
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[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. -->
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#### 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. -->
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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
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[More Information Needed]
### Recommendations
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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## Glossary [optional]
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CyberHarem/lishu_theapothecarydiaries | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Lishu (The Apothecary Diaries)
This is the dataset of Lishu (The Apothecary Diaries), containing 85 images and their tags.
The core tags of this character are `brown_hair, long_hair, earrings, hair_ornament, hair_bun, purple_eyes, hair_flower`, 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 | 85 | 62.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lishu_theapothecarydiaries/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 85 | 62.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lishu_theapothecarydiaries/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 149 | 107.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lishu_theapothecarydiaries/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/lishu_theapothecarydiaries',
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 | 21 |  |  |  |  |  | 1girl, solo, necklace, flower, makeup, double_bun |
| 1 | 6 |  |  |  |  |  | 1girl, long_sleeves, necklace, solo_focus, flower, outdoors, double_bun, grass, hanfu, upper_body |
| 2 | 6 |  |  |  |  |  | 1girl, outdoors, solo, :t, closed_mouth, pout, upper_body, closed_eyes, hair_rings, kimono, makeup, necklace |
| 3 | 6 |  |  |  |  |  | 1girl, flower, long_sleeves, open_mouth, dress, hanfu, profile, sash, solo, brown_eyes, from_side, jewelry, shawl, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | necklace | flower | makeup | double_bun | long_sleeves | solo_focus | outdoors | grass | hanfu | upper_body | :t | closed_mouth | pout | closed_eyes | hair_rings | kimono | open_mouth | dress | profile | sash | brown_eyes | from_side | jewelry | shawl | smile |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------|:---------|:---------|:-------------|:---------------|:-------------|:-----------|:--------|:--------|:-------------|:-----|:---------------|:-------|:--------------|:-------------|:---------|:-------------|:--------|:----------|:-------|:-------------|:------------|:----------|:--------|:--------|
| 0 | 21 |  |  |  |  |  | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | X | | X | | | | X | | | X | X | X | X | X | X | X | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | X | | X | | | X | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X |
|
bunkalab/medium-sample-technology | ---
license: apache-2.0
---
Sample with the keyword "Technology" taken from https://huggingface.co/datasets/fabiochiu/medium-articles
|
emilio-ariza/ESsnli | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
languages:
- es
licenses:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<100K
source_datasets:
- extended|snli
task_categories:
- text-classification
task_ids:
- natural-language-inference
---
# Dataset Card for ESsnli
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Machine-translated Spanish version of the Stanford Natural Language Inference dataset.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Machine-generated Spanish (from an English-language original corpus).
## Dataset Structure
### Data Instances
For each instance, there is a string for the premise, a string for the hypothesis, a string for the label given by the annotator and a string for the gold label, as well as data about the images that originated the sentences. Note that each premise may appear three times with a different hypothesis and label. See the [SNLI corpus viewer](https://huggingface.co/datasets/viewer/?dataset=snli) to explore more examples.
```
{"annotator_labels": ["contradiction"]
"captionID": "3618932839.jpg#4"
"gold_label": "contradiction"
"pairID": "3618932839.jpg#4r1c"
"sentence1": "El perro intenta saltar sobre el poste."
"sentence2": "Perrito durmiendo con su madre."}
```
### Data Fields
- `sentence1`: a string used to determine the truthfulness of the hypothesis
- `sentence2`: a string that may be true, false, or whose truth conditions may not be knowable when compared to the premise
- `label`: a string whose value may be either "entailment", "contradiction" or "neutral".
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
This corpus was built to remedy the scarcity of annotated Spanish-language datasets for NLI. It was generated by translating from the SNLI original dataset to Spanish using Argos. While machine translation is far from an ideal source for semantic classification, it is an aid to enlarging the data available.
### Source Data
#### Initial Data Collection and Normalization
The hypotheses (sentence1) were elicited by presenting crowdworkers with captions from preexisting datasets without the associated photos, but the vocabulary of the hypotheses still reflects the content of the photos as well as the caption style of writing (e.g. mostly present tense). The dataset developers report 37,026 distinct words in the corpus, ignoring case. They allowed bare NPs as well as full sentences. Using the Stanford PCFG Parser 3.5.2 (Klein and Manning, 2003) trained on the standard training set as well as on the Brown Corpus (Francis and Kucera 1979), the authors report that 74% of the premises and 88.9% of the hypotheses result in a parse rooted with an 'S'. The corpus was developed between 2014 and 2015.
Crowdworkers were presented with a caption without the associated photo and asked to produce three alternate captions, one that is definitely true, one that might be true, and one that is definitely false. See Section 2.1 and Figure 1 for details (Bowman et al., 2015).
The corpus includes content from the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/) and the [VisualGenome corpus](https://visualgenome.org/). The photo captions used to prompt the data creation were collected on Flickr by [Young et al. (2014)](https://www.aclweb.org/anthology/Q14-1006.pdf), who extended the Flickr 8K dataset developed by [Hodosh et al. (2013)](https://www.jair.org/index.php/jair/article/view/10833). Hodosh et al. collected photos from the following Flickr groups: strangers!, Wild-Child (Kids in Action), Dogs in Action (Read the Rules), Outdoor Activities, Action Photography, Flickr-Social (two or more people in the photo). Young et al. do not list the specific groups they collected photos from. The VisualGenome corpus also contains images from Flickr, originally collected in [MS-COCO](https://cocodataset.org/#home) and [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/).
The premises (sentence2) from the Flickr 30k corpus corrected for spelling using the Linux spell checker and ungrammatical sentences were removed. Bowman et al. do not report any normalization, though they note that punctuation and capitalization are often omitted.
#### Who are the source language producers?
A large portion of the premises (160k) were produced in the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/) by an unknown number of crowdworkers. About 2,500 crowdworkers from Amazon Mechanical Turk produced the associated hypotheses. The premises from the Flickr 30k project describe people and animals whose photos were collected and presented to the Flickr 30k crowdworkers, but the SNLI corpus did not present the photos to the hypotheses creators.
The Flickr 30k corpus did not report crowdworker or photo subject demographic information or crowdworker compensation. The SNLI crowdworkers were compensated per HIT at rates between $.1 and $.5 with no incentives. Workers who ignored the guidelines were disqualified, and automated bulk submissions were rejected. No demographic information was collected from the SNLI crowdworkers.
An additional 4,000 premises come from the pilot study of the [VisualGenome corpus](https://visualgenome.org/static/paper/Visual_Genome.pdf). Though the pilot study itself is not described, the location information of the 33,000 AMT crowdworkers that participated over the course of the 6 months of data collection are aggregated. Most of the workers were located in the United States (93%), with others from the Philippines, Kenya, India, Russia, and Canada. Workers were paid $6-$8 per hour.
### Annotations
#### Annotation process
56,941 of the total sentence pairs were further annotated in a validation task. Four annotators each labeled a premise-hypothesis pair as entailment, contradiction, or neither, resulting in 5 total judgements including the original hypothesis author judgement. See Section 2.2 for more details (Bowman et al., 2015).
The authors report 3/5 annotator agreement on 98% of the validation set and unanimous annotator agreement on 58.3% of the validation set. If a label was chosen by three annotators, that label was made the gold label. Following from this, 2% of the data did not have a consensus label and was labeled '-' by the authors.
#### Who are the annotators?
The annotators of the validation task were a closed set of about 30 trusted crowdworkers on Amazon Mechanical Turk. No demographic information was collected. Annotators were compensated per HIT between $.1 and $.5 with $1 bonuses in cases where annotator labels agreed with the curators' labels for 250 randomly distributed examples.
### Personal and Sensitive Information
The dataset does not contain any personal information about the authors or the crowdworkers, but may contain descriptions of the people in the original Flickr photos.
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
The language reflects the content of the photos collected from Flickr, as described in the [Data Collection](#initial-data-collection-and-normalization) section. [Rudinger et al (2017)](https://www.aclweb.org/anthology/W17-1609.pdf) use pointwise mutual information to calculate a measure of association between a manually selected list of tokens corresponding to identity categories and the other words in the corpus, showing strong evidence of stereotypes across gender categories. They also provide examples in which crowdworkers reproduced harmful stereotypes or pejorative language in the hypotheses.
### Other Known Limitations
The translation of the sentences was mostly unsupervised and may introduce some noise in the corpus. Machine translation from an English-language corpus is likely to generate syntactic and lexical forms that differ from those a human Spanish speaker would produce.
[Gururangan et al (2018)](https://www.aclweb.org/anthology/N18-2017.pdf), [Poliak et al (2018)](https://www.aclweb.org/anthology/S18-2023.pdf), and [Tsuchiya (2018)](https://www.aclweb.org/anthology/L18-1239.pdf) show that the SNLI corpus has a number of annotation artifacts. Using various classifiers, Poliak et al correctly predicted the label of the hypothesis 69% of the time without using the premise, Gururangan et al 67% of the time, and Tsuchiya 63% of the time.
## Additional Information
### Dataset Curators
The SNLI corpus was developed by Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning as part of the [Stanford NLP group](https://nlp.stanford.edu/).
It was supported by a Google Faculty Research Award, a gift from Bloomberg L.P., the Defense Advanced Research Projects Agency (DARPA) Deep Exploration and Filtering of Text (DEFT) Program under Air Force Research Laboratory (AFRL) contract no. FA8750-13-2-0040, the National Science Foundation under grant no. IIS 1159679, and the Department of the Navy, Office of Naval Research, under grant no. N00014-10-1-0109.
### Licensing Information
This corpus is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0).
The Stanford Natural Language Inference Corpus is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
[Needs More Information] |
biglam/bnl_newspapers1841-1879 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- de
- fr
- lb
- nl
- la
- en
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
pretty_name: BnL Newspapers 1841-1879
tags:
- newspapers
- 1800-1900
- lam
dataset_info:
features:
- name: publisher
dtype: string
- name: paperID
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: newpaperTitle
dtype: string
- name: date
dtype: timestamp[ns]
- name: metsType
dtype: string
- name: identifier
dtype: string
- name: type
dtype: string
- name: creator
dtype: string
splits:
- name: train
num_bytes: 1605420260
num_examples: 630709
download_size: 1027493424
dataset_size: 1605420260
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for BnL Newspapers 1841-1879
## Table of Contents
- [Dataset Card for bnl_newspapers1841-1879](#dataset-card-for-bnl_newspapers1841-1879)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [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)
- [size of dataset](#size-of-dataset)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://data.bnl.lu](https://data.bnl.lu)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** opendata at bnl.etat.lu
### Dataset Summary
630.709 articles from historical newspapers (1841-1879) along with metadata and the full text.
21 newspaper titles
24.415 newspaper issues
99.957 scanned pages
Transcribed using a variety of OCR engines and corrected using [https://github.com/natliblux/nautilusocr](https://github.com/natliblux/nautilusocr) (95% threshold)
Public Domain, CC0 (See copyright notice)
The newspapers used are:
- Der Arbeiter (1878)
- L'Arlequin (1848-1848)
- L'Avenir (1868-1871)
- Courrier du Grand-Duché de Luxembourg (1844-1868)
- Cäcilia (1863-1871)
- Diekircher Wochenblatt (1841-1848)
- Le Gratis luxembourgeois (1857-1858)
- L'Indépendance luxembourgeoise (1871-1879)
- Kirchlicher Anzeiger für die Diözese Luxemburg (1871-1879)
- La Gazette du Grand-Duché de Luxembourg (1878)
- Luxemburger Anzeiger (1856)
- Luxemburger Bauernzeitung (1857)
- Luxemburger Volks-Freund (1869-1876)
- Luxemburger Wort (1848-1879)
- Luxemburger Zeitung (1844-1845)
- Luxemburger Zeitung = Journal de Luxembourg (1858-1859)
- L'Union (1860-1871)
- Das Vaterland (1869-1870)
- Der Volksfreund (1848-1849)
- Der Wächter an der Sauer (1849-1869)
- D'Wäschfra (1868-1879)
### Supported Tasks and Leaderboards
### Languages
German, French, Luxembourgish
## Dataset Structure
JSONL file zipped.
### Data Instances
### Data Fields
- `identifier` : unique and persistent identifier using ARK for the Article.
- `date` : publishing date of the document e.g "1848-12-15".
- `metsType` : set to "newspaper".
- `newpaperTitle` : title of the newspaper. It is transcribed as in the masthead of the individual issue and can thus change.
- `paperID` : local identifier for the newspaper title. It remains the same, even for short-term title changes.
- `publisher` : publisher of the document e.g. "Verl. der St-Paulus-Druckerei".
- `title` : main title of the article, section, advertisement, etc.
- `text` : full text of the entire article, section, advertisement etc. It includes any titles and subtitles as well. The content does not contain layout information, such as headings, paragraphs or lines.
- `creator` : author of the article, section, advertisement etc. Most articles do not have an associated author.
- `type` : type of the exported data e.g. ARTICLE, SECTION, ADVERTISEMENT, ...
## Dataset Creation
The dataset was created by the National library of Luxembourg with the output of its newspaper digitisation program.
### Curation Rationale
The selection of newspapers represent the current state of digitisation of the Luxembourg legal deposit collection of newspapers that are in the public domain. That means all newspapers printed in Luxembourg before and including 1879.
### Source Data
Printed historical newspapers.
#### Initial Data Collection and Normalization
The data was created through digitisation. The full digitisation specifications are available at [https://data.bnl.lu/data/historical-newspapers/](https://data.bnl.lu/data/historical-newspapers/)
### Annotations
#### Annotation process
During the digitisation process, newspaper pages were semi-automatically zoned into articles. This was done by external suppliers to the library according to the digitisation specifications.
#### Who are the annotators?
Staff at the external suppliers.
### Personal and Sensitive Information
The dataset contains only data that was published in a newspaper. Since it contains only articles before 1879, no living person is expected to be included.
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
The biases in the text represent the biases from newspaper editors and journalists at the time of the publication. In particular during the period from 1940/05/10 to 1944/09/10 the Nazi occupier controlled all information published.
### Other Known Limitations
The OCR transcription is not perfect. It is estimated that the quality is 95% or better.
## Additional Information
### size of dataset
500MB-2GB
### Dataset Curators
This dataset is curated by the national library of Luxembourg (opendata at bnl.etat.lu).
### Licensing Information
Creative Commons Public Domain Dedication and Certification
### Citation Information
```
@misc{bnl_newspapers,
title={Historical Newspapers},
url={https://data.bnl.lu/data/historical-newspapers/},
author={ Bibliothèque nationale du Luxembourg},
```
### Contributions
Thanks to [@ymaurer](https://github.com/ymaurer) for adding this dataset. |
Nkumar5/RockMST | ---
dataset_info:
features:
- name: image
dtype: image
- name: audio_file
dtype: string
- name: slice
dtype: int16
splits:
- name: train
num_bytes: 79528496.375
num_examples: 1805
download_size: 79512056
dataset_size: 79528496.375
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
SoftMINER-Group/TechHazardQA | ---
license: apache-2.0
---
🎯 If you are using this dataset, please cite our paper
```
@misc{banerjee2024unethical,
title={How (un)ethical are instruction-centric responses of LLMs? Unveiling the vulnerabilities of safety guardrails to harmful queries},
author={Somnath Banerjee and Sayan Layek and Rima Hazra and Animesh Mukherjee},
year={2024},
eprint={2402.15302},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
qgiaohc/twitter_dataset_1713157676 | ---
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: 25336
num_examples: 58
download_size: 13136
dataset_size: 25336
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Marchanjo/spider-en-pt-es-fr | ---
license: cc-by-sa-4.0
---
Distributed under the Creative Commons-by-sa-4.0 respecting the ShareAlike of the [Spider Dataset](https://yale-lily.github.io/spider).
Code explanations and links for the model's checkpoints and datasets are on Github [mRAT-SQL](https://github.com/C4AI/gap-text2sql)
Here is the [Hugging Face collection](https://huggingface.co/collections/Marchanjo/mrat-sql-65a671743bb0e70b416561f6), you can download the model's checkpoints and datasets, but to understand is better to go to Github [mRAT-SQL](https://github.com/C4AI/gap-text2sql).
# mRAT-SQL-FIT
## A Multilingual Translator to SQL with Database Schema Pruning to Improve Self-Attention
Marcelo Archanjo Jose, Fabio Gagliardi Cozman
Long sequences of text are challenging in the context of transformers, due to quadratic memory increase in the self-attention mechanism. As this issue directly affects the translation from natural language to SQL queries (as techniques usually take as input a concatenated text with the question and the database schema), we present techniques that allow long text sequences to be handled by transformers with up to 512 input tokens. We propose a training process with database schema pruning (removal of tables and columns names that are useless for the query of interest). In addition, we used a multilingual approach with the mT5-large model fine-tuned with a data-augmented Spider dataset in four languages simultaneously: English, Portuguese, Spanish, and French. Our proposed technique used the Spider dataset and increased the exact set match accuracy results from 0.718 to 0.736 in a validation dataset (Dev). Source code, evaluations, and checkpoints are available at: [mRAT-SQL](https://github.com/C4AI/gap-text2sql).
[paper published in Springer-Nature - International Journal of Information Technology](https://doi.org/10.1007/s41870-023-01342-3), [here the SharedIt link](https://rdcu.be/dff19). [here the pre-print in arXiv](https://arxiv.org/abs/2306.14256).
# mRAT-SQL+GAP
## mRAT-SQL+GAP:A Portuguese Text-to-SQL Transformer
Marcelo Archanjo José, Fabio Gagliardi Cozman
The translation of natural language questions to SQL queries has attracted growing attention, in particular in connection with transformers and similar language models. A large number of techniques are geared towards the English language; in this work, we thus investigated translation to SQL when input questions are given in the Portuguese language. To do so, we properly adapted state-of-the-art tools and resources. We changed the RAT-SQL+GAP system by relying on a multilingual BART model (we report tests with other language models), and we produced a translated version of the Spider dataset. Our experiments expose interesting phenomena that arise when non-English languages are targeted; in particular, it is better to train with original and translated training datasets together, even if a single target language is desired. This multilingual BART model fine-tuned with a double-size training dataset (English and Portuguese) achieved 83% of the baseline, making inferences for the Portuguese test dataset. This investigation can help other researchers to produce results in Machine Learning in a language different from English. Our multilingual ready version of RAT-SQL+GAP and the data are available, open-sourced as mRAT-SQL+GAP at: [mRAT-SQL](https://github.com/C4AI/gap-text2sql).
BRACIS 2021: [paper published in Springer Lecture Notes in Computer Science](https://link.springer.com/chapter/10.1007%2F978-3-030-91699-2_35), [here the pre-print in arXiv](https://arxiv.org/abs/2110.03546).
Based on: RAT-SQL+GAP: [Github](https://github.com/awslabs/gap-text2sql). Paper: [AAAI 2021 paper](https://arxiv.org/abs/2012.10309) |
dim/lurk_prompts | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: text
dtype: string
- name: word
dtype: string
splits:
- name: train
num_bytes: 92132815
num_examples: 5671
download_size: 49004405
dataset_size: 92132815
---
# Dataset Card for "lurk_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Kukedlc__NeuralSirKrishna-7b | ---
pretty_name: Evaluation run of Kukedlc/NeuralSirKrishna-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Kukedlc/NeuralSirKrishna-7b](https://huggingface.co/Kukedlc/NeuralSirKrishna-7b)\
\ 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_Kukedlc__NeuralSirKrishna-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-10T23:43:45.740350](https://huggingface.co/datasets/open-llm-leaderboard/details_Kukedlc__NeuralSirKrishna-7b/blob/main/results_2024-03-10T23-43-45.740350.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\": 0.6530158884271082,\n\
\ \"acc_stderr\": 0.03204748525595896,\n \"acc_norm\": 0.6519894815779393,\n\
\ \"acc_norm_stderr\": 0.03272423435086863,\n \"mc1\": 0.602203182374541,\n\
\ \"mc1_stderr\": 0.01713393424855968,\n \"mc2\": 0.7559601871847126,\n\
\ \"mc2_stderr\": 0.014162739924097284\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.71160409556314,\n \"acc_stderr\": 0.013238394422428176,\n\
\ \"acc_norm\": 0.7372013651877133,\n \"acc_norm_stderr\": 0.012862523175351335\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7167894841665007,\n\
\ \"acc_stderr\": 0.004496369742132101,\n \"acc_norm\": 0.8904600677155945,\n\
\ \"acc_norm_stderr\": 0.003116771577319422\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\
\ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\
\ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n\
\ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\
\ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \
\ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.027943219989337142,\n\
\ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.027943219989337142\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\
\ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\
\ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\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\": 0.6647398843930635,\n\
\ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\
\ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.047840607041056527,\n\
\ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.047840607041056527\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108102,\n\
\ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108102\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\
\ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n\
\ \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5448275862068965,\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\"\
: 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"\
acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\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\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7838709677419354,\n\
\ \"acc_stderr\": 0.023415293433568525,\n \"acc_norm\": 0.7838709677419354,\n\
\ \"acc_norm_stderr\": 0.023415293433568525\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n\
\ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\
: 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\
\ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.797979797979798,\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\": 0.917098445595855,\n \"acc_stderr\": 0.01989934131572178,\n \
\ \"acc_norm\": 0.917098445595855,\n \"acc_norm_stderr\": 0.01989934131572178\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402534,\n\
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402534\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\"\
: 0.3841059602649007,\n \"acc_stderr\": 0.03971301814719197,\n \"\
acc_norm\": 0.3841059602649007,\n \"acc_norm_stderr\": 0.03971301814719197\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\
acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5277777777777778,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\
: 0.5277777777777778,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8431372549019608,\n\
\ \"acc_stderr\": 0.02552472232455334,\n \"acc_norm\": 0.8431372549019608,\n\
\ \"acc_norm_stderr\": 0.02552472232455334\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290916,\n\
\ \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290916\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\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\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.021586494001281365,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.021586494001281365\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\": 0.8263090676883781,\n\
\ \"acc_stderr\": 0.01354741565866226,\n \"acc_norm\": 0.8263090676883781,\n\
\ \"acc_norm_stderr\": 0.01354741565866226\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069367,\n\
\ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069367\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.45251396648044695,\n\
\ \"acc_stderr\": 0.016646914804438775,\n \"acc_norm\": 0.45251396648044695,\n\
\ \"acc_norm_stderr\": 0.016646914804438775\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.0256468630971379,\n\
\ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.0256468630971379\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6977491961414791,\n\
\ \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n\
\ \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.024477222856135114,\n\
\ \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.024477222856135114\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\": 0.47327249022164275,\n\
\ \"acc_stderr\": 0.01275197796767601,\n \"acc_norm\": 0.47327249022164275,\n\
\ \"acc_norm_stderr\": 0.01275197796767601\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.028332959514031208,\n\
\ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.028332959514031208\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.673202614379085,\n \"acc_stderr\": 0.018975427920507205,\n \
\ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.018975427920507205\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784593,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784593\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\
\ \"acc_stderr\": 0.025870646766169136,\n \"acc_norm\": 0.8407960199004975,\n\
\ \"acc_norm_stderr\": 0.025870646766169136\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\
\ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\
\ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.602203182374541,\n\
\ \"mc1_stderr\": 0.01713393424855968,\n \"mc2\": 0.7559601871847126,\n\
\ \"mc2_stderr\": 0.014162739924097284\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8531965272296764,\n \"acc_stderr\": 0.009946627440250684\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7126611068991661,\n \
\ \"acc_stderr\": 0.01246467706010709\n }\n}\n```"
repo_url: https://huggingface.co/Kukedlc/NeuralSirKrishna-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|arc:challenge|25_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|gsm8k|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hellaswag|10_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-10T23-43-45.740350.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-10T23-43-45.740350.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- '**/details_harness|winogrande|5_2024-03-10T23-43-45.740350.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-10T23-43-45.740350.parquet'
- config_name: results
data_files:
- split: 2024_03_10T23_43_45.740350
path:
- results_2024-03-10T23-43-45.740350.parquet
- split: latest
path:
- results_2024-03-10T23-43-45.740350.parquet
---
# Dataset Card for Evaluation run of Kukedlc/NeuralSirKrishna-7b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Kukedlc/NeuralSirKrishna-7b](https://huggingface.co/Kukedlc/NeuralSirKrishna-7b) 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_Kukedlc__NeuralSirKrishna-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-10T23:43:45.740350](https://huggingface.co/datasets/open-llm-leaderboard/details_Kukedlc__NeuralSirKrishna-7b/blob/main/results_2024-03-10T23-43-45.740350.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": 0.6530158884271082,
"acc_stderr": 0.03204748525595896,
"acc_norm": 0.6519894815779393,
"acc_norm_stderr": 0.03272423435086863,
"mc1": 0.602203182374541,
"mc1_stderr": 0.01713393424855968,
"mc2": 0.7559601871847126,
"mc2_stderr": 0.014162739924097284
},
"harness|arc:challenge|25": {
"acc": 0.71160409556314,
"acc_stderr": 0.013238394422428176,
"acc_norm": 0.7372013651877133,
"acc_norm_stderr": 0.012862523175351335
},
"harness|hellaswag|10": {
"acc": 0.7167894841665007,
"acc_stderr": 0.004496369742132101,
"acc_norm": 0.8904600677155945,
"acc_norm_stderr": 0.003116771577319422
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6444444444444445,
"acc_stderr": 0.04135176749720385,
"acc_norm": 0.6444444444444445,
"acc_norm_stderr": 0.04135176749720385
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6973684210526315,
"acc_stderr": 0.03738520676119669,
"acc_norm": 0.6973684210526315,
"acc_norm_stderr": 0.03738520676119669
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7094339622641509,
"acc_stderr": 0.027943219989337142,
"acc_norm": 0.7094339622641509,
"acc_norm_stderr": 0.027943219989337142
},
"harness|hendrycksTest-college_biology|5": {
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"acc_stderr": 0.03551446610810826,
"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"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": 0.6647398843930635,
"acc_stderr": 0.03599586301247077,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.03599586301247077
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3627450980392157,
"acc_stderr": 0.047840607041056527,
"acc_norm": 0.3627450980392157,
"acc_norm_stderr": 0.047840607041056527
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5617021276595745,
"acc_stderr": 0.03243618636108102,
"acc_norm": 0.5617021276595745,
"acc_norm_stderr": 0.03243618636108102
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4649122807017544,
"acc_stderr": 0.046920083813689104,
"acc_norm": 0.4649122807017544,
"acc_norm_stderr": 0.046920083813689104
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5448275862068965,
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"acc_norm": 0.5448275862068965,
"acc_norm_stderr": 0.04149886942192117
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.40476190476190477,
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"acc_norm": 0.40476190476190477,
"acc_norm_stderr": 0.025279850397404904
},
"harness|hendrycksTest-formal_logic|5": {
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"acc_norm": 0.4603174603174603,
"acc_norm_stderr": 0.04458029125470973
},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7838709677419354,
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"acc_norm": 0.7838709677419354,
"acc_norm_stderr": 0.023415293433568525
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.035158955511656986,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.035158955511656986
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.71,
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"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7636363636363637,
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"acc_norm": 0.7636363636363637,
"acc_norm_stderr": 0.03317505930009182
},
"harness|hendrycksTest-high_school_geography|5": {
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"acc_norm": 0.797979797979798,
"acc_norm_stderr": 0.02860620428922987
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.917098445595855,
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"acc_norm": 0.917098445595855,
"acc_norm_stderr": 0.01989934131572178
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.023901157979402534,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.023901157979402534
},
"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": 0.3841059602649007,
"acc_stderr": 0.03971301814719197,
"acc_norm": 0.3841059602649007,
"acc_norm_stderr": 0.03971301814719197
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8477064220183487,
"acc_stderr": 0.015405084393157074,
"acc_norm": 0.8477064220183487,
"acc_norm_stderr": 0.015405084393157074
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5277777777777778,
"acc_stderr": 0.0340470532865388,
"acc_norm": 0.5277777777777778,
"acc_norm_stderr": 0.0340470532865388
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8431372549019608,
"acc_stderr": 0.02552472232455334,
"acc_norm": 0.8431372549019608,
"acc_norm_stderr": 0.02552472232455334
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8059071729957806,
"acc_stderr": 0.025744902532290916,
"acc_norm": 0.8059071729957806,
"acc_norm_stderr": 0.025744902532290916
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8015267175572519,
"acc_stderr": 0.034981493854624714,
"acc_norm": 0.8015267175572519,
"acc_norm_stderr": 0.034981493854624714
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7851239669421488,
"acc_stderr": 0.037494924487096966,
"acc_norm": 0.7851239669421488,
"acc_norm_stderr": 0.037494924487096966
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7685185185185185,
"acc_stderr": 0.04077494709252627,
"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252627
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7730061349693251,
"acc_stderr": 0.03291099578615769,
"acc_norm": 0.7730061349693251,
"acc_norm_stderr": 0.03291099578615769
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.04697113923010212,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.04697113923010212
},
"harness|hendrycksTest-management|5": {
"acc": 0.7766990291262136,
"acc_stderr": 0.04123553189891431,
"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8760683760683761,
"acc_stderr": 0.021586494001281365,
"acc_norm": 0.8760683760683761,
"acc_norm_stderr": 0.021586494001281365
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.69,
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"acc_norm": 0.69,
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},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8263090676883781,
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"acc_norm": 0.8263090676883781,
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},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7398843930635838,
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"acc_norm": 0.7398843930635838,
"acc_norm_stderr": 0.023618678310069367
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.45251396648044695,
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"acc_norm": 0.45251396648044695,
"acc_norm_stderr": 0.016646914804438775
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.0256468630971379,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.0256468630971379
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6977491961414791,
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"acc_norm": 0.6977491961414791,
"acc_norm_stderr": 0.02608270069539966
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7376543209876543,
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"acc_norm": 0.7376543209876543,
"acc_norm_stderr": 0.024477222856135114
},
"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": 0.47327249022164275,
"acc_stderr": 0.01275197796767601,
"acc_norm": 0.47327249022164275,
"acc_norm_stderr": 0.01275197796767601
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6801470588235294,
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"acc_norm": 0.6801470588235294,
"acc_norm_stderr": 0.028332959514031208
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.673202614379085,
"acc_stderr": 0.018975427920507205,
"acc_norm": 0.673202614379085,
"acc_norm_stderr": 0.018975427920507205
},
"harness|hendrycksTest-public_relations|5": {
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"acc_stderr": 0.0449429086625209,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7346938775510204,
"acc_stderr": 0.028263889943784593,
"acc_norm": 0.7346938775510204,
"acc_norm_stderr": 0.028263889943784593
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8407960199004975,
"acc_stderr": 0.025870646766169136,
"acc_norm": 0.8407960199004975,
"acc_norm_stderr": 0.025870646766169136
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
"acc_stderr": 0.03588702812826371,
"acc_norm": 0.85,
"acc_norm_stderr": 0.03588702812826371
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5602409638554217,
"acc_stderr": 0.03864139923699122,
"acc_norm": 0.5602409638554217,
"acc_norm_stderr": 0.03864139923699122
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8304093567251462,
"acc_stderr": 0.02878210810540171,
"acc_norm": 0.8304093567251462,
"acc_norm_stderr": 0.02878210810540171
},
"harness|truthfulqa:mc|0": {
"mc1": 0.602203182374541,
"mc1_stderr": 0.01713393424855968,
"mc2": 0.7559601871847126,
"mc2_stderr": 0.014162739924097284
},
"harness|winogrande|5": {
"acc": 0.8531965272296764,
"acc_stderr": 0.009946627440250684
},
"harness|gsm8k|5": {
"acc": 0.7126611068991661,
"acc_stderr": 0.01246467706010709
}
}
```
## 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] |
juliozhao/dataengine_llava | ---
license: apache-2.0
---
|
shrms/chart_dataset | ---
dataset_info:
features:
- name: image_file
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 11391822
num_examples: 38634
download_size: 3135404
dataset_size: 11391822
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ConvLab/woz | ---
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: WOZ 2.0
size_categories:
- 1K<n<10K
task_categories:
- conversational
---
# Dataset Card for WOZ 2.0
- **Repository:** https://github.com/nmrksic/neural-belief-tracker/tree/master/data/woz
- **Paper:** https://aclanthology.org/P17-1163.pdf
- **Leaderboard:** None
- **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com)
To use this dataset, you need to install [ConvLab-3](https://github.com/ConvLab/ConvLab-3) platform first. Then you can load the dataset via:
```
from convlab.util import load_dataset, load_ontology, load_database
dataset = load_dataset('woz')
ontology = load_ontology('woz')
database = load_database('woz')
```
For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets).
### Dataset Summary
Describe the dataset.
- **How to get the transformed data from original data:**
- download `woz_[train|validate|test]_en.json` from https://github.com/nmrksic/neural-belief-tracker/tree/master/data/woz and save to `woz` dir in the current directory.
- Run `python preprocess.py` in the current directory.
- **Main changes of the transformation:**
- domain is set to **restaurant**.
- normalize the value of categorical slots in state and dialogue acts.
- `belief_states` in WOZ dataset contains `request` intents, which are ignored in processing.
- use simple string match to find value spans of non-categorical slots.
- **Annotations:**
- User dialogue acts, state
### Supported Tasks and Leaderboards
NLU, DST, E2E
### Languages
English
### Data Splits
| split | dialogues | utterances | avg_utt | avg_tokens | avg_domains | cat slot match(state) | cat slot match(goal) | cat slot match(dialogue act) | non-cat slot span(dialogue act) |
|------------|-------------|--------------|-----------|--------------|---------------|-------------------------|------------------------|--------------------------------|-----------------------------------|
| train | 600 | 4472 | 7.45 | 11.37 | 1 | 100 | - | 100 | 96.56 |
| validation | 200 | 1460 | 7.3 | 11.28 | 1 | 100 | - | 100 | 95.52 |
| test | 400 | 2892 | 7.23 | 11.49 | 1 | 100 | - | 100 | 94.83 |
| all | 1200 | 8824 | 7.35 | 11.39 | 1 | 100 | - | 100 | 95.83 |
1 domains: ['restaurant']
- **cat slot match**: how many values of categorical slots are in the possible values of ontology in percentage.
- **non-cat slot span**: how many values of non-categorical slots have span annotation in percentage.
### Citation
```
@inproceedings{mrksic-etal-2017-neural,
title = "Neural Belief Tracker: Data-Driven Dialogue State Tracking",
author = "Mrk{\v{s}}i{\'c}, Nikola and
{\'O} S{\'e}aghdha, Diarmuid and
Wen, Tsung-Hsien and
Thomson, Blaise and
Young, Steve",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1163",
doi = "10.18653/v1/P17-1163",
pages = "1777--1788",
}
```
### Licensing Information
Apache License, Version 2.0
|
mojuss/faq_test | ---
license: apache-2.0
---
|
CyberHarem/ushigome_rimi_bangdream | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of ushigome_rimi/牛込りみ/우시고메리미 (BanG Dream!)
This is the dataset of ushigome_rimi/牛込りみ/우시고메리미 (BanG Dream!), containing 190 images and their tags.
The core tags of this character are `short_hair, black_hair, bangs, red_eyes, hair_ornament, bow, brown_eyes, ribbon`, 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 | 210.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ushigome_rimi_bangdream/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 190 | 134.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ushigome_rimi_bangdream/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 395 | 255.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ushigome_rimi_bangdream/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 190 | 186.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ushigome_rimi_bangdream/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 395 | 342.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ushigome_rimi_bangdream/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/ushigome_rimi_bangdream',
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 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, blush, bracelet, electric_guitar, short_sleeves, solo, smile, star_(symbol), hair_bow, holding_instrument, pink_skirt, white_shirt, bowtie, frills, pink_bow, plaid_bow, pleated_skirt, polka_dot, purple_bow, standing, suspender_skirt |
| 1 | 5 |  |  |  |  |  | electric_guitar, open_mouth, 1girl, 2girls, blush, star_(symbol), :d, brown_hair, holding_instrument, looking_at_viewer, pink_background, plectrum, solo_focus, striped, blue_jacket, blunt_bangs, dress, hat, long_sleeves, skirt |
| 2 | 5 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, striped, :d, electric_guitar, frills, holding_instrument, open_mouth, skirt, sparkle, bracelet, hair_flower, boots, confetti, food, jumping, rose, short_sleeves, sleeveless, star_(symbol) |
| 3 | 10 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, :d, floral_print, hairclip, open_mouth, puffy_short_sleeves, blunt_bangs, x_hair_ornament, pink_dress, simple_background, shirt, collarbone, frills, print_dress, upper_body, white_background |
| 4 | 6 |  |  |  |  |  | 1girl, blush, long_sleeves, red_ribbon, sailor_dress, solo, hanasakigawa_school_uniform, neck_ribbon, upper_body, brown_dress, buttons, looking_at_viewer, simple_background, white_background, white_sailor_collar, blunt_bangs, collarbone, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | blush | bracelet | electric_guitar | short_sleeves | solo | smile | star_(symbol) | hair_bow | holding_instrument | pink_skirt | white_shirt | bowtie | frills | pink_bow | plaid_bow | pleated_skirt | polka_dot | purple_bow | standing | suspender_skirt | open_mouth | 2girls | :d | brown_hair | pink_background | plectrum | solo_focus | striped | blue_jacket | blunt_bangs | dress | hat | long_sleeves | skirt | sparkle | hair_flower | boots | confetti | food | jumping | rose | sleeveless | floral_print | hairclip | puffy_short_sleeves | x_hair_ornament | pink_dress | simple_background | shirt | collarbone | print_dress | upper_body | white_background | red_ribbon | sailor_dress | hanasakigawa_school_uniform | neck_ribbon | brown_dress | buttons | white_sailor_collar |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:--------|:-----------|:------------------|:----------------|:-------|:--------|:----------------|:-----------|:---------------------|:-------------|:--------------|:---------|:---------|:-----------|:------------|:----------------|:------------|:-------------|:-----------|:------------------|:-------------|:---------|:-----|:-------------|:------------------|:-----------|:-------------|:----------|:--------------|:--------------|:--------|:------|:---------------|:--------|:----------|:--------------|:--------|:-----------|:-------|:----------|:-------|:-------------|:---------------|:-----------|:----------------------|:------------------|:-------------|:--------------------|:--------|:-------------|:--------------|:-------------|:-------------------|:-------------|:---------------|:------------------------------|:--------------|:--------------|:----------|:----------------------|
| 0 | 9 |  |  |  |  |  | X | X | X | 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 | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | | X | | X | | | | X | | | | | | | | X | | X | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 3 | 10 |  |  |  |  |  | X | X | X | | | | X | | | | | | | | X | | | | | | | | X | | X | | | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | X | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | X | | X | | X | X | X | X | X | X | X | X | X |
|
hf-internal-testing/dummy_image_class_data | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': resize
splits:
- name: train
num_bytes: 555953.0
num_examples: 6
download_size: 556964
dataset_size: 555953.0
---
# Dataset Card for "dummy_image_class_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Shahzebbb/wiki_processed | ---
dataset_info:
features:
- name: chunks
dtype: string
splits:
- name: train
num_bytes: 19406680850
num_examples: 22300752
download_size: 11345406685
dataset_size: 19406680850
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
result-kand2-sdxl-wuerst-karlo/e73e5059 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 155
num_examples: 10
download_size: 1318
dataset_size: 155
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "e73e5059"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Quangnguyen711/demo | ---
license: apache-2.0
---
|
CyberHarem/heavyrain_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of heavyrain/ヘビーレイン/暴雨 (Arknights)
This is the dataset of heavyrain/ヘビーレイン/暴雨 (Arknights), containing 64 images and their tags.
The core tags of this character are `animal_ears, long_hair, horse_ears, purple_eyes, hairband, grey_hair, hair_ornament, hair_flower, white_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 | 64 | 113.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/heavyrain_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 64 | 94.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/heavyrain_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 150 | 181.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/heavyrain_arknights/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/heavyrain_arknights',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, looking_at_viewer, official_alternate_costume, solo, black_dress, white_rose, long_sleeves, striped_hairband, upper_body, closed_mouth, holding, parted_lips, breasts |
| 1 | 9 |  |  |  |  |  | 1girl, black_dress, full_body, long_sleeves, solo, black_footwear, closed_mouth, looking_at_viewer, official_alternate_costume, shoes, white_pantyhose, white_flower, bag, horse_tail, standing, high_heels, sitting |
| 2 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, solo, closed_mouth, parted_bangs, simple_background, white_jacket, white_shirt, open_jacket, upper_body, white_background, black_skirt, blue_eyes, blush, long_sleeves, white_coat |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | official_alternate_costume | solo | black_dress | white_rose | long_sleeves | striped_hairband | upper_body | closed_mouth | holding | parted_lips | breasts | full_body | black_footwear | shoes | white_pantyhose | white_flower | bag | horse_tail | standing | high_heels | sitting | parted_bangs | simple_background | white_jacket | white_shirt | open_jacket | white_background | black_skirt | blue_eyes | blush | white_coat |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-----------------------------|:-------|:--------------|:-------------|:---------------|:-------------------|:-------------|:---------------|:----------|:--------------|:----------|:------------|:-----------------|:--------|:------------------|:---------------|:------|:-------------|:-----------|:-------------|:----------|:---------------|:--------------------|:---------------|:--------------|:--------------|:-------------------|:--------------|:------------|:--------|:-------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | X | X | X | X | | X | | | X | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | | X | | | X | | X | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
|
Trelis/big_patent_sample | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1k
source_datasets:
- big_patent
task_categories:
- summarization
task_ids: []
paperswithcode_id: bigpatent
pretty_name: Big Patent Sample
tags:
- patent-summarization
---
# Sampled big_patent Dataset
This is a sampled big_patent dataset - sampled down for shorter fine-tunings.
The data is sampled with the aim of providing an even distribution across data lengths. The distribution is quite flat up until 1 million characters in length, making the dataset good for training on lengths up to 250,000 tokens.
# Dataset Card for Big Patent
## 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:** [Big Patent](https://evasharma.github.io/bigpatent/)
- **Repository:**
- **Paper:** [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://arxiv.org/abs/1906.03741)
- **Leaderboard:**
- **Point of Contact:** [Lu Wang](mailto:wangluxy@umich.edu)
### Dataset Summary
BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries.
Each US patent application is filed under a Cooperative Patent Classification (CPC) code.
There are nine such classification categories:
- a: Human Necessities
- b: Performing Operations; Transporting
- c: Chemistry; Metallurgy
- d: Textiles; Paper
- e: Fixed Constructions
- f: Mechanical Engineering; Lightning; Heating; Weapons; Blasting
- g: Physics
- h: Electricity
- y: General tagging of new or cross-sectional technology
Current defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes:
```python
from datasets import load_dataset
ds = load_dataset("big_patent") # default is 'all' CPC codes
ds = load_dataset("big_patent", "all") # the same as above
ds = load_dataset("big_patent", "a") # only 'a' CPC codes
ds = load_dataset("big_patent", codes=["a", "b"])
```
To use 1.0.0 version (lower cased tokenized words), pass both parameters `codes` and `version`:
```python
ds = load_dataset("big_patent", codes="all", version="1.0.0")
ds = load_dataset("big_patent", codes="a", version="1.0.0")
ds = load_dataset("big_patent", codes=["a", "b"], version="1.0.0")
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
Each instance contains a pair of `description` and `abstract`. `description` is extracted from the Description section of the Patent while `abstract` is extracted from the Abstract section.
```
{
'description': 'FIELD OF THE INVENTION \n [0001] This invention relates to novel calcium phosphate-coated implantable medical devices and processes of making same. The unique calcium-phosphate coated implantable medical devices minimize...',
'abstract': 'This invention relates to novel calcium phosphate-coated implantable medical devices...'
}
```
### Data Fields
- `description`: detailed description of patent.
- `abstract`: Patent abastract.
### Data Splits
| | train | validation | test |
|:----|------------------:|-------------:|-------:|
| all | 1207222 | 67068 | 67072 |
| a | 174134 | 9674 | 9675 |
| b | 161520 | 8973 | 8974 |
| c | 101042 | 5613 | 5614 |
| d | 10164 | 565 | 565 |
| e | 34443 | 1914 | 1914 |
| f | 85568 | 4754 | 4754 |
| g | 258935 | 14385 | 14386 |
| h | 257019 | 14279 | 14279 |
| y | 124397 | 6911 | 6911 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@article{DBLP:journals/corr/abs-1906-03741,
author = {Eva Sharma and
Chen Li and
Lu Wang},
title = {{BIGPATENT:} {A} Large-Scale Dataset for Abstractive and Coherent
Summarization},
journal = {CoRR},
volume = {abs/1906.03741},
year = {2019},
url = {http://arxiv.org/abs/1906.03741},
eprinttype = {arXiv},
eprint = {1906.03741},
timestamp = {Wed, 26 Jun 2019 07:14:58 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset. |
chavesestranho/madruguinha | ---
license: openrail
language:
- pt
pretty_name: madruguinha
---
Vozes do Seu Madruga (Carlos Seidel)
* Extraído por Chaves Estranho (youtube.com/chavesestranho)
* Files: 240 .WAV
* Tempo: Aproximadamente 12 minutos
* Qualidade: Excelente |
open-llm-leaderboard/details_chanwit__flux-7b-v0.2 | ---
pretty_name: Evaluation run of chanwit/flux-7b-v0.2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chanwit/flux-7b-v0.2](https://huggingface.co/chanwit/flux-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_chanwit__flux-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 2024-01-18T00:42:00.685036](https://huggingface.co/datasets/open-llm-leaderboard/details_chanwit__flux-7b-v0.2/blob/main/results_2024-01-18T00-42-00.685036.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\": 0.6573592496311628,\n\
\ \"acc_stderr\": 0.031783424194298984,\n \"acc_norm\": 0.6574655421284195,\n\
\ \"acc_norm_stderr\": 0.03243446995426543,\n \"mc1\": 0.3537331701346389,\n\
\ \"mc1_stderr\": 0.016737814358846147,\n \"mc2\": 0.5180401965777761,\n\
\ \"mc2_stderr\": 0.015565981129474472\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6331058020477816,\n \"acc_stderr\": 0.014084133118104294,\n\
\ \"acc_norm\": 0.6655290102389079,\n \"acc_norm_stderr\": 0.013787460322441374\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6825333598884684,\n\
\ \"acc_stderr\": 0.004645393477680678,\n \"acc_norm\": 0.8611830312686716,\n\
\ \"acc_norm_stderr\": 0.003450488042965005\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n\
\ \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n\
\ \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\
\ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\
\ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \
\ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n\
\ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.56,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\": 0.56,\n\
\ \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6936416184971098,\n\
\ \"acc_stderr\": 0.03514942551267438,\n \"acc_norm\": 0.6936416184971098,\n\
\ \"acc_norm_stderr\": 0.03514942551267438\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\
\ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.77,\n \"acc_stderr\": 0.04229525846816507,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.04229525846816507\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.032081157507886836,\n\
\ \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.032081157507886836\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5448275862068965,\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\"\
: 0.3994708994708995,\n \"acc_stderr\": 0.02522545028406788,\n \"\
acc_norm\": 0.3994708994708995,\n \"acc_norm_stderr\": 0.02522545028406788\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4523809523809524,\n\
\ \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n\
\ \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7870967741935484,\n \"acc_stderr\": 0.02328766512726854,\n \"\
acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.02328766512726854\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"\
acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\"\
: 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\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\": 0.029620227874790482,\n \"\
acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.029620227874790482\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n\
\ \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402534,\n\
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402534\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \
\ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.029597329730978086,\n\
\ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.029597329730978086\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\
acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"\
acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"\
acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8431372549019608,\n \"acc_stderr\": 0.025524722324553353,\n \"\
acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.025524722324553353\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.810126582278481,\n \"acc_stderr\": 0.025530100460233497,\n \
\ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.025530100460233497\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7085201793721974,\n\
\ \"acc_stderr\": 0.03050028317654585,\n \"acc_norm\": 0.7085201793721974,\n\
\ \"acc_norm_stderr\": 0.03050028317654585\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.03498149385462472,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.03498149385462472\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8347107438016529,\n \"acc_stderr\": 0.03390780612972776,\n \"\
acc_norm\": 0.8347107438016529,\n \"acc_norm_stderr\": 0.03390780612972776\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\
\ \"acc_stderr\": 0.03755265865037182,\n \"acc_norm\": 0.8148148148148148,\n\
\ \"acc_norm_stderr\": 0.03755265865037182\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.03192193448934724,\n\
\ \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.03192193448934724\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8543689320388349,\n \"acc_stderr\": 0.0349260647662379,\n\
\ \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.0349260647662379\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8504273504273504,\n\
\ \"acc_stderr\": 0.023365051491753715,\n \"acc_norm\": 0.8504273504273504,\n\
\ \"acc_norm_stderr\": 0.023365051491753715\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\": 0.8301404853128991,\n\
\ \"acc_stderr\": 0.013428186370608306,\n \"acc_norm\": 0.8301404853128991,\n\
\ \"acc_norm_stderr\": 0.013428186370608306\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.023532925431044283,\n\
\ \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.023532925431044283\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3877094972067039,\n\
\ \"acc_stderr\": 0.016295332328155818,\n \"acc_norm\": 0.3877094972067039,\n\
\ \"acc_norm_stderr\": 0.016295332328155818\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7320261437908496,\n \"acc_stderr\": 0.025360603796242553,\n\
\ \"acc_norm\": 0.7320261437908496,\n \"acc_norm_stderr\": 0.025360603796242553\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n\
\ \"acc_stderr\": 0.02549425935069491,\n \"acc_norm\": 0.7202572347266881,\n\
\ \"acc_norm_stderr\": 0.02549425935069491\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.02474862449053737,\n\
\ \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.02474862449053737\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4645390070921986,\n \"acc_stderr\": 0.02975238965742705,\n \
\ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.02975238965742705\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4791395045632334,\n\
\ \"acc_stderr\": 0.012759117066518012,\n \"acc_norm\": 0.4791395045632334,\n\
\ \"acc_norm_stderr\": 0.012759117066518012\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7279411764705882,\n \"acc_stderr\": 0.02703304115168146,\n\
\ \"acc_norm\": 0.7279411764705882,\n \"acc_norm_stderr\": 0.02703304115168146\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6830065359477124,\n \"acc_stderr\": 0.018824219512706207,\n \
\ \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.018824219512706207\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.028920583220675602,\n\
\ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.028920583220675602\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\
\ \"acc_stderr\": 0.025870646766169136,\n \"acc_norm\": 0.8407960199004975,\n\
\ \"acc_norm_stderr\": 0.025870646766169136\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896308,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896308\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061463,\n\
\ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061463\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3537331701346389,\n\
\ \"mc1_stderr\": 0.016737814358846147,\n \"mc2\": 0.5180401965777761,\n\
\ \"mc2_stderr\": 0.015565981129474472\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7932123125493291,\n \"acc_stderr\": 0.011382566829235798\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7263078089461713,\n \
\ \"acc_stderr\": 0.012281003490963449\n }\n}\n```"
repo_url: https://huggingface.co/chanwit/flux-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_01_18T00_42_00.685036
path:
- '**/details_harness|arc:challenge|25_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|gsm8k|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hellaswag|10_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-18T00-42-00.685036.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-18T00-42-00.685036.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- '**/details_harness|winogrande|5_2024-01-18T00-42-00.685036.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-18T00-42-00.685036.parquet'
- config_name: results
data_files:
- split: 2024_01_18T00_42_00.685036
path:
- results_2024-01-18T00-42-00.685036.parquet
- split: latest
path:
- results_2024-01-18T00-42-00.685036.parquet
---
# Dataset Card for Evaluation run of chanwit/flux-7b-v0.2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [chanwit/flux-7b-v0.2](https://huggingface.co/chanwit/flux-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_chanwit__flux-7b-v0.2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-18T00:42:00.685036](https://huggingface.co/datasets/open-llm-leaderboard/details_chanwit__flux-7b-v0.2/blob/main/results_2024-01-18T00-42-00.685036.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": 0.6573592496311628,
"acc_stderr": 0.031783424194298984,
"acc_norm": 0.6574655421284195,
"acc_norm_stderr": 0.03243446995426543,
"mc1": 0.3537331701346389,
"mc1_stderr": 0.016737814358846147,
"mc2": 0.5180401965777761,
"mc2_stderr": 0.015565981129474472
},
"harness|arc:challenge|25": {
"acc": 0.6331058020477816,
"acc_stderr": 0.014084133118104294,
"acc_norm": 0.6655290102389079,
"acc_norm_stderr": 0.013787460322441374
},
"harness|hellaswag|10": {
"acc": 0.6825333598884684,
"acc_stderr": 0.004645393477680678,
"acc_norm": 0.8611830312686716,
"acc_norm_stderr": 0.003450488042965005
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6074074074074074,
"acc_stderr": 0.0421850621536888,
"acc_norm": 0.6074074074074074,
"acc_norm_stderr": 0.0421850621536888
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6981132075471698,
"acc_stderr": 0.02825420034443866,
"acc_norm": 0.6981132075471698,
"acc_norm_stderr": 0.02825420034443866
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.56,
"acc_stderr": 0.049888765156985884,
"acc_norm": 0.56,
"acc_norm_stderr": 0.049888765156985884
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6936416184971098,
"acc_stderr": 0.03514942551267438,
"acc_norm": 0.6936416184971098,
"acc_norm_stderr": 0.03514942551267438
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4215686274509804,
"acc_stderr": 0.04913595201274498,
"acc_norm": 0.4215686274509804,
"acc_norm_stderr": 0.04913595201274498
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816507,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816507
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5957446808510638,
"acc_stderr": 0.032081157507886836,
"acc_norm": 0.5957446808510638,
"acc_norm_stderr": 0.032081157507886836
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5,
"acc_stderr": 0.047036043419179864,
"acc_norm": 0.5,
"acc_norm_stderr": 0.047036043419179864
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5448275862068965,
"acc_stderr": 0.04149886942192117,
"acc_norm": 0.5448275862068965,
"acc_norm_stderr": 0.04149886942192117
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3994708994708995,
"acc_stderr": 0.02522545028406788,
"acc_norm": 0.3994708994708995,
"acc_norm_stderr": 0.02522545028406788
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4523809523809524,
"acc_stderr": 0.044518079590553275,
"acc_norm": 0.4523809523809524,
"acc_norm_stderr": 0.044518079590553275
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.36,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.36,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7870967741935484,
"acc_stderr": 0.02328766512726854,
"acc_norm": 0.7870967741935484,
"acc_norm_stderr": 0.02328766512726854
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4975369458128079,
"acc_stderr": 0.03517945038691063,
"acc_norm": 0.4975369458128079,
"acc_norm_stderr": 0.03517945038691063
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.72,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.72,
"acc_norm_stderr": 0.045126085985421276
},
"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": 0.029620227874790482,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.029620227874790482
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9119170984455959,
"acc_stderr": 0.02045374660160103,
"acc_norm": 0.9119170984455959,
"acc_norm_stderr": 0.02045374660160103
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.023901157979402534,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.023901157979402534
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34444444444444444,
"acc_stderr": 0.02897264888484427,
"acc_norm": 0.34444444444444444,
"acc_norm_stderr": 0.02897264888484427
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7058823529411765,
"acc_stderr": 0.029597329730978086,
"acc_norm": 0.7058823529411765,
"acc_norm_stderr": 0.029597329730978086
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33112582781456956,
"acc_stderr": 0.038425817186598696,
"acc_norm": 0.33112582781456956,
"acc_norm_stderr": 0.038425817186598696
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8495412844036697,
"acc_stderr": 0.015328563932669237,
"acc_norm": 0.8495412844036697,
"acc_norm_stderr": 0.015328563932669237
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5092592592592593,
"acc_stderr": 0.034093869469927006,
"acc_norm": 0.5092592592592593,
"acc_norm_stderr": 0.034093869469927006
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8431372549019608,
"acc_stderr": 0.025524722324553353,
"acc_norm": 0.8431372549019608,
"acc_norm_stderr": 0.025524722324553353
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.810126582278481,
"acc_stderr": 0.025530100460233497,
"acc_norm": 0.810126582278481,
"acc_norm_stderr": 0.025530100460233497
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7085201793721974,
"acc_stderr": 0.03050028317654585,
"acc_norm": 0.7085201793721974,
"acc_norm_stderr": 0.03050028317654585
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8015267175572519,
"acc_stderr": 0.03498149385462472,
"acc_norm": 0.8015267175572519,
"acc_norm_stderr": 0.03498149385462472
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8347107438016529,
"acc_stderr": 0.03390780612972776,
"acc_norm": 0.8347107438016529,
"acc_norm_stderr": 0.03390780612972776
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8148148148148148,
"acc_stderr": 0.03755265865037182,
"acc_norm": 0.8148148148148148,
"acc_norm_stderr": 0.03755265865037182
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7914110429447853,
"acc_stderr": 0.03192193448934724,
"acc_norm": 0.7914110429447853,
"acc_norm_stderr": 0.03192193448934724
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5089285714285714,
"acc_stderr": 0.04745033255489123,
"acc_norm": 0.5089285714285714,
"acc_norm_stderr": 0.04745033255489123
},
"harness|hendrycksTest-management|5": {
"acc": 0.8543689320388349,
"acc_stderr": 0.0349260647662379,
"acc_norm": 0.8543689320388349,
"acc_norm_stderr": 0.0349260647662379
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8504273504273504,
"acc_stderr": 0.023365051491753715,
"acc_norm": 0.8504273504273504,
"acc_norm_stderr": 0.023365051491753715
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8301404853128991,
"acc_stderr": 0.013428186370608306,
"acc_norm": 0.8301404853128991,
"acc_norm_stderr": 0.013428186370608306
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7427745664739884,
"acc_stderr": 0.023532925431044283,
"acc_norm": 0.7427745664739884,
"acc_norm_stderr": 0.023532925431044283
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3877094972067039,
"acc_stderr": 0.016295332328155818,
"acc_norm": 0.3877094972067039,
"acc_norm_stderr": 0.016295332328155818
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7320261437908496,
"acc_stderr": 0.025360603796242553,
"acc_norm": 0.7320261437908496,
"acc_norm_stderr": 0.025360603796242553
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7202572347266881,
"acc_stderr": 0.02549425935069491,
"acc_norm": 0.7202572347266881,
"acc_norm_stderr": 0.02549425935069491
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7283950617283951,
"acc_stderr": 0.02474862449053737,
"acc_norm": 0.7283950617283951,
"acc_norm_stderr": 0.02474862449053737
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4645390070921986,
"acc_stderr": 0.02975238965742705,
"acc_norm": 0.4645390070921986,
"acc_norm_stderr": 0.02975238965742705
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4791395045632334,
"acc_stderr": 0.012759117066518012,
"acc_norm": 0.4791395045632334,
"acc_norm_stderr": 0.012759117066518012
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7279411764705882,
"acc_stderr": 0.02703304115168146,
"acc_norm": 0.7279411764705882,
"acc_norm_stderr": 0.02703304115168146
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6830065359477124,
"acc_stderr": 0.018824219512706207,
"acc_norm": 0.6830065359477124,
"acc_norm_stderr": 0.018824219512706207
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.0449429086625209,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7142857142857143,
"acc_stderr": 0.028920583220675602,
"acc_norm": 0.7142857142857143,
"acc_norm_stderr": 0.028920583220675602
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8407960199004975,
"acc_stderr": 0.025870646766169136,
"acc_norm": 0.8407960199004975,
"acc_norm_stderr": 0.025870646766169136
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.87,
"acc_stderr": 0.03379976689896308,
"acc_norm": 0.87,
"acc_norm_stderr": 0.03379976689896308
},
"harness|hendrycksTest-virology|5": {
"acc": 0.536144578313253,
"acc_stderr": 0.038823108508905954,
"acc_norm": 0.536144578313253,
"acc_norm_stderr": 0.038823108508905954
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.847953216374269,
"acc_stderr": 0.027539122889061463,
"acc_norm": 0.847953216374269,
"acc_norm_stderr": 0.027539122889061463
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3537331701346389,
"mc1_stderr": 0.016737814358846147,
"mc2": 0.5180401965777761,
"mc2_stderr": 0.015565981129474472
},
"harness|winogrande|5": {
"acc": 0.7932123125493291,
"acc_stderr": 0.011382566829235798
},
"harness|gsm8k|5": {
"acc": 0.7263078089461713,
"acc_stderr": 0.012281003490963449
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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Sharppen10/Dataset1 | ---
license: other
---
|
autoevaluate/autoeval-eval-conll2003-conll2003-0a1842-65397145557 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- conll2003
eval_info:
task: entity_extraction
model: Abelll/bert-finetuned-ner
metrics: []
dataset_name: conll2003
dataset_config: conll2003
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: Abelll/bert-finetuned-ner
* Dataset: conll2003
* Config: conll2003
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@chnlyi](https://huggingface.co/chnlyi) for evaluating this model. |
ticoAg/shibing624-medical-pretrain | ---
license: apache-2.0
language:
- zh
- en
tags:
- text-generation
pretty_name: medical
task_categories:
- text-generation
size_categories:
- 1M<n<10M
---
# Dataset Card for medical
中文医疗数据集
- LLM Supervised Finetuning repository: https://github.com/shibing624/textgen
- MeidcalGPT repository: https://github.com/shibing624/MedicalGPT
## Dataset Description
medical is a Chinese Medical dataset. 医疗数据集，可用于医疗领域大模型训练。
```
tree medical
|-- finetune # 监督微调数据集，可用于SFT和RLHF
| |-- test_en_1.json
| |-- test_zh_0.json
| |-- train_en_1.json
| |-- train_zh_0.json
| |-- valid_en_1.json
| `-- valid_zh_0.json
|-- medical.py # hf dataset 数据展示用
|-- pretrain # 二次预训练数据集
| |-- medical_book_zh.json
| |-- test_encyclopedia.json
| |-- train_encyclopedia.json
| `-- valid_encyclopedia.json
|-- README.md
`-- reward # 奖励模型数据集
|-- test.json
|-- train.json
`-- valid.json
```
### Original Dataset Summary
#### pretrain
- train_encyclopedia.json: 共36万条，来自医疗百科数据[FreedomIntelligence/huatuo_encyclopedia_qa](https://huggingface.co/datasets/FreedomIntelligence/huatuo_encyclopedia_qa) , 拼接 questions 和 answers，形成 text 文本字段，语句通顺，用于预训练注入医疗知识。
- medical_book_zh.json: 共8475条，来自医疗教材的文本数据，来源：https://github.com/jind11/MedQA， 原始数据集：[google drive](https://drive.google.com/u/0/uc?export=download&confirm=t&id=1ImYUSLk9JbgHXOemfvyiDiirluZHPeQw) ，只对长段落切分为2048字的小段落了。
#### finetune
- train_zh_0.json: 共195万条，来自1）中文医疗对话数据集[Toyhom/Chinese-medical-dialogue-data](https://github.com/Toyhom/Chinese-medical-dialogue-data)的六个科室医疗问诊数据，
有79万条；2）在线医疗百科 huatuo_encyclopedia_qa ，有36万条；3）医疗知识图谱 huatuo_knowledge_graph_qa，有79万条。三部分合并，共195万条。
- train_en_1.json：共11万条，来自英文医疗问诊对话数据[Kent0n-Li/ChatDoctor](https://github.com/Kent0n-Li/ChatDoctor)，合并了HealthCareMagic-100k、GenMedGPT-5k 数据集，共11万条。
#### reward
- train.json 共4000条，问题来自中文医疗对话数据集[Toyhom/Chinese-medical-dialogue-data](https://github.com/Toyhom/Chinese-medical-dialogue-data)的随机4000条提问，`response_chosen`来自该数据集的医生答复，
`response_rejected`来自本草模型[SCIR-HI/Huatuo-Llama-Med-Chinese](https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese)的答复。
### Supported Tasks and Leaderboards
中文医疗对话模型
The dataset designed for medical task training pretrained language models.
### Languages
The data are in Chinese.
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
head pretrain/train_encyclopedia.json
```json
{"text": "怀孕后嘴巴很淡怎么办？有孕妇在怀孕之后，发现自己嘴巴比较淡，出现这种情况的原因其实也非常的复杂，首先和妊娠反应有直接的关系，这是一种正常的情况，另外有些孕妇平常不注意口腔卫生，舌苔比较厚或者自己有了一些消化系统方面的疾病，这就要求人们必须要及时的进行处理。女性在怀孕之后，身体就会出现一些明显的变化，首先人们月经会停止，另外也会有恶心、呕吐等一些妊娠反应，不过这些都是正常的。有些孕妇发现自己在怀孕之后，口味发生了很大的变化，嘴巴变得非常的淡。其实这也和激素变化有直接的关系，可能是妊娠反应所致，在怀孕期间，因为受到体内激素水平的变化，所以就会有肠胃系统的改变，人们可能会出现食欲不振，消化不良等症状表现，也有一些孕妇会发现自己嘴巴没有味道，会有口苦的症状，而这也是正常的孕期反应，人们在平常要多喝一些水，多吃一些清淡营养的食物慢慢就会改善。也有可能是舌苔过厚所致，孕妇嘴巴里面没有味道，很有可能是舌苔比较重、舌苔过厚导致的，这样就会影响到味蕾对味道的敏感度，不仅嘴巴里面没有味道，甚至有些孕妇在说话的时候也会有口臭，这就要求人们在每天早上漱口的时候，必须要用牙刷刷舌苔开始，不要清理的特别深，以免人们会有呕吐，慢慢习惯之后再往深一点的清洗，一般2到3天就会得到改善。嘴巴感到比较淡，其实也和脾胃虚寒有直接的关系，消化系统疾病，内分泌疾病，营养不良等，但有可能导致舌头部位因为味蕾的敏感度下降，产生口淡之感，患者会有食欲不振的表现，发现病症及时就诊治疗。"}
```
head finetune/train_zh_0.json
```json
{"instruction": "曲匹地尔片的用法用量", "input": "", "output": "注意：同种药品可由于不同的包装规格有不同的用法或用量。本文只供参考。如果不确定，请参看药品随带的说明书或向医生询问。口服。一次50～100mg（1-2片），3次/日，或遵医嘱。"}
```
head finetune/train_en_1.json
```json
{"instruction": "If you are a doctor, please answer the medical questions based on the patient's description.", "input": "Doctor, I have been experiencing sudden and frequent panic attacks. I don't know what to do.", "output": "Well, based on what you're telling me, it sounds like you may be suffering from panic disorder. The best course of action is to start with psychotherapy and mental health counseling. Additionally, we should conduct an electrocardiogram to make sure that there are no physical issues causing your panic attacks. We will also need to perform a depression screen and a toxicology screen to rule out any other underlying causes. Finally, I would recommend a comprehensive psychological and psychiatric evaluation and therapy to help manage your symptoms."}
```
head reward/train.json
```json
{"question": "孩子感统失调，学习不行怎么办？，孩子说话晚，走路晚，现在小学二年级，学习跟不上，理解力差，运动协调性差，家里很着急，不知怎么办。", "response_chosen": "病情分析：你好!孩子说话晚，走路也晚，很可能是大脑本身发育不好引起的发育迟缓。而五岁时所致的智力检查为临界范围，那就是说孩子的智商是有问题的，也应考虑与大脑发育不好有关。指导意见：人的大脑在头一年发育最快，可塑性最强，在头三年可塑性还是可以的，超过三岁再进行训练，效果就不怎么好了。建议再给孩子做一做智力测试，如果孩子的智商还是在临界范围，那就要考虑让孩子去特殊学校进行康复训练，而不是继续在普通小学就读，否则对孩子来说，就是强人所难了。希望自己的孩子能聪明，这是每个家长都会有的心愿，但如果孩子自身的条件就是不能跟上同龄孩子，那家长也要面对这个事实的，对吗？医生询问：", "response_rejected": "建议家长先带孩子去正规医院做全面检查以确定病因和病情严重程度；同时可以进行物理治疗、康复训练等辅助治疗方法。"}
```
### Data Fields
#### 预训练数据集 pretrain
字段解释：
- text: 文本
#### 指令微调数据集 finetune
字段解释：
- instruction: 指令
- input：问题（可为空）
- output：答复
#### 奖励模型数据集 reward
字段解释：
- question: 问题
- response_chosen: 优质回答
- response_rejected: 低质回答
### Data Splits
```
> wc -l medical/*/*
500 medical/finetune/test_en_1.json
500 medical/finetune/test_zh_0.json
116617 medical/finetune/train_en_1.json
1949972 medical/finetune/train_zh_0.json
500 medical/finetune/valid_en_1.json
500 medical/finetune/valid_zh_0.json
8475 medical/pretrain/medical_book_zh.json
500 medical/pretrain/test_encyclopedia.json
361420 medical/pretrain/train_encyclopedia.json
500 medical/pretrain/valid_encyclopedia.json
100 medical/reward/test.json
3800 medical/reward/train.json
100 medical/reward/valid.json
2443484 total
```
### Licensing Information
The dataset is available under the Apache 2.0.
### Citation Information
- https://github.com/Toyhom/Chinese-medical-dialogue-data
- https://github.com/FreedomIntelligence/Huatuo-26M/blob/main/README_zh-CN.md
- https://huggingface.co/datasets/FreedomIntelligence/huatuo_encyclopedia_qa
- https://huggingface.co/datasets/FreedomIntelligence/huatuo_knowledge_graph_qa
- https://github.com/Kent0n-Li/ChatDoctor
附上几个优质的reward model dataset:
- https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise
- https://huggingface.co/datasets/sunzeyeah/chinese_chatgpt_corpus
- https://huggingface.co/datasets/Cohere/miracl-zh-queries-22-12
- https://huggingface.co/datasets/Dahoas/rm-static
### Contributions
[shibing624](https://github.com/shibing624) 整理并上传
|
HPGomes/MichaelJacksonFalsettoo | ---
license: openrail
---
|
tyzhu/lmind_hotpot_train5000_eval5000_v1_reciteonly_qa | ---
configs:
- config_name: default
data_files:
- split: train_qa
path: data/train_qa-*
- split: train_recite_qa
path: data/train_recite_qa-*
- split: eval_qa
path: data/eval_qa-*
- split: eval_recite_qa
path: data/eval_recite_qa-*
- split: all_docs
path: data/all_docs-*
- split: all_docs_eval
path: data/all_docs_eval-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: answers
struct:
- name: answer_start
sequence: 'null'
- name: text
sequence: string
splits:
- name: train_qa
num_bytes: 864508
num_examples: 5000
- name: train_recite_qa
num_bytes: 5350190
num_examples: 5000
- name: eval_qa
num_bytes: 813536
num_examples: 5000
- name: eval_recite_qa
num_bytes: 5394796
num_examples: 5000
- name: all_docs
num_bytes: 8524332
num_examples: 18224
- name: all_docs_eval
num_bytes: 8523131
num_examples: 18224
- name: train
num_bytes: 5350190
num_examples: 5000
- name: validation
num_bytes: 5394796
num_examples: 5000
download_size: 24659819
dataset_size: 40215479
---
# Dataset Card for "lmind_hotpot_train5000_eval5000_v1_reciteonly_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Tamazight-NLP/IRCAM-CORPUS | ---
license: cc
language:
- ber
- zgh
- tzm
- shi
- rif
task_categories:
- text-generation
pretty_name: IRCAM Corpus
---
# Dataset Card for IRCAM Corpus
<!-- Provide a quick summary of the dataset. -->
A text corpus containing texts written in various Tamazight dialects of Morocco published by IRCAM (Institut Royal de la Culture Amazighe).
## 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:** https://tal.ircam.ma/talam/corpus.php
- **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] |
jmamou/dolly_hhrlhf_with_dev | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: dev
path: data/dev-*
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 39966504.570578314
num_examples: 54181
- name: test
num_bytes: 3461426
num_examples: 5129
- name: dev
num_bytes: 3783396.429421683
num_examples: 5129
download_size: 25201542
dataset_size: 47211327.0
---
# Dataset Card for "dolly_hhrlhf_with_dev"
[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/17203e26 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 188
num_examples: 10
download_size: 1337
dataset_size: 188
---
# Dataset Card for "17203e26"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
allegro/klej-polemo2-in | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: 'PolEmo2.0-IN'
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# klej-polemo2-in
## Description
The PolEmo2.0 is a dataset of online consumer reviews from four domains: medicine, hotels, products, and university. It is human-annotated on a level of full reviews and individual sentences. It comprises over 8000 reviews, about 85% from the medicine and hotel domains.
We use the PolEmo2.0 dataset to form two tasks. Both use the same training dataset, i.e., reviews from medicine and hotel domains, but are evaluated on a different test set.
**In-Domain** is the first task, and we use accuracy to evaluate model performance within the in-domain context, i.e., on a test set of reviews from medicine and hotels domains.
## Tasks (input, output, and metrics)
The task is to predict the correct label of the review.
**Input** ('*text'* column): sentence
**Output** ('*target'* column): label for sentence sentiment ('zero': neutral, 'minus': negative, 'plus': positive, 'amb': ambiguous)
**Domain**: Online reviews
**Measurements**: Accuracy
**Example**:
Input: `Lekarz zalecił mi kurację alternatywną do dotychczasowej , więc jeszcze nie daję najwyższej oceny ( zobaczymy na ile okaże się skuteczna ) . Do Pana doktora nie mam zastrzeżeń : bardzo profesjonalny i kulturalny . Jedyny minus dotyczy gabinetu , który nie jest nowoczesny , co może zniechęcać pacjentki .`
Input (translated by DeepL): `The doctor recommended me an alternative treatment to the current one , so I do not yet give the highest rating ( we will see how effective it turns out to be ) . To the doctor I have no reservations : very professional and cultured . The only minus is about the office , which is not modern , which may discourage patients .`
Output: `amb` (ambiguous)
## Data splits
| Subset | Cardinality |
|:-----------|--------------:|
| train | 5783 |
| test | 722 |
| validation | 723 |
## Class distribution in train
| Class | Sentiment | train | validation | test |
|:------|:----------|------:|-----------:|------:|
| minus | positive | 0.379 | 0.375 | 0.416 |
| plus | negative | 0.271 | 0.289 | 0.273 |
| amb | ambiguous | 0.182 | 0.160 | 0.150 |
| zero | neutral | 0.168 | 0.176 | 0.162 |
## Citation
```
@inproceedings{kocon-etal-2019-multi,
title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
author = "Koco{\'n}, Jan and
Mi{\l}kowski, Piotr and
Za{\'s}ko-Zieli{\'n}ska, Monika",
booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K19-1092",
doi = "10.18653/v1/K19-1092",
pages = "980--991",
abstract = "In this article we present an extended version of PolEmo {--} a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).",
}
```
## License
```
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
```
## Links
[HuggingFace](https://huggingface.co/datasets/allegro/klej-polemo2-in)
[Source](https://clarin-pl.eu/dspace/handle/11321/710)
[Paper](https://aclanthology.org/K19-1092/)
## Examples
### Loading
```python
from pprint import pprint
from datasets import load_dataset
dataset = load_dataset("allegro/klej-polemo2-in")
pprint(dataset['train'][0])
# {'sentence': 'Super lekarz i człowiek przez duże C . Bardzo duże doświadczenie '
# 'i trafne diagnozy . Wielka cierpliwość do ludzi starszych . Od '
# 'lat opiekuje się moją Mamą staruszką , i twierdzę , że mamy duże '
# 'szczęście , że mamy takiego lekarza . Naprawdę nie wiem cobyśmy '
# 'zrobili , gdyby nie Pan doktor . Dzięki temu , moja mama żyje . '
# 'Każda wizyta u specjalisty jest u niego konsultowana i uważam , '
# 'że jest lepszy od każdego z nich . Mamy do Niego prawie '
# 'nieograniczone zaufanie . Można wiele dobrego o Panu doktorze '
# 'jeszcze napisać . Niestety , ma bardzo dużo pacjentów , jest '
# 'przepracowany ( z tego powodu nawet obawiam się o jego zdrowie ) '
# 'i dostęp do niego jest trudny , ale zawsze możliwy .',
# 'target': '__label__meta_plus_m'}
```
### Evaluation
```python
import random
from pprint import pprint
from datasets import load_dataset, load_metric
dataset = load_dataset("allegro/klej-polemo2-in")
dataset = dataset.class_encode_column("target")
references = dataset["test"]["target"]
# generate random predictions
predictions = [random.randrange(max(references) + 1) for _ in range(len(references))]
acc = load_metric("accuracy")
f1 = load_metric("f1")
acc_score = acc.compute(predictions=predictions, references=references)
f1_score = f1.compute(predictions=predictions, references=references, average="macro")
pprint(acc_score)
pprint(f1_score)
# {'accuracy': 0.25069252077562326}
# {'f1': 0.23760962219870274}
``` |
open-llm-leaderboard/details_adamo1139__Mistral-7B-AEZAKMI-v1 | ---
pretty_name: Evaluation run of adamo1139/Mistral-7B-AEZAKMI-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [adamo1139/Mistral-7B-AEZAKMI-v1](https://huggingface.co/adamo1139/Mistral-7B-AEZAKMI-v1)\
\ 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_adamo1139__Mistral-7B-AEZAKMI-v1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-16T18:53:11.719846](https://huggingface.co/datasets/open-llm-leaderboard/details_adamo1139__Mistral-7B-AEZAKMI-v1/blob/main/results_2023-12-16T18-53-11.719846.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\": 0.5806632675458097,\n\
\ \"acc_stderr\": 0.033329691230435686,\n \"acc_norm\": 0.5911675175599549,\n\
\ \"acc_norm_stderr\": 0.03419768825329829,\n \"mc1\": 0.3733170134638923,\n\
\ \"mc1_stderr\": 0.016932370557570634,\n \"mc2\": 0.5354200723033775,\n\
\ \"mc2_stderr\": 0.015605336386464858\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.552901023890785,\n \"acc_stderr\": 0.01452938016052684,\n\
\ \"acc_norm\": 0.5887372013651877,\n \"acc_norm_stderr\": 0.014379441068522085\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6337382991435969,\n\
\ \"acc_stderr\": 0.004807975515446489,\n \"acc_norm\": 0.8200557657837084,\n\
\ \"acc_norm_stderr\": 0.0038335592281586684\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5481481481481482,\n\
\ \"acc_stderr\": 0.042992689054808644,\n \"acc_norm\": 0.5481481481481482,\n\
\ \"acc_norm_stderr\": 0.042992689054808644\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6578947368421053,\n \"acc_stderr\": 0.0386073159931609,\n\
\ \"acc_norm\": 0.6578947368421053,\n \"acc_norm_stderr\": 0.0386073159931609\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\": {\n \"\
acc\": 0.6528301886792452,\n \"acc_stderr\": 0.029300101705549655,\n \
\ \"acc_norm\": 0.6528301886792452,\n \"acc_norm_stderr\": 0.029300101705549655\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.625,\n\
\ \"acc_stderr\": 0.04048439222695598,\n \"acc_norm\": 0.625,\n \
\ \"acc_norm_stderr\": 0.04048439222695598\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n\
\ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6069364161849711,\n\
\ \"acc_stderr\": 0.03724249595817731,\n \"acc_norm\": 0.6069364161849711,\n\
\ \"acc_norm_stderr\": 0.03724249595817731\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\
\ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n\
\ \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5191489361702127,\n \"acc_stderr\": 0.032662042990646796,\n\
\ \"acc_norm\": 0.5191489361702127,\n \"acc_norm_stderr\": 0.032662042990646796\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.38596491228070173,\n\
\ \"acc_stderr\": 0.045796394220704334,\n \"acc_norm\": 0.38596491228070173,\n\
\ \"acc_norm_stderr\": 0.045796394220704334\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.0416180850350153,\n\
\ \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.0416180850350153\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42857142857142855,\n \"acc_stderr\": 0.025487187147859372,\n \"\
acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.025487187147859372\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\
\ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\
\ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7032258064516129,\n\
\ \"acc_stderr\": 0.02598850079241189,\n \"acc_norm\": 0.7032258064516129,\n\
\ \"acc_norm_stderr\": 0.02598850079241189\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.43349753694581283,\n \"acc_stderr\": 0.034867317274198714,\n\
\ \"acc_norm\": 0.43349753694581283,\n \"acc_norm_stderr\": 0.034867317274198714\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\"\
: 0.61,\n \"acc_norm_stderr\": 0.04902071300001974\n },\n \"harness|hendrycksTest-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\"\
: 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586825,\n \"\
acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586825\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8082901554404145,\n \"acc_stderr\": 0.028408953626245282,\n\
\ \"acc_norm\": 0.8082901554404145,\n \"acc_norm_stderr\": 0.028408953626245282\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5769230769230769,\n \"acc_stderr\": 0.02504919787604234,\n \
\ \"acc_norm\": 0.5769230769230769,\n \"acc_norm_stderr\": 0.02504919787604234\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2962962962962963,\n \"acc_stderr\": 0.027840811495871934,\n \
\ \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.027840811495871934\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5966386554621849,\n \"acc_stderr\": 0.031866081214088314,\n\
\ \"acc_norm\": 0.5966386554621849,\n \"acc_norm_stderr\": 0.031866081214088314\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33774834437086093,\n \"acc_stderr\": 0.038615575462551684,\n \"\
acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.038615575462551684\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7504587155963303,\n \"acc_stderr\": 0.018553897629501624,\n \"\
acc_norm\": 0.7504587155963303,\n \"acc_norm_stderr\": 0.018553897629501624\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\"\
: 0.7058823529411765,\n \"acc_stderr\": 0.03198001660115071,\n \"\
acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.03198001660115071\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7215189873417721,\n \"acc_stderr\": 0.029178682304842548,\n \
\ \"acc_norm\": 0.7215189873417721,\n \"acc_norm_stderr\": 0.029178682304842548\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6233183856502242,\n\
\ \"acc_stderr\": 0.03252113489929187,\n \"acc_norm\": 0.6233183856502242,\n\
\ \"acc_norm_stderr\": 0.03252113489929187\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7022900763358778,\n \"acc_stderr\": 0.040103589424622034,\n\
\ \"acc_norm\": 0.7022900763358778,\n \"acc_norm_stderr\": 0.040103589424622034\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.71900826446281,\n \"acc_stderr\": 0.04103203830514512,\n \"acc_norm\"\
: 0.71900826446281,\n \"acc_norm_stderr\": 0.04103203830514512\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6851851851851852,\n\
\ \"acc_stderr\": 0.04489931073591312,\n \"acc_norm\": 0.6851851851851852,\n\
\ \"acc_norm_stderr\": 0.04489931073591312\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\
\ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\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\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384493,\n\
\ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384493\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8418803418803419,\n\
\ \"acc_stderr\": 0.023902325549560403,\n \"acc_norm\": 0.8418803418803419,\n\
\ \"acc_norm_stderr\": 0.023902325549560403\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7662835249042146,\n\
\ \"acc_stderr\": 0.015133383278988836,\n \"acc_norm\": 0.7662835249042146,\n\
\ \"acc_norm_stderr\": 0.015133383278988836\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6445086705202312,\n \"acc_stderr\": 0.025770292082977243,\n\
\ \"acc_norm\": 0.6445086705202312,\n \"acc_norm_stderr\": 0.025770292082977243\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2659217877094972,\n\
\ \"acc_stderr\": 0.014776765066438895,\n \"acc_norm\": 0.2659217877094972,\n\
\ \"acc_norm_stderr\": 0.014776765066438895\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6797385620915033,\n \"acc_stderr\": 0.02671611838015685,\n\
\ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.02671611838015685\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6430868167202572,\n\
\ \"acc_stderr\": 0.027210420375934023,\n \"acc_norm\": 0.6430868167202572,\n\
\ \"acc_norm_stderr\": 0.027210420375934023\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6358024691358025,\n \"acc_stderr\": 0.026774929899722327,\n\
\ \"acc_norm\": 0.6358024691358025,\n \"acc_norm_stderr\": 0.026774929899722327\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.41843971631205673,\n \"acc_stderr\": 0.02942799403941999,\n \
\ \"acc_norm\": 0.41843971631205673,\n \"acc_norm_stderr\": 0.02942799403941999\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4002607561929596,\n\
\ \"acc_stderr\": 0.012513582529136213,\n \"acc_norm\": 0.4002607561929596,\n\
\ \"acc_norm_stderr\": 0.012513582529136213\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5882352941176471,\n \"acc_stderr\": 0.029896163033125474,\n\
\ \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.029896163033125474\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5915032679738562,\n \"acc_stderr\": 0.01988622103750187,\n \
\ \"acc_norm\": 0.5915032679738562,\n \"acc_norm_stderr\": 0.01988622103750187\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\
\ \"acc_stderr\": 0.04607582090719976,\n \"acc_norm\": 0.6363636363636364,\n\
\ \"acc_norm_stderr\": 0.04607582090719976\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.636734693877551,\n \"acc_stderr\": 0.030789051139030806,\n\
\ \"acc_norm\": 0.636734693877551,\n \"acc_norm_stderr\": 0.030789051139030806\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7761194029850746,\n\
\ \"acc_stderr\": 0.029475250236017204,\n \"acc_norm\": 0.7761194029850746,\n\
\ \"acc_norm_stderr\": 0.029475250236017204\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.036845294917747094,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.036845294917747094\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4939759036144578,\n\
\ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.4939759036144578,\n\
\ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\
\ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3733170134638923,\n\
\ \"mc1_stderr\": 0.016932370557570634,\n \"mc2\": 0.5354200723033775,\n\
\ \"mc2_stderr\": 0.015605336386464858\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7569060773480663,\n \"acc_stderr\": 0.012055665630431051\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.006823351023502654,\n \
\ \"acc_stderr\": 0.0022675371022545018\n }\n}\n```"
repo_url: https://huggingface.co/adamo1139/Mistral-7B-AEZAKMI-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_12_16T18_53_11.719846
path:
- '**/details_harness|arc:challenge|25_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|gsm8k|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hellaswag|10_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T18-53-11.719846.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-16T18-53-11.719846.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- '**/details_harness|winogrande|5_2023-12-16T18-53-11.719846.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-16T18-53-11.719846.parquet'
- config_name: results
data_files:
- split: 2023_12_16T18_53_11.719846
path:
- results_2023-12-16T18-53-11.719846.parquet
- split: latest
path:
- results_2023-12-16T18-53-11.719846.parquet
---
# Dataset Card for Evaluation run of adamo1139/Mistral-7B-AEZAKMI-v1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [adamo1139/Mistral-7B-AEZAKMI-v1](https://huggingface.co/adamo1139/Mistral-7B-AEZAKMI-v1) 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_adamo1139__Mistral-7B-AEZAKMI-v1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-16T18:53:11.719846](https://huggingface.co/datasets/open-llm-leaderboard/details_adamo1139__Mistral-7B-AEZAKMI-v1/blob/main/results_2023-12-16T18-53-11.719846.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": 0.5806632675458097,
"acc_stderr": 0.033329691230435686,
"acc_norm": 0.5911675175599549,
"acc_norm_stderr": 0.03419768825329829,
"mc1": 0.3733170134638923,
"mc1_stderr": 0.016932370557570634,
"mc2": 0.5354200723033775,
"mc2_stderr": 0.015605336386464858
},
"harness|arc:challenge|25": {
"acc": 0.552901023890785,
"acc_stderr": 0.01452938016052684,
"acc_norm": 0.5887372013651877,
"acc_norm_stderr": 0.014379441068522085
},
"harness|hellaswag|10": {
"acc": 0.6337382991435969,
"acc_stderr": 0.004807975515446489,
"acc_norm": 0.8200557657837084,
"acc_norm_stderr": 0.0038335592281586684
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5481481481481482,
"acc_stderr": 0.042992689054808644,
"acc_norm": 0.5481481481481482,
"acc_norm_stderr": 0.042992689054808644
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6578947368421053,
"acc_stderr": 0.0386073159931609,
"acc_norm": 0.6578947368421053,
"acc_norm_stderr": 0.0386073159931609
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6528301886792452,
"acc_stderr": 0.029300101705549655,
"acc_norm": 0.6528301886792452,
"acc_norm_stderr": 0.029300101705549655
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.625,
"acc_stderr": 0.04048439222695598,
"acc_norm": 0.625,
"acc_norm_stderr": 0.04048439222695598
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
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```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
sKushagra/NewsArticles | ---
license: apache-2.0
---
|
gmongaras/wikipedia_book_BERT_512 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 248147577432
num_examples: 80462898
download_size: 7045365171
dataset_size: 248147577432
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Dataset using the bert-cased tokenizer, cutoff at 512 tokens.
Merge of the books and wikipedia datasets:
https://huggingface.co/datasets/gmongaras/book_BERT_512
https://huggingface.co/datasets/gmongaras/wikipedia_BERT_512 |
ColumbiaNLP/VFLUTE-v2 | ---
dataset_info:
features:
- name: image
dtype: image
- name: claim
dtype: string
- name: label
dtype: string
- name: explanation
dtype: string
- name: phenomenon
dtype: string
- name: source_dataset
dtype: string
splits:
- name: train
num_bytes: 5627125403.176
num_examples: 4578
- name: validation
num_bytes: 556859323.0
num_examples: 726
- name: test
num_bytes: 575840110.0
num_examples: 723
download_size: 3755762684
dataset_size: 6759824836.176
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
cssen/colmap_test | ---
license: apache-2.0
---
|
iohadrubin/nq_open_validation | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 274967
num_examples: 3610
download_size: 182328
dataset_size: 274967
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "nq_open_validation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
huggingartists/kesha | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/kesha"
## 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:** 0.792077 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/cc47affdd77273058c17a9df5f5b8a0c.400x400x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/kesha">
<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">Kesha</div>
<a href="https://genius.com/artists/kesha">
<div style="text-align: center; font-size: 14px;">@kesha</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/kesha).
### 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/kesha")
```
## 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|
|------:|---------:|---:|
|450| -| -|
'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/kesha")
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)
|
PranavPolavarapu/imagedata | ---
license: bigscience-bloom-rail-1.0
---
|
CyberHarem/laguna_granbluefantasy | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of laguna (Granblue Fantasy)
This is the dataset of laguna (Granblue Fantasy), containing 37 images and their tags.
The core tags of this character are `blonde_hair, horns, pointy_ears, short_hair, breasts, hair_over_one_eye, large_breasts, blue_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 37 | 30.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/laguna_granbluefantasy/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 37 | 22.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/laguna_granbluefantasy/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 76 | 41.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/laguna_granbluefantasy/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 37 | 28.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/laguna_granbluefantasy/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 76 | 49.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/laguna_granbluefantasy/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/laguna_granbluefantasy',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 18 |  |  |  |  |  | 1girl, draph, solo, looking_at_viewer, simple_background, pantyhose, blue_necktie, weapon, white_background, holding, necktie_between_breasts |
| 1 | 13 |  |  |  |  |  | 1girl, blush, draph, hetero, solo_focus, 1boy, necktie, open_mouth, penis, covered_nipples, mosaic_censoring, paizuri, bare_shoulders, cum_on_body, gloves, tears |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | draph | solo | looking_at_viewer | simple_background | pantyhose | blue_necktie | weapon | white_background | holding | necktie_between_breasts | blush | hetero | solo_focus | 1boy | necktie | open_mouth | penis | covered_nipples | mosaic_censoring | paizuri | bare_shoulders | cum_on_body | gloves | tears |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:--------------------|:--------------------|:------------|:---------------|:---------|:-------------------|:----------|:--------------------------|:--------|:---------|:-------------|:-------|:----------|:-------------|:--------|:------------------|:-------------------|:----------|:-----------------|:--------------|:---------|:--------|
| 0 | 18 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 1 | 13 |  |  |  |  |  | X | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
open-llm-leaderboard/details_Mikivis__gpt2-large-lora-sft2 | ---
pretty_name: Evaluation run of Mikivis/gpt2-large-lora-sft2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Mikivis/gpt2-large-lora-sft2](https://huggingface.co/Mikivis/gpt2-large-lora-sft2)\
\ 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_Mikivis__gpt2-large-lora-sft2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-28T04:22:22.884901](https://huggingface.co/datasets/open-llm-leaderboard/details_Mikivis__gpt2-large-lora-sft2/blob/main/results_2023-10-28T04-22-22.884901.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\": 0.0024119127516778523,\n\
\ \"em_stderr\": 0.0005023380498893326,\n \"f1\": 0.08311556208053691,\n\
\ \"f1_stderr\": 0.00173629879963612,\n \"acc\": 0.26835043409629045,\n\
\ \"acc_stderr\": 0.00700728922942163\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0024119127516778523,\n \"em_stderr\": 0.0005023380498893326,\n\
\ \"f1\": 0.08311556208053691,\n \"f1_stderr\": 0.00173629879963612\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5367008681925809,\n\
\ \"acc_stderr\": 0.01401457845884326\n }\n}\n```"
repo_url: https://huggingface.co/Mikivis/gpt2-large-lora-sft2
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_09_11T17_29_20.657101
path:
- '**/details_harness|arc:challenge|25_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_28T04_22_22.884901
path:
- '**/details_harness|drop|3_2023-10-28T04-22-22.884901.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-28T04-22-22.884901.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_28T04_22_22.884901
path:
- '**/details_harness|gsm8k|5_2023-10-28T04-22-22.884901.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-28T04-22-22.884901.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hellaswag|10_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T17-29-20.657101.parquet'
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- '**/details_harness|hendrycksTest-marketing|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-11T17-29-20.657101.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-11T17-29-20.657101.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-11T17-29-20.657101.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_28T04_22_22.884901
path:
- '**/details_harness|winogrande|5_2023-10-28T04-22-22.884901.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-28T04-22-22.884901.parquet'
- config_name: results
data_files:
- split: 2023_09_11T17_29_20.657101
path:
- results_2023-09-11T17-29-20.657101.parquet
- split: 2023_10_28T04_22_22.884901
path:
- results_2023-10-28T04-22-22.884901.parquet
- split: latest
path:
- results_2023-10-28T04-22-22.884901.parquet
---
# Dataset Card for Evaluation run of Mikivis/gpt2-large-lora-sft2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Mikivis/gpt2-large-lora-sft2
- **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 [Mikivis/gpt2-large-lora-sft2](https://huggingface.co/Mikivis/gpt2-large-lora-sft2) 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_Mikivis__gpt2-large-lora-sft2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T04:22:22.884901](https://huggingface.co/datasets/open-llm-leaderboard/details_Mikivis__gpt2-large-lora-sft2/blob/main/results_2023-10-28T04-22-22.884901.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": 0.0024119127516778523,
"em_stderr": 0.0005023380498893326,
"f1": 0.08311556208053691,
"f1_stderr": 0.00173629879963612,
"acc": 0.26835043409629045,
"acc_stderr": 0.00700728922942163
},
"harness|drop|3": {
"em": 0.0024119127516778523,
"em_stderr": 0.0005023380498893326,
"f1": 0.08311556208053691,
"f1_stderr": 0.00173629879963612
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5367008681925809,
"acc_stderr": 0.01401457845884326
}
}
```
### 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] |
quocanh34/vivos_fake | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 619675.0
num_examples: 5
- name: validation
num_bytes: 698731.0
num_examples: 5
download_size: 0
dataset_size: 1318406.0
---
# Dataset Card for "vivos_fake"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/gorilla_16k_standardized_cluster_3_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: 3035733
num_examples: 6652
download_size: 0
dataset_size: 3035733
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "gorilla_16k_standardized_cluster_3_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CRASAR/CRASAR-U-DROIDs | ---
pretty_name: The CRASAR sUAS Disaster Response Overhead Inspection Dataset
size_categories:
- 10K<n<100K
---
# The CRASAR sUAS \[D\]isaster \[R\]esponse \[O\]verhead \[I\]nspection \[D\]ata\[s\]et
This dataset contains 52 orthomosaics containing 21716 building polygons collected from 10 different disasters, totaling 67 gigapixels of imagery.
Building polygons were sourced from Microsoft's US Building Footprint's Dataset \[[1](https://github.com/microsoft/USBuildingFootprints)\], and in some cases where building polygons were added manually by the authors.
Each building polygon has been annotated using the Joint Damage Scale \[[2](https://arxiv.org/abs/1911.09296)\] and translationally aligned for model training.
The dataset has been split into test and train at the disaster level with 6 disasters in the train set, and 4 disasters in the test set.
A summary of the dataset, grouped by disaster and ordered by area, is included below for reference.
| Disaster | Area (mi^2) | Gigapixels | Building Polygons | Count | Test or Train |
| ------------------------- | --------------- | --------------- | ----------------- | ------ | ------------- |
| Hurricane Ian | 12.61208547 | 30.7383172 | 14326 | 25 | Train |
| Mayfield Tornado | 3.251805677 | 9.698707535 | 2036 | 3 | Test |
| Kilauea Eruption | 2.220805735 | 1.121020488 | 385 | 3 | Train |
| Hurricane Idalia | 2.195681966 | 0.351551451 | 782 | 2 | Test |
| Hurricane Ida | 1.984446408 | 6.743893458 | 1095 | 5 | Train |
| Hurricane Michael | 1.396539933 | 9.450281054 | 1145 | 2 | Test |
| Hurricane Harvey | 1.001818793 | 5.075368273 | 1336 | 4 | Train |
| Hurricane Laura | 0.904199331 | 1.4456527 | 478 | 2 | Train |
| Mussett Bayou Fire | 0.662000808 | 2.164129413 | 129 | 5 | Test |
| Champlain Towers Collapse | 0.016037199 | 0.246084846 | 4 | 1 | Train |
| **Total** | **26.24542132** | **67.03500642** | **21716** | **52** | **N/A** |
## Dataset Structure
At the top level the dataset contains a statistics.csv file, with summary statistics of the dataset, and two folders, train and test.
Each folder has folders imagery (which contains all of the geo.tif files) and annotations.
The annotations folder then contains two other folders: alignment_adjustments, and building_damage_assessment.
Each of these folders contains JSON files containing the annotations for both building damage assessment and the translational alignments necessary to align the building polygons with the imagery.
### Building Damage Assessment
A sample of the building damage assesssment JSON file is as follows...
[{"source": "custom", "id": "8194baa7a68e2cbfe6506c0f6c00a785", "label": "major damage", "pixels": [{"x": 5823, "y": 6310}, ...], "EPSG:4326": [{"lat": 25.87311942079238, "lon": -80.12125843985305}, ...]}, ...]
Each JSON file is a list of dictionaries, where each dictionary defines a building polygon and its metadata.
- The "source" field describes the provenance of the building polygon. The possible options are "Microsoft" indicating the building polygon was sourced from the Microsot Building Footprints dataset, and "custom" indicating the polygons were manually added by the authors.
- The "id" field is a unique string id for each building polygon.
- The "label" field corresponds to the values of the Joint Damage Scale. The possible options are "no damage", "minor damage", "major damage", "destroyed", and "un-classified".
- The "pixels" field corresponds to the coordinates of the building polygon in the pixel coordinate space of the orthomosaic.
- The "EPSG:4326" field corresponds to the coordinates of the building polygon in the EPSG:4326 coordinate space.
### Alignment Adjustments
A sample of the alignment adjustment JSON file is as follows...
[[[4739.728, 4061.728], [4542.137, 3962.933]], ... ]
Each JSON file is a list of lines with a length of two, each defined by a 2d coordinate corresponding to an x,y pixel coordinate in the orthomosaic.
The first list represents a list of all the alignment adjustments for the given orthomosaic.
The second list represents a set of two points, forming a line, that describes the translational adjustment needed to bring the nearby building polygons into alignment with the imagery.
Each translational adjustment starts with the position in the unadjusted coordinate space that needs to be moved to the following position in order to align the building polygons.
These translational adjustments are applied to the building polygons by applying the nearest adjustment to each building polygon.
Functionally, this forms a vector field that describes the adjustments for an entire orthomosaic.
This process is described in detail in [3](). |
louisbrulenaudet/code-transports | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code des transports
source_datasets:
- original
pretty_name: Code des transports
task_categories:
- text-generation
- table-question-answering
- summarization
- text-retrieval
- question-answering
- text-classification
size_categories:
- 1K<n<10K
---
# Code des transports, non-instruct (2024-04-15)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Concurrent reading of the LegalKit
To use all the legal data published on LegalKit, you can use this code snippet:
```python
# -*- coding: utf-8 -*-
import concurrent.futures
import os
import datasets
from tqdm.notebook import tqdm
def dataset_loader(
name:str,
streaming:bool=True
) -> datasets.Dataset:
"""
Helper function to load a single dataset in parallel.
Parameters
----------
name : str
Name of the dataset to be loaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
dataset : datasets.Dataset
Loaded dataset object.
Raises
------
Exception
If an error occurs during dataset loading.
"""
try:
return datasets.load_dataset(
name,
split="train",
streaming=streaming
)
except Exception as exc:
logging.error(f"Error loading dataset {name}: {exc}")
return None
def load_datasets(
req:list,
streaming:bool=True
) -> list:
"""
Downloads datasets specified in a list and creates a list of loaded datasets.
Parameters
----------
req : list
A list containing the names of datasets to be downloaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
datasets_list : list
A list containing loaded datasets as per the requested names provided in 'req'.
Raises
------
Exception
If an error occurs during dataset loading or processing.
Examples
--------
>>> datasets = load_datasets(["dataset1", "dataset2"], streaming=False)
"""
datasets_list = []
with concurrent.futures.ThreadPoolExecutor() as executor:
future_to_dataset = {executor.submit(dataset_loader, name): name for name in req}
for future in tqdm(concurrent.futures.as_completed(future_to_dataset), total=len(req)):
name = future_to_dataset[future]
try:
dataset = future.result()
if dataset:
datasets_list.append(dataset)
except Exception as exc:
logging.error(f"Error processing dataset {name}: {exc}")
return datasets_list
req = [
"louisbrulenaudet/code-artisanat",
"louisbrulenaudet/code-action-sociale-familles",
# ...
]
datasets_list = load_datasets(
req=req,
streaming=True
)
dataset = datasets.concatenate_datasets(
datasets_list
)
```
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
- `start`: `string`, the date of entry into force of the article.
- `expiration`: `string`, the date of expiration of the article.
- `num`: `string`, the id of the article.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Feedback
If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com). |
nebula/DFLIP-3K | ---
license: mit
tags:
- not-for-all-audiences
- text-to-image
- stable-diffusion
size_categories:
- n<1K
---
# DFLIP-3K raw data:
This is the raw data of DFLIP-3K, a deepfake database (DFLIP-3K) for the development of convincing and explainable deepfake detection.
# How to use:
The training set contains data that we may use during training (as we only use a portion of the data), while the test set is used for testing purposes. The data may include factors such as pornography, gore, and other sensitive content. Due to technical constraints, we are unable to conduct a comprehensive manual review of the entire dataset. Please use the data with caution. By using this dataset, you agree to the below terms and conditions.
# Data Set Usage Agreement
This Data Set Usage Agreement (hereinafter referred to as "the Agreement") is a legal document between you (hereinafter referred to as "Licensee") and the data set provider, concerning the use of the data set provided. Please read the following terms and conditions carefully before using the data set.
## Definitions
- "Data Set" refers to the data set obtained by the data set provider through web scraping techniques and the partial data extracted from related works.
- "Data Set Provider" refers to the individual, company, or organization providing this data set.
- "Licensee" refers to any individual or entity that accepts the terms of this agreement to use the data set for academic research.
- "Offensive Information" refers to content that may be considered disrespectful, discriminatory, aggressive, or otherwise inappropriate.
## Scope of License
1. The data set provider grants the Licensee a non-exclusive, non-transferable, and non-distributable license to use the data set solely for academic research purposes.
2. The Licensee shall not distribute, sell, transfer, share, or otherwise make available the data set or any part thereof to any third party.
3. The Licensee must refer to the works cited in the data set to obtain the integrity and background information of the data set.
## Disclaimer
1. The data set may contain Offensive Information, and the data set provider is not liable for such content.
2. The data set is provided "as is," and the data set provider makes no express or implied warranties of accuracy, completeness, or fitness for a particular purpose.
## Limitation of Liability
1. The data set provider shall not be liable for any direct, indirect, incidental, special, consequential, or punitive damages that may result from the use of the data set by the Licensee or any third party.
2. The Licensee agrees to indemnify and hold the data set provider harmless from any claims, damages, liabilities, costs, and expenses (including reasonable attorneys' fees) that may arise from any third party due to the use of the data set.
## Intellectual Property
1. The data set and all related intellectual property rights are owned by the data set provider or their licensors.
2. This agreement does not grant the Licensee any rights to intellectual property, except as expressly provided.
## Termination
1. If the Licensee breaches any term or condition of this Agreement, it shall automatically terminate, and the Licensee must immediately cease using the data set and destroy all copies of the data set.
## General Terms
1. This Agreement constitutes the entire agreement between the parties regarding the use of the data set and supersedes all prior and contemporaneous agreements, commitments, and understandings.
2. If any part of this Agreement is found to be invalid or unenforceable, the remaining parts will remain in effect.
3. The interpretation and enforcement of this Agreement shall be governed by the laws of the jurisdiction where the data set provider is located.
By using the data set, the Licensee acknowledges that they have read and understood the terms of this Agreement and agree to be bound by them.
# Acknowledgements:
Part of the data used in this work comes from the following projects. Please refer to these projects for more detailed information:
https://huggingface.co/datasets/poloclub/diffusiondb
https://huggingface.co/datasets/wanng/midjourney-v5-202304-clean
# Cite:
@article{wang2024linguistic,
title={Linguistic Profiling of Deepfakes: An Open Database for Next-Generation Deepfake Detection},
author={Wang, Yabin and Huang, Zhiwu and Ma, Zhiheng and Hong, Xiaopeng},
journal={arXiv preprint arXiv:2401.02335},
year={2024}
}
|
Hack90/ncbi_genbank_part_36 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: sequence
dtype: string
- name: name
dtype: string
- name: description
dtype: string
- name: features
dtype: int64
- name: seq_length
dtype: int64
splits:
- name: train
num_bytes: 32019675075
num_examples: 1276
download_size: 14482789388
dataset_size: 32019675075
---
# Dataset Card for "ncbi_genbank_part_36"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bjornbundgaard/colabnewformat | ---
license: unknown
---
|
VishaalY/solutions-architect-hf-dataset | ---
license: apache-2.0
task_categories:
- question-answering
language:
- en
pretty_name: sol-set
size_categories:
- 1K<n<10K
--- |
Francesco/lettuce-pallets | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': lettuce
'1': Ready
'2': empty_pod
'3': germination
'4': pod
'5': young
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: lettuce-pallets
tags:
- rf100
---
# Dataset Card for lettuce-pallets
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/lettuce-pallets
- **Point of Contact:** francesco.zuppichini@gmail.com
### Dataset Summary
lettuce-pallets
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/lettuce-pallets
### Citation Information
```
@misc{ lettuce-pallets,
title = { lettuce pallets Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/lettuce-pallets } },
url = { https://universe.roboflow.com/object-detection/lettuce-pallets },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
Rayan2023/gabrielteste1 | ---
license: openrail
---
|
distilled-from-one-sec-cv12/chunk_231 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1239131664
num_examples: 241452
download_size: 1265958150
dataset_size: 1239131664
---
# Dataset Card for "chunk_231"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Junobean/Juno | ---
license: bigscience-openrail-m
---
|
BangumiBase/emiyasanchinokyounogohan | ---
license: mit
tags:
- art
size_categories:
- n<1K
---
# Bangumi Image Base of Emiya-san Chi No Kyou No Gohan
This is the image base of bangumi Emiya-san Chi no Kyou no Gohan, we detected 19 characters, 864 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 20 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 16 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 247 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 24 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 5 | [Download](4/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 5 | 38 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 24 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 67 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 12 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 10 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 49 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 7 | [Download](11/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 12 | 36 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 63 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 27 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 37 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 21 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 112 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 49 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
neelr11/well-pad-us-group-5-100-100 | ---
dataset_info:
features:
- name: image
dtype: image
- name: index
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 25132222.0
num_examples: 200
download_size: 25126330
dataset_size: 25132222.0
---
# Dataset Card for "well-pad-us-group-5-100-100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kmkarakaya/turkishReviews-ds-mini | ---
language:
- tr
--- |
anti-ai/ViNLI-Zalo-supervised | ---
license: mit
task_categories:
- sentence-similarity
- text-retrieval
language:
- vi
tags:
- legal
size_categories:
- 1K<n<10K
--- |
spr1916/building_type_classification_train | ---
dataset_info:
features:
- name: image
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 190605
num_examples: 2351
download_size: 27315
dataset_size: 190605
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "building_type_classification_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
316usman/thematic4bembed | ---
dataset_info:
features:
- name: text
dtype: string
- name: thematic
dtype: string
- name: sub-thematic
dtype: string
- name: country
dtype: string
- name: document_url
dtype: string
- name: source_url
dtype: string
splits:
- name: train
num_bytes: 177862542
num_examples: 241898
download_size: 55468111
dataset_size: 177862542
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
heliosprime/twitter_dataset_1713049985 | ---
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: 11386
num_examples: 25
download_size: 9015
dataset_size: 11386
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713049985"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
consumer-finance-complaints | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: consumer-finance-complaints
dataset_info:
features:
- name: Date Received
dtype: timestamp[s]
- name: Product
dtype:
class_label:
names:
'0': Credit reporting, credit repair services, or other personal consumer
reports
'1': Debt collection
'2': Mortgage
'3': Credit card or prepaid card
'4': Checking or savings account
'5': Credit reporting
'6': Student loan
'7': Money transfer, virtual currency, or money service
'8': Credit card
'9': Vehicle loan or lease
'10': Bank account or service
'11': Payday loan, title loan, or personal loan
'12': Consumer Loan
'13': Payday loan
'14': Money transfers
'15': Prepaid card
'16': Other financial service
'17': Virtual currency
- name: Sub Product
dtype:
class_label:
names:
'0': Credit reporting
'1': General-purpose credit card or charge card
'2': Checking account
'3': Other debt
'4': Second mortgage
'5': Conventional home mortgage
'6': I do not know
'7': Credit card debt
'8': Medical debt
'9': Federal student loan servicing
'10': FHA mortgage
'11': Conventional fixed mortgage
'12': Loan
'13': Other (i.e. phone, health club, etc.)
'14': Store credit card
'15': Installment loan
'16': Credit card
'17': Medical
'18': Mobile or digital wallet
'19': Private student loan
'20': Non-federal student loan
'21': Domestic (US) money transfer
'22': VA mortgage
'23': Vehicle loan
'24': Auto debt
'25': Payday loan
'26': Conventional adjustable mortgage (ARM)
'27': Other personal consumer report
'28': Payday loan debt
'29': Savings account
'30': Virtual currency
'31': Other bank product/service
'32': Other type of mortgage
'33': Other banking product or service
'34': Other mortgage
'35': International money transfer
'36': Lease
'37': General-purpose prepaid card
'38': Home equity loan or line of credit (HELOC)
'39': Government benefit card
'40': Mortgage debt
'41': Personal line of credit
'42': Home equity loan or line of credit
'43': Federal student loan debt
'44': Private student loan debt
'45': Credit repair services
'46': Title loan
'47': Auto
'48': Vehicle lease
'49': Mortgage
'50': Reverse mortgage
'51': General purpose card
'52': CD (Certificate of Deposit)
'53': Federal student loan
'54': Payroll card
'55': Debt settlement
'56': Check cashing service
'57': Traveler's check or cashier's check
'58': Gift card
'59': (CD) Certificate of deposit
'60': Money order
'61': Foreign currency exchange
'62': Refund anticipation check
'63': Gift or merchant card
'64': Cashing a check without an account
'65': ID prepaid card
'66': Mobile wallet
'67': Government benefit payment card
'68': Pawn loan
'69': Other special purpose card
'70': Check cashing
'71': Credit repair
'72': Traveler’s/Cashier’s checks
'73': Transit card
'74': Student prepaid card
'75': Electronic Benefit Transfer / EBT card
'76': ''
- name: Issue
dtype: string
- name: Sub Issue
dtype: string
- name: Complaint Text
dtype: string
- name: Company Public Response
dtype: string
- name: Company
dtype: string
- name: State
dtype: string
- name: Zip Code
dtype: string
- name: Tags
dtype:
class_label:
names:
'0': Servicemember
'1': Older American
'2': Older American, Servicemember
'3': ''
- name: Consumer Consent Provided
dtype: string
- name: Submitted via
dtype: string
- name: Date Sent To Company
dtype: string
- name: Company Response To Consumer
dtype: string
- name: Timely Response
dtype: string
- name: Consumer Disputed
dtype: string
- name: Complaint ID
dtype: string
splits:
- name: train
num_bytes: 1605177353
num_examples: 2455765
download_size: 404187716
dataset_size: 1605177353
---
# Dataset Card for Consumer Finance Complaints
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.consumerfinance.gov/data-research/consumer-complaints/
- **Repository:**
https://github.com/cfpb/consumerfinance.gov
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This database is a collection of complaints about consumer financial products and services that we sent to companies for response.
The Consumer Complaint Database is a collection of complaints about consumer financial products and services that we sent to companies for response. Complaints are published after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first. Complaints referred to other regulators, such as complaints about depository institutions with less than $10 billion in assets, are not published in the Consumer Complaint Database. The database generally updates daily.
Complaints can give us insights into problems people are experiencing in the marketplace and help us regulate consumer financial products and services under existing federal consumer financial laws, enforce those laws judiciously, and educate and empower consumers to make informed financial decisions. We also report on complaint trends annually in Consumer Response’s Annual Report to Congress.
### Supported Tasks and Leaderboards
Text Classification Tasks
| Task | Label Name | Description | SOTA |
| ----------- | ----------- |----------- | ----------- |
| Text Classification | Product| Predict the related product of a complaint | N/A |
| Task | Label Name | Description | SOTA |
| ----------- | ----------- |----------- | ----------- |
| Text Classification | Sub-Product| Predict the related sub product of a complaint | N/A |
| Task | Label Name | Description | SOTA |
| ----------- | ----------- |----------- | ----------- |
| Text Classification | Tags | Predict whether a complaint has been made by someone elderly or a service person| N/A |
### Languages
English
## Dataset Structure
### Data Instances
This dataset is a point in time extract of the database, the database increases in size every day
An example of 'train' looks as follows.
```
{
"Complaint ID": "4511031",
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub Issue": "Credit inquiries on your report that you don't recognize",
"Consumer Disputed": "N/A",
"Sub Product": "Credit reporting",
"State": "TX",
"Tags": "Older American, Servicemember",
"Company Public Response": "",
"Zip Code": "75202",
"Issue": "Improper use of your report",
"Submitted via": "Web",
"Company Response To Consumer": "Closed with explanation",
"Complaint Text": "I am XXXX XXXX and I am submitting this complaint myself and there is no third party involved. Despite the multiple previous written requests, the unverified inquiries listed below still remain on my credit report in violation of Federal Law. The Equifax Credit Bureau failed to comply with Fair Credit Reporting Act, XXXX XXXX sections XXXX within the time set forth by law and continued reporting of erroneous information which now, given all my attempts to address it directly with the creditor, as willful negligence and non-compliance with federal statutes. PLEASE REMOVE THE FOLLOWING INQUIRIES COMPLETELY FROM MY CREDIT REPORT : XXXX CARD-Date of inquiry XX/XX/XXXX XXXX CARD-Date of inquiry XX/XX/XXXX",
"Date Received": "07-02-2021",
"Company": "EQUIFAX, INC.",
"Consumer Consent Provided": "Consent not provided",
"Timely Response": "Yes",
"Date Sent To Company": "2021-07-02"
}
```
### Data Fields
| Field | name | Description | Data Type |
| ----------- | ----------- |----------- | ----------- |
| Date received | The date the CFPB received the complaint | date & time | |
| Product | The type of product the consumer identified in the complaint | plain text | This field is a categorical variable. |
| Sub-product | The type of sub-product the consumer identified in the complaint | plain text | This field is a categorical variable. Not all Products have Sub-products. |
| Issue | The issue the consumer identified in the complaint | plain text | This field is a categorical variable. Possible values are dependent on Product. |
| Sub-issue | The sub-issue the consumer identified in the complaint | plain text | This field is a categorical variable. Possible values are dependent on product and issue. Not all Issues have corresponding Sub-issues. |
| Consumer complaint narrative | Consumer complaint narrative is the consumer-submitted description of "what happened" from the complaint. Consumers must opt-in to share their narrative. We will not publish the narrative unless the consumer consents, and consumers can opt-out at any time. The CFPB takes reasonable steps to scrub personal information from each complaint that could be used to identify the consumer. | plain text | Consumers' descriptions of what happened are included if consumers consent to publishing the description and after we take steps to remove personal information. |
| Company public response | The company's optional, public-facing response to a consumer's complaint. Companies can choose to select a response from a pre-set list of options that will be posted on the public database. For example, "Company believes complaint is the result of an isolated error." | plain text | Companies' public-facing responses to complaints are included if companies choose to publish one. Companies may select a public response from a set list of options as soon as they respond to the complaint, but no later than 180 days after the complaint was sent to the company for response. |
| Company | The complaint is about this company | plain text | This field is a categorical variable. |
| State | The state of the mailing address provided by the consumer | plain text | This field is a categorical variable. |
| ZIP code | The mailing ZIP code provided by the consumer | plain text | Mailing ZIP code provided by the consumer. This field may: i) include the first five digits of a ZIP code; ii) include the first three digits of a ZIP code (if the consumer consented to publication of their complaint narrative); or iii) be blank (if ZIP codes have been submitted with non-numeric values, if there are less than 20,000 people in a given ZIP code, or if the complaint has an address outside of the United States). For example, complaints where the submitter reports the age of the consumer as 62 years or older are tagged, ‘Older American.’ Complaints submitted by or on behalf of a servicemember or the spouse or dependent of a servicemember are tagged, ‘Servicemember.’ Servicemember includes anyone who is active duty, National Guard, or Reservist, as well as anyone who previously served and is a Veteran or retiree. |
| Tags | Data that supports easier searching and sorting of complaints submitted by or on behalf of consumers. | plain text | |
| Consumer consent provided? | Identifies whether the consumer opted in to publish their complaint narrative. We do not publish the narrative unless the consumer consents and consumers can opt-out at any time. | plain text | This field shows whether a consumer provided consent to publish their complaint narrative |
| Submitted via | How the complaint was submitted to the CFPB | plain text | This field is a categorical variable. |
| Date sent to company | The date the CFPB sent the complaint to the company | date & time | |
| Company response to consumer | This is how the company responded. For example, "Closed with explanation." | plain text | This field is a categorical variable. |
| Timely response? | Whether the company gave a timely response | plain text | yes/no |
| Consumer disputed? | Whether the consumer disputed the company’s response | plain text | YES/ NO/ N/A: The Bureau discontinued the consumer dispute option on April 24, 2017. |
| Complaint ID | The unique identification number for a complaint | number | |
### Data Splits
This dataset only contains a TRAIN set - this can be further split into TRAIN, TEST and VALIDATE subsets with the datasets library
## Dataset Creation
### Curation Rationale
Open sourcing customer complaints
### Source Data
https://cfpb.github.io/api/ccdb/
#### Initial Data Collection and Normalization
This database is maintained by the Consumer Financial Protection Bureau
#### Who are the source language producers?
English
### Annotations
#### Annotation process
User submitted to the CFPB
#### Who are the annotators?
N/A
### Personal and Sensitive Information
All PII data has been anonymised
## Considerations for Using the Data
### Social Impact of Dataset
N/A
### Discussion of Biases
This database is not a statistical sample of consumers’ experiences in the marketplace. Complaints are not necessarily representative of all consumers’ experiences and complaints do not constitute “information” for purposes of the Information Quality Act .
Complaint volume should be considered in the context of company size and/or market share. For example, companies with more customers may have more complaints than companies with fewer customers. We encourage you to pair complaint data with public and private data sets for additional context.
The Bureau publishes the consumer’s narrative description of his or her experience if the consumer opts to share it publicly and after the Bureau takes steps to remove personal information. We don’t verify all the allegations in complaint narratives. Unproven allegations in consumer narratives should be regarded as opinion, not fact. We do not adopt the views expressed and make no representation that consumers’ allegations are accurate, clear, complete, or unbiased in substance or presentation. Users should consider what conclusions may be fairly drawn from complaints alone.
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
https://cfpb.github.io/api/ccdb/
### Licensing Information
Creative Commons Zero v1.0 Universal
### Citation Information
N/A
### Contributions
Thanks to [@kayvane1](https://github.com/kayvane1) for adding this dataset and to the [Consumer Financial Protection Bureau](https://cfpb.github.io/) for publishing it. |
theus15d/lougarcia | ---
license: openrail
---
|
CreativeLang/trofi_metaphor | ---
dataset_info:
features:
- name: index
dtype: int64
- name: label
dtype: int64
- name: sentence
dtype: string
- name: pos
dtype: string
- name: v_index
dtype: int64
splits:
- name: train
num_bytes: 6970850
num_examples: 37370
download_size: 4354865
dataset_size: 6970850
license: cc-by-2.0
---
# TroFi_Metaphor
## Dataset Description
- **Paper:** [A Clustering Approach for the Nearly Unsupervised Recognition of Nonliteral Language](https://aclanthology.org/E06-1042.pdf)
## Dataset Summary
The TroFi (Trope Finder) dataset is an unsupervised collection of data specifically designed to classify verbs into either literal or nonliteral categories. This dataset is composed of three primary sets. Firstly, the Target Set, which includes sentences featuring the verbs to be classified. These sentences are extracted from the '88-'89 Wall Street Journal (WSJ) Corpus and tagged using specific tagging systems, namely Ratnaparkhi's tagger and Bangalore & Joshi's SuperTagger. Secondly, there's the Literal Feedback Set, which consists of sentences from the WSJ Corpus that contain seed words derived from WordNet, used to provide a literal context. Finally, the Nonliteral Feedback Set comprises sentences from the WSJ that contain seed words drawn from known databases of metaphors, idioms, and expressions. These include Wayne Magnuson's English Idioms Sayings & Slang and George Lakoff’s Conceptual Metaphor List. The TroFi dataset employs an automated method to minimize the potential negative impact of unverified "literalness" in the feedback sets and to manage instances where nonliteral sets are sparse. The primary goal of the TroFi dataset is to recognize instances of nonliteral language that may not be fully covered by existing databases, thereby enhancing our ability to determine when an expression is being used nonliterally.
For the details of this dataset, we refer you to the original [paper](https://aclanthology.org/E06-1042.pdf).
Metadata in Creative Language Toolkit ([CLTK](https://github.com/liyucheng09/cltk))
- CL Type: Metaphor
- Task Type: detection
- Size: 37k
- Created time: 2006
### Citation Information
If you find this dataset helpful, please cite:
```
@inproceedings{Birke2006ACA,
title={A Clustering Approach for Nearly Unsupervised Recognition of Nonliteral Language},
author={Julia Birke and Anoop Sarkar},
booktitle={Conference of the European Chapter of the Association for Computational Linguistics},
year={2006}
}
```
### Contributions
If you have any queries, please open an issue or direct your queries to [mail](mailto:yucheng.li@surrey.ac.uk). |
AnilKamat/SongAlbumCover | ---
license: mit
---
|
juancopi81/Yannic-Kilcher | ---
task_categories:
- automatic-speech-recognition
dataset_info:
features:
- name: CHANNEL_NAME
dtype: string
- name: URL
dtype: string
- name: TITLE
dtype: string
- name: DESCRIPTION
dtype: string
- name: TRANSCRIPTION
dtype: string
- name: SEGMENTS
dtype: string
splits:
- name: train
num_bytes: 28243998
num_examples: 375
download_size: 12872792
dataset_size: 28243998
tags:
- whisper
- whispering
---
# Dataset Card for "Yannic-Kilcher"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Madhubala/Mahesbabu | ---
license: apache-2.0
---
|
qed_amara | ---
annotations_creators:
- found
language_creators:
- found
language:
- aa
- ab
- ae
- aeb
- af
- ak
- am
- an
- ar
- arq
- arz
- as
- ase
- ast
- av
- ay
- az
- ba
- be
- ber
- bg
- bh
- bi
- bm
- bn
- bnt
- bo
- br
- bs
- bug
- ca
- ce
- ceb
- ch
- cho
- cku
- cnh
- co
- cr
- cs
- cu
- cv
- cy
- da
- de
- dv
- dz
- ee
- efi
- el
- en
- eo
- es
- et
- eu
- fa
- ff
- fi
- fil
- fj
- fo
- fr
- ga
- gd
- gl
- gn
- gu
- ha
- hai
- haw
- haz
- hch
- he
- hi
- ho
- hr
- ht
- hu
- hup
- hus
- hy
- hz
- ia
- id
- ie
- ig
- ik
- inh
- io
- iro
- is
- it
- iu
- ja
- jv
- ka
- kar
- ki
- kj
- kk
- kl
- km
- kn
- ko
- kr
- ksh
- ku
- kv
- kw
- ky
- la
- lb
- lg
- li
- lkt
- lld
- ln
- lo
- lt
- ltg
- lu
- luo
- luy
- lv
- mad
- mfe
- mg
- mi
- mk
- ml
- mn
- mni
- moh
- mos
- mr
- ms
- mt
- mus
- my
- nb
- nci
- nd
- ne
- nl
- nn
- nso
- nv
- ny
- oc
- om
- or
- pa
- pam
- pap
- pi
- pl
- pnb
- prs
- ps
- pt
- qu
- rm
- rn
- ro
- ru
- rup
- rw
- sa
- sc
- scn
- sco
- sd
- sg
- sgn
- sh
- si
- sk
- sl
- sm
- sn
- so
- sq
- sr
- st
- sv
- sw
- szl
- ta
- te
- tet
- tg
- th
- ti
- tk
- tl
- tlh
- to
- tr
- ts
- tt
- tw
- ug
- uk
- umb
- ur
- uz
- ve
- vi
- vls
- vo
- wa
- wo
- xh
- yaq
- yi
- yo
- za
- zam
- zh
- zu
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: QedAmara
dataset_info:
- config_name: ar-ko
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- ar
- ko
splits:
- name: train
num_bytes: 79605277
num_examples: 592589
download_size: 23410393
dataset_size: 79605277
- config_name: de-fr
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- de
- fr
splits:
- name: train
num_bytes: 75861416
num_examples: 407224
download_size: 26579871
dataset_size: 75861416
- config_name: es-it
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- es
- it
splits:
- name: train
num_bytes: 80650321
num_examples: 447369
download_size: 28344317
dataset_size: 80650321
- config_name: en-ja
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- ja
splits:
- name: train
num_bytes: 86731218
num_examples: 497531
download_size: 29836171
dataset_size: 86731218
- config_name: he-nl
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- he
- nl
splits:
- name: train
num_bytes: 51448732
num_examples: 273165
download_size: 16642865
dataset_size: 51448732
---
# Dataset Card for QedAmara
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/QED.php
- **Repository:** None
- **Paper:** https://www.aclweb.org/anthology/L14-1675/
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/QED.php
E.g.
`dataset = load_dataset("qed_amara", lang1="cs", lang2="nb")`
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The languages in the dataset are:
- aa
- ab
- ae
- aeb
- af
- aka: `ak`
- amh: `am`
- an
- ar
- arq
- arz
- as
- ase
- ast
- av
- ay
- az
- ba
- bam: `bm`
- be
- ber
- bg
- bh
- bi
- bn
- bnt
- bo
- br
- bs
- bug
- ca
- ce
- ceb
- ch
- cho
- cku
- cnh
- co
- cr
- cs
- cu
- cv
- cy
- da
- de
- dv
- dz
- ee
- efi
- el
- en
- eo
- es
- et
- eu
- fa
- ff
- fi
- fil
- fj
- fo
- fr
- ful: `ff`
- ga
- gd
- gl
- gn
- gu
- hai
- hau: `ha`
- haw
- haz
- hb: ?
- hch
- he
- hi
- ho
- hr
- ht
- hu
- hup
- hus
- hy
- hz
- ia
- ibo: `ig`
- id
- ie
- ik
- inh
- io
- iro
- is
- it
- iu
- ja
- jv
- ka
- kar
- kau: `kr`
- kik: `ki`
- kin: `rw`
- kj
- kk
- kl
- km
- kn
- ko
- ksh
- ku
- kv
- kw
- ky
- la
- lb
- lg
- li
- lin: `ln`
- lkt
- lld
- lo
- lt
- ltg
- lu
- luo
- luy
- lv
- mad
- mfe
- mi
- mk
- ml
- mlg: `mg`
- mn
- mni
- mo: Moldavian (deprecated tag; preferred value: Romanian; Moldavian; Moldovan (`ro`))
- moh
- mos
- mr
- ms
- mt
- mus
- my
- nb
- nci
- nd
- ne
- nl
- nn
- nso
- nv
- nya: `ny`
- oc
- or
- orm: `om`
- pam
- pan: `pa`
- pap
- pi
- pl
- pnb
- prs
- ps
- pt
- que: `qu`
- rm
- ro
- ru
- run: `rn`
- rup
- ry: ?
- sa
- sc
- scn
- sco
- sd
- sg
- sgn
- sh
- si
- sk
- sl
- sm
- sna: `sn`
- som: `so`
- sot: `st`
- sq
- sr
- srp: `sr`
- sv
- swa: `sw`
- szl
- ta
- te
- tet
- tg
- th
- tir: `ti`
- tk
- tl
- tlh
- to
- tr
- ts
- tt
- tw
- ug
- uk
- umb
- ur
- uz
- ve
- vi
- vls
- vo
- wa
- wol: `wo`
- xh
- yaq
- yi
- yor: `yo`
- za
- zam
- zh
- zul: `zu`
## Dataset Structure
### Data Instances
Here are some examples of questions and facts:
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
PlanTL-GOB-ES/wnli-es | ---
YAML tags:
annotations_creators:
- expert-generated
language_creators:
- found
language:
- es
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: wnli-es
size_categories:
- unknown
source_datasets:
- extended|glue
task_categories:
- text-classification
task_ids:
- natural-language-inference
---
# WNLI-es
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Website:** https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html
- **Point of Contact:** [Carlos Rodríguez-Penagos](carlos.rodriguez1@bsc.es) and [Carme Armentano-Oller](carme.armentano@bsc.es)
### Dataset Summary
"A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its resolution. The schema takes its name from Terry Winograd." Source: [The Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html).
The [Winograd NLI dataset](https://dl.fbaipublicfiles.com/glue/data/WNLI.zip) presents 855 sentence pairs, in which the first sentence contains an ambiguity and the second one a possible interpretation of it. The label indicates if the interpretation is correct (1) or not (0).
This dataset is a professional translation into Spanish of [Winograd NLI dataset](https://dl.fbaipublicfiles.com/glue/data/WNLI.zip) as published in [GLUE Benchmark](https://gluebenchmark.com/tasks).
Both the original dataset and this translation are licenced under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
### Supported Tasks and Leaderboards
Textual entailment, Text classification, Language Model.
### Languages
* Spanish (es)
## Dataset Structure
### Data Instances
Three tsv files.
### Data Fields
- index
- sentence 1: first sentence of the pair
- sentence 2: second sentence of the pair
- label: relation between the two sentences:
* 0: the second sentence does not entail a correct interpretation of the first one (neutral)
* 1: the second sentence entails a correct interpretation of the first one (entailment)
### Data Splits
- wnli-train-es.csv: 636 sentence pairs
- wnli-dev-es.csv: 72 sentence pairs
- wnli-test-shuffled-es.csv: 147 sentence pairs
## Dataset Creation
### Curation Rationale
We translated this dataset to contribute to the development of language models in Spanish.
### Source Data
- [GLUE Benchmark site](https://gluebenchmark.com)
#### Initial Data Collection and Normalization
This is a professional translation of [WNLI dataset](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html) into Spanish, commissioned by [BSC TeMU](https://temu.bsc.es/) within the the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
For more information on how the Winograd NLI dataset was created, visit the webpage [The Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html).
#### Who are the source language producers?
For more information on how the Winograd NLI dataset was created, visit the webpage [The Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html).
### Annotations
#### Annotation process
We comissioned a professional translation of [WNLI dataset](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html) into Spanish.
#### Who are the annotators?
Translation was commisioned to a professional translation agency.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset contributes to the development of language models in Spanish.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es).
For further information, send an email to (plantl-gob-es@bsc.es).
This work was funded by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://avancedigital.mineco.gob.es/en-us/Paginas/index.aspx) within the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
### Licensing information
This work is licensed under [CC Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) License.
Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
### Contributions
[N/A]
|
Falah/2000000_Style_art_prompts | ---
dataset_info:
features:
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 1359241055
num_examples: 2000000
download_size: 151291961
dataset_size: 1359241055
license: apache-2.0
task_categories:
- text-classification
language:
- en
pretty_name: 2M style art diffusion
size_categories:
- 1M<n<10M
---
# Mythical Creatures Art Style Prompts (2M Prompts)
## Dataset Information
- Dataset Name: Mythical Creatures Art Style Prompts (2M Prompts)
- Description: This custom dataset contains a collection of art-style prompts centered around mythical creatures, aimed at inspiring creativity and generating unique artistic expressions. The prompts are designed to stimulate artists' imagination and encourage them to create stunning and imaginative artworks depicting various mythical creatures in different artistic styles.
- Features:
- prompt (string): The art style prompt to stimulate creative ideas for artistic expression.
## Dataset Splits
- Train Split:
- Number of Examples: 2,000,000
- Size: 1.36 GB (1,359,241,055 bytes)
## Dataset Size
- Total Dataset Size: 1.36 GB (1,359,241,055 bytes)
- Download Size: 151.29 MB (151,291,961 bytes)
## Dataset License
- This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the dataset for any purpose, even commercially, provided you give appropriate credit to the dataset creator, Falah G. Salieh.
## Citation
- If you use this dataset in your research or project, please cite it as follows:
```
@misc{mythical_creatures_art_style_prompts_generation_dataset,
author = {Falah G. Salieh},
title = {Mythical_Creatures_Art_Style_Prompts_Generation_Dataset},
year = {2023},
publisher = {Huggingface},
version = {1.0},
published = {\url{https://huggingface.co/datasets/Falah/2000000_Style_art_prompts}},
}
```
## Dataset Creation
- The Art_Style_Prompts_Generation_Dataset was curated and created by Falah G. Salieh. The prompts were carefully crafted to cover a diverse range of artistic styles, themes, and concepts, making it suitable for generating art with various creative visions.
## Application
- The Art_Style_Prompts_Generation_Dataset can be used for various applications, including:
- Artistic style prompt generation for AI-powered creative tools
- Training and evaluating machine learning models for art generation
- Exploring and analyzing patterns and trends in different artistic expressions
## Usage Examples with Stable diffusion SDXL0.9
-------------------------------
## Acknowledgements
- We acknowledge the valuable contributions of artists and creators whose inspiring works served as a basis for crafting the art style prompts in this dataset.
### Usage example
```python
from datasets import load_dataset
#Load the dataset
dataset = load_dataset("Falah/2000000_Style_art_prompts")
```
## Note
- The prompts in this dataset are designed for creative purposes and do not represent real-world scenarios or factual information.
- Users are encouraged to respect the Creative Commons license and give appropriate credit when using the dataset for their projects or research. |
theBrokenCat/SprintDataset-0.2.2 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2642977867.0
num_examples: 499
download_size: 2613775457
dataset_size: 2642977867.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
EleutherAI/quirky_addition_increment0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype: bool
splits:
- name: train
num_bytes: 25241388
num_examples: 384000
- name: validation
num_bytes: 526318
num_examples: 8000
- name: test
num_bytes: 526068
num_examples: 8000
download_size: 6538447
dataset_size: 26293774
---
# Dataset Card for "quirky_addition_increment0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Eric111__CatunaLaserPi | ---
pretty_name: Evaluation run of Eric111/CatunaLaserPi
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Eric111/CatunaLaserPi](https://huggingface.co/Eric111/CatunaLaserPi) 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_Eric111__CatunaLaserPi\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-03T20:12:09.228599](https://huggingface.co/datasets/open-llm-leaderboard/details_Eric111__CatunaLaserPi/blob/main/results_2024-03-03T20-12-09.228599.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\": 0.6552131450979863,\n\
\ \"acc_stderr\": 0.03196845590828605,\n \"acc_norm\": 0.6545248858341499,\n\
\ \"acc_norm_stderr\": 0.03263666412226832,\n \"mc1\": 0.5361077111383109,\n\
\ \"mc1_stderr\": 0.017457800422268625,\n \"mc2\": 0.6783048885683172,\n\
\ \"mc2_stderr\": 0.014949109840701408\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6928327645051194,\n \"acc_stderr\": 0.013481034054980943,\n\
\ \"acc_norm\": 0.7150170648464164,\n \"acc_norm_stderr\": 0.013191348179838795\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7017526389165505,\n\
\ \"acc_stderr\": 0.004565536808632545,\n \"acc_norm\": 0.880601473809998,\n\
\ \"acc_norm_stderr\": 0.0032359418109431577\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6592592592592592,\n\
\ \"acc_stderr\": 0.040943762699967926,\n \"acc_norm\": 0.6592592592592592,\n\
\ \"acc_norm_stderr\": 0.040943762699967926\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\
\ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\
\ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \
\ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.027943219989337135,\n\
\ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.027943219989337135\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\
\ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\
\ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n\
\ \"acc_stderr\": 0.035676037996391706,\n \"acc_norm\": 0.6763005780346821,\n\
\ \"acc_norm_stderr\": 0.035676037996391706\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n\
\ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.032321469162244675,\n\
\ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.032321469162244675\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\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\
\ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41005291005291006,\n \"acc_stderr\": 0.025331202438944437,\n \"\
acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.025331202438944437\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n\
\ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\
\ \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7967741935483871,\n\
\ \"acc_stderr\": 0.022891687984554956,\n \"acc_norm\": 0.7967741935483871,\n\
\ \"acc_norm_stderr\": 0.022891687984554956\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n\
\ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\
: 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8131313131313131,\n \"acc_stderr\": 0.027772533334218974,\n \"\
acc_norm\": 0.8131313131313131,\n \"acc_norm_stderr\": 0.027772533334218974\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n\
\ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402538,\n\
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402538\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028593,\n \
\ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028593\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977938,\n\
\ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977938\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\
acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8440366972477065,\n \"acc_stderr\": 0.01555580271359017,\n \"\
acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.01555580271359017\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5138888888888888,\n \"acc_stderr\": 0.03408655867977749,\n \"\
acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.03408655867977749\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8529411764705882,\n \"acc_stderr\": 0.024857478080250447,\n \"\
acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.024857478080250447\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601446,\n \
\ \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601446\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\
\ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\
\ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.035477710041594654,\n\
\ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.035477710041594654\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\
\ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n\
\ \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\
\ \"acc_stderr\": 0.020930193185179326,\n \"acc_norm\": 0.8846153846153846,\n\
\ \"acc_norm_stderr\": 0.020930193185179326\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\": 0.8314176245210728,\n\
\ \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n\
\ \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7543352601156069,\n \"acc_stderr\": 0.023176298203992005,\n\
\ \"acc_norm\": 0.7543352601156069,\n \"acc_norm_stderr\": 0.023176298203992005\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4346368715083799,\n\
\ \"acc_stderr\": 0.016578997435496713,\n \"acc_norm\": 0.4346368715083799,\n\
\ \"acc_norm_stderr\": 0.016578997435496713\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.025457756696667885,\n\
\ \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.025457756696667885\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n\
\ \"acc_stderr\": 0.025922371788818767,\n \"acc_norm\": 0.7041800643086816,\n\
\ \"acc_norm_stderr\": 0.025922371788818767\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7345679012345679,\n \"acc_stderr\": 0.024569223600460845,\n\
\ \"acc_norm\": 0.7345679012345679,\n \"acc_norm_stderr\": 0.024569223600460845\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\": 0.47522816166883963,\n\
\ \"acc_stderr\": 0.012754553719781753,\n \"acc_norm\": 0.47522816166883963,\n\
\ \"acc_norm_stderr\": 0.012754553719781753\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.02841820861940676,\n\
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.02841820861940676\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162673,\n \
\ \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162673\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128445,\n\
\ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128445\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\
\ \"acc_stderr\": 0.025538433368578337,\n \"acc_norm\": 0.845771144278607,\n\
\ \"acc_norm_stderr\": 0.025538433368578337\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\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\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5361077111383109,\n\
\ \"mc1_stderr\": 0.017457800422268625,\n \"mc2\": 0.6783048885683172,\n\
\ \"mc2_stderr\": 0.014949109840701408\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8303078137332282,\n \"acc_stderr\": 0.010549542647363698\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7217589082638363,\n \
\ \"acc_stderr\": 0.012343803671422678\n }\n}\n```"
repo_url: https://huggingface.co/Eric111/CatunaLaserPi
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|arc:challenge|25_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|gsm8k|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hellaswag|10_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-03T20-12-09.228599.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-03T20-12-09.228599.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- '**/details_harness|winogrande|5_2024-03-03T20-12-09.228599.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-03T20-12-09.228599.parquet'
- config_name: results
data_files:
- split: 2024_03_03T20_12_09.228599
path:
- results_2024-03-03T20-12-09.228599.parquet
- split: latest
path:
- results_2024-03-03T20-12-09.228599.parquet
---
# Dataset Card for Evaluation run of Eric111/CatunaLaserPi
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Eric111/CatunaLaserPi](https://huggingface.co/Eric111/CatunaLaserPi) 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_Eric111__CatunaLaserPi",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-03T20:12:09.228599](https://huggingface.co/datasets/open-llm-leaderboard/details_Eric111__CatunaLaserPi/blob/main/results_2024-03-03T20-12-09.228599.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": 0.6552131450979863,
"acc_stderr": 0.03196845590828605,
"acc_norm": 0.6545248858341499,
"acc_norm_stderr": 0.03263666412226832,
"mc1": 0.5361077111383109,
"mc1_stderr": 0.017457800422268625,
"mc2": 0.6783048885683172,
"mc2_stderr": 0.014949109840701408
},
"harness|arc:challenge|25": {
"acc": 0.6928327645051194,
"acc_stderr": 0.013481034054980943,
"acc_norm": 0.7150170648464164,
"acc_norm_stderr": 0.013191348179838795
},
"harness|hellaswag|10": {
"acc": 0.7017526389165505,
"acc_stderr": 0.004565536808632545,
"acc_norm": 0.880601473809998,
"acc_norm_stderr": 0.0032359418109431577
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6592592592592592,
"acc_stderr": 0.040943762699967926,
"acc_norm": 0.6592592592592592,
"acc_norm_stderr": 0.040943762699967926
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7105263157894737,
"acc_stderr": 0.03690677986137283,
"acc_norm": 0.7105263157894737,
"acc_norm_stderr": 0.03690677986137283
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7094339622641509,
"acc_stderr": 0.027943219989337135,
"acc_norm": 0.7094339622641509,
"acc_norm_stderr": 0.027943219989337135
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7708333333333334,
"acc_stderr": 0.03514697467862388,
"acc_norm": 0.7708333333333334,
"acc_norm_stderr": 0.03514697467862388
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6763005780346821,
"acc_stderr": 0.035676037996391706,
"acc_norm": 0.6763005780346821,
"acc_norm_stderr": 0.035676037996391706
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.04878608714466996,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.04878608714466996
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.574468085106383,
"acc_stderr": 0.032321469162244675,
"acc_norm": 0.574468085106383,
"acc_norm_stderr": 0.032321469162244675
},
"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": 0.5586206896551724,
"acc_stderr": 0.04137931034482757,
"acc_norm": 0.5586206896551724,
"acc_norm_stderr": 0.04137931034482757
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41005291005291006,
"acc_stderr": 0.025331202438944437,
"acc_norm": 0.41005291005291006,
"acc_norm_stderr": 0.025331202438944437
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.48412698412698413,
"acc_stderr": 0.04469881854072606,
"acc_norm": 0.48412698412698413,
"acc_norm_stderr": 0.04469881854072606
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7967741935483871,
"acc_stderr": 0.022891687984554956,
"acc_norm": 0.7967741935483871,
"acc_norm_stderr": 0.022891687984554956
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5024630541871922,
"acc_stderr": 0.035179450386910616,
"acc_norm": 0.5024630541871922,
"acc_norm_stderr": 0.035179450386910616
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7757575757575758,
"acc_stderr": 0.03256866661681102,
"acc_norm": 0.7757575757575758,
"acc_norm_stderr": 0.03256866661681102
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8131313131313131,
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"acc_norm": 0.8131313131313131,
"acc_norm_stderr": 0.027772533334218974
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9067357512953368,
"acc_stderr": 0.02098685459328973,
"acc_norm": 0.9067357512953368,
"acc_norm_stderr": 0.02098685459328973
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.023901157979402538,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.023901157979402538
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32222222222222224,
"acc_stderr": 0.028493465091028593,
"acc_norm": 0.32222222222222224,
"acc_norm_stderr": 0.028493465091028593
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6890756302521008,
"acc_stderr": 0.030066761582977938,
"acc_norm": 0.6890756302521008,
"acc_norm_stderr": 0.030066761582977938
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3443708609271523,
"acc_stderr": 0.038796870240733264,
"acc_norm": 0.3443708609271523,
"acc_norm_stderr": 0.038796870240733264
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8440366972477065,
"acc_stderr": 0.01555580271359017,
"acc_norm": 0.8440366972477065,
"acc_norm_stderr": 0.01555580271359017
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5138888888888888,
"acc_stderr": 0.03408655867977749,
"acc_norm": 0.5138888888888888,
"acc_norm_stderr": 0.03408655867977749
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8529411764705882,
"acc_stderr": 0.024857478080250447,
"acc_norm": 0.8529411764705882,
"acc_norm_stderr": 0.024857478080250447
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7974683544303798,
"acc_stderr": 0.026160568246601446,
"acc_norm": 0.7974683544303798,
"acc_norm_stderr": 0.026160568246601446
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.695067264573991,
"acc_stderr": 0.030898610882477515,
"acc_norm": 0.695067264573991,
"acc_norm_stderr": 0.030898610882477515
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7938931297709924,
"acc_stderr": 0.035477710041594654,
"acc_norm": 0.7938931297709924,
"acc_norm_stderr": 0.035477710041594654
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7933884297520661,
"acc_stderr": 0.03695980128098824,
"acc_norm": 0.7933884297520661,
"acc_norm_stderr": 0.03695980128098824
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8148148148148148,
"acc_stderr": 0.03755265865037181,
"acc_norm": 0.8148148148148148,
"acc_norm_stderr": 0.03755265865037181
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7730061349693251,
"acc_stderr": 0.03291099578615769,
"acc_norm": 0.7730061349693251,
"acc_norm_stderr": 0.03291099578615769
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.44642857142857145,
"acc_stderr": 0.04718471485219588,
"acc_norm": 0.44642857142857145,
"acc_norm_stderr": 0.04718471485219588
},
"harness|hendrycksTest-management|5": {
"acc": 0.7669902912621359,
"acc_stderr": 0.04185832598928315,
"acc_norm": 0.7669902912621359,
"acc_norm_stderr": 0.04185832598928315
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8846153846153846,
"acc_stderr": 0.020930193185179326,
"acc_norm": 0.8846153846153846,
"acc_norm_stderr": 0.020930193185179326
},
"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": 0.8314176245210728,
"acc_stderr": 0.013387895731543604,
"acc_norm": 0.8314176245210728,
"acc_norm_stderr": 0.013387895731543604
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7543352601156069,
"acc_stderr": 0.023176298203992005,
"acc_norm": 0.7543352601156069,
"acc_norm_stderr": 0.023176298203992005
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4346368715083799,
"acc_stderr": 0.016578997435496713,
"acc_norm": 0.4346368715083799,
"acc_norm_stderr": 0.016578997435496713
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7287581699346405,
"acc_stderr": 0.025457756696667885,
"acc_norm": 0.7287581699346405,
"acc_norm_stderr": 0.025457756696667885
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7041800643086816,
"acc_stderr": 0.025922371788818767,
"acc_norm": 0.7041800643086816,
"acc_norm_stderr": 0.025922371788818767
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7345679012345679,
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"acc_norm": 0.7345679012345679,
"acc_norm_stderr": 0.024569223600460845
},
"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": 0.47522816166883963,
"acc_stderr": 0.012754553719781753,
"acc_norm": 0.47522816166883963,
"acc_norm_stderr": 0.012754553719781753
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6764705882352942,
"acc_stderr": 0.02841820861940676,
"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.02841820861940676
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6715686274509803,
"acc_stderr": 0.018999707383162673,
"acc_norm": 0.6715686274509803,
"acc_norm_stderr": 0.018999707383162673
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.0449429086625209,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.726530612244898,
"acc_stderr": 0.028535560337128445,
"acc_norm": 0.726530612244898,
"acc_norm_stderr": 0.028535560337128445
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.845771144278607,
"acc_stderr": 0.025538433368578337,
"acc_norm": 0.845771144278607,
"acc_norm_stderr": 0.025538433368578337
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
"acc_stderr": 0.0358870281282637,
"acc_norm": 0.85,
"acc_norm_stderr": 0.0358870281282637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5542168674698795,
"acc_stderr": 0.03869543323472101,
"acc_norm": 0.5542168674698795,
"acc_norm_stderr": 0.03869543323472101
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8362573099415205,
"acc_stderr": 0.028380919596145866,
"acc_norm": 0.8362573099415205,
"acc_norm_stderr": 0.028380919596145866
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5361077111383109,
"mc1_stderr": 0.017457800422268625,
"mc2": 0.6783048885683172,
"mc2_stderr": 0.014949109840701408
},
"harness|winogrande|5": {
"acc": 0.8303078137332282,
"acc_stderr": 0.010549542647363698
},
"harness|gsm8k|5": {
"acc": 0.7217589082638363,
"acc_stderr": 0.012343803671422678
}
}
```
## 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]
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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
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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. -->
[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. -->
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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
<!-- 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. -->
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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. -->
[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]
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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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JanithSilva/medical-english | ---
license: apache-2.0
---
|
BangumiBase/narutomovies | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Naruto [movies]
This is the image base of bangumi NARUTO [Movies], we detected 37 characters, 3111 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1040 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 44 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 34 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 40 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 90 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 120 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 49 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 107 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 19 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 72 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 75 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 86 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 37 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 35 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 75 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 115 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 23 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 44 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 38 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 68 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 158 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 19 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 45 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 45 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 247 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 18 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 9 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 15 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 11 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 6 | [Download](29/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 30 | 60 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 87 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 11 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 35 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 16 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 7 | [Download](35/dataset.zip) |  |  |  |  |  |  |  | N/A |
| noise | 111 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
nevillemthw/sample-floor-plans | ---
license: openrail
---
|
2A2I-R/dibt_10k_prompts_ranked_arabic | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: quality
list:
- name: status
dtype: string
- name: user_id
dtype: string
- name: value
dtype: string
- name: metadata
dtype: string
- name: avg_rating
dtype: float64
- name: num_responses
dtype: int64
- name: agreement_ratio
dtype: float64
- name: raw_responses
sequence: int64
- name: kind
dtype: string
splits:
- name: train
num_bytes: 10601581
num_examples: 10331
download_size: 4323538
dataset_size: 10601581
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
muwenxin/bishe | ---
license: unknown
---
|
aisc-team-c1/MMedBench | ---
license: cc-by-4.0
language:
- en
- zh
- ja
- fr
- ru
- es
tags:
- medical
task_categories:
- question-answering
configs:
- config_name: spanish
data_files: "Spanish.jsonl"
- config_name: english
data_files: "English.jsonl"
---
*This is a dataset repository made for the AISC class at Harvard Medical School. Please find the original dataset repository here: https://huggingface.co/datasets/Henrychur/MMedBench*
# MMedBench
[💻Github Repo](https://github.com/MAGIC-AI4Med/MMedLM) [🖨️arXiv Paper](https://arxiv.org/abs/2402.13963)
The official benchmark for "Towards Building Multilingual Language Model for Medicine".
## Introduction
This repo contains MMedBench, a comprehensive multilingual medical benchmark comprising 45,048 QA pairs for training and 8,518 QA pairs for testing. Each sample includes a question, options, the correct answer, and a reference explanation for the selection of the correct answer.
To access the data, please download MMedBench.zip. Upon extracting the file, you will find two folders named Train and Test. Each folder contains six .jsonl files, each named after its respective language. Each line in these files represents a sample, with the following attributes for each sample:
|Key |Value Type |Description |
|------------------|-------------------|-----------------------------------------|
|question |String | A string of question |
|options |Dict | A dict where key is the index ‘A,B,C,D,E’ and value is the string of option| |
|answer_idx |String | A string of right answer idxs. Each idx is split by ','|
|rationale |String | A string of explanation for the selection of the correct answer |
|human_checked |Bool | Whether the rationale has been manually checked. |
|human_check_passed |Bool | Whether the rationale has passed manual check. |
Our [GitHub](https://github.com/MAGIC-AI4Med/MMedLM) provides the code for finetuning on the trainset of MMedBench. Check out for more details.
## News
[2024.2.21] Our pre-print paper is released ArXiv. Dive into our findings [here](https://arxiv.org/abs/2402.13963).
[2024.2.20] We release [MMedLM](https://huggingface.co/Henrychur/MMedLM) and [MMedLM 2](https://huggingface.co/Henrychur/MMedLM2). With an auto-regressive continues training on MMedC, these models achieves superior performance compared to all other open-source models, even rivaling GPT-4 on MMedBench.
[2023.2.20] We release [MMedC](https://huggingface.co/datasets/Henrychur/MMedC), a multilingual medical corpus containing 25.5B tokens.
[2023.2.20] We release [MMedBench](https://huggingface.co/datasets/Henrychur/MMedBench), a new multilingual medical multi-choice question-answering
benchmark with rationale. Check out the leaderboard [here](https://henrychur.github.io/MultilingualMedQA/).
## Evaluation on MMedBench
The further pretrained MMedLM 2 showcast it's great performance in medical domain across different language.
| Method | Size | Year | MMedC | MMedBench | English | Chinese | Japanese | French | Russian | Spanish | Avg. |
|------------------|------|---------|-----------|-----------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|
| GPT-3.5 | - | 2022.12 | ✗ | ✗ | 56.88 | 52.29 | 34.63 | 32.48 | 66.36 | 66.06 | 51.47 |
| GPT-4 | - | 2023.3 | ✗ | ✗ | 78.00 | 75.07 | 72.91 | 56.59 | 83.62 | 85.67 | 74.27 |
| Gemini-1.0 pro | - | 2024.1 | ✗ | ✗ | 53.73 | 60.19 | 44.22 | 29.90 | 73.44 | 69.69 | 55.20 |
| BLOOMZ | 7B | 2023.5 | ✗ | trainset | 43.28 | 58.06 | 32.66 | 26.37 | 62.89 | 47.34 | 45.10 |
| InternLM | 7B | 2023.7 | ✗ | trainset | 44.07 | 64.62 | 37.19 | 24.92 | 58.20 | 44.97 | 45.67 |
| Llama\ 2 | 7B | 2023.7 | ✗ | trainset | 43.36 | 50.29 | 25.13 | 20.90 | 66.80 | 47.10 | 42.26 |
| MedAlpaca | 7B | 2023.3 | ✗ | trainset | 46.74 | 44.80 | 29.64 | 21.06 | 59.38 | 45.00 | 41.11 |
| ChatDoctor | 7B | 2023.4 | ✗ | trainset | 43.52 | 43.26 | 25.63 | 18.81 | 62.50 | 43.44 | 39.53 |
| PMC-LLaMA | 7B | 2023.4 | ✗ | trainset | 47.53 | 42.44 | 24.12 | 20.74 | 62.11 | 43.29 | 40.04 |
| Mistral | 7B | 2023.10 | ✗ | trainset | 61.74 | 71.10 | 44.72 | 48.71 | 74.22 | 63.86 | 60.73 |
| InternLM\ 2 | 7B | 2024.2 | ✗ | trainset | 57.27 | 77.55 | 47.74 | 41.00 | 68.36 | 59.59 | 58.59 |
| MMedLM~(Ours) | 7B | - | ✗ | trainset | 49.88 | 70.49 | 46.23 | 36.66 | 72.27 | 54.52 | 55.01 |
| MMedLM\ 2~(Ours) | 7B | - | ✗ | trainset | 61.74 | 80.01 | 61.81 | 52.09 | 80.47 | 67.65 | 67.30 |
- GPT and Gemini is evluated under zero-shot setting through API
- Open-source models first undergo training on the trainset of MMedBench before evaluate.
## Contact
If you have any question, please feel free to contact qiupengcheng@pjlab.org.cn.
## Citation
```
@misc{qiu2024building,
title={Towards Building Multilingual Language Model for Medicine},
author={Pengcheng Qiu and Chaoyi Wu and Xiaoman Zhang and Weixiong Lin and Haicheng Wang and Ya Zhang and Yanfeng Wang and Weidi Xie},
year={2024},
eprint={2402.13963},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
alexshengzhili/SciCapAbstractsOCR0350K | ---
license: mit
dataset_info:
features:
- name: image_file
dtype: string
- name: id
dtype: string
- name: caption
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: first_mention
dtype: string
- name: response
dtype: string
- name: title
dtype: string
- name: abstract
dtype: string
splits:
- name: train
num_bytes: 1435472978
num_examples: 352018
- name: validation
num_bytes: 357379
num_examples: 93
download_size: 782241323
dataset_size: 1435830357
---
|
mukundmurali-mm/oci-steampipe | ---
license: other
license_name: oci-steampipe
license_link: https://github.com/turbot/steampipe-plugin-oci/blob/main/docs/LICENSE
---
|
luisHuang/test_MakeDataset1 | ---
license: openrail
---
|
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